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DIRECTOR’S SPEECH ON 6TH ASAIS 2017 Ass. Wr. Wb. First of all, I would like to say a warm greeting to all distinguished keynote speakers from International Islamic University Malaysia, Associate Professor. Dr. Dzuljastri Abdul Razak, Mr. Bejoy Jose from PT. Yokogawa Singapore, Prof. Dr. Ir. Setiasyah Toha, M.Sc. from Bandung Institute of technology; all speakers and participants. It is my pleasure to welcome all of you to The 6th ASAIS 2017 organized by P3M (The Centre of Research and Public Services) of Politeknik Negeri Jakarta. The theme of this international seminar this year is called: “The Impact of Sustainable Global Technology Development on Competitive Research and Society Services” which is in line with the current issue happening in global world. I particularly believe that this seminar will be very beneficial for us in order to make us ready and aware in entering Asian Free Trade Community (AFTA). I also believe that the development of technology will bring us into the borderless world which eases us to do our daily activities. Furthermore, it assists us to meet global demands which are now becoming very crucial in order to catch up the changes of the world. Even it will help us improve the quality of our teaching and learning in the process. In line with that, as long as we live within society, we must do something beneficial for our society. The distinguished speakers and participants, As the Director of PNJ, I would like to say once again that this international seminar offers several informative talks as well as networking opportunities. It means that all of you are welcome to initiate collaborations in research and society services. I wish all of you a successful and enriching seminar experience. Thank you very much. Wass. Wb.

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BOARDS OF COMMITTEE Advisors : Direktur Politeknik Negeri Jakarta Pembantu Direktur I. Unit in charge : Kepala P3M Chairman : Dr. Isdawiman ST., MT Vice Chairman : Dr. Yogi Widyawati M.Hum Secretary : Ir. Anis Rosyidah MT

Prihatin Oktivasari, SSi., MSi Treasurer : Nurmalisna, SH Reviewer Paper Section Coordinators : Dr. A. Tossin Alamsyah MT

Dr. Drs. Agus Edy Pramono, ST., M.Si Putera Agung Maha Agung, Ph.D. Event Section Coordinator : Dr. Dra. Iis Mariam, M.Si

Dr. Muslimin Linguistic Section Coordinator : Dra. Mawarta Onida, M.Si.

Dr. Sylvia rozza, SE MSi Proceedings Section Coordinator : Dr. Nining Latianingsih SH MH

Eva Azra Latifa ST., MT Sponsorship Coordinators : Sri Danaryani ST MT Publication and Documentation Section Coordinators : Hata Maulana ., S.Si., M.TI

Sugianto, Amd Bayu Pratama Putra, ST. Azhar Aditya S.ST.

Caterers Section Coordinators : Ir. Sri Danaryani. MT

Muryeti, SSi., MSi Secretariate General Assistant : Bayu Pratama Putra, ST. Sugino Rafiih The Office Of The Secretariat : Pusat Penelitian dan Pengabdian kepada Masyarakat(P3M)

Gedung Q, Lantai 2, Politeknik Negeri Jakarta, Kampus Baru UI Depok, Tlp. 021 7270036 ext 236, Email : [email protected]. Website: http://asais.pnj.ac.id/

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PREFACE

This proceedings contain sorted papers from Annual South East Asian International Seminar (ASAIS) 2017. ASAIS 2017 is the sixth annual international event organized by Pusat Penelitian dan Pengabdian (P3M) Politeknik Negeri Jakarta Indonesia. This event is a forum for researchers for discussing and exchanging the information and knowledge in their areas of interest. It aims to promote activities in research, development and application on technology, commerce, and humanities. We would like to express our gratiture to all technical commite members who have given their efforts to support this seminar. We also would like to express our sincere gratitude to Higher Education Republic of Indonesia. Finally we also would to like to thank to all of the keynote speakers, the authors, the participant and all parties for the success of ASAIS 2017.

Editorial Team.

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TABLE OF CONTENTS

DIRECTOR SPEECH ON ASAIS 2017………………………………………....................................i

ASAIS 2017 COMMITTEE.............................................................................................................ii

PREFACE ............................................................................................................................................iii

TABLE OF CONTENTS .....................................................................................................................iv

TITLES OF TECHNOLOGY AND ENGINEERING PAPER ............................................................v

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TITLES OF TECHNOLOGY AND ENGINEERING PAPER

CODE TITLE PRESENTER PAGE

TEC – 01 DEVELOPMENT OF VISUAL SENSORY AIDS USING EMBEDDED SYSTEM FOR BLIND PERSON

Budi Setiadi and Tata Supriyadi 1

TEC – 02 OPTIMISATION MODEL OF ELECTRIFICATION RATIO USING SOLAR PHOTOVOLTAÏQUE: CASE STUDY IN KUPANG REGENCY

Rusman Sinaga, Armansyah H.Tambunan, Prastowo and Bintang C.H. Simangunsong

7

TEC – 03 STUDY ON THE EFFECT ELECTROMAGNETIZATION BIODIESEL FUEL SAVING IN DIESEL ENGINES

Tatun H. Nufus, Radite P.A. Setiawan, Wawan Hermawan and Armansyah H. Tambunan

13

TEC – 04 IDENTIFICATION OF POWER QUALITY THROUGH ONLINE DATA MONITORING

Isdawimah, Ismujianto and Nguyen Phuoc Lock

23

TEC – 05 CROSSFLOW AND PROPELLER TURBINE PERFORMANCE ON HEAD 3 M MHP SYSTEM TO CAPACITY OF WATER FLOW

Paulus Sukusno, Andi Ulfiana and Benhur Nainggolan

31

TEC – 06 MOBILE SHORTEST PATH APPLICATION SEARCH PEMPEK STORE IN PALEMBANG WITH DIJKSTRA METHOD SOLUTION

Aryanti and Ikhthison Mekongga 39

TEC – 07 DESIGN AND ANALYSIS OF SPAR I BEAM PROFILE USING COMPOSITE MATERIAL IN UAV STRUCTURE

Lenny Iryani, Fithri N. P., Andi M. Kadir and, Bambang Irawan

45

TEC – 08 MOBILE SCADA APPLICATION OF REMOTE TERMINAL UNIT FOR WATER DISTRIBUTION PROCESS

Murie Dwiyaniti, Kendi Moro N and Tohazen

51

TEC – 09 KINETICS AND THERMO-DYNAMICS OF GOLDS ABSORBTION WITH CHITOSAN FROM THE SHRIMP SHELL OF NTB

Dwi Sabda Budi Prasetya, Ahmadi and Dwi Pangga

59

TEC – 10 CIRCULAR MICROSTRIP PATCH ANTENNA FOR WiFi COMMUNICATION AT 2,4 GHz.

Nuhung Suleman, Yenniwarti Rafsyam and Agus Wagyana

65

TEC – 11 FUZZY LOGIC IMPLEMENTATION FOR DC MOTOR SPEED CONTROL ON AUTOMATIC PATIENT BEDS BASED ON RADIO CONTROL

Ikhthison Mekongga and Aryanti 69

TEC – 12 THE IMPLEMENTATION OPENBTS USING UNIVERSAL SOFTWARE RADIO PERIPHERAL (USRP) BASED ASTERISK SYSTEM

Hafidudin, Muhamad Fahru Rizal and Dadan Nur Ramadhan

73

TEC – 13 MAKING OF CALIBRATED DIGITALPRINTER MACHINE FOR CARTONMATERIAL

Heribertus Rudi K. and AnggAnggarini

81

TEC – 14 MODELLING AND SIMULATION CFD ANALYSIS IN RUNNER FOR AXIAL TURBINE TYPE MICRO HYDRO POWER PLANT WITH LOW HEAD

Gun Gun Ramdlan Gunadi, Candra Damis Widiawaty, Fachruddin, Jusafwar, Adi Syuriadi, and Jauhari Ali

87

TEC – 15 THE EFFECT OF ELECTRODE Sutanto, Hidjan and Nanang 95

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CODE TITLE PRESENTER PAGE

DISTANCE CHANGES ON ELECTRICAL CURRENT AND TURBIDITY ON WASTEWATER TREATMENT BY ELECTRO-COAGULATION AND ADSORPTION

Rohadi

TEC – 16 APPLICATION SHALE RATING SYSTEM TO HAMBALANG HILL CLAYSHALE PERFORMANCE

Putera Agung Maha Agung 103

TEC – 17 DRONE AD-HOC NETWORKS (DRANETS)

Abdul Aziz Abdullah, Shahrin Shahib and Nur Azman Abu

113

TEC – 18 VIRTUAL MAP PNJ: DETECT THE LOCATION OF 3D OBJECTS WITH GPS BASED AUGMENTED REALITY MARKERLESS

Hata Maulana 125

TEC – 19 DESIGN OF DOUBLE CROSS DIPOLE ANTENNA FREQUENCY 137 MHz FOR NOAA SATELLITE RECEIVER

Yenniwarti Rafsyam, Indra Z, Eri Ester Khairas, Jonifan, Topik Teguh Estu

131

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TEC – 20 EFFECT OF CARBOXYMETHYL CHITOSAN IN DEINKING PRO-CESS ON THE OPTICAL PROPER-TIES OF PAPER

Muryeti, Estuti Budi Mulyani 137

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Development of Visual Sensory Aids Using Embedded System for Blind Person

Budi Setiadi, Tata Supriyadi

Lecturer of Electrical Engineering Department, Polytechnic Bandung Email : [email protected] [email protected]

Abstract

The use of stick aids (manuals or electronics) in the pedestrian path for blind people has become a necessity. However, it can not solve the problem completely. Limited by distance, disrupt the main function of the hand, must be touched directly to the object or pedestrian path. This research replaces the function of the stick with the camera as a substitute for the senses of sight. The camera detects yellow line texture of the pedestrian path. Image data from the camera is processed on miniPC to perform the feature extraction feature using HOG algorithm (Histograms of Oriented Gradients) and SVM (Support Vector Machine) for feature classification. The end result of data processing is converted to sound. Product design is made to resemble a hat with additional camera on the front. The test results obtained the success rate and accuracy of positive data 62.50% and 59.3% negative data. Keywords: blind people, image processing, HOG algorithm, SVM algorithm, computer vision 1. INTRODUCTION The independence of the blind people to use the five senses that still function in daily activities is still very low. This is supported by WHO (World Health Organization) data in 2011, estimating that there are about 285 million people worldwide who are suffering from neutral disability. Good disabilities are experienced from birth (permanent) or at the time after birth. Approximately 249 million belong to the category with non-independent vision (requires assistance to recognize the conditions around it). And seen from the perspective of the field of psychology, that 83% of information obtained by humans derived from the interaction with the environment (G.Aditya, P.Divya, C.Apoorva Chaudhary, 2016) White can is one technique to recognize the environment independently. The independence of the blind people to do the activity down the pedestrian path and

recognize the surrounding objects is done by maximizing the five sense senses. The use of the five senses directly, using the foot to recognize the texture of the pedestrian path and hand to recognize the object around him. Or use an auxiliary media tool to manipulate the pedestrian path and recognize the surrounding objects (B.Sukhdeep, P.Akansha, R.Anindita, D.Arpan, 2016). The most common problems of environmental recognition techniques are the safety, cleanliness and difficulty of using stick aids. The direct use of the senses of foot feels very risky injury. The direct use of the senses of hand is a risk for health and safety. And the difficulty of using the stick aids because it must be touched directly to the pedestrian path and surrounding objects. Use of aids can damage an object, limited distance, can not recognize the shape of the object, and disrupt the main function of the hand.

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2. METHODOLOGY In the field of computer vision utilization of an algorithm is needed to help the way image processing work in detecting objects, especially yellow row texture object pedestrian path. The HOG algorithm is used to extract the feature on the object of the image by using a yellow stripe texture object of the pedestrian path. And the SVM algorithm is used for feature classification. HOG algorithm is used to extract the feature on the image object that has been done resize process by using yellow stripe texture object of pedestrian path. Furthermore, the process of converting RGB image (Red, Green, Blue) into grayscale, which then continued by calculating the value of each pixel gradient. Next determine the number of orientation bin that will be used in the histogram (spatial orientation binning). However, earlier in the gradient compute process the training drawing is divided into several cells and grouped into larger sizes called blocks. And for normalization process block used R-HOG geometry calculation. This process is done because there are overlapping blocks. In contrast to the process of making an image histogram that uses the pixel intensity value of an image or a particular part of the image for making its histogram (Navneet Dalal and Bill Triggs, 2003). SVM is a machine learning algorithm with Structural Risk Minimization (SRM) working principle with the target of finding the best hyperplane that separates two categories in input space. The basic principle of SVM is linear classifier and serves to detect objects to be detected in a window. SVM Classifier is used to separate

yellow line textures of pedestrian path and pedestrian paths or paths without yellow line texture. The SVM Classifier and classification algorithm used separate an optimal hyperplan (C. Nello, S. Bernhard, 2000)

3.1. DESIGN

This research focuses on how to implement HOG, SVM, and color algorithm into single chip embedded system mini PC. The system is divided into 2 (two) parts, hardware and software. 3.2. HARDWARE System block diagrams consist of input-camera, data processing on mini PC, and headset-output, as shown in Figure 1.

CAMERA DATA PROCESSING HEADSET

POWER SUPPLY

Figure 1 Diagram Block Of Hardware

Camera block is passive, because it will be active when it gets command from the data processor. Furthermore, when active, will do the task of capturing the digital image of the pedestrian path and send back to the data processor. Data processing block is the brain of the system, which plays a role rule, process data into sound informations. Subsequently issued in the form of sound to block headset. While the power supply is the source energy for all module blocks. The realization of hardware using the camera Pi as a substitute input the senses of sight. Mini PC Raspberry Pi model B as a data processing algorithm HOG, SVM, and voice.

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Headset as output sound information for the blind, as shown in Figure 2.

Figure 2 Hardware Realization

3.3. SOFTWARE

The operating system platform used linux-pedora, and the phyton3 programming language. Data processing, as shown in Figure 3. The first stage of initialization, that is logic conditioning or the initial signal of all input devices, processes and outputs. Furthermore, the camera is mounted in front of the head with a slope of 15 °, take a digital image yellow texture object pedestrian path used as input data processing. Next resized the image to a resolution of 800x600 pixels. Furthermore, digital image resized process results are still in the form of RGB color changed to grayscale color. Next is the process of calculating the gradient value of each pixel in the picture. Next process is made to make each cell in the picture into a histogram, in this process required the bin to know the value of the gradient. Furthermore, the process of normalization of the block, caused each cell value occurs overlap because the process is done more than once and the result is a feature of the detected object. The next process of detecting 64x128 windows, which is the process of selecting object features according to object training data that is done detection per pixel in the picture.

START

INITIALIZATION

TAKE PICTURE

NORMALIZATION OF COLORS

SPATIAL ORIENTATION BINNING

COUNT THE GRADIEN

NORMALIZATION OF BLOCK

DETECTOR WINDOWS

SVM CLASSIFICATION

VOICE INFORMATION

END

RESIZE THE IMAGE

Figure 3 Flowchart Data Processing The next process is done object classification using SVM, aims to find the best hyperplane of the object for later distinguished objects. The next process of the final process of decision-making in the form of voices. 3. ANALYSIS AND DISCUSSION Camera Pi is used to retrieve data acquisition for HOG and SVM algorithm process. Still image capture and sunny weather conditions. Data acquisition is divided into 2, that is positive image (training object to be in detection) and negative image (training object that is not in detection), as shown in Figure 4.


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Figure 4. Positive Image Training Data (+)

Figure 5. Negative Image Training Data (-)

The composition of training data test with the amount of positive image data 8 and negative image 16. Training data with resized resolution to 64x128, and the image will be in the detection is always resized to 800x600. Testing done as much as 2 (two) times with sound output.

.Testing to 1:

Testing to 2:

4. CONCLUSION The use of HOG and SVM algorithms can recognize yellow line texture of pedestrian path with an average success rate of 62.5% for positive training data testing and 59.3% for negative training data testing.

5. REFERENCES [1] A.Abdulrahman, A.Areej, M.Sarah,

A.Altaf, 2016. Ultrasonic sensors gloves for blind people using Lilypad Arduino, International Journal of New Computer Architectures and their Applications (IJNCAA), Volume 6 Issue No.1, ISSN 2220-9085 (Online); ISSN 2412-3587 (Print).

[2] A.Shradha, P.Amar, G.Shubham, K.Hanmant, 2016. Automated Mobility and Orientation System for Blind, International Research Journal of Engineering and Technology (IRJET), Volume 03 Issue 04 April 2016, e-ISSN: 2395 -0056 p-ISSN: 2395-0072.

[3] B.Sukhdeep, P.Akansha, R.Anindita, D.Arpan, 2016. Blind Navigation System, Internationa Journal Of Innovative Research in Science and Engineering (IJIRSE), Volume 02 Issue 04 April, ISSN 2454-9665.

[4] C. Nello, S. Bernhard, 2002. Support Vector Machines and Kernel Methods The New Generation of Learning Machnes. Al Magazine Volume 23 Number 3.

[5] G.Aditya, P.Divya, C.Apoorva Chaudhary, 2016. Electronic Travel Aids ETA for Blind Assistance, International Journal of Engineering Science and Computing (IJESC), Volume 06 Issue 03 March, ISSN 2321-3361.

[6] K.Sri Hari Rao, K.Jyothi, Shaik.Mahamood, 2015. Secure Navigation for the Blind People by Using RFID, INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN ELECTRICAL, ELECTRONICS, INSTRUMENTATION AND CONTROL ENGINEERING (IJIREEICE ), Volume 03 Issue 12 December, ISSN (Online) 2321 – 2004 ISSN (Print) 2321 – 5526.

[7] Navneet Dallal, Bill Triggs, 2003. Histograms of Oriented Gradients for Human Detection. http://lear.inrialpes.fr

[8] Nur, Muhammad, M.Abdul, R.Tedy, 2015. Pembuatan Prototipe

Data Training

Test result

+ - Positif Negatif Success Failed Success Failed

8 16 5 3 10 6

Data Training

Test result

+ - Positif Negatif Success Failed Success Failed

8 16 5 3 9 7


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Kacamata Elektronik untuk Tuna Netra Berbasis Mikrokontroler menggunakan Sensor Ultrasonik. Journal Coding, Computer Systems Untan Volume 03, Issue 2 , (Print) : 88-99

[9] S.Dhananjeyan, Dr.K.Mohana Sundaram, A.Kalaiyarasi, Dr. P. G. Kuppusamy, 2016. Design and Development of Blind Navigation System using GSM and RFID

Technology, Indian Journal of Science and Technology, Volume 09 January, ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645.

[10] S.Jiayin Wenlong, C.Yupeng, C.Xuefu, 2016. The Design of a Guide Device with Multi-Function to Aid Travel for Blind Person, International Journal of Smart Home (IJSH), Volume 10 No 04, ISSN: 1975-4094.


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Optimization model of electrification ratio using solar photovoltaic: case study in Kupang Regency

Rusman Sinaga1,* Armansyah H.Tambunan2, Prastowo2, Bintang C.H. Simangunsong2

1 State Polytechnic of Kupang, Po.Box.139, Penfui. Kupang 85361, Indonesia 1, 2 Graduate School, Bogor Agricultural University, Po.Box. 220. Darmaga. Bogor 16680,

Indonesia [email protected], [email protected]

Abstract

The Electrification ratio in Indonesia is 89.10%, which means there are 7, 245,728 of 66, 489,400 households don’t have access to Electrical Energy Sources (EES). Kupang Regency is one of the Regency in Indonesia which has a low electrification ratio. 29,542 of 78,109 households haven't access to EES spread over 29 villages (electrification ratio of 62%). Solar Photovoltaic in Kupang Regency 5 MWp capacity has been operating but directly connected (On-Grid) to the State Electricity Company (PLN), it is not able to help rural communities that are difficulties to reach due to its geographical conditions. The aim of this research was design the electrification ratio optimization model with the consideration of CO2 emission reduction using Solar Photovoltaic. The method of research using dynamic modeling approach. The result of the research shows that electrification ratio can be achieved optimum estimated in 2020-2021 if the addition of capable power each year 4,000 kW. The addition of capable power 2,000 kW/year, can reach the optimum electrification ratio in 2023-2024 and if the addition of capable power is only 1,000 kW/year, the optimum electrification ratio can be achieved in 2030-2031. Diversification of DPG into Solar PV can reduce CO2 emissions by 98.8%. Keywords : Solar Photovoltaic, electrification, CO2 emissions, Kupang Regency, Model 1. INTRODUCTION Electrical Energy is one form the energy most commonly used in the modern world, that can be easily converted into other forms of energy and can safely and efficiently be distributed over the distance. Electricity is required in almost every stage of economic activity from the upstream to downstream as to the operation of household appliances, information and communication equipment, education equipment, medical equipment, woodworking equipment, lighting, electrical machinery for driving such as water pumps, cooling machine, electric heating and others [1]. Electricity will affect the development of the economy and society welfare.

The current electrification ratio in Indonesia is 89.10%. The number of households that do not have access to EES is 7, 245,728 of 66, 489,400.

Some provinces even have electrification ratios below 60%, such as Jambi, West Sulawesi, West Papua and East Nusa Tenggara (ENT) due to lack of electricity infrastructure. The State Electricity Company of Indonesia (PLN) for East Nusa Tenggara region noted that there are 535,418 out of 1, 126,400 households in ENT Province have no access to EES with electrification ratio of 52.47% [2]. Meanwhile, Central Bureau of Statistics of Kupang Regency [3], noted that there are 29,542 of 78,109 households in 29 of the 177 villages in Kupang Regency ENT Province have no access to EES (electrification ratio is 62%). On the other hand, in line with Paris agreement, Indonesia has expressed its commitment to reduce Greenhouse Gas (GHG) emissions by 29% on its own efforts, or 41% with International support, by the year 2030 [4], [5]. Indonesia commitment in Paris


mailto:[email protected]:[email protected]

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agreement will be difficult to achieve If electricity production for increasing the electrification ratio is accomplished by using fossil energy sources.

Indonesia lies on the equator and has a tropical climate so that the solar energy received is very abundant that can be utilized as an alternative in overcoming the energy crisis and decrease CO2 emissions. Rahardjo & Fitriana [6] has studied the potential of solar energy resources by concluding that the average solar radiation intensity in East Nusa Tenggara is 5,117 Wh/m2/day, which has the potential to generate electrical energy. This study is supported by research Sinaga [7] on the effect of environmental parameters on the output of Solar PV in Kupang Regency. This study concludes that in the morning, noon and afternoon Illumination of sunlight rays affect the energy output on the Solar Photovoltaic, If Illumination increases 1 Lux then the energy output will increase 0.001 Wh. During the day Temperature effect on the energy output on the Solar PV, If Temperature increases 1 degree then the power output of Solar PV will increase 0.121 Wh.

2. METHODOLOGY This research was designed with system approach method using the dynamic models [8]-[11]. The dynamic model used in this research to model the production of electricity using Solar Photovoltaic (Solar PV) compared to the Diesel Power Generation (DPG) for optimizing the electrification ratio and with the consideration of CO2 emission reduction. The methods of data collection are done by literature study, survey, observation and compilation of reports.

Emission Factor Analysis (EFA) was done by calculating CO2 emissions using Tier 1 at IPCC as follows: 1) Calculating the amount of electrical

energy generated at the power plant per year by using equation (1) [12]:

tCPEelect ×= (1) Where, Eelect is electrical energy generated in a year (kWh), CP is capable power (kW), and t is effective working time in a year (h). 2) Calculating CO2 emissions using

equation (2) [13] : EFEe electCO ×=2 (2)

Where eCO2 is CO2 emissions (ton), and EF is emission factor (ton/kWh). The CO2 EF for DPG has been established by UNDP empirically is 0.786 Kg/kWh [14] and CO2 EF for Solar PV is 0.0094 Kg/kWh [15]. The electrification ratio is the ratio of the electrified household to the number of households [16].

3. ANALYSIS AND DISCUSSION Table 1 show the condition of electrification in Kupang Regency, which is processed from the statistical data of the Central Bureau of Statistics of Kupang Regency for the sub-district. The table shows that 9 out of 24 sub-district have electrification ratio less than 50%, as many as 29,542 households do not have access to electrical energy sources, which is about 38% of the total number of households making electrification ratio of Kupang Regency to become 62%. 29 villages from 9 sub-districts do not have access to PLN's electricity source.

The Solar PV Off-Grid is designed to produce electrical energy that can be stored in the battery so that stored energy can be used both at night and during the day. DPG is the Power


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Generation designed to produce electrical energy using diesel fuel oil as primary energy source. Both types of power plants can be used in remote areas far from PLN's power grid to raise the elctrification ratio, but both have different characteristics. In this study, the difference is shown in the design of optimizing the electrification ratio by considering the decrease in CO2 emissions as illustrated by the stock-flow diagram in Fig.1.

The modeling of electrification ratio optimization in this study uses three scenarios of two types of power generation (Solar PV and DPG), namely: the first scenario uses 1,000 kW capable power, the second scenario uses 2,000 kW capable power and the third scenario uses 4,000 kW capable power. The assumption is that every household uses the tariff R1 = 450 W. The results show that in the first scenario, the electrification ratio reaches an optimum (100%) estimated in 2030-2031, while in the second scenario, the electrification ratio is estimated to be optimal in 2023- 2024 and in the third scenario it is estimated that the electrification ratio reaches optimum in 2020-2021. The comparison of the electrification ratio of the three scenarios is presented in Fig. 2.

The number of CO2 emissions produced for optimizing the electrification ratio using the DPG in the first scenario is 38,041 tons, the second scenario 35,115 tons and the third scenario 35,115 tons. If using Solar PV, then the estimated CO2 emissions for the first scenario is 455 tons, the second scenario is 420 tons and the third scenario is 420 tons. Diversification of DPG into Solar PV can reduce CO2 emissions by 98.8%. The simulation of CO2 emission ratio

of DPG with Solar PV is presented in Fig. 3.

4. CONCLUSION Electrification ratio can be achieved optimum estimated in 2020-2021 if the addition of capable power each year 4,000 kW. The addition of capable power 2,000 kW / year, can reach the optimum electrification ratio in 2023-2024 and if the addition of capable power is only 1,000 kW / year, the optimum electrification ratio can be achieved in 2030-2031. CO2 emission reduction factor with diversified model of PLTD to PLTS is 2.46 Ton / kW / Year. 5. REFERENCES [1] Novakovic and A. Nasiri,

Introduction to electrical energy systems. Electrical Engineering and Computer Science Department, College of Engineering and Applied Sciences, University of Wisconsin-Milwaukee, USA. Elsevier Inc. 1, 20 (2016)

[2] Perusahaan Listrik Negara. Statistik PLN. Jakarta. Sekretariat PT. PLN (PERSERO). 1 (2015)

[3] Badan Pusat Statistik. Kabupaten Kupang Dalam Angka. Kupang: Badan Pusat Statistik Kabupaten Kupang. 1 (2015)

[4] Pusat Data dan Teknologi Informasi Energi dan Sumberdaya Mineral. Data Inventory Emisi GRK Sektor Energi. Jakarta. Pusat Data dan T I ESDM. 1 (2015)

[5] Undang-Undang Republik Indonesia Nomor 16. Paris Agreement to the United Nations Framework Convention on Climate Change. Kementerian Hukum dan HAM. 1 (2016)

[6] Rahardjo I, Fitriana I. Analisis Potensi Pembangkit Listrik Tenaga Surya di Indonesia.


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P3TKKE, BPPT. 11, 43-52 (2015).

[7] Sinaga R. 2011. Pengaruh Parameter Lingkungan dan Penempatan Posisi Modul Terhadap Luaran Energi PLTS Menggunakan Solar Cell 50 Wp 12 Volt. Jurnal Studia Teknologia. 4 (2):110-120

[8] Tao Z, Liu Z, Changxin Z. 2011. Research on The Prospects of Low-Carbon Economic Development in China Based on LEAP Model. Energy Procedia. 5, 695–699

[9] McPherson M, Bryan K. Long-Term Scenario Alternatives and Their Implications: LEAP Model Application of Panama's Electricity Sector. Energy Policy. 68, 146-157 (2014)

[10] Debnath KB, Mourshed M, Chew SPK. Modelling and forecasting energy demand in rural households of Bangladesh. Energy Procedia. 75, 731-737 (2015)

[11] Parkinson SC, Djilali N. Long Term Energy Planning With Uncertain Environmental Performance Metrics. Applied Energy. 147, 402-412 (2015)

[12] Sugiyono A. Peran PLTN dalam Mendukung Komitmen Pemerintah untuk Mengurangi Emisi CO2. Prosiding Seminar Pengembangan Energi Nuklir Tahun 2010, PPEN BATAN. 1,199 (2010)

[13] Didit W. Analisis Pembangkit Listrik Tenaga Biogas Dengan Pemanfaatan Kotoran Sapi di Kawasan Usaha Peternakan Sapi. Master thesis. UI. 103 (2011)

[14] Sherwani AF, Usmani JA, Varun. Life Cycle Assesment of Solar PV Based Electricity Generation System: A Review. Renewable and Sustainable Energy Reviews.14, 540-544 (2010)

[15] United Nation Development Program. Indonesia: Microturbine Cogeneration Technology Application Project. Jakarta. UNDP. 95 (2007)

[16] DJK. Statistik Keteagalistrikan. Kementerian Energi dan Sumberdaya Mineral (2016)

[17] BPS. Amabi Oefeto Dalam Angka. Badan Pusat Statistik Kab. Kupang. (2016)

[18] BPS. Amarasi Dalam Angka. Badan Pusat Statistik Kab. Kupang. (2016)

[19] BPS. Amarasi Selatan Dalam Angka. Badan Pusat Statistik Kab. Kupang. (2016)

[20] BPS. Amarasi Barat Dalam Angka. Badan Pusat Statistik Kab. Kupang. (2016)

[21] BPS. Amfoang Barat Daya Dalam Angka. Badan Pusat Statistik Kab. Kupang. (2016)

[22] BPS. Amfoang Barat Laut Dalam Angka. Badan Pusat Statistik Kab. Kupang. (2016)

[23] BPS. Amfoang Selatan Dalam Angka. Badan Pusat Statistik Kab. Kupang. (2016)

[24] 8. BPS. Amfoang Timur Dalam Angka. Badan Pusat Statistik Kab. Kupang. (2016)

[25] BPS. Amabi Oefeto Timur Dalam Angka. Badan Pusat Statistik Kab. Kupang. (2016)

[26] BPS. Amfoang Tengah Dalam Angka. Badan Pusat Statistik Kab. Kupang. (2016)

[27] BPS. Amarasi Timur Dalam Angka. Badan Pusat Statistik Kab. Kupang. (2016)

[28] BPS. Amfoang Utara Dalam Angka. Badan Pusat Statistik Kab. Kupang. (2016)

[29] BPS. Fatuleu Tengah Dalam Angka. Badan Pusat Statistik Kab. Kupang. (2016)

[30] BPS. Fatuleu Barat Dalam Angka. Badan Pusat Statistik Kab. Kupang. (2016)


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[31] BPS. Fatuleu Dalam Angka.Badan Pusat Statistik Kab. Kupang. (2016)

[32] BPS. Kupang Barat Dalam Angka. Badan Pusat Statistik Kab. Kupang. (2016)

[33] BPS. Kapang Tengah Dalam Angka. Badan Pusat Statistik Kab. Kupang. (2016)

[34] BPS. Kupang Timur Dalam Angka. Kupang. Badan Pusat Statistik Kab. Kupang. (2016)

[35] BPS. Nekamese Dalam Angka. Badan Pusat Statistik Kab. Kupang. (2016)

[36] BPS. Semau Dalam Angka. Badan Pusat Statistik Kab. Kupang. (2016)

[37] BPS. Semau Selatan Dalam Angka. Badan Pusat Statistik Kab. Kupang. (2016)

[38] BPS. Sulamu Dalam Angka 2016. Badan Pusat Statistik Kab. Kupang. (2016)

[39] BPS. Taebenu Dalam Angka 2016. Badan Pusat Statistik Kab. Kupang. (2016)

[40] BPS. Takari Dalam Angka 2016. Badan Pusat Statistik Kab. Kupang . (2016)

Table 1 Electrification condition in Kupang Regency

Nr. Sub-District NV VnE HP HnP HnE NH ER(%) S

1 Amabi Oefeto 7 - 1.691 75 233 1.999 85 [17] 2 Amabi Oefeto Timur 10 - 2.749 - 778 3.527 78 [18] 3 Amarasi 9 - 3.836 196 - 4.032 95 [19] 4 Amarasi Barat 8 - 2.678 747 1.321 4.746 56 [20] 5 Amarasi Selatan 5 - 2.265 51 185 2.501 91 [21] 6 Amarasi Timur 4 - 1.750 106 187 2.043 86 [22] 7 Amfoang Barat Daya 4 1 202 796 15 1.013 20 [23] 8 Amfoang Barat Laut 6 5 32 750 869 1.651 2 [24] 9 Amfoang Selatan 7 - 685 344 758 1.787 38 [25] 10 Amfoang Tengah 4 2 98 123 778 999 10 [26] 11 Amfoang Timur 5 4 20 455 800 1.275 2 [27] 12 Amfoang Utara 6 3 567 90 240 897 63 [28] 13 Fatuleu 10 3 1.897 551 3.776 6.224 30 [29] 14 Fatuleu Barat 5 5 0 710 1.571 2.281 0 [30] 15 Fatuleu Tengah 4 - 550 80 667 1.297 42 [31] 16 Kupang Barat 12 - 3.699 48 209 3.956 94 [32] 17 Kupang Tengah 8 - 7.545 655 794 8.994 84 [33] 18 Kupang Timur 13 - 6.830 472 2.905 10.207 67 [34] 19 Nekamese 11 - 2.182 236 93 2.511 87 [35] 20 Semau 8 - 1.395 - 287 1.682 83 [36] 21 Semau Selatan 6 - 1.167 10 172 1.349 87 [37] 22 Sulamu 7 1 2.506 11 1.384 3.901 64 [38] 23 Taebenu 8 - 2.993 208 580 3.781 79 [39] 24 Takari 10 5 1.230 1.160 3.066 5.456 23 [40]

Jumlah 177 29 48.567 7.874 21.668 78.109 62 Remark: NV: Number of Villages, VnE: Number of villages without electrification, HP: Number of households has been

electrified from PLN, HnP: Number of households were not electrified from PLN, HnE: Number of households were not electrified, ER: Electrification Ratio, NF: Number of households, S :Sources.


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NH1GRH1

HF1

HHE1

HHNE1

ER 1

P-SPV-DP1PR1

SPV-DP F

DR-HHE

LA1CKW-Ha

eCO2 RDPG

EF-DPG eCO2-DPG eCO2-SPV

eCO2 RSPV1

EF-SPV1

Energy

Time D

%eCO2

CO2 emissionreduction

2.46 ton

Fig. 1. Stock-flow diagram of optimization electrification ratio in Kupang Regency

17 18 19 20 21 22 23 24 25 26 27 28 2950

60

70

80

90

100%

ER 1ER 2ER 3

YearsNon-commercial use only!

Fig. 2. Comparison of electrification ratios for 1, 2 and 3 scenarios

17 18 19 20 21 22 23 24 25 26 27 28 290

10,000

20,000

30,000

40,000ton

eCO2-DPG1eCO2-DPG2eCO2-DPG3


17 18 19 20 21 22 23 24 25 26 27 28 290

500

1,000

1,500

ton

eCO2-SPV1eCO2-SPV2eCO2-SPV3


Fig. 3. Simulate the amount of CO2 emissions by using Solar PV and DPG

17 18 19 20 21 22 23 24 25 26 27 28 290

5,000

10,000

15,000

20,000

25,000

30,000Hh

HHNE1HHNE2HHNE3


17 18 19 20 21 22 23 24 25 26 27 28 2940,000

50,000

60,000

70,000

80,000

90,000

100,000

110,000Hh

HHE1HHE2HHE3


Figure 4. Simulation of household decline has no electricity and an increase in the number of households had electricity


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BIODIESEL MAGNETIZATION TO FUEL SAVINGS ON DIESEL ENGINES

Tatun H Nufus1, Armansyah H Tambunan 2, Radite Praeko AS2, Wawan Hermawan2

1 Energy Conversion Engineering Study Program, Faculty of Mechanical Engineering Politeknik Negeri Jakarta, Indonesia, Kampus UI Depok, 16425

E-mail: [email protected] 2 Agricultural Engineering Study Program, Faculty of Agricultural Technology

Bogor Agricultural Institute, Indonesia Kampus IPB Dramaga, PO BOX 220, Bogor, Jawa Barat

Abstract

Energy consumption of a machine is strongly influenced by the efficiency and effectiveness of combustion in the combustion chamber. The hypothesis of using electromagnetic fields in the fuel flow before entering the combustion chamber is one of the ways to obtain better combustion. The purpose of this research are: (1) Analyzing mechanism of applying electromagnetic field to fuel channel so as to save fuel consumption (2) Analyzing the influence of electromagnet on biodiesel fuel to diesel engine performance. This research will use experimental methods combined with theoretical analysis in explaining the phenomenon. Variation of magnetic field is produced by changing the number of coil windings 5000, 7000 and 9000, diameter wire 0.15 mm then observed electromagnetic phenomenon of fuel saving on diesel engine with VSM (Magnetometer Sampling Vibration) and FTIR (Fourier Transform Infra Red). Further observation of optimum diesel engine performance parameters especially specific fuel consumption, thermal efficiency). Test results with agricultural machinery (pump) occurred 17% fuel savings and 16.73% Keywords: electromagnetic, fuel saver, cluster, de-cluster, combustion

1. INTRODUCTION The use of fuel oil as a source of energy has increased significantly in line with population growth and technology. Along with this the level of exhaust emissions also increased. Fuel oil is a non-renewable energy source so that one day it will be difficult to obtain fuel and will eventually run out. Efforts to overcome this is to make savings in fuel use or search for alternative energy sources. Several studies have been conducted in order to save energy by improving the efficiency of combustion, including the mixing of additives to the fuel resulting in increased octane and cetane value, better combustion process and increased engine power (Dani M et al. 2004; Nurhandiansah. Fuel magnetization, by installing a permanent magnet on the fuel line to the combustion chamber effecting

decreased fuel consumption (9-30%) and reduced HC exhaust emissions (5-32%) and reduced CO (5-34.3% ) (Govindasamy P. 2007; Faris AS 2012; Jain S et al. 2012; Singh AK et al., 2013; Patel P et al., 2014; Kumar PV et al., 2014; Urge V et al.2014; Chaware K et al. 2015). But both have the disadvantage that almost all the chemical additives that are widely circulated and used by the community contain metals that harm to human health. On the other hand the use of permanent magnet kemagnetanya nature will decrease with the passage of time.

Another way to reduce the negative effects is made an instrument capable of functioning as a substitute for chemical additives of fuel and permanent magnets. The nature of this instrument is non-chemical and uses the physical energy of a magnet


mailto:[email protected]

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generated by an electric current (electromagnetic field). This instrument when mounted on gasoline and diesel engines can minimize fuel consumption up to 12.8-30% and reduce the level of HC exhaust emissions by 44-58% and CO decreased by 35-80%. (Guo H et al. 2000; Onkoronkwo C et al. 2010; Habbo ARA et al. 2010; Fuhaid N. 2011; Siregar H et al., 2012). This electromagnetic field does not contain harmful and safe elements used in diesel engine vehicles (Gaikwad DR et al 2014; Kumar PV 2014; Salih AM. Et al. 2015; Kolhe AV et al., 2014).

Based on the above statement it appears that the researchers only observed the performance of various engines due to fuel magnetization, but the theory underlying the phenomenon of fuel magnetization in improving the efficiency of combustion has not been described in more detail, whether the cause of this phenomenon the existence of cluster-de cluster on fuel, molecular polarity of materials more regular fuel or the excitation of electron fuel molecules. Therefore in this study will be discussed the phenomenon and its application on diesel engines. 2. THEORY 2.1. MAGNETIC DEVICE Biot-Savart’s law as shown in Figure 3. An electromagnetic devise made of galvanized cylinder with radius R, and length L, wounded with copper coils (N cycles) and applied on electric current (i), will generate electromagnetic fields along the axis, i.e the line where point P is located (Figure 1).

Figure 1. Devise for generating Electromagnetic Fields.

(Reitz JR, Milford FJ, Christy RW. 1991)

By dividing the length of the cylinder into elements (dz), each of which contains Ndz/L coils, the magnetic induction at point P could be calculated as equation 1 (Reitz JR et al, 1979):

( )[ ]∫ +−=L

oz

Rzz

dzL

RNizB

02/322

0

2

0 2)(µ

Where, ( )

( )[ ]3

2/3220

2

22'

2

222

0

0

0

2010

sin(

sin

sin1cos)(cot)(

sincos)(

cotcot

tantan

=+−

=

==→=

=−

−=−=−

−==

α

αα

ααααα

αα

αα

αα

RRzz

dRdz

ecff

Rzz

RzzRzz

zLzR

Introducing equation 2 to equation 1, the magnetic induction at the point P could be calculated by using equation (3):

( )( )

αααµ α

απ

dRR

LRNi

zB oz ∫−

−=2

13

22

0 sin/sin/

2)(

∫−

=1

2

sin2

απ

α

ααµ d

LNio

(2)

(3)

(1)


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( )[ ]21 coscos2 ααπµ

+−−=LNio

+=

2coscos 21 ααµ

LNio

If the length of the solenoid is much larger than the radius, and z0 does not approaching zero or L, then α1 and α2 can be formulated as in equation (4)

02

01 ; zL

RzR

−== αα

Maintaining quadratic terms in the expansion of cos α1 and cos α2, we obtain equation (5)

( )( )

−−−= 2

0

2

20

2

0 441

zLR

zR

LNizB oz

µ

if the radius of the solenoid is small, the magnetic field will be formulated as in eqution (6)

L

iNB oµ=

The permeability (μ) of the other materials is defined as multiplication of μo and called as relative permeability μr. Then, if the coil is wounded to a certain material, equation 6 can be rewritten as equation (7).

LiNB oµµ=

2.2. DIESEL PERFORMANCE The performance of agricultural diesel engines is similar to that of a diesel engine in general. This performance demonstrates the level of success in converting chemical energy contained in fuel to mechanical energy. For that, there are several parameters that are used as a measure of performance or performance for the machine can work optimally according to the purpose of the user. There are several parameters used to evaluate the performance of the diesel engine: a. Torque

The ability of the machine to produce the work is shown by the value of torque it produces. And in everyday situations torque is used for vehicle acceleration to get high speed. Torque is the multiplication of tangential forces with arm length. The formula for calculating torque on the engine (Figure 2) is as follows:

Torque = P. R (N.m) Where: P = force (N) R = lenght waterbrake dynamometer (m)

Figure 2. Dynamometer

The rotating rotor or part is connected to the stator using a non-fixed clutch such as electromagnetic, hydraulic or mechanical friction, the function of this coupling to convert the engine power to another form of power for easy measurement. The rotor and stator are supported by bearings that have small frictional losses. In the stator section there is an arm where at the end of the arm is mounted a force gauge. When the rotor is rotating the stator will rotate due to the non-fixed coupling relationship, but the stator rotation is held by a force gauge mounted on the end of the arm with a certain distance from the rotary axis. The force gauge will measure the magnitude of the force F (kg) due to the torque the rotor gives to the stator. Engine torque is obtained by multiplying the force on the end of the arm with the distance x:

T = Fx x = distance (m)

(7)

(6)

(4)

(5)


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F = force (kg) b. Break Horse Power The purpose of the operation of the machine is to generate power or power. Brake horse power is the power generated from the engine output shaft calculated based on the rate of work per unit time. The power value is proportional to the resulting force and its linear velocity or proportional to its shaft torque and angular velocity. To calculate the motor power used formulation:

bhp = ω . T bhp = 2π . n . T (Watt) bhp = 2π . n . T / 746 (hp)

T = Torsi (N.m) n = Center waterbrake dynamometer (rps) c. Spesific fuel consumption Fuel consumption (fuel consumption) is the amount of fuel used by the machine over a certain time unit. Whereas, sfc (specific fuel consumption) is the amount of fuel consumption of the engine over a certain time unit to produce an effective power. Since the calculation of sfc is based on bhp (brake horse power) it is called bsfc (brake specific fuel consumption). If in the test data obtained about the use of fuel m (kg) in time s (seconds) and the power generated by bhp (hp), then the fuel consumption per hour is:

)/(mbb3600 hourkgs

bbm ⋅=

the fuel consumption specific is:

bhpSfc bbm3600

⋅= (kg/kW.hour)

bbm = fuel consumption per unit time (kg/secon or kg/hour) s = time (secon) sfc = specific fuel consumption (kg/hp.hour) d.Thermal efficiency (ηth) Thermal efficiency is the amount of heat energy utilization stored in the fuel to be converted into effective power by internal combustion engine. Each fuel has a different calorific value so the resulting thermal efficiency will also differ. Theoretically the thermal efficiency of the fuel is expressed in the equation:

(bhp)= power Output = Q×

= LHV ( )

3. METHODOLOGY 3.1. MATERIALS AND METHOD Schematic diagram of the experiment is given in Figure 3. 45 ml of fuel is included in galvanized tubes that have been welded with coil wire of 9000 and given an electric current from 12 Volt batteries for 1200s at room temperature. Electromagnetic field was generated by using a galvanum tube (2.54 cm diameter and 10 cm length) wounded with 0.15 mm diameter of copper wire. The magnetic field was variated by using different number of coil, i.e 5000, 7000 and 9000 coils, with DC voltage of 12 volts. The intensity of the electromagnetic field was measured with Digital Teslameter Model MG-801, as shown in Figure 4.


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Figure 4. Measuring the strength of electromagneitc field

3.2. BIODIESEL SAMPLES Biodiesel samples were collected from Pertamina (Indonesia fuel company). It is a mixture of diesel fuel with FAME (Fatty Acid Methyl Ester) in proportion of 10% (Kementrian ESDM RI, 2013). A 45 ml biodiesel sample was placed in the galvanum tube. The sample was exposed to the electromagnetic field in 20 min at constant room temperature before used for measurement of viscosity and vibration.

3.3. FUEL CHARACTERISTICS a. Viscosity Fuel sample viscosities of B0, B10, B40, B70, and B100 were measured by using a modified oswald viscometer equit with magnetic ball and censoring coils connected to sound data detector as shown in Figure 3. Time of magnetic ball in fluid (tb) was calculated as soon as the ball passed the first coil and the second coil with the distance between these coils was 107 mm. The device was calibrated by using water as sample in order to obtain time accuracy of 1 μs. The fuel viscosity measurement was conducted with 5 replications. The viscosity was then calculated by using Eq (2).

( )L

gtr fbb9

2 2 ρρη

−= (2)

Here r is radius of magnetic ball (0.961 mm), g is acceleration of gravity (9.8 m/s2), ρb is density of the

ball (310,467 kg/m3), and ρf is density of the fuel. b. Vibration of the fuel molecules Molecular interactions of fuel samples were investigated by analyzing their infrared spectra. The Infra-red spectra were obtained using Fourier Transform Infrared (FTIR) spectroscopy (IR Prestige-21, Shimadzu Co. Ltd). The FTIR spectroscopy is equipped with L-alanine-doped triglycine sulfate (DLATGS) detector. The equipment was set at 4 cm-1 resolution, 20 scans accumulation, and absorbance (% A) measurement mode with wavenumber ranging from 4000 to 400 cm-1 in order to determine the functional groups which were formed in the fuel. Samples of 1 μL were mixed with 0.5 g KBr. The FTIR measurements were carried out at room temperature.

c. Magnetic moment Vibrating Sample Magnetometer (VSM) instrument (Oxford VSMI.2H) was used to measure the magnetic properties of the fuel samples as a function of magnetic field. The VSM had amplitudes of 1–1.5 mm. The fuel sample with volume of 10 μl was placed in the coiled tube. 4. ANALYSIS AND DISCUSSION EFFECTS ON KINEMATIC VISCOSITY Figure 5 shows the viscosity of various fuel blends exposed to electromagnetic fields at time intervals. Even though the biodiesel used in the experiment met the Indonesian National Standard (INS) (Kementrian ESDM RI, 2013), its viscosity was higher than petrodiesel, The electromagnetic exposure was proven to lower kinematic viscosity of the fuel samples (Rosensweig et al., 1969; Marques et al.,1997; Tung et al., 2003). Longer exposure time to


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electromagnetic field gave lower viscosity to the fuel, but not significant after 1200 second of exposure time. From the Figure 5, it can also be seen that biodiesel is more sensitive to electromagnetic exposure than petrodiesel, which is proved by more reduction of the viscosity value. It is noted that viscosity value is not only determined by the tensile strength of molecules but also by the state of molecular orientation at liquid (fuel sample)–solid (magnetic ball) interface (Tung et al., 2001; Nakano, 2003; Sengupta et al., 2014). It can be expected that the effect of magnetic exposure to the viscosity can be continued for longer exposure time by changing distribution of the molecular orientation at the interface.

4.2. EFFECTS OF ELECTROMAGNETIC EXPOSURE ON FUEL MOLECULE INTERACTIONS

Figure 6 shows the intensity of infrared absorption of petrodiesel and biodiesel fuels from FTIR observation at various wavenumber. Each peak in the graph shows the existence of functional groups. It was seen that petrodiesel and biodiesel have chemical bonds composed of C-H. However, peak existence at wavenumber of 1743 and 1176 cm-1, which represent C=O and C-O bonds, clearly shows the difference between petrodiesel and biodiesel (Berman et al., 2016; Ferrao et al., 2011; Furlan et al., 2012).

Figure 6 shows FTIR observation of B0, B10, B40, B70, and B100 fuels at varied exposure time to various wavenumber of electromagnetic field. It can be seen that spectrum of each fuel have identical shape and peak positions regardless the exposure

time. This means that the electromagnetic exposure time did not alter molecular structure of those fuels, which also prove that ionization might not occur. Furthermore, by comparing the fuel spectra after electromagnetic exposure to the original fuel spectra, the increment of the absorption intensity for each functional group was observed. The absorption intensity can be correlated to with molecular vibration of functional groups. The electromagnetic exposure causes the more number of molecules to vibrate. This phenomenon was consistent for all functional groups existing in the fuel samples, and thereby consistent for all fuel samples regardless of the fuel’s structure.

The vibrational increment of functional groups indicates that the polarization and transition of dipole moments of molecules occur due to the displacements of the fuel molecules and alteration of magnetic moment of those molecular interactions. Furthermore, molecular attracting energy of functional groups is determined by their vibrational frequency in which the higher frequency the lower the absolute value of molecular attracting energy. This is the reason of the fuel properties such as viscosity and surface tension (Faris et al., 2012), which are influenced by the molecular attracting energy, decrease after the fuels exposed to electromagnetic field.

The affinity of fuel molecules is determined by the frequency of the molecular vibration. Accordingly, low transmission, which leads to high absorption, imposes low molecular affinity which means less energy necessary to break the inter-atomic bonds apart. Therefore, the molecular


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affinity among molecules decreases after the electromagnetic exposure.

4.3. EFFECTS ON DIPOLE MOMENT Petrodiesel, biodiesel and its blend are considered as paramagnetic material in which each molecule of the fuel has dipole moment influenced by electromagnetic field. Figure 8 shows the dipole moment obtained from VSM measurement. It can be seen that the electromagnetic exposure causes the increase of the dipole moment, regardless of the biodiesel blending proportion. It means that the fuel molecules arranges themselves according to the direction of electromagnetic field or, in other word, its dipole direction was arranged properly (Sheldon et al., 2005; Kuwako et al.,1997; Nittoh et al., 2012). The main constituent molecule of the fuel sample is hydrocarbon (C-H) that has unpaired electron spin moments. When it is exposed to electromagnetic field, the induced magnetic moment becomes weak. A strong electromagnetic field exposing hydrocarbon molecules causes intermolecular hydrocarbons to repel each other (de-clustering), which creates an optimal distance between molecules of hydrocarbons and oxygen. The polarized molecules are relatively more active and oriented in accordance with the direction of the electromagnetic field.

4.4. PERFORMANCE ENGINE

a. Specific fuel consumption Figure 5.8 shows the graph of the relationship between the sfc and the load, the generator using fuel B100 magnetized with 3 pieces of fuel that has magnetic power of 969.23 Gauss at 12 volt voltage, it appears that the value of sfc on the generator is lower than the generator with the material

diesel fuel and B20 are not magnetized. The minimum sfc for filter 1 is 0.141 kg / hp.jam at 10.36 kW engine load rotation. The largest sfc value generated by Genset diesel fuel without magnetization is 0.210 kg / Hp.jam at the same load. On average, with the addition of a magnetic field (filter 1) instrument on fuel B20 decreases the sfc by 29% by diesel fuel standards, but when compared with a non-magnetized B20 the magnitude of 6.7%

b. Thermal Efficiency Figure 5.9 illustrates the thermal efficiency of the load engine function with an increasing graphic trend ranging from low to optimum, then decreasing with increasing engine speed. At low rotation, the mixing of fuel takes less optimum, so burning that happened less perfect. At the optimum point turbulence of fuel and burning time reaches the best condition so that get the highest efficiency. In addition to the engine rotation is too high just turbulence that occurs large enough so that mixing of fuel and air both but the time of the burning so quickly that much fuel is wasted.

5. CONCLUSION The results of the experiment, it can be explained that the magnetization of the fuel causes: 1. Fuel magnetization causes more

fuel molecules to vibrate This indicates the increasing number of molecules that have the attraction between the small molecules.

2. Increasing magnetic moment creates the regularity of fuel molecules

3. Decreased viscosity these three phenomena can inform a better chance of burning. Test results with agricultural


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machinery (pump) occurred 17% fuel savings and 16.73%.

6. REFERENCES [1] Ajaj R, Praihad Tipole, Virendra

Bhojwani, suhas Desmukh. 2013. Effect of Magnetic Field Strength on Hydrocarbon fuel viscosity and engine performance, International Jounal of Mechanical Enggineering, 1(7). 94-98.

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polarization of Cd Subtituted Fe3O4 films prepared by reactive sputtering. Journal Thin Solid Films. 594. 162-167.

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[23] Salim B, Toifur M. Pemanfaatan sensor induksi untuk Fuida. Proseding seminar Sains, 2012.

[24] Singh AK, Solank RM. 2013, Investigation of fuel saving in annealing lehr through magnetic material fuel sarver, International Journal of Science and Research.6(14): 178-180.

[25] Siregar H, Nainggolan R. 2012. Electromagnetic Fuel Saver for Enhanching The Performance of The Diesel Engine. Global Journal of Research in Engineering Mechanical and Mechanics Engineering. Global Journal Inc (USA). 12(6):1-4.

[26] Stuart B. 2002. Infrared Spectroscopy (Fundamenetal and Applications). ANTS. Wiley.

[27] Urge V, Dhobe A, Lutade S, Mudafale K. 2014, Performance of internal combustion (CI) engine under the influence of strong permanent magnetic field. IOSR Journal of Mechanical and Civil Engineering. 11-17.


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Figure 3 Schematic diagram of the experiment

Figure5 fuel viscosity Figure 6 the intensity of infrared absorption of biodiesel

Figure 7 Moment magnetic of biodiesel Figure 8 shows the graph of the relationship between the sfc and the load


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Identification of Power Quality through Online Data Monitoring

Isdawimah1, Ismujianto1,Nguyen Phuoc Loc2 1Electrical Department, Politeknik Negeri Jakarta Kampus UI Depok, Indonesia

2Faculty of Electric-Electronics and Computer, Kien Giang Vocational College, Vietnam [email protected]

Abstract

The decrease in the quality of power is caused by several factors, such as increasing the use of non-linear loads in the industry and in residential, the increasing growth of the renewable energy power plants which causes the increase of harmonic distortion, the disturbance that causes swell, sag and voltage flicker. This decrease in power quality can lead to overheating of equipment or transmission, permanent damage to some sensitive electronic equipment, reducing the life of the equipment and causing read errors on kWh meters. Provision of good quality and constant electrical energy source can be done by controlling several things, such as voltage, current, frequency, phase angle, power and harmonic order. Controlling of power quality can be done if the value of existing quantities is known continuously to be compared with the standard value to be achieved. Considering the value changes that occur so quickly and the required data is the latest data, it is necessary to do continuous monitoring with a good data acquisition system. This latest data will be the basis for improving the quality of electric power, so as to obtain good power and in constant quality. The monitoring system consists of data retrieval program, voltage and current sensors, analog to digital signal conversion circuit (ADC), data display using PC, and wireless information dissemination. A monitoring system is created to collect data, convert analog signal data into digital signals, display and store data on PC that can be accessed wirelessly. Data will be sent to the control system to improve the quality of Electric Power. Keywords: Power Quality, Monitoring, Real Time, Wireless Access, Controller 1. INTRODUCTION

Electric power quality problems include disruptions with a wide range, which can disrupt the operation of industrial machinery and cause production losses.The cause of the decrease inpower quality, among others, under voltage and over voltage, or called voltage flicker where the voltage lost for a moment.Voltage flicker for 0.5-3 seconds [1] or more could cause the computer to shut down, loss of data memory, loss of motor load, tripping conditions at adjustable speeds, and finaly cause losses due to failure of production process.For example, the result of the investigation of the cause of the failure of the operation of the machine based on the power quality data [2]. In addition, harmonic distortion can result in excessive heat on the equipment and on the

conductor and cause read error on kWh meter [3].

Considering the value changes that occur so quickly and the required data is the latest data, it is necessary to do continuous measurement(monitoring) with a good data acquisition system.The monitoring system should pay attention to a good of time resolution, in order to obtain data monitoring of optimal power quality [4], in order to obtain data monitoring of optimal power quality [4]. This Monitoring System has been used on smart grid system to inform real time about the use of electric energy and the cost to be paid by users [5]. In 2012, power quality monitoring in Malaysia uses conventional measuring instruments, Fluke 1750 [6]. Monitoring of the quality of power in a three phase system is used as a basis for re-configuration of


mailto:[email protected]

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neural-network [7]. Voltage monitoring using Neural Network on distribution systems in remote areas is done using only signals from utility substations [8]. In the application of power quality control system, the need for data retrieval and its processing becomes more complex. So the problem is how to get the latest measurement data in real time with a good of time resolution, so that data monitoring of power quality to be optimal. The latest data will be used as a basis for improving the quality of electrical energy sources, so as to obtain good power with constant quality. With the latest data, the improvement of power quality will be right on target.

2. THEORY State of the art in this research is the display of real-time power quality data that can be accessed via internet and accurate data acquisition method to get all information about electric power quality. The monitoring system created not only displays kWh values, but also other quantities related to the quality of electrical power,such as: waveform, voltage fluctuation, current, frequency, power, power factor, energy and THD (Figure 1). With the latest data, the increase in the quality of electric power can be done continuously according to the type of interference that ultimately get good power and constant quality.

Given the very dynamic data which changes its value so fast, then used LabVIEW software that is able to monitor and obtain data quickly and accurately.For example the use of LabVIEW software to monitor the performance of connected PV to the grid, by measuring environmental variables (ambient temperature and solar radiation) and harmonic levels (THD) generated by inverters. The

system is equipped with a transducer, a communication network of FP 1000 modules and a DAQ (data-acquisition) unit.

The results showed that there were variations of Voltage (95% -103%) of normal voltage with almost stable frequency (59.998 Hz- 60.001 Hz), power factor 0.925 and THD 4.16%. With reference to IEEE Std 929-2000 standard, this measurement meet the quality standards of PV power connected to the grid [9]. Another example is the collection of integrated PV system performance data with building (BIPV) connected to the grid in Gejiang province, China.Data collection is based on IEC Standard 61724, while data processing is based on IEA-PVPS T2-01 standard [10].The data collected is sent to the DAQ unit for processing.Signals from Boxes A and B are transmitted to PC by ethernet bus, while other signals from the meteorological data collection system are sent by wireless stations. Data in PC is processed by LabVIEW software and displayed in real time in PC and can be accessed via internet.

Sine Voltage and Current Acquisition

Effective VoltageVrms (V)

Efektif CurrentIrms (A)

FrequencyF (Hz)

Power FactorPF

Active PowerP (W)

Apparent PowerAP (VA)

Real-Time Acquisition(Real Time Clock)

Reactive PowerRP (VAR)

Active Power UsageU (Wh, kWh, MWh)

Figure 1. The amount of electricity shown in the monitoring system


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Harmonic distortion leads to various disturbances such as: increase in current in the neutral path, overheating of the transformer, mechanical vibrations in the motor, the failure work of the circuit breaker and the reading error of the measuring device [11].For example, harmonic distortion of high frequency switching in inverters over a range of 3 kHz to 150 kHz has led to reading error on kWh meters [12].

The preliminary research that has been made is about the harmonic effect of the power source on the measurement result of kWh meter.A significant kWh meter measurement error (7.7% -17.2%) occurs in loads using the power source with a high-frequency switching (21.7 kHz-100 kHz) with THD values of voltages and currents exceeding the permitted ranges [3].This is overcome by a signal sniffer method that takes into account of lost power due to switching [13].Furthermore, from the results of monitoring and data acquisition will be used to improve the quality of electric power, based on the classification of power quality disturbance refers to Table 1.

Table 1.Classification of power quality disturbance*

* Source: IEEE Power and Energy Society, 2015

3. METHODOLOGY The monitoring system performed is shown in Figure 2. Given the very dynamic data that changes its value so fast, it is used LabVIEW software that is able to monitor and obtain data quickly and accurately.The

monitoring system consists of data retrieval programs using LabVIEW software, voltage and current sensors, analog to digital signal converter (ADC),data display using PC and wireless information dissemination by using internet.The task of monitoring system is to collect data, convert analog signals into digital signals, display and store data on PC.Data on PC is processed by LabVIEW software, displayed in real time and accessible via internet.The data is then converted by the data acquisition system into an analog signal and sent to the control system.Data processing based on EN50160 standard. While the classification of power quality disruption based on IEEE Power and Energy Society (Table 1).

The magnitude of the electricity that is monitored includes: current, voltage, frequency, power factor, active power, reactive power, apparent power and active power usage, as shown in Figure 1.Data retrieval is done on lighting SDP panel and power SDP panel in Electrical Workshop and Laboratory (Figure 3),as there are many non linear loads in this location.Data collection is carried out under various conditions, including: at the low load, ie on holidays and during breaks; high load on weekdays. Data is taken every three seconds for 8-10 hours per day.

4. ANALYSIS AND DISCUSSION The electric power system at Worskhop and Laboratory of the Department of Electrical Engineering PNJ comes from a PLN source on a 20 KV distribution network which is dropped the voltage to 380/220 V by a power transformer, and a genset operated by an ATS panel.Currently, the capacitor bank is available but not yet installed, so it has not been able to improve the power factor in the power


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source.Figure 4 shows the block diagram and front panel of the LabView programming, while the location of data capture and display data can be seen in Figure 5.The block diagram and front panel are based on the amount of electricity that will be measured, displayed and stored in the PC.

Based on the results of the voltage monitoring in Figure 6, we can see a very dynamic voltage fluctuation, with a value of about 0.92% - 6.8% of the nominal voltage (220V).The voltage value tends to be higher than the nominal voltage, so the fluctuation is positive. Most of the voltage values in working hours are still at the allowable tolerance of 5%, but at break time (around 12:00) the voltage rises sharply up to 6.8%.

This is likely due to the simultaneous release of the load at break time.The voltage spike can be recorded properly, because the data is taken every 3 seconds.When every hour is obtained 1200 data, then in a day (8 hours work) obtained 9600 data for each magnitude of electricity. In addition to working days, the monitoring system is also capable of measuring the quality of electric power on a holiday.Based on the data obtained, we can see the unbalanced load distribution between phases, especially in the third phase (phase T). The current and power measured at phase T are much smaller than the phases of R and S,consequently the voltage at the phase T is less fluctuating than the voltage at the other phase. Low indicated power factor, less than 0.85.

5. CONCLUSION Based on the results of monitoring the magnitude of electricity, it can be identified the quality of electric power

system in Electrical Workshop and Laboratory Department of Electrical Engineering PNJ, as follows: 1. The voltage value of each phase fluctuates (0.92% - 6.8%) over its nominal value (over voltage), where the greatest fluctuation occurs during break time. 2. Monitoring system is made to become capable of measuring electrical magnitude every 3 seconds, both on working day and also holiday. It's just necessary to add backup electrical energy, so that the PC can still operate at the time of power outages. 3. Unbalanced load distribution between phases, especially in the third phase (phase T). , where the load is much smaller than the other phase. 4. The rearrangement of load distribution is necessary, so that the voltage, current and power of the three phases are balanced. 5. The capacitor bank is required to improve the low power factor.Part 5 consists of the conclusion and suggestions if any.

6. ACKNOWLEDGMENT Gratitude to such kind of programs which are supported by Decentralization Research “Skim Penelitian Produk Terapan” in which is allocation for Polytechnic State of Jakarta under contract number: 356/PL3.18/SPK/2017, that makes this research could be realized and released.

7. REFERENCES [1] IEEE Power and Energy Society,

2015. “Electrical Signatures of Power System Failures”. IEEE The Institute of Electrical and Electronic Engineers, Inc

[2] Soner Emec*, Jörg Krüger, Günther Seliger, 2016. “Online fault-monitoring in machine tools


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based on energy consumption analysis and non-invasive data acquisition for improved resource-efficiency”, 13th Global Conference on Sustainable Manufacturing - Decoupling Growth from Resource Use, www.elsevier.com/locate/procedia

[3] Isdawimah, R. Setiabudy, and R. Gunawan, 2014. "The Effect of High Switching Frequency on Inverter Against Measurements of kWh-Meter," IPTEK Journal of Proceedings Series, vol. 1, pp. 102-108.

[4] Mahdi Hajian, Asghar Akbari Foroud, Ali Akbar Abdoos, 2013, “New automated power quality recognition system for online/offline monitoring”. Neuro computing Journal Vol.128: 389–406 journal homepage: www.elsevier.com/locate/neucom

[5] Bochu Subhash and V.Rajagopal, 2014. “Overview of Smart Metering System in Smart Grid Scenario”. Power and Energy Systems: Towards Sustainable Energy (PESTSE 2014)

[6] F. Salim, K. M. Nor, D. M. Said, 2012, “Experience in Online Power Quality Monitoring Through VPN”International Conference High Quality of Power (ICHQP) 15th, IEEE

[7] Martin Valtierra-Rodriguez et al.,2013, “Reconfigurable instrument for neural-network based power-quality monitoring in 3-phase power systems”. IET Journal Generator Transmission Distribution, 2013, Vol. 7, Iss. 12, pp. 1498–1507

[8] Alex S. Silva, Ricardo C. dos Santosa, Fernando B. Bottura, Mário Oleskovicz, 2017, “Development and evaluation of a prototype for remote voltage monitoring based on artificial

neural networks”, Engineering Applications of Artificial Intelligence Journal, Vol. 57: 50–60. journal homepage: www.elsevier.com/locate/engappai

[9] J. Aristizabal, J. Hernandez, W. Moreno, G. Gordillo, 2005. “Development of a system for measuring the parameters determining the quality of the electrical power generated by grid-connected PV systems”. Conference Record of the Thirty-first IEEE Photovoltaic Specialists Conference, Page(s):1738 – 1741.

[10] Z. Xinjing and B. Li, 2011. "Development of a data acquisition system for grid-connected photovoltaic systems," in Electrical and Control Engineering (ICECE) International Conference on, pp. 5227-5230.

[11] Roger Dugan, Mark F. Mc. Granaghan, 2004. “Electrical Power Systems Quality”, Second Edition McGraw-Hill.

[12] J. Kirchhof and G. Klein, 2009. "“Result of the optinus project-deficits and Uncertainties in Photovoltaic Invertor Test Procedures," in 24th European Photovoltaic Solar Energy Conference and Exhibition, pp. 1-4.

[13] Isdawimah, R. Setiabudy, and R. Gunawan, 2015. "Improving kWh-Meter Performance at PV on Grid System By Multiplying the Number of Sampling Signal," Journal of Theoretical and Applied Information Technology, vol. 71 No.2, pp. 302-309.

[14] European Standard EN50160, 2004. “Voltage characteristics of electricity supplied by public distribution system”, Leonardo Power Quality Initiative.Adams


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B, Alden J, and Harris N (2006) Regional development and spatial

planning in an enlarged European Union. Aldershot: Ashgate.

Figure 2. Monitoring system circuit diagram Figure 3. Location of installation of monitoring system

Figure 4.Block diagram and front panel of the LabView programming

Figure 5. Installation location of monitoring system

.


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Figure 6. Voltage fluctuations per phase on weekdays


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CROSSFLOW AND PROPELLER TURBINE PERFORMANCE ON HEAD 3 M MHP SYSTEM

TO CAPACITY OF WATER FLOW

Paulus Sukusno, Andi Ulfiana, Benhur Nainggolan Lecturer Department of Mechanical Engineering State Polytechnic of Jakarta

1Email: p.sukusno.100 @ gmail.com

Abstract

The aim of this study is to determine the efficiency of turbine crossflow and turbine propellers in Micro Hydro Power (MHP) system head 3 meters, 3 ince pipe diameter, by adjusting the flowing water flow (debid) capacity to the turbine. The study was conducted on PLTMH system using crossflow debid turbine arranged with guide vane and with valve. the MHP system uses a turbine propeller turbine with valves. The results of the crossflow turbine debid was adjusted with a guide vane maximum efficiency of 18.7% and maximum power of 60.3 [W], and the debid was adjusted with a valve maximum efficiency of 17.3% and a maximum power of 60 [W]. Study on turbine propellers maximum efficiency of 18.8% and maximum power of 74.1 [W]. Keywords: Turbine, propeller, crossflow, guide vane, valve, electric 1. INTRODUCTION Micro Hydro Power (MHP) is a small-scale power plant suitable for use in rural areas with hilly natural conditions and running water throughout the year or anywhere, by blocking and / or running water somewhere so that a head of> 2 meters can be made MPH system. (Permadi E, et al., 2013), and (Helena MR, et al., 2012). State Polytechnic of Jakarta (PNJ) has made a prototype of MHP system and has been utilized for research (Sukusno P, et al., 2009, 2012, 2014) that is practical and reliable MHP system and angle impeller angle influence to MHP system efficiency. The above research has not utilized the wasted water flow from the system, whereas the water flow can still be utilized for power plants. Research on zero head MHP located on the surface of lake or river water surface area has been done (Steffi D, et al 2011 and Jana H, et al 2011).

Head (height of water) is done by stemming the flow of river water and or flowing water to a place so as to obtain sufficient head for PLTMH system (Vicente L, et al. 2012, Yaakoba OB, et al., 2014, Vineesh V, et al. 2012, Zainuddin H, et al., 2012). Formulation of the problem, to get enough heads can be by stemming the flow of river water and or flowing water kesuatu place so that obtained a height of water (head) is enough or (head> 2 meters) can be made MHP system. And to increase the efficiency of water energy conversion into mechanical energy is done with various water turbine units, so that obtained the optimal efficiency of the MHP system and how to harness the energy that has been wasted water to be recovered. Problem approach, in solving the problem is done by way of approach as follows:


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a. Research by making MHP system in natural location certainly not easy in doing the measurement, to facilitate measurement in research, MHP system made in laboratory scale. b. The approach taken in this research, ie river water source and dam with 3 m head is assumed by making the turbine input water bath and the water flow is obtained from the pump. c. The MHP system in this study uses a closed cycle cycle system, and is located in the PNJ Energy Engineering Laboratory. The specific objective of the research is to develop a low-head MHP system to improve system efficiency by varying the types of turbine or impeller type. The end result of this research; decent prototypes as a means of research center and review of low-head MHP systems, appropriate for student practicum, national / regional seminar articles and national / international journal articles. The novelty of this research is the MHP system, the water entering the turbine can be arranged simultaneously (alternately for turbine propeller and crossflow turbine).

2. THEORY Hydraulic power (Ph), is the input power of turbine or power owned by water, the magnitude is:

Ph = ρ Q g H [Watt] (1)

Description: ρ = density of water, [kg / m3] Q = flow rate of water, discharge, [m3 /s, l /s] g = earth's gravitational acceleration, [m /s2]

H = total falling water level (total head), [m] The water flow rate (Q), the Bernoulli equation written to measure the flow rate of the dam (form V) that fills the entire suppressed weir along a current line as follows:

Q = K 815

2g . Tg(θ /2). H5/2 [m3/s]

(2)

Description: K = flow coefficient on the dam, K = 0.582 θ = angle of dam form V, (= 540) Z = base distance is blocked to the base of the dam flow, [m] H = base distance of form V flow to surface, [m] Output power (Pout), output power generated from the generator according to the formula below.

Pout = V. I [Watt] (3)

Input power (Pin), is the input power of the turbine or power that belongs to the water entering the turbine, which is equal to the hydraulic power, the magnitude is:

Pin = ρ Q g H [Watt] (4)

Generator Efficiency (η), generator generated is the ratio of input power to the generator (Pin) compared to output power (Pout) generated generator. The efficiency of MHP (η), is equal to the power generated due to loss or generator power output (Pout) to power regardless of disadvantage, or hydraulic power (Pin), so η is the ratio of output power to the turbine input power.

η = Pin

Pout x 100 % (5)


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The series of turbine units (generators), to make a practical and reliable MHP, the mechanical system of the plant; turbine, transmission, and generator assembled into one so easily mounted and removed from the holder. 3. METHODOLOGY The research method will be observed directly on the object under study (exprimental), that is, in the low head (head 3 meter) MHP system using turbine propeller type Ф60, then to increase efficiency, water out system (wasted) is utilized to drive crossflow trubin. The first experiment was conducted on a 3 meter head MHP system with turbine propellers, the system's outflow water was used to move the turbine-type crossflow unit. The research scheme as in figure 2. Materials and test equipment: a. MHP system head 3 meters b. Water flow regulator (debid) c. Crossflow turbine unit d. Unit turbine propellers e. Unit of electrical system

equipment f. Measuring tools; voltmeter,

ampermeter, tachometer, flowmeter

The water flow rate controller (discharge) on the propeller turbine uses a valve. A water flow velocity (debid) and water flow controller on a crossflow turbine using a guide vane.

4. ANALYSIS AND DISCUSSION Data analysis

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