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TRANSCRIPT
Zuhusna Adilla Binti IbrahimB011110121
Supervisor : Encik Mohamad Fani bin Sulaima
Distribution Network Reconfiguration (DNR) Using Improved Artificial Bee Colony (IABC) For Energy Saving
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Motivation
In Malaysia, the growing industrialization and increasing standard of living has considerably increased the usage of energy. The increasing demand of the electrical energy is quietly related to the power demand. In order to cope the demand of the electricity, the distribution system has become more complex and causing power loss always occurred while distributing the electric. To reduce the power loss, the network distribution system needs to be reconfigured.
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• The demand for the electricity is rising due to the increasing population group.
• The distribution system has become more complex.• The current drawn increasing during the distribution of
electricity which lead to the instability.• As the system unstable, the power losses will occur.
Problem Statements
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Research BackgroundPower System
Generation Transmission Distribution
LoopMeshRadial
DNRAct of
opening and closing
switches
Easy to analyze and isolate fault
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Research BackgroundOptimization Technique
Heuristic Artificial Intelligence
ABCGAANN
Works by mimicking bee
behavior of finding food
source
Optimal Flow
Pattern (OFP)
Branch Exchange Method (BEM)
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Scope
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Previous WorkAuthor Project Title Method Used Description Comment
R.J Safri, M.M.A Salama, A.Y Chikhani
Distribution System Reconfiguration for Loss Reduction : A New Algorithm based on a set of Quantified Heuristic Rules
Quantified Heuristic Rules
Aim to reduce power losses
The method serves as pre-processor by removing the undesirable switching
Does not perform the complex analysis load flow.
This proposed method does not perform the load flow analysis
A new artificial intelligence technique is proposed
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Author Project Title Method Used
Description Comment
S. Ganesh Network Reconfiguration of Distribution System Using Artificial Bee Colony Algorithm
ABC algorithm technique
Aim to minimize power losses
The ABC is tested on the 33-bus system
Compared with Refined Generic Algorithm (RGA) and Tabu Search Algorithm (TSA)
ABC has the best performance in minimizing power losses.
Does not apply the improved ABC algorithm
Does not improve the voltage profile
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Author Project Title Method Used
Description Comment
M. Assadian, M.M Farsangi, Hossein
GCPSO in cooperation with graph theory to distribution network reconfiguration for energy saving
Guaranteed Convergence Particle Swarm Optimization (GCPSO) and Particle Swarm Optimization (PSO)
Objectives are to reduce power loss and enhancement of voltage profile
Compared with applied GA + GCPSO
Results show that the GA and GCPSO are better than conventional PSO in term of energy saving.
The paper does not show the cost saving
The proposed method does not show the value of energy saved.
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METHODOLOGY
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MethodologyStart
Initialization Phase
Employed Bee Phase
Onlooker Bee Phase
Scout Bee Phase
Memorize the best solution
Exceed maximum
cycle?
Stop
No
Yes
Flowchart of ABC
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Improved Artificial Bee Colony (IABC) Technique
• Inspired by the improved strategies of Particle Swarm Optimization (PSO)• An inertial weight w inspired by PSO evolution equation and its improving
strategies are added.• The benefits of using this technique are: Maximize the exploitation capacity Balanced the exploitation and exploration phase
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Start
Initialization Phase
Employed Bee Phase
(Weight is added here)
Onlooker Bee Phase
Scout Bee Phase
Memorize the best solution
Exceed maximum
cycle?
Stop
No
Yes
Flowchart of IABC
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Energy Saving Formulation
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RESULTS AND DISCUSSION
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Test System Analysis
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• In this system, the 33-bus initial configuration are consists of:
• 1 feeder, 32 normally closed tie line and 5 normally open tie lines.
• The normally open tie lines are represented by 33, 34, 35, 36 and 37 branches.
Sectionalizing Switch
Tie Switch
Figure 1: IEEE 33-bus radial original network configuration
Test System Analysis
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• The IABC algorithm is tested on 33-bus network system for 30 times.
• From the 30 run times, only 12 of them are radial.
• The best combination of switches that has been chosen is at 20 because value of power loss at this 20th running times is the lowest which is 107.1 kW and has the fastest computational time (1222.6623s).
• The best combination switches are opened at S31, S6, S21, S13 and, S37
Test System Analysis
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Figure 4.2: The Power Loss after IABC Network Reconfiguration
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Power Losses
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Test System Analysis
Figure 5.1: Power Loss (kW) Comparison between the Network Reconfiguration
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Figure 5.2: Loss Reduction Comparison between the Network Reconfiguration
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Voltage Profile
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Figure 4.5: Voltage Profile of the Three Network Reconfiguration System25
Energy Saving & Cost Saving
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Company SAIDI (Minute)2008 2009 2010 2011 2012 2013
TNB
68.31
56.72
88.1
63.25
49.30
56.20
Data from SAIDI (TNB)
Table 4.3: The Average SAIDI data in Peninsular Malaysia [22]
Region Electricity Average Selling Price (sen/kWh)
Peninsular Malaysia 33.88
Table 4.4: The Electricity Average Selling Price (sen/kWh) [22]
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Energy Saving
Network Reconfiguration
Initial Network ABC IABC
Total Power Loss (kW)
202.71 134.26 107.10
Energy (kWh) 4 833.82 3201.56 2553.90Total loss Cost for
one day (RM)1 637.70 1084.69 865.26
Table 5.2: The total energy and total cost loss in one day
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Total Cost Loss
Figure 5.3: The Monthly Cost Loss of the Network Reconfiguration
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Figure 5.4: Total Cost Loss for a Year
Total Cost Loss
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Conclusion
• IABC algorithm technique has shown a good performance in minimizing the power loss when it is compared to the ABC and other optimization method
• Succeeded in reducing the energy losses in the distribution network system• The objectives of this study have been achieved successfully
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Recommendation
• Tested on 14-kV and 69-kV IEEE test bus system in order to get better outcomes and analysis.
• To consider the Distribution Generators (DGs) in the future.
• To consider the power quality.
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References[1] R.J Safri, M.M.A Salama and A.Y Chickani, “Distribution system reconfiguration for loss reduction: a new algorithm based on a set of quantified heuristic rules”, Proceedings of Electrical and Computer Engineering, Vol. 1, Canada , pp. 125-130,1994.[2] S. Ganesh, “Network Reconfiguration of Distribution System Using Artificial Bee Colony Algorithm”, International Journal of Electrical, Robotics, Electronics and Communication Engineering, Vol.8, No. 2, pp. 403-409, 2014.[3] M. Assadian, M. M. Farsangi, Hossein Nezamabadi, “GCPSO in cooperation with graph theory to distribution network reconfiguration for energy saving”, Energy Conversion and Management vol. 51,pp. 418-417, 2010. [22] Suruhanjaya Tenaga, Performance and Statistical Information on Electricity Supply Industry in Malaysia, pp. 22-24, 2013.[14] M. Rohani, H. Tabatabaee & A. Rohani, “Reconfiguration Optimization for Loss reduction in Distribution Networks using Hybrid PSO Algorithm and Fuzzy Logic”, MAGNT Research Report, Vol. 2(5), pp. 903-911, 2011
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