jing - uk-india bi-lateral workshop on sustainable energy and smart grid

energy, power & intelligent control

Saving energy through intelligent modelling,

control, and optimization

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Dr Jing Deng

http://jing-deng.com

Energy, Power and Intelligent Control

School of Electronics, Electrical Engineering and Computer Science

Queen's University Belfast

27/03/2014

[email protected]






Content

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1. Background.

2. EPSRC/RCUK (EP/G042594/1), “UK-China Science Bridge in Sustainable

Energy and Built Environment (UC-SEBE)”.

3. 2010-2013, EPSRC (EP/G059489/1), “Thermal Management in Polymer

Processing”.

4. EPSRC (EP/L001063/1), “Intelligent Grid Interfaced Vehicle Eco-charging

(iGIVE)” (http://i-give.org.uk).

http://i-give.org.uk/




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Ultra Power Saving Mode powered by PowerXtend

WebXtend: boots extra 25%NavXtend: boots extra 25%GameXtend: boots extra 50%

10% Battery last for 24 hours

Background


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• DU Battery Saver• Easy Battery Saver• Battery Widget Reborn• JuiceDefender• Battery Doctor

Background


5Background


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eQ-3 MAX! Wireless Radiator Thermostat (~£25)

eQ-3 MAX! Wireless Window Sensor (~£20)

eQ-3 MAX! Eco Switch (~£20)

eQ-3 MAX! Cube LAN Gateway (~£45)

Background

http://www.eq-3.de


7Background

Renewable energy resources

EV Battery

Economic dispatch

Openpmu.org

Fault detection and diagnosis

Model-based Model-based

Intelligent Energy and health monitoring

Modelling, control, and optimization


1. UK-China Science Bridge

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Intelligent

system & control

Power system

Civil engineering

Queen’s Science Bridge: pursue energy and infrastructure issues in a

sustainable manner, via enhanced uptake of technology, knowledge and

expertise and strengthened collaborations

http://www.google.co.uk/imgres?q=%E4%B8%8A%E6%B5%B7%E4%BA%A4%E9%80%9A%E5%A4%A7%E5%AD%A6&hl=en&gbv=2&biw=1024&bih=445&tbm=isch&tbnid=ZgDiDaXGT0NviM:&imgrefurl=http://www.nipic.com/show/3/82/797f70d1da3304cd.html&docid=N7--XA4bw3NFiM&imgurl=http://pica.nipic.com/2008-03-03/20083394124752_2.jpg&w=1000&h=387&ei=IeaoT7WgD6aB4gSFlYGiCQ&zoom=1&iact=hc&vpx=617&vpy=27&dur=827&hovh=139&hovw=361&tx=265&ty=83&sig=100557456488270525119&page=3&tbnh=67&tbnw=174&start=25&ndsp=15&ved=1t:429,r:4,s:25,i:136



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05

101520253035

Full time PhD Exchanged students

Undergraduate Research fellows

Trained 30 PhD students, 20 exchange students, 11 undergraduate students, and 4

research fellows.

Engineering leaderships



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Invited lectures Research training Honours

30 invited lectures and seminars, 10 research training activities, 14 visiting professorships

Academic engagements



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partent journal conference

163 conference papers; 44 journal papers

Joint publications



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1. Joint Laboratory on Energy and Automation with Baosteel, SAIC, SHU

Joint project won Science and Technology Progress

Award by Shanghai Municipal Government

Creation prize from Shanghai International

Industrial Fair 2010

first prize of Chinese Machinery Industry

Science and Technology



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2. Joint Laboratory on Autonomous Service Robots

Autonomous wheelchair project in Shanghai EXPO 2010



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1). 2010 International Conference on Life System Modeling and Simulation& 2010 International Conference on Intelligent Computing for Sustainable Energy and Environment

3). UK-China Science Bridge Forum & 2nd International Conference on Intelligent Computing for Sustainable Energy and Environment

2). UK-China workshop and PhD student summer school on smart grids

3. Jointly organized international conferences/workshops



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Knowledge Transfers

Intelligent optimized algorithms deployed in Shanghai Waigaoqiao & Caohejing power stations

Wireless data acquisition deployed in Nantong sewage treatment company

Hybrid network measurement and monitoring system used in Baosteel company



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In situ testing of concrete

in the ‘Bird’s Nest Stadium’ Beijing

QUB structural health

monitoring station at Hangzhou Bay Bridge

QUB energy efficiency

monitoring system

Knowledge Transfers



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Knowledge Transfers

Crown cap surface

detection systems for a Chinese company, detecting

280,000 caps/hour.

Technologies have been used in

developing new modules for SUPMAX Distributed Control System series for energy and environment applications

by SAIC, deployed in many countries and regions.


2. Thermal Management in Polymer Processing

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The aim of the proposal is to develop methods and technologies to facilitate the efficient use of thermal energy in existing polymer processing plant operation and in the design of future plants.


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Develop monitoring and control techniques to optimise energy use and quality in extrusion

• Development of inferential techniques to monitor melting stability.

• Development of low cost techniques to monitor power consumption on-line

• Development of an ‘expert’ system for machine set-up and on-line optimisation

WP3


Melt pressure

Melt temperature

Feed rate

Barrel temperature

Screw speed

Viscosity

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energy, power & intelligent control21

Killion KTS-100 laboratory single-screw extruder

Geometrical screw parameters

DC motor power (kW) 2.24

Screw diameter (mm) 25

No. of barrel temperature zones 3

Additional temperature zones connected

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Operating speed range (rpm) 0-115

Extruder Specifications

2. Thermal energy monitoring- the extruder



2. Thermal energy monitoring- the heating and cooling

Zone 1, Heating band1.296kw



Clamp ring heating band0.4964kw

Adapter heating band0.106kw

Controller circuit0.0016kw

Other circuits0.06kw

Cooling fan0.04637kw

Heating and cooling elements of the single screw extruder

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L1 L2 NL3

L1:• Controller circuits• Zone 3 heating and cooling• Motor drive power supply

L2:• Zone 1 heating and cooling• Zone 4 heating

L3: • Zone 2 heating and cooling• Zone 5 heating

2. Thermal energy monitoring- power supply2. Thermal Management in Polymer Processing


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PIDController

Heating band

Cooling Fan ExtruderBarrel Zone

Temperature

SetTemperature

AFM215-303DURAKOOL Mercury displacement contactor

Time-proportional control

2. Thermal energy monitoring- the controller2. Thermal Management in Polymer Processing


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More close to the actual

power consumption



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Advantage: • Additional power consumption measurement• More accurate thermal energy monitoring• Expensive power meter is not required

Separate power supply

2. Thermal energy monitoring- the advantages2. Thermal Management in Polymer Processing


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Plot of energy consumption by different zones, screw speed at 10, cooling temperature at 25 degreeTemperature settings 170-180-190, material: LDPE 2102TN32W, MFR:2.5g/10min at 190 °C and 2.16 kg

2. Thermal energy monitoring- monitor separate heating zones2. Thermal Management in Polymer Processing


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Extruder Killion KTS-100Material SABIC LDPE 2100TN00WCooling temperature setting: 25Temperature setting: 170-180-190Screw speed: 40 rpmData file: 20120720C

2. Thermal energy monitoring- monitor separate heating zones2. Thermal Management in Polymer Processing


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L1 N



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Power in

Power out



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Those rising edges contain high-frequency energy from harmonics of the PWM signal's frequency. Because a motor presents an inductive load to the inverter circuits, its inductance filters much of the high-frequency energy. The high frequencies do little to rotate the motor, but the energy in those frequencies must go somewhere, and the high-frequency energy dissipates as heat.

Measure PWM motor efficiency


http://www.tmworld.com/design/design-and-prototyping/4379582/Measure-PWM-motor-efficiency


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Motor Apparent power consumption

Power factor

Active power

Screw speed

Voltage

current

current

Screw speed



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V_a = R_a * I + K_v * w

R_a = 12.4222;K_v = 0.0038

V_a = 12.4222 * I + 0.0038 * N



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Viscosity measurement

On-line rheometer In-line rheometer Off-line rheometer



2. Viscosity monitoring

3/09/2012 Queen's University Belfast

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Viscosity calculation



2. Viscosity monitoring

3/09/2012 Queen's University Belfast

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Viscosity calculation

By substituting typical values



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Table 1: The comparison of forward and backward selection

Advantage Disadvantage

Forward Fast/less computing Constrained minimization

Backward Slow/much computing Unconstrained minimization

• Forward selection method (constrained minimisation)

y

X1X1 θ1

e = y – X1 θ1

y

X1X1

= y – X1 θ1-X2 θ2

X2

X2 θ2

e

θ 1



1 2 k n

j

Selected termsStage 1: Forward model selectionStage 2: Backward model refinement

- Loop 1 ……..- Loop 2 ……..- Loop 3 ……..………

Candidate terms pool

Two-stage selection

• Remains efficient and effective from FRA• Eliminates optimization constraint in FRA• Reduces the training error without increasing model size



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Consider a general nonlinear model

Write in a matrix form



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A optimal design criterion

where is known as the design matrix

The new cost function becomes



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define

Some properties of R



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Also define some auxiliary matrices



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Recursive updating

Net contribution of a new term to the cost function



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Employing Branch and Bound



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The net contribution of a new term to the cost function

where



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3. Intelligent Grid Interfaced Vehicle Eco-charging (iGIVE)


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WP1) Framework of energy and information flows

WP2) Power flow control-based battery model

WP3) Bi-directional traction drive charging system

WP4) Model for environment friendly EV charging

WP5) Optimal dispatching strategy for EV

WP6) Holistic system model for the impact of EV actors

3. Intelligent Grid Interfaced Vehicle Eco-charging (iGIVE)


Future collaboration

• Uk and china agree £20 million low carbon innovation programme (announced on 06 March 2014).

• Energy Resilient Manufacturing (EPSRC, 04/04/2014)

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Questions ?

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Jing DENG

EPIC Research Cluster

[email protected]

jing - uk-india bi-lateral workshop on sustainable energy and smart grid

Education