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Fuzzy Controller of a Small Wind-Fuel Cell Hybrid Energy System
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Emerging Technologies in Energy Engineering
• Wind and Solar energy technologies are the forerunners• Hydrogen based energy conversion bears good potential
Source: Worldwatch Institute Source: Plug Power Inc., NY
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Renewable Resources
•Wind Power Resources Allocation & Application in
He’nan
Author:[Lu Minghua /Kang Yan/ Liu Guoshun]
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Hybrid Energy Systems in Stand-alone Applications
• Energy from a renewable source depends on environmental conditions
• In a Hybrid Energy System, a renewable source is combined with energy storage and secondary power source(s).
• Mostly used in off-grid/remote applications• Could be tied with a distributed power generation network.
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Wind-Fuel Cell Hybrid Energy System• A wind turbine works as a primary power source• Excess energy could be used for hydrogen production by an
electrolyzer• During low winds, a fuel-cell delivers the electrical energy using
the stored hydrogen• Power converters and controllers are required to integrate the
system
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Model Formulation
Models Developed for:
• Wind Turbine• PEM Fuel Cell• Electrolyzer • Power ConvertersApproach:
• Components are integrated into a complete system through control and power electronic interfaces
• Simulation done in MATLAB-Simulink®
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Wind Energy Conversion System (WECS) Small wind turbine:WG-150 (Jiujiang
Device) Wind field PM DC generator Controller
• Reference speed generator• Fuzzy logic controller
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Small WECS
Power in the wind:
Captured power:
3windwtwind VA
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1P
3effwtpa VA
2
1CP
Power 50 W ~ 10 KW
Diameter 1 ~ 7 m
Hub-height ~ 30 m
Control/Regulation Stall, Yaw, Pitch, Variable speed
Over-speed Protection Horizontal/Vertical furling
Generator DC, Permanent Magnet Alternator
Application Stand-alone, Grid connections
Model Formulation 9
PEM Fuel Cells
Polymer membrane is sandwiched between two electrodes, containing a gas diffusion layer (GDL) and a thin catalyst layer.
The membrane-electrode assembly (MEA) is pressed by two conductive plates containing channels to allow reactant flow.
H2
H2
H2
O2
O2
O2
Gas diffusion layer
Flow channels
Catalyst later
Conductive plates
Electrolyte
Electric load
Anode Cathode
FuelI In
H2
H2O
1/2O2
H2O
Electrolyte
Oxidant in
Depleted Fuel Depleted oxidant
Positive Ion
Negative Ion
2e-Load
Model Formulation 10
Alkaline Electrolyzer
Aqueous KOH is used as electrolyte Construction similar to fuel cell
Model Formulation 11
Fuel Cell Model FormulationElectrochemical Model Cell voltage & Stack voltage:
Open circuit voltage:
Activation overvoltage:
Ohmic overvoltage
ohmicactNernstcell EV
ENernst
Ract
Rint
Cdl
+
Vcell
-
Ifc
dlact
act
dl
fcact
CR
V-
C
I=
dt
dV
intfcohmic RI
actactV
cellfcstack VNV
5.0'O
'H
fcfc
3-Nernst 22
pplnF2
RT)+15.298-(T10×5.8229.1=E
Model Formulation 12
Power Electronic Converters
• Variable DC output of the Wind turbine/Fuel cell is interfaced with a 180 V DC bus
• Load voltage: 220 V, 50Hz• Steady state modeling of DC-DC converters• Simplified inverter model coupled with LC filter
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Controller DesignControl Problem
I. Below rated wind speed: Extract maximum available power
II. Near-rated wind speed:Maintain constant rated power
III. Over-rated wind speed : Decrease rotor speed (shut-down)
I II III
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Design of Fuzzy Logic Controller
The controller is a 2 input, 2 output fuzzy controller with 7 membership functions for the inputs, and 7 for the outputs.
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Fuzzification
The 7 membership functions were assigned the linguistic labels of Positive Large, Positive Medium, Positive Small, Zero, Negative Small, Negative Medium, and Negative Large.
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fuzzification.m
function [ fuzzy ] = fuzzification( data, rules )
% Define linguistics plarge = 1; pmedium = 2; psmall = 3; zero = 4; nsmall = 5; nmedium = 6; nlarge = 7;
if data >= rules( plarge )
fuzzy = plarge;
elseif data >= rules( pmedium )
fuzzy = pmedium;
elseif data > rules( zero )
fuzzy = psmall;
elseif data == rules( zero )
fuzzy = zero;
elseif data <= rules( nlarge )
fuzzy = nlarge;
elseif data <= rules( nmedium )
fuzzy = nmedium;
elseif data <= rules( nsmall )
fuzzy = nsmall;
elseif data < rules( zero )
fuzzy = nsmall;
end;
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Fuzzy Rule-base
The rule-base was implemented with a two input, two output system. All the inputs use the same linguistic modifier’s of positive large (pl), positive medium (pm), positive small (ps), zero (z), negative small (ns), negative medium (nm), and negative large (nl). Based on the linguistics, 49 rules were established and outputs were chosen based on the desired output for the system.
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Defuzzification
function [ crisp ] = fuzzification( data, rules )
crisp = rules( data );
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System Integration
Wind-fuel cell system interconnection
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MATLAB-Simulink® Simulation
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Constant temperature in fuel cell & electrolyzer assumed Step changes in
• Wind speed• Load resistance• Hydrogen pressure
Simulation
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Results System response with random wind
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Fuel cell performance (step response)
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Power converter performance (step response)
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Summary
High settle time for the wind turbine Controlled operation of the wind
turbine, fuel cell, electrolyzer and power converter found to be satisfactory
Coordination of power flow within the system achieved
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REFERENCES
http://www.fuelcell-magazine.com/eprints/free/johnsonmattheyapril03.pdf
http://www.ecn.nl/bct/solupor.en.html
http://www.efcf.com/reports/E04.pdf
http://www.gatech.edu/news-room/release.php?id=618
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Thank You
For your attention & presence