Transcript Modeling and Simulation of a Small Wind Energy Conversion

Fuzzy Controller of a Small Wind-Fuel Cell
Hybrid Energy System
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Emerging Technologies in Energy Engineering
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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
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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
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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:
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Wind Turbine
PEM Fuel Cell
Electrolyzer
Power Converters
Approach:
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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)
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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
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3
P


A
V
Power in the wind: wind
wt wind
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1
3
Captured power: Pa  C p AwtVeff
2
Power
50 W ~ 10 KW
Diameter
1~7m
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
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PEM Fuel Cells
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Polymer membrane is sandwiched
between two electrodes,
containing a gas diffusion layer
(GDL) and a thin catalyst layer.
Conductive plates
H2
O2
H2
O2
H2
O2
Flow channels
Gas diffusion layer
Catalyst later
Electrolyte
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The membrane-electrode assembly
(MEA) is pressed by two
conductive plates containing
channels to allow reactant flow.
2e-
Electric load
Load
FuelI In
Oxidant in
H2
H 2O
Positive Ion
Negative Ion
1/2O2
H 2O
Depleted Fuel
Depleted oxidant
Anode
Cathode
Electrolyte
Model Formulation
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Alkaline Electrolyzer
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Aqueous KOH is used as electrolyte
Construction similar to fuel cell
Model Formulation
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Fuel Cell Model Formulation
Electrochemical Model
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Cell voltage & Stack voltage:
Vcell  E Nernst   act   ohmic
Vstack  N fcVcell
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Open circuit voltage:
E Nernst =1.229  8.5×10- 3 (T fc -298.15 )+
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Activation overvoltage:
Vact  act
dVact I fc
V
=
- act
dt
Cdl RactCdl
RT fc
2F

 
ln p'H 2 pO' 2
Cdl
0 .5

Ifc
Rint
Ract
+
Vcell
ENernst
-

Ohmic overvoltage
ohmic  I fc Rint
Model Formulation
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Power Electronic Converters
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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
Model Formulation
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Controller Design
Control 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)
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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
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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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function [ fuzzy ] = fuzzification( data, rules )
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% Define linguistics
plarge = 1;
pmedium = 2;
psmall = 3;
zero = 4;
nsmall = 5;
nmedium = 6;
nlarge = 7;
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if data >= rules( plarge )
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fuzzification.m
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;
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Fuzzy Rule-base
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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
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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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Simulation
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Constant temperature in fuel cell & electrolyzer assumed
Step changes in
• Wind speed
• Load resistance
• Hydrogen pressure
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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
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http://www.fuelcellmagazine.com/eprints/free/johnson
mattheyapril03.pdf
http://www.ecn.nl/bct/solupor.en.ht
ml
http://www.efcf.com/reports/E04.pd
f
http://www.gatech.edu/newsroom/release.php?id=618
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Thank You
For your attention & presence
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