1 fuzzy controller of a small wind-fuel cell hybrid energy system

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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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