1 Fuzzy Controller of a Small Wind-Fuel Cell Hybrid Energy System

2 Emerging Technologies in Energy Engineering Wind and Solar energy technologies are the forerunners Hydrogen based energy conversion bears good potential Source: Worldwatch InstituteSource: Plug Power Inc., NY

3 Renewable Resources Wind Power Resources Allocation & Application in He’nan Wind Power Resources Allocation & Application in He’nan Author: [Lu Minghua /Kang Yan/ Liu Guoshun]

4 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.

5 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

6 Model Formulation Models Developed for: Wind Turbine PEM Fuel Cell Electrolyzer Power Converters Approach: Components are integrated into a complete system through control and power electronic interfaces Simulation done in MATLAB-Simulink ®

7 Wind Energy Conversion System (WECS)  Small wind turbine:WG-150 (Jiujiang Device)  Wind field  PM DC generator  Controller Reference speed generator Fuzzy logic controller

8 Small WECS Power in the wind: Captured power: Power50 W ~ 10 KW Diameter1 ~ 7 m Hub-height~ 30 m Control/RegulationStall, Yaw, Pitch, Variable speed Over-speed ProtectionHorizontal/Vertical furling GeneratorDC, Permanent Magnet Alternator ApplicationStand-alone, Grid connections

Model Formulation9 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.

Model Formulation10 Alkaline Electrolyzer  Aqueous KOH is used as electrolyte  Construction similar to fuel cell

Model Formulation11 Fuel Cell Model Formulation Electrochemical Model  Cell voltage & Stack voltage:  Open circuit voltage:  Activation overvoltage:  Ohmic overvoltage

Model Formulation12 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

13 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) I IIIII

14 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.

15 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.

16 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;

17 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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19 Defuzzification function [ crisp ] = fuzzification( data, rules ) crisp = rules( data );

20 System Integration Wind-fuel cell system interconnection

21 MATLAB-Simulink ® Simulation

22  Constant temperature in fuel cell & electrolyzer assumed  Step changes in Wind speed Load resistance Hydrogen pressure Simulation

23 Results System response with random wind

24 Fuel cell performance (step response)

25 Power converter performance (step response)

26 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

27 REFERENCES magazine.com/eprints/free/johnson mattheyapril03.pdf magazine.com/eprints/free/johnson mattheyapril03.pdf ml f f room/release.php?id=618

28 Thank You For your attention & presence