Wind Energy Conversion System Using Advanced Speed Control and Model-Based Loss Minimization

This paper presents a new optimization strategy for a stand-alone wind energy conversion system (WECS). The WECS is comprised of a variable-speed wind turbine (WT) with a vector-controlled self-excited induction generator (SEIG), a three-phase full-bridge converter, and a DC-bus containing the excit...

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Veröffentlicht in:IEEE transactions on energy conversion 2023-06, Vol.38 (2), p.1-10
Hauptverfasser: Bubalo, Matija, Basic, Mateo, Vukadinovic, Dinko, Grgic, Ivan
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Basic, Mateo
Vukadinovic, Dinko
Grgic, Ivan
description This paper presents a new optimization strategy for a stand-alone wind energy conversion system (WECS). The WECS is comprised of a variable-speed wind turbine (WT) with a vector-controlled self-excited induction generator (SEIG), a three-phase full-bridge converter, and a DC-bus containing the excitation capacitor, batteries, and a load. The control strategy incorporates an advanced model-based SEIG loss minimization and fuzzy-logic WT optimization. The latter utilizes a hedge-algebra speed controller to ensure fast response with practically no overshoot in the whole WT operating range, which cannot be achieved with the conventional proportional-integral (PI) controller. Consequently, the WT optimization time step is shortened and its convergence accelerated. The proposed SEIG loss minimization is based on the corresponding mathematical model that accounts for magnetic saturation and variable stray load and iron losses. Simultaneous optimization of the WT and SEIG is enabled, which results in greater total energy output compared to the successive WT-SEIG optimization. The proposed control strategy is run in real-time using the DS1103 board (dSpace) with a 1.5 kW SEIG driven by an emulated WT. It is experimentally evaluated over a wide WT operating range and compared with several competing strategies involving successive optimization, PI speed control and/or less elaborate SEIG models.
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The WECS is comprised of a variable-speed wind turbine (WT) with a vector-controlled self-excited induction generator (SEIG), a three-phase full-bridge converter, and a DC-bus containing the excitation capacitor, batteries, and a load. The control strategy incorporates an advanced model-based SEIG loss minimization and fuzzy-logic WT optimization. The latter utilizes a hedge-algebra speed controller to ensure fast response with practically no overshoot in the whole WT operating range, which cannot be achieved with the conventional proportional-integral (PI) controller. Consequently, the WT optimization time step is shortened and its convergence accelerated. The proposed SEIG loss minimization is based on the corresponding mathematical model that accounts for magnetic saturation and variable stray load and iron losses. Simultaneous optimization of the WT and SEIG is enabled, which results in greater total energy output compared to the successive WT-SEIG optimization. The proposed control strategy is run in real-time using the DS1103 board (dSpace) with a 1.5 kW SEIG driven by an emulated WT. 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The WECS is comprised of a variable-speed wind turbine (WT) with a vector-controlled self-excited induction generator (SEIG), a three-phase full-bridge converter, and a DC-bus containing the excitation capacitor, batteries, and a load. The control strategy incorporates an advanced model-based SEIG loss minimization and fuzzy-logic WT optimization. The latter utilizes a hedge-algebra speed controller to ensure fast response with practically no overshoot in the whole WT operating range, which cannot be achieved with the conventional proportional-integral (PI) controller. Consequently, the WT optimization time step is shortened and its convergence accelerated. The proposed SEIG loss minimization is based on the corresponding mathematical model that accounts for magnetic saturation and variable stray load and iron losses. Simultaneous optimization of the WT and SEIG is enabled, which results in greater total energy output compared to the successive WT-SEIG optimization. The proposed control strategy is run in real-time using the DS1103 board (dSpace) with a 1.5 kW SEIG driven by an emulated WT. It is experimentally evaluated over a wide WT operating range and compared with several competing strategies involving successive optimization, PI speed control and/or less elaborate SEIG models.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TEC.2022.3221215</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0001-5207-9070</orcidid><orcidid>https://orcid.org/0000-0001-7796-4551</orcidid><orcidid>https://orcid.org/0000-0001-8680-0524</orcidid><orcidid>https://orcid.org/0000-0003-3987-3792</orcidid></addata></record>
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subjects Controllers
Core loss
Electric converters
Energy conversion
Field-oriented control
Fuzzy logic
Generators
hedge algebra
induction generator
Induction generators
Iron
Magnetic flux
Magnetic saturation
Mathematical models
model-based optimization
MPPT
Optimization
Proportional integral
Rotors
Saturation magnetization
Speed control
Stators
Wind power
wind turbine
Wind turbines
title Wind Energy Conversion System Using Advanced Speed Control and Model-Based Loss Minimization
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