Improved differential evolution-based Elman neural network controller for squirrel-cage induction generator system

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Title:	Improved differential evolution-based Elman neural network controller for squirrel-cage induction generator system
Authors:	林法正;Lin, Faa-Jeng;Tan, Kuang-Hsiung;Tsai, Chia-Hung
Contributors:	資訊電機學院電機工程學系
Keywords:	AC‐DC power converter;AC‐DC power convertors;Algorithms;asynchronous generators;Control systems;Controllers;DC‐AC power inverter;DC‐link voltage square control;Dynamical systems;Dynamics;Electric potential;electric power conversion;Electric power generation;evolutionary computation;grid‐connected wind power applications;IDE‐based ENN intelligent controller algorithm;improved differential evolution‐based Elman neural network controller;invertors;learning (artificial intelligence);learning rate optimization;lookup table;machine control;network structure;Neural networks;neurocontrollers;online learning algorithm;power balance principle;power generation control;power grid;power grids;squirrel‐cage induction generator system;steady‐state responses;three‐phase SCIG system;transient response;transient responses;voltage control;wind power plants;wind speed variations;wind turbine emulator control characteristics;wind turbines
Date:	2016-07-01
Issue Date:	2026-04-23 14:15:36 (UTC+8)
Publisher:	Institution of Engineering and Technology;The Institution of Engineering and Technology
Abstract:	摘要： An improved differential evolution (IDE) algorithm-based Elman neural network (ENN) controller is proposed to control a squirrel-cage induction generator (SCIG) system for grid-connected wind power applications. First, the control characteristics of a wind turbine emulator are introduced. Then, an AC/DC converter and a DC/AC inverter are developed to convert the electric power generated by a three-phase SCIG to the grid. Moreover, the dynamic model of the SCIG system is derived for the control of the square of DC-link voltage according to the principle of power balance. Furthermore, in order to improve the transient and steady-state responses of the square of DC-link voltage of the SCIG system, an IDE-based ENN controller is proposed for the control of the SCIG system. In addition, the network structure and the online learning algorithm of the ENN are described in detail. Additionally, according to the different wind speed variations, a lookup table built offline by the dynamic model of the SCIG system using the IDE is provided for the optimisation of the learning rates of ENN. Finally, to verify the control performance, some experimental results are provided to verify the feasibility and the effectiveness of the proposed SCIG system for grid-connected wind power applications. 出版者： The Institution of Engineering and Technology 出版日期： 2016-07-01 出處： IET renewable power generation, 2016-07, Vol.10 (7), p.988-1001 資源來源： Wiley Online Library Open Access 版權： The Institution of Engineering and Technology 識別號： ISSN: 1752-1416 識別號： EISSN: 1752-1424 識別號： DOI: 10.1049/iet-rpg.2015.0329
Appears in Collections:	[Department of Electrical Engineering] journal & Dissertation

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