Hybrid ANFIS‐GA‐based control scheme for performance enhancement of a grid‐connected wind generator. Issue 7 (12th April 2018)

• Record Type: Journal Article
• Authors: Soliman, Mahmoud A.; Hasanien, Hany M.; Azazi, Haitham Z.; El‐kholy, Elwy E.; Mahmoud, Sabry A.
• Is Part Of: IET renewable power generation. Volume 12:Issue 7(2018)
• Page Start: 832
• Page End: 843
• Publication Date: 2018-04-12
• Subjects: hybrid power systems -- adaptive control -- fuzzy reasoning -- fuzzy control -- neurocontrollers -- genetic algorithms -- wind power plants -- power grids -- power generation control -- wind turbines -- synchronous generators -- permanent magnet generators -- frequency convertors -- power convertors -- invertors -- nonlinear control systems -- robust control -- power system dynamic stability -- power system transient stability -- learning (artificial intelligence); hybrid cascaded ANFIS‐GA‐based control scheme -- power grid connected wind generator -- adaptive neurofuzzy inference system -- genetic algorithm -- variable‐speed wind energy conversion system -- variable speed wind turbine -- permanent‐magnet synchronous generator -- frequency converter -- generator‐side converter -- grid side inverter -- robustness controller -- nonlinear controller -- artificial neural network -- learning design procedure -- Zaafarana wind farm -- Egypt -- optimised proportional–integral controllers -- grey wolf optimiser algorithm -- MATLAB/Simulink environment -- transient stability -- dynamic stability -- grid‐connected wind generator systems; Renewable energy sources -- Periodicals; 333.79405
• DOI: 10.1049/iet-rpg.2017.0576
• Languages: English
• ISSNs: 1752-1416

Abstract

Abstract : This study presents a novel application of a hybrid adaptive neuro‐fuzzy inference system (ANFIS)‐genetic algorithm (GA)‐based control scheme to enhance the performance of a variable‐speed wind energy conversion system. The variable‐speed wind turbine drives a permanent‐magnet synchronous generator, which is connected to the power grid through a frequency converter. A cascaded ANFIS‐GA controller is introduced to control both of the generator‐side converter and the grid‐side inverter. ANFIS is a non‐linear, adaptive, and robustness controller, which integrates the merits of the artificial neural network and the FIS. A GA‐based learning design procedure is proposed to identify the ANFIS parameters. Detailed modelling of the system under investigation and its control strategies are demonstrated. For achieving realistic responses, real wind speed data extracted from Zaafarana wind farm, Egypt, are considered in the analyses. The effectiveness of the ANFIS‐GA controller is compared with that obtained using optimised proportional–integral controllers by the novel grey wolf optimiser algorithm taking into consideration severe grid disturbances. The validity of the ANFIS‐GA control scheme is verified by the extensive simulation analyses, which are performed using MATLAB/Simulink environment. With the ANFIS‐GA controller, the dynamic and transient stability of grid‐connected wind generator systems can be further enhanced.