Elman Neural Network for Dynamic Control of Wind Power Systems

Abstract:

This paper presents Elman neural network for the dynamic control strategies of a hybrid power system that include wind/photovoltaic/diesel system. Wind and PV power are the primary power sources of the system to take full advantages of renewable energy, and the diesel-engine is used as a backup system. A simulation model for the hybrid energy system has been developed using MATLAB/Simulink. To achieve a fast and stable response for the real power control, the intelligent controller consists of a Radial Basis Function Network (RBFN) and an modified Elman Neural Network (ENN) for maximum power point tracking (MPPT). The pitch angle of wind turbine is controlled by ENN, and the PV system uses RBFN, where the output signal is used to control the DC / DC boost converters to achieve the MPPT.

• Periodical: Applied Mechanics and Materials (Volumes 479-480)
• Pages: 570-574
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.479-480.570
• Online since: December 2013
• Authors: Cong Hui Huang, Chih Ming Hong, Yih Feng Su, Song Mao Lee, Chuan Sing Jhuang, Bo Yan Shi, Bo Ren Shi
• Keywords: Diesel Engine, Elman Neural Network (ENN), Maximum Power Point Tracking (MPPT), Photovoltaic (PV) System, Radial Basis Function Network

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