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Implementation supervisory controller for hybrid wind microgrid system using adaptive neural MIMO model

This paper investigates a novel forward adaptive neural model which is applied for modeling and implementing the supervisory controller of the hybrid wind microgrid system. The nonlinear features of the hybrid wind microgrid system are thoroughly modeled based on the adaptive identification process using experimental input-output training data. | TAÏP CHÍ PHAÙT TRIEÅN KH&CN, TAÄP 18, SOÁ K6- 2015 Implementation supervisory controller for hybrid wind microgrid system using adaptive neural MIMO model Ho Pham Huy Anh Nguyen Ngoc Son Ho Chi Minh city University of Technology, VNU-HCM, Vietnam Tran Thien Huan Ho Chi Minh city University of Technology and Education, Vietnam (Manuscript Received on July 15, 2015, Manuscript Revised August 30, 2015) ABSTRACT This paper investigates a novel forward adaptive neural model which is applied for modeling and implementing the supervisory controller of the hybrid wind microgrid system. The nonlinear features of the hybrid wind microgrid system are thoroughly modeled based on the adaptive identification process using experimental input-output training data. This paper proposes the novel use of a back propagation (BP) algorithm to generate the adaptive neural-based supervisory controller for the hybrid wind microgrid system. The simulation results show that the proposed adaptive neuralbased supervisory controller trained by Back Propagation learning algorithm yields outstanding performance and perfect accuracy. Keywords: hybrid wind microgrid system, back propagation learning algorithm (BP), adaptive neural-based supervisory controller, wind turbine, modeling and identification 1. INTRODUCTION Hybrid renewable energy systems can be classified into two main types: grid-connected and standalone. The renewable energy sources can be PV or wind generators (or both), according to the availability of solar radiation or wind velocity (or both) at the system site. Batteries are often used as a backup source to supply the system when the renewable energy source is unavailable. Other backup sources can be used with or without batteries such as fuel cells (e.g. electrolysers, supercapacitors and flywheel energy storage). Diesel generators could be used as secondary sources of renewable energy. The standalone system might provide dc power, ac power, or both dc and ac .