Title: Wind turbine generator selection and comprehensive evaluation based on BPNN optimised by PSO

Authors: Wei Sun; Zhipeng Xu

Addresses: Department of Economics and Management, North China Electric Power University, Baoding, Hebei, China ' Department of Economics and Management, North China Electric Power University, Baoding, Hebei, China

Abstract: With the development of the electric power system in China, wind power, as a clean energy, can be utilised to optimise the structure of electrical energy. By reducing the emission of pollutants, it will benefit the sustainable development of the national economy and environment. In wind power projects, scientific and rational choices for the wind turbine generator in actual wind farm are critical since it is directly related to the economic benefits of wind power projects. By analysing the status of current wind power capacity at the scale of the globe and China, wind power is projected to play an increasingly important role in the future. On this basis, we developed the comprehensive evaluation system of wind turbine generator selection and established a comprehensive evaluation model based on BP neural network which was optimised by particle swarm. A real example was employed to verify the validity of the proposed method, thus can provide guideline of the evaluation of the wind turbine generators selection in wind farms.

Keywords: wind turbine generators selection; comprehensive evaluation; BP neural network; particle swarm optimisation; parameter optimisation.

DOI: 10.1504/IJADS.2017.087188

International Journal of Applied Decision Sciences, 2017 Vol.10 No.4, pp.364 - 381

Received: 16 Nov 2016
Accepted: 04 May 2017
Published online: 06 Oct 2017 *

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