A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data

作     者：Sharifian, Amir Ghadi, M. Jabbari Ghavidel, Sahand Li, Li Zhang, Jiangfeng 

作者机构：Guilan Reg Elect Co POB 41377-18775 Rasht Iran Univ Technol Sydney Fac Engn & Informat Technol POB 123 Broadway NSW 2007 Australia 

出 版 物：《RENEWABLE ENERGY》 (Renew. Energy)

年 卷 期：2018年第120卷

页      面：220-230页

核心收录：

学科分类：0820[工学-石油与天然气工程] 080703[工学-动力机械及工程] 08[工学] 0807[工学-动力工程及工程热物理] 

主　　题：Type-2 fuzzy neural network PSO algorithm Medium-term and long-term wind power forecasting Uncertain information 

摘      要：Nowadays, due to some environmental restrictions and decrease of fossil fuel sources, renewable energy sources and specifically wind power plants have a major part of energy generation in the industrial countries. To this end, the accurate forecasting of wind power is considered as an important and influential factor for the management and planning of power systems. In this paper, a novel intelligent method is proposed to provide an accurate forecast of the medium term and long-term wind power by using the uncertain data from an online supervisory control and data acquisition (SCADA) system and the numerical weather prediction (NWP). This new method is based on the particle swarm optimization (PSO) algorithm and applied to train the Type-2 fuzzy neural network (T2FNN) which is called T2FNN-PSO. The presented method combines both of fuzzy system s expert knowledge and the neural network s learning capability for accurate forecasting of the wind power. In addition, the T2FNN-PSO can appropriately handle the uncertainties associated with the measured parameters from SCADA system, the numerical weather prediction and measuring tools. The proposed method is applied on a case study of a real wind farm. The obtained simulation results validate effectiveness and applicability of the proposed method for a practical solution to an accurate wind power forecasting in a power system control center. (C) 2017 Elsevier Ltd. All rights reserved.