智能电网铁磁谐振时DFIG负序方向元件的智能算法

IF 2 Q4 ENERGY & FUELS
Salman Rezaei
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引用次数: 4

摘要

智能电网包括公用事业系统和分布式可再生能源发电,如风能和太阳能。智能电网系统代表了公用事业系统、分布式发电(DG)和消费者之间能源和通信设施的双向流动。包括风电场在内的智能电网严重暴露于高阶非线性,如铁磁共振。它会导致风电场中保护继电器的误操作。利用PSCAD/EMTDC软件,研究了电力系统铁磁谐振对风电场双馈异步发电机和负序定向元件运行的影响。针对智能电网坚持自修复保护的特点,提出了一种基于小波变换、神经网络和时域铁磁共振分析的智能算法,用于NSDE判别铁磁共振。该算法适当地符合智能电网保护策略。它将铁磁共振与其他非线性异常区分开来,并能够区分不同类型的铁磁共振。为了满足智能电网的要求,设计了预测电网铁磁谐振发生的算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Intelligent Algorithm for Negative Sequence Directional Element of DFIG during Ferroresonance in Smart Grid
Smart grid comprises utility system and Distributed renewable power Generations such as wind and solar energy. The smart grid system represents bidirectional flowing of energy and communication facilities among utility system, Distributed Generations (DG) and consumers. Smart grid including wind farm is seriously exposed to high magnitudes of nonlinearities like ferroresonance. It causes mal operation of protective relays in wind farm. This paper investigates impact of ferroresonance in utility system on operation of DFIG (Doubly-Fed Induction Generator) and Negative Sequence Directional Element (NSDE) in Wind Park by means of PSCAD/EMTDC software. As smart grid insists on a self-healing protection, an intelligent algorithm based on wavelet transform, neural network and ferroresonance analysis in time domain is proposed for NSDE to discriminate ferroresonance. The algorithm appropriately conforms to smart grid protection strategy. It discriminates ferroresonance from other nonlinear abnormalities and is able to distinguish different types of ferroresonance. To accord with smart grid requirements, the algorithm is designed to forecast occurrence of ferroresonance in the grid.
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来源期刊
Technology and Economics of Smart Grids and Sustainable Energy
Technology and Economics of Smart Grids and Sustainable Energy Economics, Econometrics and Finance-Economics and Econometrics
CiteScore
3.90
自引率
18.20%
发文量
33
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