Data analytics and computational methods for anti-islanding of renewable energy based Distributed Generators in power grids

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• 作者：Shashank Vyas^a ; Rajesh Kumar^a ; ^{rkumar.ee@gmail.com} ; Rajesh Kavasseri^b
• 关键词：Distributed Generators ; Islanding ; Feature extraction ; Classification ; Prediction
• 刊名：Renewable and Sustainable Energy Reviews
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：69
• 期：Complete
• 页码：493-502
• 全文大小：394 K
• 卷排序：69

文摘

The centralized generation based model of power delivery remains inefficient due to unavoidable losses and limited reach of the related infrastructure to penetrate into inaccessible areas. Distributed generation based on cleaner sources like wind, solar, biomass etc. can provide energy access to all in a standalone configuration called microgrid. However such distributed generators can also be interfaced with the utility grid and support power flow and ensure supply to connected consumers during utility outages. Grid availability impacted by its vulnerability to extreme events is a major issue affecting wide-spread deployment of such systems. The paper gives an account of major computational intelligence based techniques addressing the problem of islanding in power grids having renewable energy based distributed generators connected to them. The various methods reported have been analyzed in terms of their working methodologies, tools used, accuracy, speed and other relevant aspects. In light of the current state of the art and a need to add more resiliency to the operation of grid-connected distributed generation systems, a new prospect, with preliminary results, will be discussed to address the issue of islanding that can be applied as an effective strategy by utilities to ensure smoother operation of the power grid.