Situational intelligence for online coherency analysis of synchronous generators in power system

2016 North American Power Symposium (NAPS) Pub Date : 2016-09-01 DOI:10.1109/NAPS.2016.7747878

Yawei Wei, Iroshani Jayawardene, Paranietharan Arunagirinathan, Ke Tang, G. Venayagamoorthy

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引用次数: 3

Abstract

This paper presents a situational intelligence (SI) based approach to carry out coherency analysis of synchronous generator in a power system in an online manner. A cellular computational network (CCN) is used as the SI algorithm. CCN is a framework for distributed multi-timescale frequency prediction by utilizing the local and neighboring phasor measurement units (PMUs). The predicted frequency values are utilized for coherency analysis. The advantages of the CCN are scalability and distributedness which caters for on-line predicted coherency analysis for large power systems. The multi-time scale frequency predictions mitigates or minimizes delays in power system measurements and provides an insight to the power system coherent behavior apriori. The simulation studies on the New York-New England IEEE benchmark power system are presented to demonstrate that CCN based SI can be utilized in online coherency analysis. Predicted measurements can enhance resiliency to bad data. Furthermore, it is possible to utilize this approach for adaptive control of wide area power systems.

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电力系统同步发电机在线相干性分析的态势情报

本文提出了一种基于情景情报的在线同步发电机相干性分析方法。采用细胞计算网络(CCN)作为SI算法。CCN是一种利用本地和邻近相量测量单元(pmu)进行分布式多时间尺度频率预测的框架。利用预测的频率值进行相干分析。CCN具有可扩展性和分散性等优点，适合大型电力系统在线预测相干分析。多时间尺度频率预测减轻或最小化了电力系统测量中的延迟，并提供了对电力系统先验相干行为的洞察。通过对纽约-新英格兰IEEE基准电力系统的仿真研究，证明了基于CCN的SI可以用于在线相干分析。预测的测量可以增强对坏数据的弹性。此外，该方法还可用于广域电力系统的自适应控制。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2016 North American Power Symposium (NAPS)

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