Situational intelligence for online coherency analysis of synchronous generators in power system
Situational intelligence for online coherency analysis of synchronous generators in power system
复制标题
电力系统同步发电机在线相干分析的态势智能
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
G. Venayagamoorthy
中科院分区：
文献类型：
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作者：
Yawei Wei;Iroshani Jayawardene;Paranietharan Arunagirinathan;Ke Tang;G. Venayagamoorthy
提出了一种基于情景智能的电力系统同步发电机同调分析方法。一个蜂窝计算网络（CCN）被用作SI算法。CCN是一种利用本地和相邻相量测量单元（PMU）进行分布式多时间尺度频率预测的框架。预测的频率值用于相干性分析。CCN的优点是可扩展性和分布式，适合大型电力系统的在线预测同调分析。多时间尺度频率预测减轻或最小化电力系统测量中的延迟，并提供对电力系统相干行为的先验洞察。纽约-新英格兰IEEE基准电力系统的仿真研究表明，基于CCN的SI可以用于在线同调分析。预测测量可以增强对坏数据的弹性。此外，它是可能的，利用这种方法的广域电力系统的自适应控制。
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.