Evaluation of Reactive Power and Voltage Support Capability of Power Grid Based on Big Data

Qing He, Wenchao Yang, Longyan He, Yuqing Mao
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Abstract

With the development of new energy power grid construction, the failure of the power grid and the accidents caused by the collapse of the whole network are increasing, which are mainly attributed to the voltage drop of the power grid caused by reactive power shortage. Therefore, it is necessary to evaluate the reactive voltage support capacity of the power grid, customize quantitative indexes based on the evaluation content, and conduct data mining for the massive data in the operation of the power grid to form visual data content for operation and maintenance personnel to maintain and monitor the power grid. Based on the above background and with the clustering method as the technical core, this paper formulated the evaluation system of power grid reactive voltage support capability based on big data to carry out technical evaluation of power grid reactive voltage support capability. On this basis, this article establishes a complete set of reactive voltage support capability evaluation systems through the basic framework of data mining to improve the versatility of the indexes by optimizing the correlation coefficient and adding dynamic correlation indexes to adapt to multiple data sample categories.
基于大数据的电网无功电压保障能力评估
随着新能源电网建设的发展,电网故障和全网瘫痪事故不断增加,主要原因是无功缺电导致电网电压下降。因此,有必要对电网无功电压支撑能力进行评估,并根据评估内容定制量化指标,对电网运行中的海量数据进行数据挖掘,形成可视化的数据内容,供运维人员对电网进行维护和监控。基于上述背景,本文以聚类方法为技术核心,制定了基于大数据的电网无功电压保障能力评价体系,对电网无功电压保障能力进行技术评价。在此基础上,本文通过数据挖掘的基本框架建立了一套完整的无功电压保障能力评价体系,通过优化相关系数和增加动态相关指标来提高指标的通用性,以适应多种数据样本类别。
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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