基于GSA的用电信息采集异常精确定位研究

Research of accurate anomaly positioning of electric energy data acquisition based on GSA

随着电力信息化建设不断推进,涉及的各类电力设备越来越多,为了保证电力系统的可靠正常运行,辨识系统中的用电信息采集异常并对异常点进行精确定位是电力系统安全稳定运行的前提和条件。文章涉及了电网中的用电信息采集领域,论文研究了建立在神经网络和聚类分析基础上的GSA异常数据定位算法,该算法运用神经网络完成对测量数据的预处理,并对聚类分析后的结果进行判断,完成异常数据的精确定位。仿真比较表明,该算法能较好地实现电力系统中用电信息采集异常的精确定位功能,具有一定的有效性与实用性,有效避免了异常数据的漏检与误检。

With the continued progress of the power informatization, more and more kinds of the electrical equipments are involved. To ensure reliable and normal operation of power system, the system would need to identify anomaly of electric energy data acquisition and do accurate positioning to the abnormal nodes. In terms of energy data acquisition field in power system, this article researches the GSA anomaly positioning algorithm based on neural network and clustering analysis. The algorithm accomplishes the pretreatment of surveying data via the neural network, and then judges the result of clustering analysis so as to finish the positioning of abnormal nodes. Testing shows that this algorithm can well accomplish accurate anomaly positioning of electric energy data acquisition in power system, which presents effectiveness and practicality, and effectively avoid the undetected and false-detected abnormal nodes.