计及雷击情况的基于PDT-SVM暂降源辨识方法研究

目前,电压暂降已成为影响最突出的电能质量问题之一,为有效分析雷击对电网暂降的影响程度,对雷击导致电压暂降的情况进行了详细分析,准确辨识了包括雷击导致暂降情况在内的4种暂降类型,为合理划分暂降责任提供重要依据。文中首先分析了雷击故障导致暂降的有效值波形与普通短路故障之间的区别,归纳了短路故障、雷击、变压器投切及感应电机启动4种暂降类型电压有效值波形的特点,引入5个暂降电压特征指标,并建立了暂降类型辨识特征矩阵。然后采用基于粒子群聚类优化的决策树支持向量机(PDT-SVM)分类器对4种暂降类型进行辨识。分类器的训练与测试数据均来自电网实测暂降电压数据,与工程实际密切贴合。最后,算例分析结果验证了算法的有效性和准确性。

PDT-SVM-based sag source identification considering lightning strike

At present, voltage sag has become one of the most prominent power quality problems.In order to effectively analyze the impact of lightning stroke on power grid sag, the situation of voltage sag caused by lightning stroke is analyzed in detail.Four types of temporary relief, including lightning induced temporary relief, are identified accurately, which provides an important basis for the rational division of temporary relief liability.Firstly, the difference between the effective waveform of temporary sag caused by lightning stroke fault and the common short circuit fault is analyzed.The characteristics of RMS waveforms of four kinds of sag types, short circuit fault, lightning stroke, transformer switching and induction motor starting, are summarized.Five characteristic indices of sag voltage are introduced and the characteristic matrix of sag type identification is established.Then four types of sags are identified by using decision tree support vector machine (PDT-SVM) classifier based on particle swarm optimization.The training and testing data of the classifier come from the measured sag voltage data of the power grid, which is closely in line with the engineering practice.Finally, the validity and accuracy of the algorithm are verified by the analysis results of an example.