摘要
电压暂降发生频率高、影响范围广、造成危害大。针对电力监测系统中带有事故源信息的电压暂降监测数据非常有限且不易获得的问题,提出了一种基于半监督支持向量机的电压暂降源识别方法。分析了各种电压暂降事故源,利用短时傅里叶变换(STFT)对电压暂降信号进行时频分析,提取出各类暂降特性参数,运用半监督支持向量机对其进行训练与识别,实现在少量带事故源标签电压暂降监测数据下电压暂降源的可靠识别。算例结果显示,在少量标签数据下半监督支持向量机比传统支持向量机具有更高的暂降源识别精度。
Voltage sag has the characteristics of high frequency, wide influence and great harm, etc. This paper proposed a voltage sag source identification method based on the semi-supervised support vector machine（SVM） in view of the situation that the labeled data with accident source information was very limited and not easy to obtain in the power monitoring system. All kinds of voltage sag sources were analyzed. The short time Fourier transform（STFT） was used for time-frequency analysis. All kinds of voltage sag characteristic parameters were extracted and the semi-supervised SVM was adopted for training and identification to realize the reliable identification of voltage sag sources under the conditions that there was a small number of labeled voltage sag monitoring data. Example results show that the semisupervised SVM has higher identification accuracy than the traditional SVM in the case of a small number of labeled data.
作者
蒋小伟
吕干云
武阳
JIANG Xiao-wei,LV Gan-yun,WU Yang(School of Electric Power Engineering, Nanjing Institute of Technology,Nanjing 211167, Chin)
出处
《电工电气》
2018年第5期23-26,31,共5页
Electrotechnics Electric
基金
国家自然科学基金项目(51577086)
江苏省研究生教育教学改革研究与实践课题(JGLX16_86)
江苏省配电网智能技术与装备协同创新中心开放基金项目(XTCX201611)
关键词
电压暂降
电压暂降源识别
短时傅里叶变换
半监督支持向量机
标签数据
voltage sag
identifcation of voltage sags source
short time Fourier transform
semi-supervised support vector machine
labeled data
