基于支持向量机的电力系统暂态分类

N. Hamzah, Fahteem Hamamy Anuwar, Z. Zakaria, N. Tahir
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引用次数: 5

摘要

本文讨论了支持向量机在电能质量扰动分类中的应用。电力系统暂态对电力系统设备和敏感负载的可靠性构成严重威胁。电力系统暂态的原因有很多,即短路、电容器组开关、包括电动机和变压器在内的大型感性负载的开关以及雷电。首先,利用PSCAD软件对IEEE 30总线系统进行建模,生成电容开关和雷电引起的不同类型的暂态数据。使用小波技术进行特征提取。接下来,将小波系数,即小波能量的最小值和最大值作为支持向量机的输入进行分类。初步结果表明,支持向量机能够以径向基函数(RBF)为核心对瞬态源进行分类
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of transient in power system using support vector machine
In this paper, application of SVM to classify disturbances in power quality is discussed. Power system transient can pose a serious threat to the reliability of power system apparatus and sensitive loads. There are numerous causes of power system transient namely short circuits, capacitor bank switching, switching of large inductive loads that include motors and transformers as well as lightning. Firstly, an IEEE 30 bus system is modeled using the PSCAD software to generate the different type of transient data caused by capacitor switching and lightning. Feature extraction is performed using wavelet technique. Next, the wavelet coefficients specifically the minimum and maximum values of the wavelet energy served as inputs for the SVM for classification purpose. Initial results showed that SVM is capable to classify the transient source with Radial Basis Function (RBF) as the kernel
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