【摘要】 为提高暂态电能质量扰动分类识别的精度,提出了一种基于高阶累积量与支持向量机的暂态电能质量扰动信号的分类识别算法。该算法利用高阶累积量提取脉冲暂态与振荡暂态2类扰动的3阶与4阶统计特征,并选取各阶统计结果中的极大值个数、极小值个数以及最大值、最小值共8个特征量作为支持向量机的输入。利用Matlab产生仿真数据对此方法进行了仿真验证,结果表明,高阶累积量可以有效表征暂态扰动特征,且受噪声影响小;结合支持向量机可有效分类识别这2类暂态扰动,在训练样本为50组,核函数选择为线性核函数时,识别率可达到99%;当混合有其他扰动分量时,该方法也有效。更多还原

【Abstract】 To improve the precision of classification and recognition of transient power quality disturbances,a new classification and recognition algorithm for transient power quality disturbance signals,which is based on high-order cumulants(HOC) and support vector machine(SVM),is proposed.In the proposed algorithm,the 3-order and 4-order impulse statistical characteristics of two kinds of disturbances,i.e.,the impulse transient and oscillation transient,are extracted from HOC,and eight characteristic quantities,i.e.,the number of maximum values,the number of minimum values,the maximum and the minimum in 3-order and 4-order statistical results respectively,are chosen as the input of SVM.The proposed algorithm is verified by Matlab to obtain simulation data.Simulation results show that the transient disturbance characteristics can be effectively characterized by HOC and the disturbance characteristics are less influenced by noises;combining with SVM the two kinds of transient disturbances can be effectively recognized and identified.When training samples are 50 groups and the linear kernel function is chosen as the kernel function,the recognition rate can reach 99%;when other disturbance components are mixed with,the proposed algorithm is effective too.更多还原