一种新的电能质量扰动分类方法

Design of a new classification method for power quality disturbance

在电力系统中,由于加入了大量非线性用电设备,导致电能质量大幅度降低。对大规模电力系统进行实时性电压扰动识别,是提升电能质量的有效手段之一。为降低谐波扰动信号对电力网络的影响,通过对电压扰动信号建模,采用局部均值分解与神经网络相结合来构建分类模型,实现对七种电压扰动信号的分类处理。局部均值分解通过三层分解,将电压扰动信号转化为电压幅频信息的乘积函数,送入神经网络进行分类。仿真结果证明了该方法的有效性。

In the power system, due to the addition of a large number of nonlinear electrical equipment, the power quality is greatly reduced.Real-time voltage disturbance identification for large-scale power system is one of the effective means to improve the power quality.In order to reduce the influence of harmonic disturbance signal on the power network, the voltage disturbance signal is modeled, the classification model is constructed by using local mean decomposition and neural network, and the classification and processing of seven kinds of voltage disturbance signal are realized.The local mean decomposition transforms the voltage disturbance signal into the product function of the voltage amplitude frequency through three-layer decomposition, and sends it to the neural network for classification.The simulation results have proved the effectiveness of the method.