基于PSO-MP算法和RBF神经网络的电能质量扰动识别

The identification of power quality disturbance based on PSO-MP algorithm and RBF neural network

准确识别扰动信号类型对分析和治理电能质量问题具有重要意义。文中提出一种基于粒子群优化匹配追踪算法(PSO-MP)和RBF神经网络的电能质量扰动识别方法。首先,构建工频原子库将工频信号提取出来,得到的残余信号能更好地体现扰动信号差异性;再利用PSO优化匹配追踪算法以减小计算量,并结合离散Gabor原子库对残余扰动信号进行稀疏分解,准确提取其原子参数;最后将原子参数以及残余信号在原子上的投影的均值和标准偏差作为特征量,利用RBF神经网络对扰动信号进行识别。仿真算例表明,该方法能够有效地识别几种常见的电能质量扰动,且具有抗噪性能强、计算量小等优点。

It is of great significance to accurately identify the type of disturbance signal to analyze and control the power quality problem. In this paper,a new method of power quality disturbance identification based on matching pursuit optimized by particle swarm optimization（ PSO-MP） and RBF neural network is proposed. Firstly,in order to let the residue signal to better reflect the different disturbance signal difference,the fundamental atomic library is constructed to extract the fundamental frequency signals; Then,the MP algorithm is optimized by PSO to reduce the calculation amount,which combines with discrete Gabor atom libraries to accurately extract atomic parameters of residual disturbance signal by sparse decomposition,Finally,the RBF neural network is used to identify disturbance signals by features,which is the mean and standard deviation of the atomic parameter and projection of residual signal on the atom.Simulation examples show that the proposed method can effectively identify several common power quality disturbances with a small amount of computation and good anti-noise performance.