混沌粒子群优化小波神经网络在电能质量扰动信号分类中的应用

Application of Wavelet Neural Network Based on Chaotic Particle Swarm Optimization Algorithm in Power Quality Disturbance Signals Classification

为更加有效地对电能质量扰动信号进行分类,将小波变换和神经网络进行有机结合,构建4层小波神经网络模型,同时将混沌引入到粒子群优化算法中,通过混沌运动的特性,提高网络模型训练的收敛速度和精度。使用训练好的网络模型,对正常电压和几种常见电能质量扰动进行分类。结果表明,混沌粒子群优化小波神经网络能够有效地对电能质量扰动进行分类,且具有抗干扰性强,稳定性好的优点。另外,与粒子群优化算法和BP算法相比,使用混沌粒子群优化算法能够更好地对电能质量扰动进行分类,具有更高的分类准确率。

In order to classify power quality disturbance signals more effectively,the wavelet transform and neural network are organically combined to build a four-layer wavelet neural network model in this paper.Besides, the chaotic is embedded into particle swarm optimization algorithm to improve the convergence speed and accuracy of the network model training based on the property of chaotic.Then the trained network model is used to classify the normal voltage and several common power quality disturbances.The classification result shows that the wavelet neural network optimized by chaotic particle swarm optimization algorithm can effectively classify power quality disturbances, and has the advantage of strong interference resistance and good stability.Meanwhile, compared with particle swarm optimization algorithm and BP algorithm, classifying the power quality disturbances based on chaotic particle swarm optimization algorithm has the higher accuracy rate of classification.