基于提升小波和改进BP神经网络的配电网系统电能质量扰动定位与识别

Localization and identification of power quality disturbance in distribution network system based on lifting wavelet and improved BP neural network

针对配电网系统电能质量扰动的非平稳性、突变性和短时持续性问题,提出一种基于提升小波和改进BP神经网络的扰动定位与识别新方法。首先用Euclidean分解算法得到db4小波提升方案;然后对扰动信号进行提升小波分解,结合模极大值对扰动突变点峰值进行定位检测;再利用自适应学习率和增加动量项相结合的方法对BP神经网络改进并进行扰动识别训练。仿真结果表明,该方法能更好地获取扰动时刻信息,定位快速且精度高,能有效地克服传统BP神经网络易陷入局部极小点和收敛速度慢的缺点,对配电网系统电能质量扰动识别率高。

According to the non-stationary, mutability and short duration of power quality disturbance in distribution network system, a new method to localize and identify disturbance is proposed based on lifting wavelet and improved BP neural network. At first, the Euclidean decomposition principle is used to obtain db4 wavelet lifting scheme. Then, disturbance signals are decomposed through lifting wavelet analysis, and the mutation peak of power quality disturbance is localized using lifting wavelet modulus maxim. At last, traditional BP algorithm is improved by combining increasing momentum method with self-adaption learning rate method, and improved BP neural network is used to identify disturbance signals. The simulation results show that the proposed method can better localize the disturbances＇ start-stop time of distribution network system with relatively high accuracy, and can effectively overcome the shortcomings of traditional BP neural network which is easy to fall into local minimum and has slow convergence speed, and can identify power quality disturbance in distribution network system with high discrimination ratio.