基于小波包对数能量熵与BP神经网络的孤岛检测方法  

Based on Wavelet Packet Log Energy Entropy and BP Neural Network Islanding Detection Method

作　　者：王涛[1] 张兴[1] 杜成孝[1] 

机构地区：[1]合肥工业大学电气与自动化工程学院,合肥230009

出　　处：《电气工程学报》2016年第6期9-12,32,共5页Journal of Electrical Engineering

基　　金：国家自然科学基金资助项目(51277051)

摘　　要：针对传统被动式孤岛检测法存在检测时间长、盲区(NDZ)大,而主动式孤岛检测法影响电能质量的缺点,提出一种新的基于小波包对数能量熵(WPLEE)与BP神经网络的孤岛检测方法。该方法首先采集公共耦合点(PCC)处的电压信号,再将该电压信号分别进行小波包变换,然后通过对数能量熵进行算法处理来获取适合于孤岛检测的特征向量,该特征向量通过BP神经网络进行模式识别来判断系统是否发生孤岛现象,特别在逆变器输出功率和本地负载功率匹配时。实验和仿真结果表明,该方法均能准确、有效地判断出是否存在孤岛状态,同时与传统的被动式孤岛检测方法相比检测速度快,检测盲区小,不会对电能质量产生不良影响。The detecting time is long and non-detection zone（NDZ） is large for traditional passive islanding detection methods,while active methods have some negative effects on power quality.A novel islanding detection method is proposed based on wavelet packet log energy entropy（WPLEE） and BP neural network.Firstly,the voltage of point of common coupling（PCC） are gathered,and then WPLEE is adopted to analyze the voltage signal.Secondly,by log energy entropy to get adapted to the characteristics of islanding detection algorithm processing variable.Lastly,BP neural network used these characteristic variable to pattern recognition and determine whether there is an island phenomenon,especially in the case of the inverter output power and load power matching.The simulation and experiment results show that this method is faster than the traditional passive methods in islanding detection,and the non-detection zone is smaller and there isn＇t a negative impact on power quality.

关 键 词：孤岛检测 小波包对数能量熵 BP神经网络 特征量 

分 类 号：TM46[电气工程—电器]