摘要
针对当前常用多逆变器并网孤岛检测方法的不足,提出一种基于小波变换与神经网络的孤岛检测方法。该法利用小波变换提取公共耦合点(PCC)处的电压信号与逆变器输出电流信号的特征量,再采用BP神经网络通过模式识别以判断孤岛现象是否发生。Matlab仿真和实验结果表明,该方法能快速检测出孤岛状态,检测盲区小且对电能质量无影响,有较高的准确性和可靠性。
In view of the insufficiency of commonly used islanding detection methods of muhi grid-connected inverters, the islanding detection scheme based on wavelet and neural network is proposed. In this method, wavelet transform is adopted to extract feature vectors from the voltage of PCC point and the output current of inverter, and then pattern recognition is exerted by BP neural network to determine whether there is an island phenomenon. The results of simulation and experiment show that the detecting time is short and the non-detection zone is very small. This method doesn＇ t deteriorate the output power quality and the accuracy and reliability is very high.
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2015年第1期138-145,共8页
Acta Energiae Solaris Sinica
基金
国家自然科学基金(51277051)
安徽省自然科学基金(QYX2012B14)
关键词
逆变器
光伏系统
孤岛检测
小波变换
神经网络
inverter
photovohaic system
islanding detection
wavelet transform
neural network
