基于WPES-SAE的MPPT控制器多故障诊断方法
|
摘要
针对光伏发电系统最大功率点跟踪(MPPT)控制器多故障模式情形时难以有效进行故障准确识别,此处提出一种基于堆栈自动编码器(SAE)的功率变换器多故障诊断方法。首先,选取合适的电路测试点并采集电压信号;其次,采用小波包分解方法对各测点电压信号进行故障特征提取,并以小波包能量谱(WPES)作为功率变换器的故障特征向量;最后,采用4层SAE深度学习网络实现多故障模式的准确分类。仿真实验表明,所提方法诊断率可达100%,优于反向传播神经网络(BPNN)及支持向量机(SVM)所得诊断结果,验证了所提方法的准确性和有效性。
Aiming at the difficulty in identifying the multiple faults for maximum power point tracking(MPPT)controller,which is an important part in photovoltaic power generation system.Thus,a multiple faults diagnosis method based on stacked autoencoder(SAE)is proposed.First,selecting the appropriate circuit test points and collecting the voltage signals.Secondly,the wavelet packet decomposition method is used to extract the fault features from the voltage signals,and the wavelet package energy spectrum(WPES)is used as the fault feature vector for power converter.Finally,a four-layer SAE deep learning network is applied to accurately classify the multiple fault modes.Simulation results show that the proposed method has a classification rate of 100%,which is better than the results obtained by back propagation neural network(BPNN)and support vector machine(SVM).Also,the accuracy and effectiveness of the proposed method are verified.
Aiming at the difficulty in identifying the multiple faults for maximum power point tracking(MPPT)controller,which is an important part in photovoltaic power generation system.Thus,a multiple faults diagnosis method based on stacked autoencoder(SAE)is proposed.First,selecting the appropriate circuit test points and collecting the voltage signals.Secondly,the wavelet packet decomposition method is used to extract the fault features from the voltage signals,and the wavelet package energy spectrum(WPES)is used as the fault feature vector for power converter.Finally,a four-layer SAE deep learning network is applied to accurately classify the multiple fault modes.Simulation results show that the proposed method has a classification rate of 100%,which is better than the results obtained by back propagation neural network(BPNN)and support vector machine(SVM).Also,the accuracy and effectiveness of the proposed method are verified.
引文
[1]Zhang P,Li W,Wang Y,et al.Reliability Assessment of Photovoltaic Power Systems:Review of Current Status and Future Perspectives[J].Applied Energy,2013,104(4):822-833.
[2]Dhumale R B,Lokhande S D.Neural Network Fault Diagnosis of Voltage Source Inverter Under Variable Load Conditions at Different Frequencies[J].Measurement,20 1 6,91(4):565-575.
[3]崔江,叶纪青,陈未,等.一种基于M-ary支持向量机的功率变换器故障分类方法[J].中国电机工程学报,2016,36(22):6231-6237.
[4]杨晓冬,王崇林,史丽萍.一种新型的IGBT开路故障诊断实现方法[J].电力电子技术,2014,48(3):39-41.
[5]姜媛媛,王友仁,吴祎,等.基于小波包能量谱和ELM的光伏逆变器多故障在线诊断[J].仪器仪表学报,2015,36(9):2145-2152.
[6]Khateb A E,Rahim N A,Selvaraj J,et al.Fuzzy Logic Controller Based SEPIC Converter for Maximum Power Point Tracking[J].IEEE Trans.on Industry Applications,2014,50(4):2349-2358.
[2]Dhumale R B,Lokhande S D.Neural Network Fault Diagnosis of Voltage Source Inverter Under Variable Load Conditions at Different Frequencies[J].Measurement,20 1 6,91(4):565-575.
[3]崔江,叶纪青,陈未,等.一种基于M-ary支持向量机的功率变换器故障分类方法[J].中国电机工程学报,2016,36(22):6231-6237.
[4]杨晓冬,王崇林,史丽萍.一种新型的IGBT开路故障诊断实现方法[J].电力电子技术,2014,48(3):39-41.
[5]姜媛媛,王友仁,吴祎,等.基于小波包能量谱和ELM的光伏逆变器多故障在线诊断[J].仪器仪表学报,2015,36(9):2145-2152.
[6]Khateb A E,Rahim N A,Selvaraj J,et al.Fuzzy Logic Controller Based SEPIC Converter for Maximum Power Point Tracking[J].IEEE Trans.on Industry Applications,2014,50(4):2349-2358.