摘要
针对光伏阵列电气工作参数包含复杂的暂态过程及工频干扰噪声严重影响故障诊断模型性能的问题,提出一种基于最大功率点的稳态时间序列预处理方法.首先,以自动过滤数据中的暂态过程和干扰噪声,获取连续的稳态时间序列电气特征数据,作为故障诊断模型的输入参数;然后,提出一种基于长短期记忆网络的深度网络模型,以实现对光伏阵列常见故障的检测及分类;最后,在一个小型光伏并网发电系统及其Simulink仿真模型上,进行故障模拟及仿真,以验证所提出的故障诊断方法.实验结果表明,所提出的故障诊断方法具有良好的精度和泛化性能,并且优于常规的反向传播神经网络和循环神经网络.
The electrical operating parameters of the photovoltaic array include complex transient processes and power frequency interference noise,which seriously affect the quality of fault characteristics and the performance of the diagnostic algorithm.In response to this problem,this paper first proposes a steady-state time series preprocessing method based on the maximum power point to automatically filter the transient process and interference noise in the data to obtain continuous steady-state time series electrical characteristic data,as the input parameters of the fault diagnosis model.Then,a deep network model based on long short-term memory(LSTM)is proposed to realize the detection and classification of common faults in photovoltaic arrays.Finally,in a small photovoltaic grid-connected power generation system and on the Simulink simulation model,fault simulation and simulation are performed to verify the proposed fault diagnosis method.The experimental results show that the proposed fault diagnosis method has good accuracy and generalization performance,and is better than back propagation neural network(BPNN)and recurrent neural network(RNN).
作者
戴森柏
陈志聪
吴丽君
林培杰
程树英
DAI Senbai;CHEN Zhicong;WU Lijun;LIN Peijie;CHENG Shuying(Institute of Micro-Nano Devices and Solar Cells,College of Physics and Information Engineering,Fuzhou University,Fuzhou,Fujian 350108,China)
出处
《福州大学学报（自然科学版）》
CAS
北大核心
2022年第1期54-60,共7页
Journal of Fuzhou University(Natural Science Edition)
基金
国家自然科学基金资助项目(61601127)
福建省科技厅高校产学合作资助项目(2016H6012)
福建省科技厅引导性基金资助项目(2019H0006)。
关键词
光伏阵列
故障诊断
最大功率点
时间序列
长短期记忆网络
photovoltaic array
fault diagnosis
maximum power point
time series
long short-term memory(LSTM)
