基于FCM-FM算法的光伏阵列故障诊断
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摘要
工作于自然环境的光伏阵列故障频发,及时对故障进行定位和分类对于提高光伏电站运行水平具有重要意义。针对光伏阵列的常见故障类型(短路、开路、局部遮挡等),基于运行数据提出无监督模糊C均值(FCM)聚类与模糊隶属(FM)算法相结合的光伏阵列故障诊断方法。论文首先对光伏阵列典型故障的产生机理进行分析并提取故障特征参数;然后,采用FCM聚类方法对光伏阵列典型故障样本数据进行分类,得到不同故障的聚类中心;最后,利用FM算法计算运行数据与聚类中心的隶属度,判定故障类型。基于数字模拟实验和实证测试,验证上述方法的有效性。分析结果表明,本文方法可有效判别光伏阵列的典型故障,诊断结果准确、可靠。
Faults often occur in photovoltaic arrays which work in the natural environment.Locating and classifying the faults timely is of great significance to improve the operating level of photovoltaic power stations.Aiming at several common faults(short circuit,open circuit and partial occlusion) of photovoltaic array,a new method of photovoltaic array fault diagnosis using operation data and combining fuzzy C-means(FCM) algorithm and fuzzy membership(FM) algorithm is proposed in this paper.Firstly,the occurrence mechanism of typical faults of PV array is carried out,and the fault feature parameters are extracted.Then,the FCM algorithm is used to classify the fault samples of PV array and the clustering centers of various faults are obtained.Finally,the FM algorithm is used to calculate the membership degree of the fault data about the clustering centers and determine the fault types.The simulation and experimental tests are both adopted to verify effectiveness of the methods.The results show that the proposed fault diagnosis method can effectively identify the typical faults of PV array and the diagnostic results are accurate and reliable.
Faults often occur in photovoltaic arrays which work in the natural environment.Locating and classifying the faults timely is of great significance to improve the operating level of photovoltaic power stations.Aiming at several common faults(short circuit,open circuit and partial occlusion) of photovoltaic array,a new method of photovoltaic array fault diagnosis using operation data and combining fuzzy C-means(FCM) algorithm and fuzzy membership(FM) algorithm is proposed in this paper.Firstly,the occurrence mechanism of typical faults of PV array is carried out,and the fault feature parameters are extracted.Then,the FCM algorithm is used to classify the fault samples of PV array and the clustering centers of various faults are obtained.Finally,the FM algorithm is used to calculate the membership degree of the fault data about the clustering centers and determine the fault types.The simulation and experimental tests are both adopted to verify effectiveness of the methods.The results show that the proposed fault diagnosis method can effectively identify the typical faults of PV array and the diagnostic results are accurate and reliable.
引文
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[3]韩世军,朱菊,毛吉贵,等.基于粒子群优化支持向量机的变压器故障诊断[J].电测与仪表,2014,51(11):71-75,90.DOI:10.3969/j.issn.1001-1390.2014.11.015.
[4]LI Z H,WANG Y Z,ZHOU D Q,et al.An intelligent method for fault diagnosis in photovoltaic array[M]//XIAO T Y,ZHANG L,MA S W.System Simulation and Scientific Computing.Berlin,Heidelberg:Springer,2012:10-16.DOI:10.1007/978-3-642-34396-4_2.
[5]王元章,吴春华,周笛青,等.基于BP神经网络的光伏阵列故障诊断研究[J].电力系统保护与控制,2013,41(16):108-114.
[6]KARATEPE E,BOZTEPE M,?OLAK M.Development of a suitable model for characterizing photovoltaic arrays with shaded solar cells[J].Solar energy,2007,81(8):977-992.DOI:10.1016/j.solener.2006.12.001.
[7]NOGUCHI T,TOGASHI S,NAKAMOTO R.Shortcurrent pulse-based maximum-power-point tracking method for multiple photovoltaic-and-converter module system[J].IEEE transactions on industrial electronics,2002,49(1):217-223.DOI:10.1109/41.982265.
[8]CHOWDHURY S R,SAHA H.Maximum power point tracking of partially shaded solar photovoltaic arrays[J].Solar energy materials and solar cells,2010,94(9):1441-1447.DOI:10.1016/j.solmat.2010.04.011.
[9]MIYATAKE M,INADA T,HIRATSUKA I,et al.Control characteristics of a fibonacci-search-based maximum power point tracker when a photovoltaic array is partially shaded[C]//Proceedings of the 4th International Power Electronics and Motion Control Conference.Xi'an,China:IEEE,2004:816-821.
[10]薛鹏,樊波,程培源.基于模糊C均值聚类的模拟电路故障诊断[J].移动电源与车辆,2008(1):36-39.DOI:10.3969/j.issn.1003-4250.2008.01.011.
[11]杜文霞,句希源,吕锋.基于模糊聚类算法的变压器故障诊断研究[J].变压器,2009,46(8):65-69.DOI:10.19487/j.cnki.1001-8425.2009.08.018.
[12]KRISHNAPURAM R,KELLER J M.A possibilistic approach to clustering[J].IEEE transactions on fuzzy systems,1993,1(2):98-110.DOI:10.1109/91.227387.
[13]LIU X K,ZHUO F,CHEN Y,et al.Development of fast simulation models for photovoltaic generation system based on Simulink[C]//Proceedings of 2015 IEEE Energy Conversion Congress and Exposition.Montreal,QC,Canada:IEEE,2015:3265-3270.