利用FusionSolar平台的光伏发电系统直流侧电气数据资源,通过研究热斑故障对光伏组串输出特性的影响作用,提出一种基于时间序列波形特征的热斑故障诊断方法。通过分析热斑产生和演变机理,以及与其他类型故障在I-V曲线和时间序列中的特征差异,获得热斑在电流和电压时间序列中的波形变化规律;构造适应于时序图波形特征的热斑故障的函数形式,提取故障诊断特征向量;结合现场运维经验,建立模糊推理故障诊断系统,实现热斑故障的原因判定和程度估算。实验结果表明：热斑故障在组串输出的电流/电压时序图波形中,具有独特、对应的变化关系,所构造的函数形式能够明确表征波形变化规律,所建立的模糊推理系统可实现热斑故障的有效、可靠诊断。
Abstract
Based on the electrical data resources of the direct current （DC） side of the photovoltaic power generation system on the FusionSolar platform, this paper studies the influence of hot spot faults on the output characteristics of photovoltaic arrays, proposes a hot spot fault diagnosis method based on the waveform characteristics of time series. By analyzing the generation and evolution mechanism of hot spots, as well as the characteristic differences between hot spots and other types of faults in I-V curves and time series, the waveform variation rule of hot spots in current and voltage time series was obtained; At same time, the function of hot spot fault characterized the waveform feature of time series diagram is constructed and the fault diagnosis feature vector is also extracted; Combined with the field operation and maintenance experience, a fuzzy inference fault diagnosis system was established to confirm the reason and estimate the degree of hot spot faults. The experimental results show that the hot spot fault has a unique and corresponding variation relation in the current/voltage time series diagram, the function can clearly characterize the waveform variation rule, and the fuzzy inference system can realize the effective and reliable diagnosis of hot spot fault.
关键词
光伏组串 /
故障诊断 /
时间序列 /
模糊推理 /
热斑
Key words
photovoltaic group strings /
fault diagnosis /
time series /
fuzzy inference /
hot pot
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] DENG S F, ZHANG Z, JU C H, et al.Research on hot spot risk for high-efficiency solar module[J]. Energy procedia, 2017, 130: 77-86.
[2] 马铭遥, 张志祥, 刘恒, 等. 基于I-V特性分析的晶硅光伏组件故障诊断[J]. 太阳能学报, 2021, 42(6): 130-137.
MA M Y, ZHANG Z X, LIU H, et al.Fault diagnosis of crystalline silicon photovoltaic module based on I-V characteristic analysis[J]. Acta energiae solaris sinica, 2021, 42(6): 130-137.
[3] WEI D, WEI M Y, CAI H, et al.Parameters extraction method of PV model based on key points of I-V curve[J]. Energy conversion and management, 2020, 209: 112656.
[4] 魏缪宇, 卫东, 郭倩, 等. 局部异物遮挡状态下光伏单元输出特性与故障诊断方法[J]. 太阳能学报, 2021, 42(5): 260-266.
WEI M Y, WEI D, GUO Q, et al.Output characteristics and fault diagnosis method of PV unit under partial shading[J]. Acta energiae solaris sinica, 2021, 42(5): 260-266.
[5] CHEN H T, YI H, JIANG B, et al.Data-driven detection of hot spots in photovoltaic energy systems[J]. IEEE transactions on systems, man, and cybernetics: systems, 2019, 49(8): 1731-1738.
[6] BHARADWAJ P, KARNATAKI K, JOHN V.Formation of hotspots on healthy PV modules and their effect on output performance[C]//2018 IEEE 7th World Conference on Photovoltaic Energy Conversion(WCPEC), Waikoloa, HI, USA, 2018.
[7] BRESSAN M, GUTIERREZ A, GUTIERREZ L G, et al.Development of a real-time hot-spot prevention using an emulator of partially shaded PV systems[J]. Renewable energy, 2018, 127: 334-343.
[8] HUANG J M, WAI R J, GAO W.Newly-designed fault diagnostic method for solar photovoltaic generation system based on I-V curve measurement[J]. IEEE access, 2019, 7: 70919-70932.
[9] MA M Y, LIU H, ZHANG Z X, et al. Rapid diagnosis of hot spot failure of crystalline silicon PV module based on I-V curve[J]. Microelectronics reliability, 2019, 100-101: 113402.
[10] ZHU H L, SHI Y C, WANG H Z, et al.New feature extraction method for photovoltaic array output time series and its application in fault diagnosis[J]. IEEE journal of photovoltaics, 2020, 10(4): 1133-1141.
[11] LU X Y, LIN P J, CHENG S Y, et al.Fault diagnosis for photovoltaic array based on convolutional neural network and electrical time series graph[J]. Energy conversion and management, 2019, 196: 950-965.
[12] LIU G Y, ZHU L, WU X P, et al.Time series clustering and physical implication for photovoltaic array systems with unknown working conditions[J]. Solar energy, 2019, 180: 401-411.
[13] PAN J N, HE W G, SHI Y C, et al.Uncertainty analysis based on non-parametric statistical modelling method for photovoltaic array output and its application in fault diagnosis[J]. Solar energy, 2021, 225: 831-841.
[14] TAO Y K, BAI J B, PACHAURI R K, et al.Parameter extraction of photovoltaic modules using a heuristic iterative algorithm[J]. Energy conversion and management, 2020, 224: 113386.
[15] SILVESTRE S, DA SILVA M A, CHOUDER A, et al. New procedure for fault detection in grid connected PV systems based on the evaluation of current and voltage indicators[J]. Energy conversion and management, 2014, 86: 241-249.
[16] SOLÓRZANO J, EGIDO M A. Automatic fault diagnosis in PV systems with distributed MPPT[J]. Energy conversion and management, 2013, 76: 925-934.
基金
浙江省基础公益研究计划（LGG22E070003; LGG20E070003）
PDF(2034 KB)
