一种基于极限学习机和皮尔逊相关系数的光伏阵列故障快速诊断方法

摘要
图/表
参考文献(15)
相关文章 (15)

全文: PDF (5390 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要

光伏阵列故障会造成能量损失,甚至引发供电中断或火灾事故。因此,对故障的快速识别至关重要。本文提出一种光伏阵列故障快速诊断方法,用于快速感知故障及故障发生的时刻。通过分析光伏阵列常见故障的信号变化规律,提出利用正常运行时的功率波形训练一个极限学习机预测模型,用于预测短时功率的变化;接着计算实测波形和预测波形的皮尔逊相关系数;若光伏阵列发生故障,相关系数将低于一定的阈值,从而识别故障的发生。实测实验验证了所提方法具有很强的故障辨识能力,准确率达到99.37%。所提方法的故障辨识时间仅为4.355ms,亦可作为光伏阵列故障录波的启动方法使用。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	陈世群
	高伟
	陈孝琪
	涂彦昭
	杨艳

关键词 ：光伏阵列, 故障录波, 极限学习机（ELM）, 皮尔逊相关系数

Abstract：

The failure of the photovoltaic (PV) array can cause energy loss and even cause power supply interruption or fire accident. Therefore, it is very important to identify the fault quickly. A new fast fault diagnosis method for PV array is proposed to quickly perceive the fault and the time of fault occurrence in this paper. Based on the analysis of the signal variation rule of PV array traditional faults, a prediction model of extreme learning machine (ELM) is investigated to train the power waveform during normal operation, which is adopted to predict the short-time power variation. Then Pearson's correlation coefficient of measured waveforms and predicted waveforms are calculated. If the PV array fails, the correlation coefficient will be below the threshold, thus identifying the occurrence of the fault. Experimental results show that the proposed method has strong ability of fault identification, and the accuracy reaches 99.37%. The fault identification time of the proposed method is only 4.355ms, and it can be used as the start-up method of PV array fault recording.

Key words： photovoltaic array fault recording extreme learning machine (ELM) Pearson's correlation coefficient

收稿日期: 2021-02-26

基金资助:

江苏省住房和城乡建设厅指导项目（2019ZD001287）; 江苏淮安市科技局自然科学研究计划项目（HAB201905）

作者简介: 陈世群（1995—）,男,福建福州人,硕士,研究方向为新能源故障诊断研究。

引用本文:

陈世群, 高伟, 陈孝琪, 涂彦昭, 杨艳. 一种基于极限学习机和皮尔逊相关系数的光伏阵列故障快速诊断方法[J]. 电气技术, 2021, 22(10): 57-64. CHEN Shiqun, GAO Wei, CHEN Xiaoqi, TU Yanzhao, YANG Yan. A fast fault diagnosis method for photovoltaic array via extreme learning machine and Pearson's correlation coefficient. Electrical Engineering, 2021, 22(10): 57-64.

链接本文:

http://dqjs.cesmedia.cn/CN/Y2021/V22/I10/57

[1] 国家能源局. 国家能源局2021年一季度网上新闻发布会文字实录[EB/OL]. [2021-01-30]. http://www.nea. gov.cn/2021-01/30/c_139708580.htm.
[2] 陈国灯, 林培杰, 赖云锋, 等. 基于电流时间序列的光伏故障在线监测系统[J]. 电气技术, 2018, 19(6): 20-25.
[3] PEI Tingting, HAO Xiaohong.A fault detection method for photovoltaic systems based on voltage and current observation and evaluation[J]. Energies, 2019, 12(9): 1-16.
[4] 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.
[5] 程泽, 董梦男, 杨添剀, 等. 基于自适应混沌粒子群算法的光伏电池模型参数辨识[J]. 电工技术学报, 2014, 29(9): 245-252.
[6] GEORGIJEVIC N, JANKOVIC M, SRDIC S, et al.The detection of series arc fault in photovoltaic systems based on the arc current entropy[J]. IEEE Transactions on Power Electronics, 2016, 31(8): 5917-5930.
[7] ZHAO Ye, PALMA J D, MOSESIAN J, et al.Line-line fault analysis and protection challenges in solar photovoltaic arrays[J]. IEEE Transactions on Industrial Electronics, 2013, 60(9): 3784-3795.
[8] PILLAI D S, NATARAJAN R.An MPPT-based sensorless line-line and line-ground fault detection technique for PV systems[J]. IEEE Transactions on Power Electronics, 2019, 34(9): 8646-8659.
[9] 孙祥晟, 陈芳芳, 贾鉴, 等. 基于经验模态分解的神经网络光伏发电预测方法研究[J]. 电气技术, 2019, 20(8): 54-58.
[10] HARIHARAN R, MANICKAM C, ILANGO G S, et al.A method to detect photovoltaic array faults and partial shading in PV systems[J]. IEEE Journal of Photo- voltaics, 2016, 6(5): 1278-1285.
[11] MOHAMMED K A, FAISAL K, JAY J, et al.A comprehensive review of catastrophic faults in PV arrays: types, detection, and mitigation techniques[J]. IEEE Journal of Photovoltaics, 2015, 5(3): 982-997.
[12] GAO Wei, WAI Rongjong, CHEN Shiqun.Novel PV fault diagnoses via SAE and improved multi-grained cascade forest with string voltage and currents measures[J]. IEEE Access, 2020(8): 133144-133160.
[13] HUANG Guangbin, ZHOU Hongming, DING Xiaojian, et al.Extreme learning machine for regression and multiclass classification[J]. IEEE Transactions on Systems Man & Cybernetics Part B, 2012, 42(2): 513-529.
[14] 师愉航, 汲胜昌, 张凡, 等. 变压器油箱表面运行变形振型特性研究[J]. 电工技术学报, 2019, 34(5): 202-209.
[15] CHEN Shiqun, YANG Gengjie, GAO Wei, et al.Photovoltaic fault diagnosis via semi-supervised ladder network with string voltage and current measures[J]. IEEE Journal of Photovoltaics, 2021, 11(1): 219-231.