FAULT DIAGNOSIS METHODS OF PHOTOVOLTAIC ARRAYS BASED ON GRADIENT BOOSTING REGRESSION TREE-SELF-TRAINING BAYESIAN OPTIMIZED SUPPORT VECTOR MACHINE

Cao Lan; Zhou Chenggong; Yuan Binxia; Shen Yin'gang

doi:10.19912/j.0254-0096.tynxb.2024-0278

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (6) : 289-297. DOI: 10.19912/j.0254-0096.tynxb.2024-0278

FAULT DIAGNOSIS METHODS OF PHOTOVOLTAIC ARRAYS BASED ON GRADIENT BOOSTING REGRESSION TREE-SELF-TRAINING BAYESIAN OPTIMIZED SUPPORT VECTOR MACHINE

Cao Lan, Zhou Chenggong, Yuan Binxia, Shen Yin'gang

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Abstract

A fault diagnosis method for PV arrays, based on the gradient-boosted regression tree-self-training Bayesian optimized support vector machine, is proposed aiming at the difficulty of measuring open-circuit voltage directly by conventional monitoring systems. The experimental results show that in the case where the open-circuit voltage cannot be directly measured, the accuracy rate of the gradient-boosted regression tree-self-trained Bayesian optimized support vector machine algorithm in predicting open-circuit voltage and using it as a characteristic parameter for fault diagnosis is 94.96%, and a running time of 4.876 s, which has better accuracy and a shorter diagnosis time.

Key words

semi-supervised learning / Bayesian networks / photovoltaic arrays / fault diagnosis / support vector machines

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Cao Lan, Zhou Chenggong, Yuan Binxia, Shen Yin'gang. FAULT DIAGNOSIS METHODS OF PHOTOVOLTAIC ARRAYS BASED ON GRADIENT BOOSTING REGRESSION TREE-SELF-TRAINING BAYESIAN OPTIMIZED SUPPORT VECTOR MACHINE[J]. Acta Energiae Solaris Sinica. 2025, 46(6): 289-297 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0278

References

[1] PEINADO GONZALO A, PLIEGO MARUGÁN A, GARCÍA MÁRQUEZ F P. Survey of maintenance management for photovoltaic power systems[J]. Renewable and sustainable energy reviews, 2020, 134: 110347.
[2] LI B, DELPHA C, DIALLO D, et al.Application of artificial neural networks to photovoltaic fault detection and diagnosis: a review[J]. Renewable and sustainable energy reviews, 2021, 138: 110512.
[3] VAN GOMPEL J, SPINA D, DEVELDER C.Satellite based fault diagnosis of photovoltaic systems using recurrent neural networks[J]. Applied energy, 2022, 305: 117874.
[4] LODHI E, WANG F Y, XIONG G, et al.An AdaBoost ensemble model for fault detection and classification in photovoltaic arrays[J]. IEEE journal of radio frequency identification, 2022, 6: 794-800.
[5] 唐圣学, 王彦丰, 乔乃珍. 光伏发电系统直流串联故障电弧检测方法研究[J]. 太阳能学报, 2023, 44(11): 31-39.
TANG S X, WANG Y F, QIAO N Z.Study on detection method of DC fault arc based on photovoltaic power generation system[J]. Acta energiae solaris sinica, 2023, 44(11): 31-39.
[6] NAVEEN VENKATESH S, SUGUMARAN V.Machine vision based fault diagnosis of photovoltaic modules using lazy learning approach[J]. Measurement, 2022, 191: 110786.
[7] MELLIT A.An embedded solution for fault detection and diagnosis of photovoltaic modules using thermographic images and deep convolutional neural networks[J]. Engineering applications of artificial intelligence, 2022, 116: 105459.
[8] KORKMAZ D, ACIKGOZ H.An efficient fault classification method in solar photovoltaic modules using transfer learning and multi-scale convolutional neural network[J]. Engineering applications of artificial intelligence, 2022, 113: 104959.
[9] 任惠, 夏静, 卢锦玲, 等. 基于红外图像和改进MobileNet-V3的光伏组件故障诊断方法[J]. 太阳能学报, 2023, 44(8): 238-245.
REN H, XIA J, LU J L, et al.Research on fault diagnosis of photovoltaic modules based on infrared images and improved MobileNet-V3[J]. Acta energiae solaris sinica, 2023, 44(8): 238-245.
[10] 陈辉, 张傲, 孙帅, 等. 基于改进DeepLabv³+的含噪声热红外图像光伏热斑检测方法[J]. 太阳能学报, 2023, 44(11): 23-30.
CHEN H, ZHANG A, SUN S, et al.Photovoltaic thermal spot detection method with noisy thermal infrared image based on improved DeepLabv³+[J]. Acta energiae solaris sinica, 2023, 44(11): 23-30.
[11] HUANG J M, WAI R J, GAO W.Newly-designed fault diagnostic method for solar photovoltaic generation system based on IV-curve measurement[J]. IEEE access, 2019, 7: 70919-70932.
[12] 陶彩霞, 王旭, 高锋阳. 基于深度信念网络的光伏阵列故障诊断[J]. 中国电力, 2019, 52(12): 105-112.
TAO C X, WANG X, GAO F Y.Fault diagnosis of photovoltaic array based on deep belief network[J]. Electric power, 2019, 52(12): 105-112.
[13] CHEN Z C, WU L J, CHENG S Y, et al.Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics[J]. Applied energy, 2017, 204: 912-931.
[14] ZHAO Q, SHAO S, LU L X, et al.A new PV array fault diagnosis method using fuzzy C-mean clustering and fuzzy membership algorithm[J]. Energies, 2018, 11(1): 238.
[15] ZHU H L, LU L X, YAO J X, et al.Fault diagnosis approach for photovoltaic arrays based on unsupervised sample clustering and probabilistic neural network model[J]. Solar energy, 2018, 176: 395-405.
[16] XU J Y, WANG W B, GONG T, et al.Fault simulation and characteristic analysis of photovoltaic array[C]//2023 5th Asia Energy and Electrical Engineering Symposium (AEEES). Chengdu, China, 2023: 546-550.
[17] SAKTHIVEL S S, ARUNACHALAM V, JAGATHEESAN K.Detection, classification, and location of open-circuit and short-circuit faults in solar photovoltaic array: an approach using single sensor[J]. IEEE journal of photovoltaics, 2023, 13(6): 986-990.
[18] VAPNIK N.V. Statistical learning theory[M].New York, NY, USA: Wiley-Interscience, 1998, 100-120.
[19] SRINIVAS N, KRAUSE A, KAKADE S M, et al. Gaussian process optimization in the bandit setting: No regret and experimental design[EB/OL].2009: 0912.3995. https://arxiv.org/abs/0912.3995v4.
[20] LI J N, ZHU Q S, WU Q W.A self-training method based on density peaks and an extended parameter-free local noise filter for k nearest neighbor[J]. Knowledge-based systems, 2019, 184: 104895.
[21] LI J N, ZHU Q S, WU Q W, et al.An effective framework based on local cores for self-labeled semi-supervised classification[J]. Knowledge-based systems, 2020, 197: 105804.
[22] LI J N, ZHU Q S.Semi-supervised self-training method based on an optimum-path forest[J]. IEEE access, 2019, 7: 36388-36399.
[23] FRIEDMAN H.J. Greedy function approximation: a gradient boosting machine, Ann. Statist[J]. 2001, 29(5), 1189-1232.
[24] ZHU X J, GHAHRAMANI. Z, LAFFERTY. J.Semi-supervised learning using Gaussian fields and harmonic functions[C]//Proceedings of the Twentieth International Conference on Machine Learning (ICML). Washington DC, USA, 2003: 912-919.