KNOWLEDGE EXPRESSION AND GRAPH CONSTRUCTION OF HOT SPOT EFFECT FAULTS OF PHOTOVOLTAIC MODULES

Wang Daolei; Zhang Zhen; Zheng Xin; Fang Mengxin; Zhu Rui; Zhao Wenbin

doi:10.19912/j.0254-0096.tynxb.2024-0786

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (9) : 538-546. DOI: 10.19912/j.0254-0096.tynxb.2024-0786

KNOWLEDGE EXPRESSION AND GRAPH CONSTRUCTION OF HOT SPOT EFFECT FAULTS OF PHOTOVOLTAIC MODULES

Wang Daolei¹, Zhang Zhen¹, Zheng Xin², Fang Mengxin¹, Zhu Rui¹, Zhao Wenbin¹

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Abstract

This paper proposes a method for constructing a knowledge graph for hot-spot failures. First, the hot-spot failure knowledge representation is standardized, and relevant text information is collected to create a corpus. Secondly, an improved RoBERTa-BiLSTM-MHA-CRF model is used to identify entities related to hot-spot failures. Based on this, the BERT model is used for relationship extraction. Finally, the results of these two parts are integrated to construct a complete hot-spot failure knowledge graph. Experimental results show that the proposed entity recognition model achieves an accuracy of 96.02% for hot-spot failure knowledge, a recall rate of 93.56%, and an F1 value of 94.77%. This method precisely constructs a knowledge graph for hot spot faults in photovoltaic modules,enabling intelligent fault diagnosis of PV modules and offering strong support for subsequent applications.

Key words

deep learning / photovoltaic modules / natural language processing / knowledge graph / RoBERTa-BiLSTM-MHA-CRF

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Wang Daolei, Zhang Zhen, Zheng Xin, Fang Mengxin, Zhu Rui, Zhao Wenbin. KNOWLEDGE EXPRESSION AND GRAPH CONSTRUCTION OF HOT SPOT EFFECT FAULTS OF PHOTOVOLTAIC MODULES[J]. Acta Energiae Solaris Sinica. 2025, 46(9): 538-546 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0786

References

[1] 伊纪禄, 刘文祥, 马洪斌, 等. 太阳电池热斑现象和成因的分析[J]. 电源技术, 2012, 36(6): 816-818.
YI J L, LIU W X, MA H B, et al. Solar hot spot phenomenon and its analysis[J]. Chinese journal of power sources, 2012, 36(6): 816-818.
[2] 王道累, 李超, 李明山, 等. 基于深度卷积神经网络的光伏组件热斑检测[J]. 太阳能学报, 2022, 43(1): 412-417.
WANG D L, LI C, LI M S, et al. Solar photovoltaic modules hot spot detection based on deep convolutional neural networks[J]. Acta energiae solaris sinica, 2022, 43(1): 412-417.
[3] 王道累, 李明山, 姚勇, 等. 改进SSD的光伏组件热斑缺陷检测方法[J]. 太阳能学报, 2023, 44(4): 420-425.
WANG D L, LI M S, YAO Y, et al. Method of hotspot detection of photovoltaic panels modules on improved SSD[J]. Acta energiae solaris sinica, 2023, 44(4): 420-425.
[4] 蔡洁聪, 吕洪坤, 朱凌云, 等. 光伏发电站热斑检测技术综述[J]. 电源技术, 2021, 45(5): 683-685.
CAI J C, LYU H K, ZHU L Y, et al. Review of hot spot detection technology in photovoltaic power station[J]. Chinese journal of power sources, 2021, 45(5): 683-685.
[5] 王道累, 姚从荣, 李超, 等. 光伏组件热斑效应智能化检测的综述及展望[J]. 太阳能学报, 2024, 45(5): 527-536.
WANG D L, YAO C R, LI C, et al. Overview and prospect of intelligent detection of hot spot effect of photovoltaic modules[J]. Acta energiae solaris sinica, 2024, 45(5): 527-536.
[6] 俞伊丽, 张展耀, 接晓霞, 等. 基于知识图谱与SCD文件的智能变电站二次检修安全措施自动生成技术研究[J]. 电力系统保护与控制, 2024, 52(2): 129-142.
YU Y L, ZHANG Z Y, JIE X X, et al. Automatic generation technology of secondary safety measures in an intelligent substation based on a knowledge graph and SCD files[J]. Power system protection and control, 2024, 52(2): 129-142.
[7] 武霁阳, 李强, 陈潜, 等. 知识图谱框架下基于深度学习的HVDC系统故障辨识[J]. 电力系统保护与控制, 2023, 51(20): 160-169.
WU J Y, LI Q, CHEN Q, et al. Fault identification of an HVDC system based on deep learning in the framework of a knowledge graph[J]. Power system protection and control, 2023, 51(20): 160-169.
[8] 黄简, 杨程, 冯天波, 等. 面向风电机组运维的知识图谱构建研究与应用[J]. 电力系统保护与控制, 2024, 52(8): 167-177.
HUANG J, YANG C, FENG T B, et al. Research and application of knowledge graph construction for wind turbine operation and maintenance[J]. Power system protection and control, 2024, 52(8): 167-177.
[9] LIU Y H, OTT M, GOYAL N, et al. RoBERTa: a robustly optimized BERT pretraining approach[EB/OL].2019: arXiv: 1907.11692. https://arxiv.org/abs/1907.11692
[10] ZHANG P F, LI T R, WANG G Q, et al.Multi-source information fusion based on rough set theory: a review[J]. Information fusion, 2021, 68: 85-117.
[11] LI J N, LI D X, SAVARESE S, et al. BLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models[EB/OL].2023: arXiv: 2301.12597. https://arxiv.org/abs/2301.12597
[12] YANG X, ZHANG H W, CAI J F.Deconfounded image captioning: a causal retrospect[J]. IEEE transactions on pattern analysis and machine intelligence, 2023, 45(11): 12996-13010.
[13] 蔡莉, 王淑婷, 刘俊晖, 等. 数据标注研究综述[J]. 软件学报, 2020, 31(2): 302-320.
CAI L, WANG S T, LIU J H, et al. Survey of data annotation[J]. Journal of software, 2020, 31(2): 302-320.
[14] YUN W, ZHANG X, LI Z D, et al.Knowledge modeling: a survey of processes and techniques[J]. International journal of intelligent systems, 2021, 36(4): 1686-1720.
[15] 张吉祥, 张祥森, 武长旭, 等. 知识图谱构建技术综述[J]. 计算机工程, 2022, 48(3): 23-37.
ZHANG J X, ZHANG X S, WU C X, et al. Survey of knowledge graph construction techniques[J]. Computer engineering, 2022, 48(3): 23-37.
[16] HUANG Z H, XU W, YU K. Bidirectional LSTM-CRF models for sequence tagging[EB/OL].2015: arXiv: 1508.01991. https://arxiv.org/abs/1508.01991
[17] 戴志辉, 张富泽, 张近月, 等. 基于MacBERT-BiLSTM-CRF模型的继电保护装置缺陷知识图谱构建方法[J]. 电力系统保护与控制, 2024, 52(20): 131-143.
DAI Z H, ZHANG F Z, ZHANG J Y, et al. Construction method of a defect knowledge map of a relay protection device based on a MacBERT-BiLSTM-CRF model[J]. Power system protection and control, 2024, 52(20): 131-143.
[18] CHUNG Y A, ZHU C G, ZENG M.SPLAT: speech-language joint pre-training for spoken language understanding[C]//Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Online, 2021: 1897-1907.
[19] CHO K, VAN MERRIENBOER B, GULCEHRE C, et al.Learning phrase representations using RNN encoder-decoder for statistical machine translation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing(EMNLP). Doha, Qatar, 2014: 1724-1734.