考虑波浪作用和时序特性的海上漂浮式光伏阵列故障诊断方法

doi:10.19595/j.cnki.1000-6753.tces.250113

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摘要

围绕近海漂浮式光伏发电场在海上风浪环境下长期稳定运行与智慧运维的需求,该文基于海上环境与光伏电气系统运行状况的一体化智能监测装置,提出一种考虑波浪作用和时序特性的故障诊断方法。首先将海上波浪运动规律与漂浮式光伏阵列的等效电路模型相结合,构建多模型融合的漂浮式光伏发电模型。在此基础上仿真分析正常状态下和海上多种故障类型的发电特性,针对海上环境下特有的故障类型进行特征提取,构建时序故障特征矩阵,所提出的特征提取算法考虑了波浪环境对故障特征量的影响和故障发展的时序特性。最后,提出空间注意力机制（SAM）与卷积-双向长短期记忆神经网络（CNN-BiLSTM）结合的故障诊断模型并进行了仿真及波浪实验数据验证。该模型可兼顾波浪影响的局部特征与故障发展的时序特征的提取,结果表明所提出的基于时序故障特征矩阵的诊断方法相较于其他方法有4.4%~20.3%的准确率提升。

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	高爽
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关键词 ：海上漂浮式光伏, 光伏发电模型, I-V特性曲线, 光伏阵列故障诊断, 故障时序特性提取

Abstract：

With the global transition towards decarbonization, photovoltaic (PV) power generation has become a key component in achieving carbon neutrality. Offshore floating photovoltaic systems, in particular, have gained attention due to their land-saving nature and the cooling effect of water, which improves power generation efficiency. However, these systems face significant operational challenges due to the harsh marine environment, including the effects of waves, salt spray, and contamination, which can lead to frequent system faults. This paper proposes a fault diagnosis method for offshore floating PV arrays that integrates wave effects and the temporal characteristics of fault progression. It aims at improving system reliability and efficiency in such dynamic environments.
The study first developed an integrated floating PV array model that incorporates both the dynamic behavior of ocean waves and the electrical characteristics of the PV system. By combining wave motion dynamics with the equivalent electrical circuit model of the floating PV array, a multi-model fusion approach was constructed to simulate the power generation characteristics under both normal and fault conditions. These simulations accounted for a variety of faults that could occur in offshore floating PV systems, such as open-circuit faults, short-circuit faults, contamination, aging, and hotspot issues. For each fault type, the system’s performance was evaluated through simulation to identify key fault indicators and characteristic behaviors, focusing on the changes observed in the I-V (current-voltage) curves under different conditions.
A novel fault feature extraction algorithm was introduced, which accounted for the dynamic effects of waves on tilt angles and the temporal characteristics of fault development. The algorithm extracted critical features, including open-circuit voltage, short-circuit current, maximum power point voltage, maximum power current, inflection points on the I-V curves, and environmental factors such as irradiance and temperature. These features were used to construct a fault feature vector that captured both instantaneous fault characteristics and the evolving nature of faults over time.
The primary contribution of this work was the development of a fault diagnosis model that combined Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM) networks, and a Spatial Attention Mechanism (SAM). This hybrid model enhanced the ability to capture both local fault features and temporal dependencies, which were crucial for accurately diagnosing faults in the dynamic marine environment. The CNN component extracted local features from the fault data, while the BiLSTM network captured the temporal dynamics of fault progression. The SAM module helped improve the model’s focus on critical regions of the input data, further enhancing diagnostic performance.
The proposed model was rigorously tested using simulation data and experimental data obtained from a wave simulation setup. The diagnostic performance of the CNN-BiLSTM-SAM model was compared with other approaches, including BP neural networks, CNN-SAM networks, and SE-TCN (Squeeze-and-Excitation Temporal Convolutional Networks). The results showed that the CNN-BiLSTM-SAM model significantly outperformed the other methods in fault detection accuracy. Specifically, the proposed model achieved an average improvement of 11.6% in the F1-score for faults such as contamination and aging, and up to a 50.1% improvement for hotspot faults, which exhibited significant temporal development.
In addition to simulation validation, the model’s robustness was further confirmed through experimental testing in a wave environment. The model maintained high diagnostic accuracy even when subjected to real-world wave dynamics, demonstrating its potential for real-time fault detection in offshore PV systems. The comparison of the confusion matrix and ROC (Receiver Operating Characteristic) curves confirmed that the CNN-BiLSTM-SAM model achieved the highest accuracy, with an AUC (Area Under Curve) value of 0.988, indicating superior fault classification performance compared to other models.

Key words： Offshore floating photovoltaics photovoltaic generation model fault diagnosis of photovoltaic arrays I-V curve time-sequence characteristic extraction of faults

收稿日期: 2025-01-16

PACS:

TM615

基金资助:

国家重点研发计划资助项目（2022YFB4200704）

通讯作者: 高爽女,1985年生,博士,副教授,硕士生导师,研究方向为海上漂浮式光伏电站智能监测及故障预警、分布式电源灵活并网与运行控制、电动汽车与电网互动技术等。E-mail：sgao@tju.edu.cn

作者简介: 李辰昊男,2000年生,硕士研究生,研究方向为海上光伏电站智能监测与故障诊断。 E-mail：lichenhao_1229@tju.edu.cn

引用本文:

高爽, 李辰昊, 李泽宇, 孔祥玉, 刘孟孟. 考虑波浪作用和时序特性的海上漂浮式光伏阵列故障诊断方法[J]. 电工技术学报, 0, (): 20250113-20250113. Gao Shuang, Li Chenhao, Li Zeyu, Kong Xiangyu, Liu Mengmeng. Fault Detection of Offshore Floating Photovoltaic Power System Considering Wave Motion and Time-Sequence Characteristics. Transactions of China Electrotechnical Society, 0, (): 20250113-20250113.

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