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<p>With the acceleration of the global energy transition, photovoltaic (PV) power generation, as an essential component of clean energy, has become increasingly important in promoting green and low-carbon development and achieving sustainable energy goals. However, the time-series data generated by PV power generation systems often contains outliers caused by equipment failures or environmental changes. Previous studies have shown that reconstruction-based anomaly detection methods are deficient in simultaneously modeling the global dependencies and local dynamics of time-series data, and are susceptible to model overfitting. Against the background of rapid expansion in PV installed capacity and sustained growth in monitoring data, coupled with a scarcity of precise annotations, unsupervised anomaly detection in multivariate time series has become a critical issue for ensuring the safe and economical operation of large-scale PV plants. For this reason, an unsupervised anomaly detection model based on GTBAD (GVSAO-Transformer-BiLSTM for anomaly detection) is proposed in this article. The model introduces an improved Snow Ablation Optimization algorithm (GVSAO) during the offline training phase to optimize model parameters, thereby enhancing global search capability and improving the efficiency of parameter optimization. The encoder employs a Transformer module with positional encoding to capture global dependencies in the time series, and regularization techniques are incorporated at each layer to mitigate overfitting. During decoding, a BiLSTM module is used to further exploit local temporal patterns via bidirectional recurrent modeling, enabling comprehensive utilization of contextual information in PV data. Finally, the reconstructed sequence is generated through a fully connected output layer, and anomaly detection is performed by comparing the reconstruction error against a predefined threshold. In this paper, we conduct comparison experiments between the GTBAD model and existing anomaly detection methods on four public datasets and verify the effectiveness of each module through ablation experiments. In addition, this study incorporates attention visualization and variable-level attribution analysis to enhance model interpretability and improve its operational usability. The experimental results show the effectiveness and superiority of the GTBAD model in PV anomaly detection.</p>

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GTBAD: GVSAO-Transformer-BiLSTM-based time-series anomaly detection for photovoltaic power generation

  • 期刊论文

  • 发表日期：

    2026年3月9日

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  • Lin Zhu,
  • Shaojuan Ma,
  • Changlin Xu,
  • Xinyi Xu,
  • Haichuan Du

摘要

With the acceleration of the global energy transition, photovoltaic (PV) power generation, as an essential component of clean energy, has become increasingly important in promoting green and low-carbon development and achieving sustainable energy goals. However, the time-series data generated by PV power generation systems often contains outliers caused by equipment failures or environmental changes. Previous studies have shown that reconstruction-based anomaly detection methods are deficient in simultaneously modeling the global dependencies and local dynamics of time-series data, and are susceptible to model overfitting. Against the background of rapid expansion in PV installed capacity and sustained growth in monitoring data, coupled with a scarcity of precise annotations, unsupervised anomaly detection in multivariate time series has become a critical issue for ensuring the safe and economical operation of large-scale PV plants. For this reason, an unsupervised anomaly detection model based on GTBAD (GVSAO-Transformer-BiLSTM for anomaly detection) is proposed in this article. The model introduces an improved Snow Ablation Optimization algorithm (GVSAO) during the offline training phase to optimize model parameters, thereby enhancing global search capability and improving the efficiency of parameter optimization. The encoder employs a Transformer module with positional encoding to capture global dependencies in the time series, and regularization techniques are incorporated at each layer to mitigate overfitting. During decoding, a BiLSTM module is used to further exploit local temporal patterns via bidirectional recurrent modeling, enabling comprehensive utilization of contextual information in PV data. Finally, the reconstructed sequence is generated through a fully connected output layer, and anomaly detection is performed by comparing the reconstruction error against a predefined threshold. In this paper, we conduct comparison experiments between the GTBAD model and existing anomaly detection methods on four public datasets and verify the effectiveness of each module through ablation experiments. In addition, this study incorporates attention visualization and variable-level attribution analysis to enhance model interpretability and improve its operational usability. The experimental results show the effectiveness and superiority of the GTBAD model in PV anomaly detection.