Photovoltaic power prediction and fault diagnosis method based on LSTM and transformer

The prediction and fault diagnosis of photovoltaic power generation are crucial for the efficient utilization of clean energy, but traditional methods have limitations such as low efficiency and strong subjectivity. To this end, an innovative hybrid model combining bidirectional long short-term memory network and Transformer is proposed, integrating the improved Golden Jackal optimization algorithm, time convolutional neural network, and grey wolf algorithm optimized variational mode decomposition to achieve signal adaptive purification, bidirectional temporal dependency capture, and global attention focusing. The experimental results show that the research model has achieved high-precision prediction, with an average accuracy of 95.03% in all seasons, an average absolute error of 4.86 kW, a relative root mean square error of 8.46%, and no significant cross seasonal differences, verifying the stability of the model. In terms of fault diagnosis, the waveform difference of the fault signal is up to 35.2 mm, and the accuracy of classifying five types of faults is 96.87%. SHAP analysis shows that instantaneous irradiance and historical power have the highest contribution, and the component string current is negatively correlated, which conforms to the physical laws of photovoltaics. In terms of deployment, the complete model has a parameter count of 8.77 M and an accuracy rate of 97.08%, making it suitable for high-precision offline and cloud scenarios. These results indicate that the proposed model improves both fault recognition efficiency and prediction accuracy. This study provides a valuable reference for building accurate photovoltaic power prediction and fault diagnosis models under complex environments, thus promoting the development of clean energy.