A Hybrid Artificial Intelligence for Fault Detection and Diagnosis of Photovoltaic Systems Using Autoencoders and Random Forests Classifiers

Abstract

The increasing sophistication of grid-connected photovoltaic (GCPV) systems necessitates advanced fault detection and diagnosis (FDD) methods to ensure operation efficiency and security. In this paper, a novel two-stage hybrid AI architecture is analyzed that couples an autoencoder using Long Short-Term Memory (LSTM) for unsupervised anomaly detection with an RF classifier for focused fault diagnosis. The architecture is critically compared to that of a baseline-only RF baseline on a synthetic dataset. The results of this two-stage hybrid AI show a strong overall accuracy of (83.1%). The hybrid model’s first stage trains only on unlabeled healthy data, reducing the reliance on extensive and often unavailable labeled fault datasets. This design has the safety-critical advantage of marking unfamiliar faults as anomalies instead of committing to a misclassification. By integrating anomaly detection with classification, the architecture enables early stage screening of faults and targeted categorization, even in data-scarce scenarios. This offers a scalable, interpretable solution suitable for deployment in real-world GCPV systems where robustness and early detection are critical. While the method exhibits reduced sensitivity to subtle or recurring faults, it demonstrates strong reliability in confidently detecting distinct and significant anomalies. Additionally, the approach improves interpretability, facilitating clearer identification of performance constraints such as the autoencoder’s moderate fault sensitivity (AUC = 0.61). This study confirms the hybrid approach as a very promising FDD solution, in which the architectural advantages of safety and maintainability offer a more worthwhile proposition to real-world systems than incremental improvements in a single accuracy measure.