Fault diagnosis of photovoltaic strings by using machine learning‐based stacking classifier

Abstract

Photovoltaic (PV) modules are prone to short circuits, open circuits, cracks, which can bring serious harmful effects. It is difficult to establish the corresponding PV fault models to diagnose the status of PV strings. The paper proposes a machine learning‐based stacking classifier (MLSC) for accurate fault diagnosis of PV strings. Specifically, for the operating state of PV modules, the parameter sensitivity algorithm is used to analyze the impact of characteristic factors on the characteristics of PV modules. Then based on the characteristic factors (irradiance, temperature, current, and power), MLSC is proposed to realize the accurate fault diagnosis of PV strings. This structure of MLSC is to integrate all kinds of classifiers by stacking to play the characteristics of each classifier. It combines the characteristics of various types of machine learning algorithms to improve the overall classification effect by using the advantages of each classifier. Finally, experiments reveal that MLSC improves the accuracy of PV fault diagnosis.

Artificial Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review

“…Convolutional Neural Networks (CNNs) are frequently adopted due to their high accuracy (100%), with several studies applying them for detecting a range of PV panel faults [39][40][41]. Additionally, hybrid models such as ensemble learning and stacking classifiers also reported significant accuracy enhancements [42,43]. Regarding speed and efficiency, the CNN approach in one study was particularly emphasized for its rapid fault identification [41].…”