Lijun Wu

TL;DR: Both the simulation and experimental results show that the optimized KELM based fault diagnosis model can achieve high accuracy, reliability, and good generalization performance.

TL;DR: The comparison results indicate that the generalization performance of the proposed RF based model is better than the one of the decision tree based model, therefore, the proposed optimal RF based method is an effective and efficient alternative to detect and classify the faults of PV arrays.

TL;DR: A novel intelligent fault detection and diagnosis method for photovoltaic arrays based on a newly designed deep residual network model trained by the adaptive moment estimation deep learning algorithm, which can automatically extract features from raw current-voltage curves and ambient irradiance and temperature, and effectively improve the performance with a deeper network.

TL;DR: In this article, an improved adaptive Nelder-Mead simplex (NMS) hybridized with the artificial bee colony (ABC) metaheuristic, EHA-NMS, is proposed to improve parameters identification of PV models.

TL;DR: Comparison studies with other well-known optimization algorithms indicate that the MSSO method has the best performance among these methods in terms of efficiency, robustness and accuracy.