This paper presents an advanced fault diagnosis framework for renewable energy systems by leveraging a novel Adaptive Polynomial Kolmogorov Arnold Network (Adaptive PolyKAN). The proposed method is evaluated on two distinct applications: a wind energy conversion system and a grid-connected photovoltaic (PV) system, each characterized by complex, nonlinear fault patterns. A comprehensive comparison is conducted against a range of classical and neural classifiers, including Random Forest (RF), Support Vector Machine (SVM), and others. Experimental results demonstrate that Adaptive PolyKAN consistently achieves superior classification accuracy, reaching 99.96 % for wind data and 95.61 % for PV data, outperforming conventional methods across all performance metrics. To improve computational efficiency, an autoencoder-based dimensionality reduction strategy is incorporated, resulting in a reduction of execution time by over 88 % and memory usage by 40 %, while preserving high diagnostic accuracy, maintaining 99.96 % on the wind data and increasing to 96.47 % on the PV data. The results confirm the robustness, adaptability, and efficiency of the proposed framework, highlighting its potential for intelligent fault diagnosis in complex renewable energy systems.
Attouri et al. (Wed,) studied this question.