Table 1.

Comparative analysis of PV inverter fault diagnosis approaches.

Approach	Key methods	Advantages	Limitations
Model-based16 & Signal-based20	Sliding mode observers; extended Kalman filters; Adaptive observers; FFT; STFT; wavelet transform	Provides insightful diagnostics; good at detecting transient faults; effective for periodic fault detection	Requires accurate system models; performance degrades with noise; limited adaptability; struggles with complex systems
Statistical methods25	K-nearest neighbor; Wolf optimization; independent component analysis; random forest; ensemble methods	Robust pre-processing capabilities; good for handling imbalanced data; effective feature extraction; better performance through hybrid approaches	Limited representation learning; may require extensive feature engineering; performance depends on data quality
Deep learning33,34	1D-CNN; 2D-CNN; hybrid CNN (HCNN); CNN-LSTM; pyramid-structured networks	High diagnostic accuracy; strong pattern recognition; good at handling complex data; real-time detection capability	Requires extensive preprocessing; limited generalization; high computational demands; limited spatial dependency handling