Fault diagnosis in renewable-integrated distribution systems using EMD-GAF and ANN

Abstract

The increasing integration of distributed renewable energy sources and dynamic loads has made fault detection in modern distribution systems significantly more challenging. Traditional protection schemes often fail to accurately distinguish between faults and non-fault disturbances such as switching events, islanding, or power quality anomalies, which can lead to delayed or incorrect responses. This paper proposes a fast and reliable fault diagnosis technique integrating Empirical Mode Decomposition (EMD), Gramian Angular Fields (GAF), and Artificial Neural Networks (ANN) to detect, classify, and locate faults in renewable-integrated distribution networks. Three-phase current and voltage signals are first decomposed using EMD to extract low-frequency residues, that are then transformed into two-dimensional GAF visual patterns. Cosine similarity compares these patterns against reference healthy conditions for fault detection.

For fault localization, an ANN is trained using statistical features from four levels of EMD residues. The proposed method achieves over 99.5% accuracy in fault detection and classification using only 0.25 cycles of post-fault data and single-point current and voltage measurements at the substation, even under noisy (20 dB SNR) and high-impedance (up to 5 $\Omega$) conditions. It outperforms existing signal-analysis-based and visual-pattern-based techniques by accurately distinguishing faults from switching and islanding events, making it a robust and scalable solution for real-time smart grid protection. Furthermore, the method achieves up to 99.04% bus-level fault localization accuracy and reduces distance-to-fault errors by over 25% compared to existing techniques, further enhancing suitability for protection and precise fault location.