Fault detection and classification using deep learning method and neuro‐fuzzy algorithm in a smart distribution grid

Abstract

Abstract This article proposes a deep learning (DL) model made of Long Short Term Memory (LSTM) and Adaptive Neuro Fuzzy Inference System (ANFIS) to detect fault in smart distribution grid assisted by communication systems using smart meter data. In smart grid, data analysis for fault identification and detection is crucial for grid monitoring. Nowadays, there are several DL techniques developed for smart grid data analysis applications. To solve this problem, a novel data analysis model based on deep learning and Neuro‐fuzzy algorithm is developed for fault location in a smart power grid. First, the LSTM is applied for training the data samples extracted from the smart meters. Then, an ANFIS algorithm is implemented for fault detection and identification from the trained data. Finally, faults are located with the higher accuracy. With this intelligent method proposed, single‐phase, two‐phase and three‐phase faults can be identified using a restricted amount of data. The novelty of the proposed method compared with other methods is the capability of fast training and testing even with large amount of data. To verify the effectiveness of our methodology, an intelligent model of the IEEE 13‐node network is used. The effectiveness and robustness of the proposed model are evaluated using several parameters such as accuracy, precision‐recall, F1‐score, Receiver Operating Characteristic (ROC) curve and complexity time. The obtained results indicate that the proposed deep learning model outperforms existing deep learning methods in the literature for fault detection and classification with 99.99% accuracy.