Soft computing based smart grid fault detection using computerised data analysis with fuzzy machine learning model

Electrical grids are more dependable, secure, and significant smart grid (SG) technologies. For effective and dependable electricity distribution, new risks are raised by its high reliance on digital communication technologies. The best grid monitoring and control skills are essential for system reliability. Among other things, SG applications include three key challenges: managing big data volumes, having enough real-time capable measurement instruments, and two-way low-latency communication. This study proposes a unique method for detecting faults in the smart grid via the use of data monitoring and classification using a fuzzy machine learning model. Here, enhanced smart sensor metering performed in the cloud at the network's edge has been used to track data from the smart grid. Fuzzy reinforcement encoder adversarial NN has then been used to categorise the tracked data. Experimental analysis is carried out in terms of scalability, reliability, accuracy, mean average precision, throughput. The potential use of the current grid can be increased, and fault frequency can be decreased, with better monitoring technologies and predictive techniques. Proposed technique attained accuracy of 93 %, throughput of 94 %, reliability of 81 %, mean average precision of 89 %, scalability of 92 %.