Automatic fault identification in WSN‐based smart grid environment

Abstract

SummaryWireless sensor network (WSN) plays a vital role in the smart grid (SG) environment. Due to the fault tolerance characteristics, cost reduction, and large‐scale convergence, SG introduces many unique challenges caused by system and functional devices. To solve this problem, a WSN‐based SG network is used to identify faults. During data transmission, faulty nodes occur in the transmission line. The node failures, calibration, network failures, low battery, dead nodes, environmental changes, software failures, and so on lead to the interruption in data delivery and spoil the entire WSN‐based SG network. In order to tackle these problems, the new WSN model is designed to detect the faults in the transmission line based on the SG environment. This paper uses the Adaptive Zigbee‐Aquila communication protocol (AZACP) to find the optimal shortest path for transferring data. AZACP finds the shortest optimal path for transmitting the sensed data to the base station with low cost and less time consumption. Fault detection automatically identifies the fault in the transmission line and isolates the faulty nodes to ensure efficient data transmission in WSN. Here, enhanced recurrent equilibrium neural network (ERENN) is introduced to identify the fault in data transmission. The proposed approach provides better performance in terms of evaluating performance metrics like throughput, delay, reliability, average residual energy, number of total transmissions, network lifetime, efficiency, and bit error rate (BER).