Automatic Fault Identification in WSN Based Smart Grid Environment

Abstract

Wireless Sensor Network (WSNs) plays a vital role in smart grid (SG) environment. Due to the fault tolerance characteristics, cost reduction and large scale convergence. SG introduce many unique challenges caused by system and functional devices. To solve this problem, a WSN based SG network is used for identifying faults. During the process of data transmission, faulty nodes are occurred in the transmission line. Due to the nodes failures, calibration, network failures, low battery, dried sensors, environmental changes and software failures etc., which leads 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. In this paper, Adaptive Zigbee-Aquila communication protocol (AZACP) is used to find the optimal shortest path for transferring data. AZACP finds the shortest optimal path for transmitting the sensed data to base station with low cost and less time consumption. Fault detection is the process of automatically identifying the fault in the transmission line and isolate the faulty nodes to ensure the efficient data transmission in WSN. Here, Enhanced Recurrent Equilibrium Neural Network (ERENN) is introduced to identify the fault in data transmission. It recognize the strength of the signal to transmit the sensed data and checks the quality of the data in transmission line between the nodes. The proposed approach is implemented in MATLAB software and compared with existing approaches like Adaptive Error Control (AEC), Gallager Humble Spira (GHS), Genetic Algorithm-Ticket Based Routing (GA-TBR), Improved Grid based Routing and Charging (IGRC) and Emperor Penguin Optimized Self-healing Strategy (EPOSH). The proposed approach provides better performance in terms of evaluating performance metrics like throughput, delay, reliability, average residual energy, number of total transmission, network lifetime, efficiency and Bit Error Rate (BER).