High Voltage Transformer Condition Monitoring Using Memristive Echo State Networks

The importance of intelligent transformers in the contemporary society cannot be overstated. Timely detection of damage or failure in transformers is crucial. This study evaluates many factors through the use of sensors that include oil level indicators, all of which are connected to the transformers. The data collected from these sensors is subsequently processed using a memristive Echo state network framework to monitor for errors. Two distinct ESN topologies, namely ‘sparsely connected’ and ‘stochasticity improved reservoir connections’, are utilized to ensure a precise prediction of the oil level in the transformer. By proactively detecting possible difficulties with the transformers, necessary steps may be swiftly initiated. Empirical findings demonstrate that the real-time forecasts for oil level and associated variables surpass alternative methods, exhibiting reduced error rates and enhanced precision in predictions.