Automatic Electrical Equipment Control System using Non-Stationary Markov Chain Optimization

In these days, electrical equipment control systems are playing a major role in various industries including power plants, distribution systems and renewable energies. The optimizations techniques are utilized for the electrical equipment control including the existing Markov Chain Optimization (MCO) technique have limitations. The existing MCO technique gives inefficient failures as it assumes that the previous transitions are always constant. In this paper, Non-Stationary Markov Chain (NSMC) optimization technique is proposed for the automatic electric equipment control system. Initially, the optimization technique started with state variable initialization with time parameter as it improves the reliability of state variable sequence. Then, the transition probability and sojourn for each state is calculated by using transition probability and the transition states are recognized as functional and failure states. After that, the transition states are evaluated and the matrix approach is utilized to find the time duration taken for the failure in each state. The transition matrix helps electrical equipment system to operate smoothly and ensures the reliability by predicting the failures. From the results, the proposed NSMC optimization model provided better results than the existing CAD-IOMPSE by providing results in terms of accuracy (89.46%), recall (88.43%) and F1-score (90.64%) respectively.