Aging assessment of power transformer insulation oil using hybrid meta-heuristic trained artificial neural network

Abstract

In today's economic state, power transformer remains as the most expensive equipment in electrical system, in which insulation oil has been taken a significant role for performing a prominent operation. Since the insulation oil happens to degrade soon due to aging, high temperature and chemical reactions such as the oxidation, the periodic checking of oil followed by its replacement is necessary to stop the unexpected failure of the transformer. Moreover, it will be very advantageous if it happens to implement an automated model for predicting the age of transformer oil from time to time. The main intent of this paper is to develop an age assessment framework of transformer insulation oil using intelligent approaches. Here, diverse parameters associated with the transformer such as Breakdown Voltage (BDV), moisture, resistivity, tan delta, interfacial tension, and flash point is given as input for predicting the age of the insulation oil. These data have been already collected using 20 working power transformers operated at various substations in Punjab, India. In the proposed model, the collected parameters are subjected to a well-performing machine learning algorithm termed as Artificial Neural Network (ANN) in order to predict the age of the insulation oil. As a main contribution, the existing training algorithm in ANN so called as Levenberg–Marquardt (LM) is replaced by a hybrid meta-heuritics algorithm. The newly developed hybrid algorithm merges the idea of Crow Search Algorithm (CSA), and Particle Swarm Optimization (PSO), and the new algorithm is termed as Particle Swarm-based Crow Search Algorithm (PS-CSA). The new training algorithm optimizes the weight of ANN using the hybrid CS-PSO updating procedure, in such a way that the difference between the predicted and actual outcome is minimum. Hence, this age prediction of transformer insulation oil will be beneficial for the environs to avoid the drastic condition.