A cascade of artificial neural networks to predict transformers oil parameters

In this paper artificial neural networks have been constructed to predict different transformers oil parameters. The prediction is performed through modeling the relationship between the insulation resistance measured between distribution transformers high voltage winding, low voltage winding and the ground and the breakdown strength, interfacial tension acidity and the water content of the transformers oil. The process of predicting these oil parameters statuses is carried out using various configurations of neural networks. First, a multilayer feed forward neural network with a back-propagation learning algorithm was implemented. Subsequently, a cascade of these neural networks was deemed to be more promising, and four variations of a three stage cascade were tested. The first configuration takes four inputs and outputs four parameter values, while the other configurations have four neural networks, each with two or three inputs and a single output; the output from some networks are pipelined to some others to produce the final values. Both configurations are evaluated using real-world training and testing data and the accuracy is calculated across a variety of hidden layer and hidden neuron combinations. The results indicate that even with a lack of sufficient data to train the network, accuracy levels of 84% for breakdown voltage, 95% for interfacial tension, 56% for water content, and 75% for oil acidity predictions were obtained by the cascade of neural networks.