Recognizing the Density of Transformer Oil Based one GA-BPNN with MFU Technology

Abstract

On the basis of the principle of multi-frequency ultrasound, genetic algorithm GA and back propagation neural network BPNN, this paper proposed a prediction study of density of transformer oil. Taking 110 sets of transformer oil belonged to China southern power grid as an example, a prediction model of density of transformer oil was established based on BPNN, with the 242 dimensional multi-frequency ultrasonic data of oil sample as the input and density as the output. By adjusting the number of hidden layer neurons, the network was trained. Moreover, the genetic algorithm GA was introduced to optimize the network parameters. All results show that compared with the traditional standard BPNN model, the output value of density of transformer oil with the GA-BPNN model is much close to the real value with small errors, which lays a solid foundation to test transformer oil other parameters with tell multi-frequency ultrasonic technology