Study on Method of Identifying Dissolved Gases in Transformer Oil Based on Improved Artificial Neural Network Algorithm
Dissolved Gas-in-oil Analysis (DGA) plays an important role in fault diagnosis of power transformers. BP (Back Propagation) algorithm is used to diagnosis for dissolves gases in the oil of transformer in this paper. But typical BP algorithm has some defects, such as converging slowly, searching space possessing local minima and oscillation. The algorithm using additional momentum method and L-M (Lerenberg-Marquardt) to train BP Neural Network has been proved to have good performance in avoiding the local trap and converging slowly. So this paper adopts BP artificial neural network with algorithm of additional momentum method and L-M in diagnosis of dissolves gases in the oil. A mass of gases samples are analyzed in the algorithm and the results are compared with the swatches forecasted. The comparison result indicates that the improved algorithm has better classify capability for single-gases swatch as well as high diagnosis precision.
transformer oil,additional momentum method,typical bp algorithm,bp neural network,local minimum,gases sample,improved artificial neural network,fault diagnosis,bp artificial neural network,high diagnosis precision,improved algorithm,dissolved gases,local trap,oscillations,neural network,artificial neural network,local minima,search space,power transformer
Oscillation,Computer science,Transformer oil,Control theory,Algorithm,Transformer,Maxima and minima,Momentum,Backpropagation,Artificial neural network