Abstract
Through the dissolved gas analysis (DGA) in transformer oil, the fault of the power transformer can be diagnosed. However, the DGA method has the disadvantage of low accuracy because it couldn’t exactly reflect the nonlinear relationship between the characteristic gases and fault types. Radial basis function neural network (RBFNN) has the advantage of dealing with complex nonlinear problems, so it can be applied to transformer fault diagnosis based on DGA. The centers, widths and weights has important effects on the performance of the RBFNN. However, it is difficult to find the global optimal solution of these parameters when RBFNN training. This paper creatively designs a method to improve these parameters of RBFNN, firstly using the K-means algorithm to optimize the centers and widths of RBFNN, then using the genetic algorithm-backpropagation (GA-BP) algorithm optimize the weights. Finally, establish the K-means RBF-genetic backpropagation (KRBF-GBP) algorithm model through a large amount of training data. The test results show that the fault diagnosis accuracy of the KRBF-GBP algorithm is 96.4%, higher than the unoptimized RBFNN with 71.43%.
Highlights
Power transformers are an essential part of the power system [1]
This paper proposes a strategy to optimize the parameters of Radial basis function neural network (RBFNN) based on Dissolved gas analysis (DGA) data that by using the K-means and the genetic algorithm-backpropagation (GA-BP) algorithm optimizes the parameters of the RBFNN, establish the K-means RBF-genetic backpropagation (KRBF-GBP) algorithm model
8 Conclusion An optimization scheme of RBFNN parameters is proposed, and the KRBF-GBP algorithm model is established. It can be seen from the test result that the KRBF-GBP algorithm improves the accuracy of the transformer fault diagnosis
Summary
Power transformers are an essential part of the power system [1]. Dissolved gas analysis (DGA) is a method of transformer fault diagnosis by analyzing the composition and content of dissolved gas in transformer oil. Radial basis function neural network (RBFNN) is an excellent forward network [6], it has strong advantages in processing complex nonlinear mapping problems, the hidden layer transforms the input vector from the low dimensional space into the high dimensional space, the linear inseparability problem which in the low dimensional space is linear separable in the high dimensional space [7] It can realize the classification of fault very well and could improve the accuracy when applying it in transformer fault diagnosis based on DGA. This paper proposes a strategy to optimize the parameters of RBFNN based on DGA data that by using the K-means and the genetic algorithm-backpropagation (GA-BP) algorithm optimizes the parameters of the RBFNN, establish the K-means RBF-genetic backpropagation (KRBF-GBP) algorithm model It can be seen from the test result that this model has a high fault diagnosis rate in transformers and has great application value
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