A novel model based on wavelet LS-SVM integrated improved PSO algorithm for forecasting of dissolved gas contents in power transformers

Finding out the transformer incipient faults and their development trend has always been a central issue for electric power companies. In this paper, a novel approach combing wavelet technique with least squares support vector machine (LS-SVM) for forecasting of dissolved gases in oil-immersed power transformers has been proposed. The algorithm of particle swarm optimization (PSO) with mutation is developed to optimize the parameters of constructed wavelet LS-SVM regression (W-LSSVR). The existence of admissible wavelet kernels is proven by theoretic analysis. Evaluation of forecasting performance is based upon the measures of mean absolute percentage error (MAPE) and squared correlation coefficient (r2). On the basis of the proposed approach, a procedure is put forward to serve as an effective tool and experimental results show that this approach is capable of forecasting the dissolved gas contents accurately. Comparing with the back propagation neural network (BPNN), the radial basis function neural network (RBFNN), the generalized regression neural network (GRNN), and the SVM regression (SVR) in two practical cases (taken hydrogen as an example here), the MAPEs of the proposed approach are significantly better than that of the four methods (5.4238% vs 19.1458%, 11.7361%, 7.7395%, 8.3248%; 2.1567% vs 18.9453%, 10.2451%, 7.8636%, 2.4628%) respectively.