Abstract
The water content in oil is closely related to the deterioration performance of an insulation system, and accurate prediction of water content in oil is important for the stability and security level of power systems. A novel method of measuring water content in transformer oil using multi frequency ultrasonic with a back propagation neural network that was optimized by principal component analysis and genetic algorithm (PCA-GA-BPNN), is reported in this paper. 160 oil samples of different water content were investigated using the multi frequency ultrasonic detection technology. Then the multi frequency ultrasonic data were preprocessed using principal component analysis (PCA), which was implemented to obtain main principal components containing 95% of original information. After that, a genetic algorithm (GA) was incorporated to optimize the parameters for a back propagation neural network (BPNN), including the weight and threshold. Finally, the BPNN model with the optimized parameters was trained with a random 150 sets of pretreatment data, and the generalization ability of the model was tested with the remaining 10 sets. The mean squared error of the test sets was 8.65 × 10−5, with a correlation coefficient of 0.98. Results show that the developed PCA-GA-BPNN model is robust and enables accurate prediction of a water content in transformer oil using multi frequency ultrasonic technology.
Highlights
In different insulation systems, transformer oil is an important insulating medium in power transformers, and the water content in oil is an important factor in determining the insulation life of transformers [1,2,3]
Since the number of optimal hidden layer neurons gives uncertainty in the initial modeling, included 150 random sets, and the prediction accuracy of the model was tested with the remaining the range of the number of hidden layer neurons was determined by empirical formula [32,33,34]: 10 sets
√ was determined by empirical formula [32,33,34]: the range of the number of hidden layer neurons n < m+l+a
Summary
Transformer oil is an important insulating medium in power transformers, and the water content in oil is an important factor in determining the insulation life of transformers [1,2,3]. Many problems in the insulating system, such as breakdown voltage reduction, dielectric loss increase and the acceleration of the chemical reaction of organic matter, are caused by a higher water content in oil [4,5,6,7]. The technology and methods of water content detection in transformer oil have been studied by many scholars domestically and abroad [8,9,10,11,12]. Detection methods of water content in transformer oil, including off-line and on-line, have been widely reported. The measurement of water content based on humidity sensors and the reduction of temperature error have been studied by some scholars [8]
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