Abstract
The random vector functional link (RVFL) network is suitable for solving nonlinear problems from transformer fault symptoms and different fault types due to its simple structure and strong generalization ability. However, the RVFL network has a disadvantage in that the network structure, and parameters are basically determined by experiences. In this paper, we proposed a method to improve the RVFL neural network algorithm by introducing the concept of hidden node sensitivity, classify each hidden layer node, and remove nodes with low sensitivity. The simplified network structure could avoid interfering nodes and improve the global search capability. The five characteristic gases produced by transformer faults are divided into two groups. A fault diagnosis model of three layers with four classifiers was built. We also investigated the effects of the number of hidden nodes and scale factors on RVFL network learning ability. Simulation results show that the number of implicit layer nodes has a large impact on the network model when the number of input dimensions is small. The network requires a higher number of implicit layer neurons and a smaller threshold range. The size of the scale factor has significant influence on the network model with larger input dimension. This paper describes the theoretical basis for parameter selection in RVFL neural networks. The theoretical basis for the selection of the number of hidden nodes, and the scale factor is derived. The importance of parameter selection for the improvement of diagnostic accuracy is verified through simulation experiments in transformer fault diagnosis.
Highlights
Urban construction and sustainable development have become the focus of modern social development. e sustainable development of a city is inseparable from the safe and stable operation of the urban power grid
Dissolved gas analysis (DGA) method is an effective method for transformer fault diagnosis
It can be found that by optimizing the number of hidden nodes in the classifier RVFL4, the diagnostic accuracy of the random vector functional link (RVFL) neural network for training data is improved by 2.86%, and the diagnostic accuracy of test data is increased by 17.50%
Summary
Urban construction and sustainable development have become the focus of modern social development. e sustainable development of a city is inseparable from the safe and stable operation of the urban power grid. E random vector function link (RVFL) network is a single hidden layer feedforward neural network [21,22,23,24,25,26,27,28,29,30,31,32,33] It is widely used in deep and transfer learning [34,35,36,37]. It is verified with a transformer fault diagnosis model
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