Abstract
Transformers are the main equipment for power system operation. Undiagnosed faults in the internal components of the transformer will increase the downtime during operation and cause significant economic losses. Efficient and accurate transformer fault diagnosis is an important part of power grid research, which plays a key role in the safe and stable operation of the power system. Existing traditional transformer fault diagnosis methods have the problems of low accuracy, difficulty in effectively processing fault characteristic information, and superparameters that adversely affect transformer fault diagnosis. In this paper, we propose a transformer fault diagnosis method based on improved particle swarm optimization (IPSO) and multigrained cascade forest (gcForest). Considering the correlation between the characteristic gas dissolved in oil and the type of fault, firstly, the noncode ratios of the characteristic gas dissolved in the oil are determined as the characteristic parameter of the model. Then, the IPSO algorithm is used to iteratively optimize the parameters of the gcForest model and obtain the optimal parameters with the highest diagnostic accuracy. Finally, the diagnosis effect of IPSO-gcForest model under different characteristic parameters and size samples is analyzed by identification experiments and compared with that of various methods. The results show that the diagnostic effect of the model with noncode ratios as the characteristic parameter is better than DGA data, IEC ratios, and Rogers ratios. And the IPSO-gcForest model can effectively improve the accuracy of transformer fault diagnosis, thus verifying the feasibility and effectiveness of the method.
Highlights
Transformer fault will endanger the safe and stable operation of the whole power system
E dissolved gas analysis (DGA)-based transformer fault diagnosis method can analyze the equipment status information and detect the potential risks of the transformer in time, which is the key to ensuring the reliable and efficient operation of the equipment. erefore, we proposed a transformer fault diagnosis method to improve the accuracy of transformer diagnosis, in which the key parameters of gcForest model were optimized by improved particle swarm optimization (IPSO) algorithm
(3) It is proposed that using noncode ratios as the characteristic parameter of the model can significantly improve the accuracy of transformer fault diagnosis
Summary
Transformer fault will endanger the safe and stable operation of the whole power system. E model has the advantages of high parallel learning efficiency and strong representation learning ability It is widely used in the fields of hyperspectral image classification [19], complex machine processing status monitoring [20], turbine fault intelligent diagnosis [21], and other fields with good results. Erefore, we proposed a transformer fault diagnosis method to improve the accuracy of transformer diagnosis, in which the key parameters of gcForest model were optimized by IPSO algorithm. (2) Under the premise of the highest diagnostic accuracy, the IPSO algorithm is used to iterate and update automatically to find the optimal value of the parameters in the gcForest model, which overcomes the problem of low accuracy caused by the traditional empirical selection of parameters.
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