Abstract:Transformer is one of the main equipment in the power system and power quality of the power system mainly depends on the working of the power transformer. Leakage reactance is one of the main parameters of the transformer. The design of the transformer has a significant impact on the leakage reactance of the transformer. The mechanical strength of the transformer mainly depends on the leakage reactance of the transformer. This paper investigates the leakage reactance of the transformer in normal conditions and… Show more
“…As pointed out in the literature [14][15][16], several types of faults that are often encountered in transformers mainly include short circuit faults, insulation damage faults and overheating faults. Among them, various short circuit faults in transformers, such as single phase to ground, two phases to ground or three phases to ground, are most serious.…”
A transformer is an important part of the power system. Existing transformer fault diagnosis methods are still limited by the accuracy and efficiency of the solution and excessively rely on manpower. In this paper, a novel neural network is designed to overcome this issue. Based on the traditional method of judging the ratio of dissolved gas in transformer internal insulation oil, a fast fault diagnosis model of a transformer was built with an improved probabilistic neural network (PNN). The particle swarm optimization (PSO) algorithm was used to find the global optimal smoothing factor and improve the fault diagnosis accuracy of PNN. The transformer fault diagnosis model based on improved PNN not only eliminates the influence of human subjective factors but also significantly improves the diagnosis speed and accuracy, meeting the requirements for real-time application in practical projects. The feasibility and effectiveness of the method proposed in this paper are illustrated by a case study of actual data. Through analysis and comparison, the diagnostic accuracy of the proposed method is 10% higher than that of the general BPNN and 5% higher than that of the traditional PNN on the premise of ensuring the efficiency of the solution.
“…As pointed out in the literature [14][15][16], several types of faults that are often encountered in transformers mainly include short circuit faults, insulation damage faults and overheating faults. Among them, various short circuit faults in transformers, such as single phase to ground, two phases to ground or three phases to ground, are most serious.…”
A transformer is an important part of the power system. Existing transformer fault diagnosis methods are still limited by the accuracy and efficiency of the solution and excessively rely on manpower. In this paper, a novel neural network is designed to overcome this issue. Based on the traditional method of judging the ratio of dissolved gas in transformer internal insulation oil, a fast fault diagnosis model of a transformer was built with an improved probabilistic neural network (PNN). The particle swarm optimization (PSO) algorithm was used to find the global optimal smoothing factor and improve the fault diagnosis accuracy of PNN. The transformer fault diagnosis model based on improved PNN not only eliminates the influence of human subjective factors but also significantly improves the diagnosis speed and accuracy, meeting the requirements for real-time application in practical projects. The feasibility and effectiveness of the method proposed in this paper are illustrated by a case study of actual data. Through analysis and comparison, the diagnostic accuracy of the proposed method is 10% higher than that of the general BPNN and 5% higher than that of the traditional PNN on the premise of ensuring the efficiency of the solution.
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