Open AccessApplication of Particle Swarm Optimization Algorithm in Power Transformer Fault Diagnosis
Author(s) -
Zhong Cao,
Chen Chen,
Yu Chen,
Lei Song,
Huirui Zhou,
Guo Qun Zhao,
Guo Hua Jiang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1624/4/042022
Subject(s) - particle swarm optimization , transformer , dissolved gas analysis , algorithm , transformer oil , computer science , fault (geology) , electric power system , power (physics) , engineering , voltage , electrical engineering , seismology , geology , physics , quantum mechanics
Fault diagnosis of power transformer is indispensable for power system reliability. To improve the function of fault diagnosis and overcome the “code absence” problem of the traditional ratio method, this paper presents a novel approach for oil chromatographic fault diagnosis based on particle swarm optimization algorithm. The PSO algorithm is used to obtain the optimal three-ratio value that can best represent various fault types of power transformers, and then the change trend of the characteristic gas of the power transformer is analyzed to predict the possible faults. Combining the stereogram method and the optimal three-ratio method, A comprehensive fault diagnosis method for based on oil chromatographic distraction is obtained. In the end, simulation of the actual oil chromatographic data of the transformer verifies the accuracy and effectiveness of the proposed method.