Open AccessFault Diagnosis of Power Transformer in Mine
Author(s) -
Lu Chen,
Shudong Wang,
Shiyong Fan,
Haiyan Shi,
Liang Zhang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/738/1/012015
Subject(s) - dissolved gas analysis , petri net , ambiguity , fault (geology) , transformer , computer science , fuzzy logic , data mining , transformer oil , algorithm , reliability engineering , artificial intelligence , engineering , voltage , electrical engineering , geology , programming language , seismology
In view of the ambiguity of power system transformer faults in mines, the traditional gas analysis method cannot directly judge the possibility of fault occurrence based on gas concentration. A fault diagnosis method for mine transformers based on intuitionistic fuzzy Petri nets is proposed. The relationship between characteristic gas and fault is described by intuitionistic fuzzy set, and a new type of intuitive fault diagnosis model is established. The membership degree and non-affiliation degree are introduced into the model. The intuitionistic fuzzy inference algorithm is designed. By obtaining and processing the specific parameters such as the weight of the connecting arc and the threshold of the transition in the fault diagnosis model, the fault diagnosis process is transformed into the intuitionistic fuzzy inference process using the intuitionistic fuzzy Petri net. Finally, the membership degree and non-affiliation degree of each fault are obtained to judge the possibility of fault occurrence. It is verified by the fact that the above-mentioned mine transformer fault reasoning method can quickly judge the possibility of fault occurrence according to the concentration of gas.