Title :
Development of an Artificial Intelligent Diagnosis System for Transformer Fault
Author :
Qiping, Yang ; Wude, Xue ; Zhida, Lan
Author_Institution :
Shanghai Inst. of Electr. Power
Abstract :
This paper introduces artificial intelligence system method and describes the developing and application in transformer fault diagnosis. An artificial intelligent system (TFDAI) design includes selection of input, network topology, synaptic connection weights, two-passageway, and output. This paper introduces the new intelligence technology in the transformer fault diagnosis -TFDAI System. TFDAI based data processing and diagnostic techniques are described in detail. It consists of expert system (ES ), artificial neural network (ANN), guide-rule, two-passageway and their characteristics are presented. This paper mentions the two-passageway structure of the artificial intelligence system and with practical examples. ES and ANN are connected by the two-passageway effectively. It states that artificial intelligence system for transformer early hidden faults achieves the possibility and accuracy of primary diagnosis
Keywords :
artificial intelligence; expert systems; fault diagnosis; neural nets; power engineering computing; power transformer testing; artificial intelligence; artificial neural network; data processing; expert system; guide-rule; network topology; synaptic connection; transformer fault diagnosis; two-passageway; Artificial intelligence; Artificial neural networks; Dissolved gas analysis; Fault diagnosis; Gas insulation; Intelligent systems; Oil insulation; Petroleum; Power transformer insulation; Power transformers; Artificial Intelligence (AI) Two-passageway; Artificial neural network (ANN); Expert system (ES); Transformer fault diagnosis;
Conference_Titel :
Transmission and Distribution Conference and Exhibition: Asia and Pacific, 2005 IEEE/PES
Conference_Location :
Dalian
Print_ISBN :
0-7803-9114-4
DOI :
10.1109/TDC.2005.1546920