Title :
Application of Artificial Intelligence (AI) in Power Transformer Fault Diagnosis
Author :
Yang Qi-ping ; Li Meng-qun ; Mu Xue-Yun ; Wang Jun
Author_Institution :
Shanghai Univ. of Electr. Power, Shanghai, China
Abstract :
This paper introduces the new intelligence technology in the transformer fault diagnosis - artificial intelligence system (TFDAI). An artificial intelligence system design includes selection of input, network topology, synaptic connection weight, and output. TFDAI module structure, data processing and diagnostic techniques are described in detail. It consists of expert system (ES) and artificial neural network (ANN). This paper covers TFDAI developing and application. It states that artificial intelligence system is very useful tool for transformer early hidden faults achieves the possibility and accuracy of primary diagnosis.
Keywords :
expert systems; fault diagnosis; network topology; neural nets; power engineering computing; power transformer protection; artificial intelligence system; artificial neural network; data processing; diagnostic techniques; expert system; network topology; power transformer fault diagnosis; synaptic connection weight; Artificial intelligence; Computational intelligence; Fault diagnosis; Power transformers; Artificial Intelligence; Artificial neutral network; Expert System; Power Transformer fault diagnosis;
Conference_Titel :
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3835-8
Electronic_ISBN :
978-0-7695-3816-7
DOI :
10.1109/AICI.2009.497
