Title :
Genetic Programming based Fuzzy Mapping Function Model for fault diagnosis of power transformers
Author :
Zhang Zheng ; Fang Kangling ; Huang Weihua
Author_Institution :
Coll. of Inf. Sci. & Eng., Wuhan Univ. of Sci. & Technol., Wuhan
Abstract :
A genetic programming based fuzzy mapping functions (GPFMF) model is proposed in this paper to diagnose the insulation fault types of power transformers. The proposed GPFMF model constructs the fuzzy relationship between input and output fuzzy variables by genetic programming algorithms. The fuzzy relationship is represented as one of candidates which have the form of tree-like combinations of series of fuzzy implication operators with fuzzy input variables. Then the best fuzzy mapping function is evolved by genetic operations and evolution. Based on the proposed GPFMF model, an insulation fault diagnosis system for power systems is designed to detect the insulation fault types of power transformers. Compared with the normal fuzzy IEC code method, the GPFMF models can generate fuzzy mapping functions from fuzzy input and output examples and has higher performance than normal fuzzy method.
Keywords :
fault diagnosis; fuzzy set theory; genetic algorithms; power transformer insulation; fault diagnosis; fuzzy IEC code method; genetic operations; genetic programming based fuzzy mapping functions; insulation fault diagnosis system; power systems; power transformers; tree-like combinations; Electrical fault detection; Fault detection; Fault diagnosis; Genetic programming; Input variables; Power system faults; Power system modeling; Power transformer insulation; Power transformers; Trees - insulation; Dissolved gas analysis; Fuzzy implication operators; Fuzzy mapping function; Genetic programming; Insulation fault diagnosis;
Conference_Titel :
Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-2113-8
Electronic_ISBN :
978-1-4244-2114-5
DOI :
10.1109/WCICA.2008.4593092
