Fault Diagnosis of Power Systems Using Intuitionistic Fuzzy Spiking Neural P Systems
- Others:
- Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial
- Universidad de Sevilla. TIC193: Computación Natural
- National Natural Science Foundation of China
- Chunhui Project Foundation of the Education Department of China
- Research Foundation of the Education Department of Sichuan Province, China
Description
In this paper, intuitionistic fuzzy spiking neural P (IFSNP) systems as a variant are proposed by integrating intuitionistic fuzzy logic into original spiking neural P systems. Compared with a common fuzzy set, intuitionistic fuzzy set can more finely describe the uncertainty due to its membership and non-membership degrees. Therefore, IFSNP systems are very suitable to deal with fault diagnosis of power systems, specially with incomplete and uncertain alarm messages. The fault modeling method and fuzzy reasoning algorithm based on IFSNP systems are discussed. Two examples are used to demonstrate the availability and effectiveness of IFSNP systems for fault diagnosis of power systems. Case studies involve single fault, complex fault, and multiple faults with protection device failures and incorrect tripping signals.
Abstract
National Natural Science Foundation of China No. 61472328
Abstract
Chunhui Project Foundation of the Education Department of China Z2016143
Abstract
Chunhui Project Foundation of the Education Department of China Z2016148
Abstract
Research Foundation of the Education Department of Sichuan Province, China 17TD0034. Paper no. TSG-01301-2016
Additional details
- URL
- https://idus.us.es/handle//11441/116055
- URN
- urn:oai:idus.us.es:11441/116055
- Origin repository
- USE