Artículo
Fault Diagnosis of Power Systems Using Intuitionistic Fuzzy Spiking Neural P Systems
Autor/es | Peng, Hong
Wang, Jun Ming, Jun Shi, Peng Pérez Jiménez, Mario de Jesús Yu, Wenping Tao, Chengyu |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2018 |
Fecha de depósito | 2021-07-13 |
Publicado en |
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Resumen | 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, ... 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. |
Agencias financiadoras | 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 |
Identificador del proyecto | 61472328
Z2016143 Z2016148 17TD0034. Paper no. TSG-01301-2016 |
Cita | Peng, H., Wang, J., Ming, J., Shi, P., Pérez Jiménez, M.d.J., Yu, W. y Tao, C. (2018). Fault Diagnosis of Power Systems Using Intuitionistic Fuzzy Spiking Neural P Systems. IEEE Transactions on Smart Grid, 9 (5), 4777-4784. |
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