PSS AND IPFC POD controllers coordinated tuning by an Adaptive Genetic Algorithm with Hyper-mutation

Descripción del Articulo

Flexible AC transmission systems (FACTS) are a modern technology to increase controllability in power systems. This work presents an analysis of Interline Power Flow Controller (IPFC) which is a FACTS device. This device control and manage power flow in transmission lines. Supplementary damping cont...

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Detalles Bibliográficos
Autores: Cordero Bautista, Luis Gustavo, Bueno de Araujo, Percival
Formato: artículo
Fecha de Publicación:2018
Institución:Universidad Nacional de Ingeniería
Repositorio:Revistas - Universidad Nacional de Ingeniería
Lenguaje:español
OAI Identifier:oai:oai:revistas.uni.edu.pe:article/541
Enlace del recurso:https://revistas.uni.edu.pe/index.php/tecnia/article/view/541
Nivel de acceso:acceso abierto
Materia:estabilizadores de potencia
hyper-mutación
algortimo genético adaptativo
Small-signal Stability, Power System Stabilizers, Interline Power Flow Controller, Power Oscillation Damping, Adaptive Genetic Algorithm, Hyper-mutation.
Descripción
Sumario:Flexible AC transmission systems (FACTS) are a modern technology to increase controllability in power systems. This work presents an analysis of Interline Power Flow Controller (IPFC) which is a FACTS device. This device control and manage power flow in transmission lines. Supplementary damping controller is installed on IPFC Proportional Integral (PI) control. Power Oscillation Damping (POD) and Power System Stabilizers (PSS) contribute to power system stability. This works represents the electric power system and Interline Power Flow Controller FACTS device by a current sensitivity model (CSM). This work focuses on small-signal stability studies using an Adaptive Genetic Algorithm and Hyper-mutation (AGAH) to design simultaneously controller parameters. Adaptive Genetic Algorithm aims to find optimal controller parameters to enhance greatly stability of the power system. This paper considers two areas 14 bus symmetrical system in order to assess proposed algorithm. Simulations are carried out in MatLab platform in order to compare genetic algorithm with proposed algorithm performance. Results show AGAH outweighed AG by time convergence and accuracy.
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