Short-term planning of electric power distribution networks using multiobjective genetic algorithim

• Autor(a) principal: Pereira Jr., Benvindo [UNESP]
• Data de Publicação: 2011
• Outros Autores: Cossi, Antonio [UNESP], Mantovani, José Roberto [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.2316/P.2011.756-050; http://hdl.handle.net/11449/72897
• topic: Capacitor Banks; Multiobjective Genetic Algorithm; Power Distribution Planning; Voltage Regulators; Capacitor bank; Distribution lines; Distribution systems; Energy quality; Inference process; Long line; Multi objective; Multi-objective genetic algorithm; Non-dominated sorting genetic algorithms; NSGA-II; Optimized allocation; Power distribution planning; Power factors; Radial distribution systems; Search spaces; Short term planning; Specialized knowledge; Voltage variation; Capacitors; Electric circuit breakers; Electric power distribution; Electric power factor; Fuzzy logic; Genetic algorithms; Reactive power; Voltage regulators; Local area networks

The high active and reactive power level demanded by the distribution systems, the growth of consuming centers, and the long lines of the distribution systems result in voltage variations in the busses compromising the quality of energy supplied. To ensure the energy quality supplied in the distribution system short-term planning, some devices and actions are used to implement an effective control of voltage, reactive power, and power factor of the network. Among these devices and actions are the voltage regulators (VRs) and capacitor banks (CBs), as well as exchanging the conductors sizes of distribution lines. This paper presents a methodology based on the Non-Dominated Sorting Genetic Algorithm (NSGA-II) for optimized allocation of VRs, CBs, and exchange of conductors in radial distribution systems. The Multiobjective Genetic Algorithm (MGA) is aided by an inference process developed using fuzzy logic, which applies specialized knowledge to achieve the reduction of the search space for the allocation of CBs and VRs.