Published June 1, 2019 | Version v1
Journal article

Optimal Multi-objective Placement of Wind Turbines Considering Voltage Stability, Total Loss and Cost Using Fuzzy Adaptive Modified Particle Swarm Optimization Algorithm

  • 1. Yasouj University, Electrical Engineering Department, Faculty of Engineering (Iran, Islamic Republic of)
  • 2. University of Isfahan, Department of Electrical Engineering (Iran, Islamic Republic of)

Description

The proper placement of distributed generations, especially wind turbines, is a challenging issue in distribution networks. In this regard, this paper employs the fuzzy adaptive modified particle swarm optimization (FAMPSO) to determine the locations of wind turbines in a radial distribution network by considering power losses, operation cost reduction and voltage stability improvement as the objective functions. Considering the nature of these objective functions and load flow necessity, wind turbine placement is a nonlinear and complicated numerical problem. Therefore, the multi-objective FAMPSO and Pareto optimal methods are employed for compromising between the objective functions. Moreover, during the simulation procedure, a set of non-dominated solutions is stored in an external memory. This method is applied to a 69-bus distribution network for algorithm verification.

Additional details

Identifiers

Publishing Information

Journal Title
Electrical and computer engineering (Shiraz)
Journal Volume
43
Journal Issue
2
Journal Page Range
p. 343-359
ISSN
2228-6179

INIS

Country of Publication
Iran, Islamic Republic of
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
54088420
Subject category
S42: ENGINEERING; S97: MATHEMATICAL METHODS AND COMPUTING;
Descriptors DEI
ALGORITHMS; COMPUTERIZED SIMULATION; ELECTRIC POTENTIAL; FUZZY LOGIC; NONLINEAR PROBLEMS; OPERATION; OPTIMIZATION; POWER LOSSES; SPATIAL DISTRIBUTION; WIND TURBINES
Descriptors DEC
DISTRIBUTION; ENERGY LOSSES; EQUIPMENT; LOSSES; MACHINERY; MATHEMATICAL LOGIC; SIMULATION; TURBINES; TURBOMACHINERY

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Copyright
Copyright (c) 2019 Shiraz University