Abstract
This paper proposes a chaotic biogeography based optimization (CBBO) for solving optimal reactive power dispatch (ORPD) problem. Based on biogeography based optimization (BBO) theory proposed by Dan Simon in 2008, a new artificial intelligence with full models and equations have been used to achieve the best solution for objective function of ORPD such as total power loss, voltage deviation and voltage stability index while satisying various constraints of power balance, voltage limits, transformers tap changer limits and switchable capacitor bank limits. The BBO has been enhanced its search ability by adding chaotic theory. Therefore, the proposed CBBO can obtain better solutiong quality than BBO for optimization problems. The proposed method has been tested on the IEEE-30 and IEEE-118 bus systems and the obtained results have been verified with other methods. The result comparison has indicated that the CBBO can be a promise method for dealing the ORPD problem
Highlights
The main objective of optimal reactive power dispatch (ORPD) [1] in electrical power system is to minimize the objective function via the optimal adjustment of the power system control variables, while at the same time satisfying various equality and inequality constraints
Some objective functions in ORPD to evaluate the quality of power system is real power loss, voltage deviation at load buses [2], voltage stability index [3]
By supplying the full theory and model of chaotic biogeography based optimization (CBBO), we proved the useful of this algorithm by testing on IEEE-30 bus system and IEEE-118 bus system
Summary
The main objective of optimal reactive power dispatch (ORPD) [1] in electrical power system is to minimize the objective function via the optimal adjustment of the power system control variables, while at the same time satisfying various equality and inequality constraints. Most of them are based on the biological model like evolutionary and behavior in species, were used such as evolutionary programming (EP) [9], genetic algorithm (GA) [10], differential evolution (DE) [11], ant colony optimization (ACO) [12] and particle swarm optimization (PSO) [13]. These methods can improve the solutions for ORPD it is more complex and slow in performance. The results is compared with the other paper to evaluate the advantage or disadvantage of this method
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