Abstract
Gravitational Search Algorithm (GSA) is a heuristic method based on Newton’s law of gravitational attraction and law of motion. In this paper, to further improve the optimization performance of GSA, the memory characteristic of Particle Swarm Optimization (PSO) is employed in GSAPSO for searching a better solution. Besides, to testify the prominent strength of GSAPSO, GSA, PSO, and GSAPSO are applied for the solution of optimal reactive power dispatch (ORPD) of power system. Conventionally, ORPD is defined as a problem of minimizing the total active power transmission losses by setting control variables while satisfying numerous constraints. Therefore ORPD is a complicated mixed integer nonlinear optimization problem including many constraints. IEEE14-bus, IEEE30-bus, and IEEE57-bus test power systems are used to implement this study, respectively. The obtained results of simulation experiments using GSAPSO method, especially the power loss reduction rates, are compared to those yielded by the other modern artificial intelligence-based techniques including the conventional GSA and PSO methods. The results presented in this paper reveal the potential and effectiveness of the proposed method for solving ORPD problem of power system.
Highlights
Optimal reactive power dispatch (ORPD), as one of the significant optimization problems in power system operation, is to minimize the given objective function such as total active power transmission losses (PLoss) by optimizing settings of control variables while satisfying a set of constraints during the entire dispatch period
The results presented in this paper reveal the potential and effectiveness of the proposed method for solving ORPD problem of power system
The exertion of merging individual superiorities into a new algorithm has become wide in various engineering fields, which avoids their own disadvantages by benefiting from each other’s advantages; for example, a method composed of chaotic embedded Backtracking Search Optimization Algorithm (BSA) and Binary Charged System Search (BCSS) algorithm is proposed for solving Short-Term Hydrothermal Generation Scheduling (SHTGS) in [11]; in [12], the authors present a new hybrid evolutionary algorithm based on new fuzzy adaptive Particle Swarm Optimization (PSO) algorithm and NelderMead simplex search method to solve distribution feeder reconfiguration problem; the combination of the vertical search algorithm and presented lateral search algorithm is used to solve the midterm schedule for thermal power plants problem in [13]
Summary
Optimal reactive power dispatch (ORPD), as one of the significant optimization problems in power system operation, is to minimize the given objective function such as total active power transmission losses (PLoss) by optimizing settings of control variables while satisfying a set of constraints during the entire dispatch period. PSO tends to be trapped into prematurity in the latter period of searching, which directly lessens the possibility of acquiring the better solution; different from the algorithms based on biology, GSA is a memoryless algorithm, which is adverse to recording of the optimal value during the process of searching According to these features, the exertion of merging individual superiorities into a new algorithm has become wide in various engineering fields, which avoids their own disadvantages by benefiting from each other’s advantages; for example, a method composed of chaotic embedded Backtracking Search Optimization Algorithm (BSA) and Binary Charged System Search (BCSS) algorithm is proposed for solving Short-Term Hydrothermal Generation Scheduling (SHTGS) in [11]; in [12], the authors present a new hybrid evolutionary algorithm based on new fuzzy adaptive PSO algorithm and NelderMead simplex search method to solve distribution feeder reconfiguration problem; the combination of the vertical search algorithm and presented lateral search algorithm is used to solve the midterm schedule for thermal power plants problem in [13].
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