Abstract
The power loss in electrical power systems is an important issue. Many techniques are used to reduce active power losses in a power system where the controlling of reactive power is one of the methods for decreasing the losses in any power system. In this paper, an improved particle swarm optimization algorithm using eagle strategy (ESPSO) is proposed for solving reactive power optimization problem to minimize the power losses. All simulations and numerical analysis have been performed on IEEE 30-bus power system, IEEE 118-bus power system, and a real power distribution subsystem. Moreover, the proposed method is tested on some benchmark functions. Results obtained in this study are compared with commonly used algorithms: particle swarm optimization (PSO) algorithm, genetic algorithm (GA), artificial bee colony (ABC) algorithm, firefly algorithm (FA), differential evolution (DE), and hybrid genetic algorithm with particle swarm optimization (hGAPSO). Results obtained in all simulations and analysis show that the proposed method is superior and more effective compared to the other methods.
Highlights
The reactive power optimization approach is important for power quality, system stability, and optimal operation of electrical power systems
The optimization methods along with fuzzy logic [10] have been used to adjust the optimal setting of power system variables, containing flexible AC transmission systems (FACTs) devices, where the power system losses have been reduced by the optimal placement of thyristor-controlled series compensation (TCSC) and static VAR compensator (SVC)
Capability, and performance of the proposed Eagle Strategy with Particle Swarm Optimization (ESPSO) on reactive power optimization problem, it is implemented to IEEE 30-bus test system, IEEE 118-bus test system, and a real distribution subsystem
Summary
The reactive power optimization approach is important for power quality, system stability, and optimal operation of electrical power systems. The reactive power optimization approach can minimize the power losses and improve the voltage profiles Many conventional methods such as dynamic programming, linear and nonlinear programing, interior point method, genetic algorithm, and quadratic programming have been employed for solving reactive power optimization problem [1,2,3,4,5]. A dynamic weights based particle swarm optimization (PSO) algorithm has been used for reducing power loss [11]. This approach has been implemented to IEEE 6-bus system. Authors selected the main objective of the study as the definition of the load buses
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