Abstract
This paper presents a novel differential evolution (DE) algorithm, with its improved version (IDE) for the benchmark functions and the optimal reactive power dispatch (ORPD) problem. Minimization of the total active power loss is usually considered as the objective function of the ORPD problem. The constraints involved are generators, transformers tapings, shunt reactors, and other reactive power sources. The aim of this study is to discover the best vector of control variables to minimize power loss, under the premise of considering the constraints system. In the proposed IDE, a new initialization strategy is developed to construct the initial population for guaranteeing its quality and simultaneously maintaining its diversity. In addition, to enhance the convergence characteristic of the original DE, two kinds of self-adaptive adjustment strategies are employed to update the scaling factor and the crossover factor, respectively, in which the detailed information about the two factors can be exchanged for each generation dynamically. Numerical applications of different cases are carried out on several benchmark functions and two standard IEEE systems, i.e., 14-bus and 30-bus test systems. The results achieved by using the proposed IDE, compared with other optimization algorithms, are discussed and analyzed in detail. The obtained results demonstrated that the proposed IDE can successfully be used to deal with the ORPD problem.
Highlights
Differential evolution (DE) algorithm is one of the most popular intelligent optimization algorithms and was first put forward by Storn and Price in 1995 [23]
Awad et al [31] propounded an efficient differential evolution (DE) algorithm for optimal active-reactive power dispatch problems, in which an arithmetic recombination crossover factor and a new scaling factor based on Laplace distribution were adapted to enhance the performance of the original DE algorithm
F and CR are adaptively controlled by evolutional generation, and the detailed information can be exchanged for each generation dynamically. e proposed improved differential evolution (IDE) is used to optimize a series of benchmark functions and applied to IEEE 14-bus system and IEEE 30-bus system, aiming at minimizing the loss of active power and achieving good reactive power optimization effect
Summary
Differential evolution (DE) algorithm is one of the most popular intelligent optimization algorithms and was first put forward by Storn and Price in 1995 [23]. Awad et al [31] propounded an efficient DE algorithm for optimal active-reactive power dispatch problems, in which an arithmetic recombination crossover factor and a new scaling factor based on Laplace distribution were adapted to enhance the performance of the original DE algorithm. Pulluri et al [41] introduced an enhanced self-adaptive DE with mixed crossover algorithm for dealing with the multiobjective optimal power flow (MO-OPF) problems with conflicting objectives. Based on the previous work, this paper proposes an improved differential evolution (IDE) algorithm for solving ORPD problems. E proposed IDE is used to optimize a series of benchmark functions and applied to IEEE 14-bus system and IEEE 30-bus system, aiming at minimizing the loss of active power and achieving good reactive power optimization effect. The main objective is optimizing generator fuel cost, active power loss or voltage stability, etc. e united form of the objective function is formulated as Minimize : fi(x, u), i 1, 2, . . . , N,
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