Integrated Strategies of Backtracking Search Optimizer for Solving Reactive Power Dispatch Problem

Abstract

This paper proposes backtracking search optimizer (BSO) for solving the reactive power dispatch (RPD) problem. The RPD problem is highly nonlinear, nonconvex optimization problem and is consisting of both continuous and discrete control variables. It aims to find the optimal settings of the generator voltages, tap positions of tap changing transformers, and the amount of reactive compensation that able to optimize the transmission power losses. BSO has simple structure and single control parameter. It has two new tuned mutation and crossover operators that control the amplitude of the search-direction matrix and search space boundaries. In this paper, five diversified generation strategies of mutation factor of BSO to solve the RPD. The proposed BSO strategies are applied to the IEEE standard 14-, 30-, 57-bus test systems, and a real power system at West Delta region system as a part of the unified Egyptian network. Simulation results show that the BSO strategy, which almost generates mutation factor concentrated on the mean value of the normal distribution (BSO 4) assigns multidirections through the search space and supports the diversity of populations. To show the effectiveness and superiority of proposed BSO, the obtained results are compared with different algorithms reported in the literature.Index Terms-Backtracking search optimizer (BSO), evolutionary algorithms, power losses, reactive power dispatch (RPD), statistical indices, tuned mutation strategies.

Multi-Objective Optimal Reactive Power Dispatch of Power Systems by Combining Classification-Based Multi-Objective Evolutionary Algorithm and Integrated Decision Making

“…Due to the powerful ability of MOEAs to find widely distributed POSs by only one simulation run, MOEAs are widely used to solve the MORPD model. While MOEAs include enormous algorithms such as water cycle algorithm (NGBWC) [14], backtracking search optimizer (BSO) [15], whale optimization algorithm (WOA) [16], and grey wolf optimizer (GWO) [17], etc. Furthermore, for better performance, some improvements have been made based on the original algorithms.…”

An Improved Lightning Attachment Procedure Optimizer for Optimal Reactive Power Dispatch With Uncertainty in Renewable Energy Resources

“…ORPD problem is a non-convex, complex, and non-linear optimization problem. Thus, many efforts have been introduced for solving the ORPD by applying numerous optimization techniques including the Backtracking Search Optimizer (BSO) [2], Particle Swarm Optimization (PSO) [3], Ant Lion Optimizer (ALO) [4], Improved Ant Lion Optimization algorithm (IALO) [5] , Whale Optimization Algorithm (WOA) [6], Improved Social Spider Optimization Algorithm (ISSO) [7], Differential Evolution (DE) [8], Moth Swarm Algorithm (MSA) [9], Evolutionary Algorithm (EA) [10], Modified Differential Evolution (MDE) [11], Jaya Algorithm (JA) [12], Modified Sine Cosine Algorithm (MSCA) [13], Lightning Attachment Procedure Optimization (LAPO) [14], Gravitational Search Algorithm (GSA) [15], Biogeography-Based Optimization (BBO) [16], Teaching Learning Based Optimization (TLBO) [17], Harmony Search Algorithm (HAS) [17], Grey Wolf Optimizer (GWO) [18], Comprehensive Learning Particle Swarm Optimization (CLPSO) [19], Chemical Reaction Optimization (CRO) [20], Improved Gravitational Search Algorithm (IGSA) [21], Improved Pseudo-Gradient Search Particle Swarm Optimization (IPG-PSO) [22], Firefly Algorithm (FA) [23], Fractional Particle Swarm Optimization Gravitational Search Algorithm [24], hybrid GWO-PSO optimization [25], Oppositional Salp Swarm Algorithm (OSSA) [26], diversity-enhanced particle swarm optimization (DEPSO) [27].…”

The Optimization of the Location and Capacity of Reactive Power Generation Units, Using a Hybrid Genetic Algorithm Incorporated by the Bus Impedance Power-Flow Calculation Method

“…The former includes annealing and fuzzy clustering [2], the evolution approach [3], genetic algorithms (GAs) [4], krill herd algorithms [5], particle swarm optimization (PSO) [6], seeker algorithms [7], and whale optimization [8]. The search algorithms contain backtracking [9], cuckoo [10], differential [11], direct [12], and harmony search methods [13]. The optimization algorithms (as conventional methods) involve decomposition methods [1], dynamic programming [14], gradient-based optimization [15], interior-point methods [16], linear approximation [17], mixed integer programming [18,19], quadratic programming [20], teaching-based learning [21], and decision-making algorithms [22].…”