Modified Differential Evolution algorithm for multi-objective VAR management

Pareto-optimal front of linear combination of PL and VD objectives using MDE. [Display omitted] •Modified DE algorithm proposed for the multi-objective VAR management problem.•DE algorithm is modified with time varying chaotic mutation and crossover.•VAR management problem solved for minimization of PLoss and voltage deviation both.•The MDE, SaDE, EPSDE and classical DE algorithms were applied on IEEE 30-bus test system.•MDE provided faster convergence and better solution than SaDE, EPSDE and classical DE. Reactive power or VAR management is one of the most crucial tasks required for proper operation and control of a power system. Reactive power management is carried out to reduce losses and to improve voltage profile in a power system, by adjusting the reactive power control variables such as generator voltages, transformer tap-settings and other sources of reactive power such as capacitor banks or FACTS devices. VAR management provides better system security, improved power transfer capability and overall system operation. VAR management is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. In this paper, the VAR management problem is formulated as a nonlinear constrained multi-objective optimization problem with equality and inequality constraints for minimization of real power losses and voltage deviation simultaneously. This multi-objective problem is solved using Differential Evolution (DE), which is a population based search algorithm. For avoiding the time and the effort in tuning the parameters of DE algorithm, a modified DE algorithm with time varying chaotic mutation and crossover is proposed for solving the multi-objective VAR management problem. Weighing factor method has been employed for finding Pareto optimal set for VAR management problem. Fuzzy membership function is used to obtain the best compromise solution out of the available Pareto-optimal solutions. Effectiveness of the proposed modified DE algorithm based approach has been demonstrated on IEEE 30-bus system and is found to be superior to classical DE and its variants Self-adaptive Differential Evolution (SaDE) and Ensemble of Mutation and Crossover Strategies and Parameters in Differential Evolution (EPSDE) in terms of convergence behavior and solution quality.