A review of meta-heuristic algorithms for reactive power planning problem <s> Meta-heuristic optimization algorithms <s> This paper presents a new optimization model EPSO, evolutionary particle swarm optimization, inspired in both evolutionary algorithms and in particle swarm optimization algorithms. The fundamentals of the method are described, and an application to the problem of loss minimization and voltage control is presented, with very good results. <s> BIB001 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Meta-heuristic optimization algorithms <s> Evolutionary particle swarm optimization (EPSO) is a robust optimization algorithm belonging to evolutionary methods. EPSO borrows the movement rules from particle swarm optimization (PSO) and uses it as a recombination operator that evolves under selection. This paper presents a reactive power planning approach taking advantage of EPSO robustness, in a model that considers simultaneously multiple contingencies and multiple load levels. Results for selected problems are summarized including a trade-off analysis of results. <s> BIB002 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Meta-heuristic optimization algorithms <s> Abstract People have paid more attention to enhancing voltage stability margin since voltage collapses happened in some power systems recently. This paper proposes an optimal reactive power flow (ORPF) incorporating static voltage stability based on a multi-objective adaptive immune algorithm (MOAIA). The main idea of the proposed algorithm is to add two parts to an existing immune algorithm. The first part defines both partial affinity and global affinity to evaluate the antibody affinity to the multi-objective functions. The second part uses adaptive crossover, mutation and clone rates for antibodies to maintain the antibodies diversity. Hence, the proposed algorithm can achieve a dynamic balance between individual diversity and population convergence. The paper describes ORPF’s multi-objective functional mathematical model and the constraint conditions. The problems associated with the antibody are also discussed in detail. The proposed method has been tested in the IEEE-30 system and compared with IGA (immune genetic algorithm). The results show that the proposed algorithm has improved performance over the IGA. <s> BIB003 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Meta-heuristic optimization algorithms <s> This paper presents a new formulation of multi-objective reactive power and voltage control for power system. The objectives are active power loss, voltage deviation and the voltage stability index of the system. The load constrains and operational constrains are also taken into consideration. The multi-objective formulation of the problem requires a global performance index of the problem. A pseudogoal function derived on the basis of the fuzzy sets theory gives a unique expression for the global objective function, eliminating the use of weighing coefficients or penalty terms. Both objective functions and constraints are evaluated by membership functions. The inequality constrains are embedded into the fitness function by pseudogoal function which guarantees that the searched optimal solution is feasible. Moreover, a new type of evolutionary algorithm, particle swarm optimization (PSO), has been adopted and improved for this problem. To improve the performance of PSO, a fuzzy adaptive PSO (FAPSO) is proposed. A fuzzy system is employed to adaptively adjust the parameters of PSO, such as the inertia weight and learning factors, during the evolutionary process. The proposed approach has been examined and tested with promising numerical results of the IEEE 30-bus and IEEE 118-bus power systems. <s> BIB004 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Meta-heuristic optimization algorithms <s> Static Var compensator (SVC) is normally used in power system to improve voltage profile and reduce system power losses. In this paper, a relatively new optimization technique named as the improved harmony search algorithm (IHS) is applied to determine optimal location and size of SVC devices in a transmission network. A multi-criterion objective function comprising of both operational objectives and investment costs is considered. The results on the 57-bus test system showed that the IHS algorithm give lower power loss and better voltage improvement compared to the particle swarm optimization technique in solving the SVC placement and sizing problem. <s> BIB005 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Meta-heuristic optimization algorithms <s> Abstract This paper addresses an application of modified NSGA-II (MNSGA-II) by incorporating controlled elitism and dynamic crowding distance (DCD) strategies in NSGA-II to multiobjective optimal reactive power dispatch (ORPD) problem by minimizing real power loss and maximizing the system voltage stability. To validate the Pareto-front obtained using MNSGA-II, reference Pareto-front is generated using multiple runs of single objective optimization with weighted sum of objectives. For simulation purposes, IEEE 30 and IEEE 118 bus test systems are considered. The performance of MNSGA-II, NSGA-II and multiobjective particle swarm optimization (MOPSO) approaches are compared with respect to multiobjective performance measures. TOPSIS technique is applied on obtained non-dominated solutions to determine best compromise solution (BCS). Karush–Kuhn–Tucker (KKT) conditions are also applied on the obtained non-dominated solutions to substantiate a claim on optimality. Simulation results are quite promising and the MNSGA-II performs better than NSGA-II in maintaining diversity and authenticates its potential to solve multiobjective ORPD effectively. <s> BIB006 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Meta-heuristic optimization algorithms <s> The optimal Reactive Power Dispatch (RPD) problem is a nonlinear constrained optimization problem. This paper presents true multi-objective solution set for multi-objective RPD problem. The objective functions are real power loss minimization and control variable adjustment costs. In this paper, a Modified Non-Dominated Sorting Genetic Algorithm version II (MNSGA-II) is proposed for solving RPD problem. For maintaining good diversity in the performance of NSGA-II, the concept of Dynamic Crowding Distance (DCD) is implemented in NSGA-II algorithm and given name as MNSGA-II. The standard IEEE 30-bus and IEEE 118 bus test systems are used. The results obtained using MNSGA-II are compared with NSGA-II and validated with reference Pareto front generated by conventional weighted sum method using Covariance Matrix Adapted Evolution Strategy (CMA-ES). The performance of NSGA-II and MNSGA-II are compared with respect to best, mean, worst and standard deviation of multi-objective performance measures.The results show the effectiveness of MNSGA-II and confirm its potential to solve the multi-objective RPD problem. <s> BIB007 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Meta-heuristic optimization algorithms <s> This paper discusses the application of Modified Non-Dominated Sorting Genetic Algorithm-II (MNSGA-II) to multi-objective Reactive Power Planning (RPP) problem. The three objectives considered are minimization of combined operating and VAR allocation cost, bus voltage profile improvement and voltage stability enhancement. For maintaining good diversity in nondominated solutions, Dynamic Crowding Distance (DCD) procedure is implemented in NSGA-II and it is called as MNSGA-II. The standard IEEE 30-bus test system, practical 69-bus Indian system and IEEE 118-bus system are considered to analyze the performance of MNSGA-II. The results obtained using MNSGA-II are compared with NSGA-II and validated with reference pareto-front generated by conventional weighted sum method using Covariance Matrix Adapted Evolution Strategy (CMA-ES). The performance of NSGA-II and MNSGA-II are compared with respect to best, mean, worst and standard deviation of multi-objective performance measures namely gamma, spread, minimum spacing and Inverted Generational Distance (IGD) in 15 independent runs. The results show the effectiveness of MNSGA-II and confirm its potential to solve the multi-objective RPP problem. A decision-making procedure based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used for finding best compromise solution from the set of pareto-solutions obtained through MNSGA-II. <s> BIB008 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Meta-heuristic optimization algorithms <s> Abstract This paper presents the application of Multi Objective Differential Evolution (MODE) algorithm to solve the Voltage Stability Constrained Reactive Power Planning (VSCRPP) problem. Minimization of total cost of energy loss and reactive power production cost of capacitors and maximization of voltage stability margin are taken as the objectives in the Reactive Power Planning (RPP) problem. The L -index of the load buses is taken as the indicator of voltage stability. In the proposed approach, generator bus voltage magnitudes, transformer tap settings and reactive power generation of capacitor bank are taken as the control variables and are represented as the combination of floating point numbers and integers. The MODE emphasizes the non dominated solutions and simultaneously maintains diversity in the non dominated solutions. DE/randSF/1/bin strategy scheme of Differential Evolution with self tuned parameter which employs binomial crossover and difference vector based mutation is used for the VSCRPP problem. A fuzzy based mechanism is employed to get the best compromise solution from the pareto front to aid the decision maker. The proposed reactive power planning model is implemented on two test systems, IEEE 30 bus and IEEE 57 bus test systems. The simulation results of the proposed optimization approach show that MODE is better in maintaining diversity and optimality of solutions. <s> BIB009
Meta-heuristic Optimization Algorithms (MOAs) are extensively used in solving multi-objective optimization problems since they can find multiple optimal solutions in a single run. Different MOAs are applied efficiently to solve the RPP problem. Table 2 shows a comparison between various MOAs that have been applied to the RPP problem. Since, the settings of their key parameters have a large impact on their performance, the adaptive MOAs have been developed recently and applied to the RPP problem. Some of the adaptive MOAs reported are as follows: the IHS algorithm BIB005 , Chaotic DE algorithm  , JADE-vPS algorithm  , adaptive model of IA BIB003 , EPSO BIB001 BIB002 , improved model of DE algorithm  BIB009 , SARGA  , FAPSO algorithm BIB004 , and MNSGA-II BIB008 BIB006 BIB007  . Although the adaptive models of MOAs reduce the complexity of parameter selection, the selected adaptation strategy influences on their performance and they have a high computational burden that needs more calculations to adapt the parameters.