Hybrid - Particle Swarm Optimization and Differential Evolution for Reduction of Real Power Loss and Preservation of Voltage Stability Limits

In this paper, a Hybrid algorithm based on - Particle Swarm Optimization (PSO) and Differential Evolution (DE) is used for solving reactive power dispatch problem. It needs progressing the population to create the individual optimal positions by means of the PSO algorithm, and then the algorithm come in DE phase and progresses the individual optimal positions by smearing the DE algorithm. In order to comprehend co-evolution of DE and PSO algorithm, an information-sharing mechanism is presented, which progresses the capability of the algorithm to fence out of the local optimum. Additionally, in optimization procedure, we espouse the hybrid inertia weight stratagem, time-varying acceleration coefficients tactic and arbitrary scaling factor stratagem. The proposed Hybrid algorithm based on - Particle Swarm Optimization and Differential Evolution (H-PSDE) has been tested on standard IEEE 30, 57,118 bus test systems and simulation results show clearly about the better performance of the proposed algorithm in reducing the real power loss. Keywords:Optimal Reactive Power; Transmission loss; Particle Swarm Optimization; Differential Evolution; Global Search; Local Search; Inertia Weight

Reduction of Real Power Loss and Safeguarding of Voltage Constancy by Artificial Immune System Algorithm

In this paper, Artificial Immune System (AIS) algorithm is used for solving reactive power problem. Artificial Immune System Algorithm, also termed as the machine learning approach to Artificial Intelligence, are powerful stochastic optimization techniques with potential features of random search, hill climbing, statistical sampling and competition. Artificial immune system algorithmic approach to power system optimization these ideas are embedded into proposed algorithm for solving reactive dispatch problem. In order to evaluate the proposed algorithm, it has been tested in standard IEEE 30,118 bus systems and compared to other specified algorithms. Simulation results show better performance of the proposed AIS algorithm in reducing the real power loss and preservation of voltage stability

New Metaheuristic Algorithms for Reactive Power Optimization

Optimal reactive power dispatch (ORPD) is significant regarding operating the practice safely and efficiently. The ORPD is beneficial to recover the voltage profile, diminish the losses and increase the voltage stability. The ORPD is a complicated optimization issue in which the total active power loss is reduced by detecting the power-system control variables, like generator voltages, tap ratios of tap-changer transformers, and requited reactive power, ideally. This study offers new approaches based on Shuffled Frog Leaping Algorithm (SFLA) and Tree Seed Algorithm (TSA) to solve the best ORPD. The results of the approaches are offered set against the current results studied in the literature. The recommended algorithms were tested by IEEE-30 and IEEE-118 bus systems to discover the optimal reactive power control variables. It was observed that the obtained results are more successful than the other algorithms

Ant lion optimizer for solving optimal reactive power dispatch problem in power systems

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Reduction of Real Power Loss by Improved Evolutionary Algorithm

This paper presents an Improved Evolutionary Algorithm (IEA), to solve optimal reactive power dispatch problem. In IEA objective space is disintegrated into a set of sub objective spaces by a set of route vectors. In the evolutionary procedure, each sub objective space has a solution. In such a way, the diversity of achieved solutions can be upheld. In addition, if a solution is conquered by other solutions, the solution can produce more newfangled solutions than those solutions, which makes the solution of each sub objective space converge to the optimal solutions as far as conceivable. The planned IEA has been tested in standard IEEE 30, 118 bus test systems and simulation results show clearly the improved performance of the planned algorithm in declining the real power loss. Keywords: Evolutionary Algorithm, genetic operators, optimal reactive power, Transmission loss

Real Power Loss Reduction by Enhanced Imperialist Competitive Algorithm

In this paper, an Enhanced Imperialist Competitive (EIC) Algorithm is proposed for solving reactive power problem. Imperialist Competitive Algorithm (ICA) which was recently introduced has shown its decent performance in optimization problems. This innovative optimization algorithm is inspired by socio-political progression of imperialistic competition in the real world. In the proposed EIC algorithm, the chaotic maps are used to adapt the angle of colonies movement towards imperialist’s position to augment the evading capability from a local optima trap. The ICA is candidly stuck into a local optimum when solving numerical optimization problems. To overcome this insufficiency, we use four different chaotic maps combined into ICA to augment the search ability. Proposed Enhanced Imperialist Competitive (EIC) algorithm has been tested on standard IEEE 30 bus test system and simulation results show clearly the decent performance of the proposed algorithm in reducing the real power loss

Enriched Particle Swarm Optimization for Solving Optimal Reactive Power Dispatch Problem

In this paper, a different approach, Enriched particle Swarm optimization (EPSO) Algorithm for solving optimal reactive power dispatch problem has been presented. Particle swarm optimization is affected by early convergence, no assurance in finding optimal solution. This paper proposes EPSO using multiple sub swarm PSO in blend with multi exploration space algorithm. The particles are alienated into equal parts and arrayed into the number of sub swarms available. Multi-exploration space algorithm is used to obtain an optimum solution for each sub swarm and these solutions are then arrayed yet into a new swarm to obtain the best of all the solution. The proposed EPSO algorithm has been tested on standard IEEE 30 bus test system and simulation results show the commendable performance of the proposed algorithm in reducing the real power loss. Keywords:Optimal Reactive Power, Transmission loss, Enriched particle Swarm optimization, Multi-exploratio