K. Lenin

Hybridization of Firefly and Water Wave Algorithm for Solving Reactive Power Problem

In this paper, a hybrid algorithm as the combination of Firefly and Water Wave algorithm (FWW) has been proposed to solve the Reactive power problem. The firefly algorithm is a meta-heuristic technique which is widely used for solving the optimization problems. The water wave optimization algorithm is also a nature inspired based algorithm. Both algorithms collectively improved the performance of search. The water wave algorithm is work on the combinatorial optimization and utilized as application of firefly algorithm. Hence we merge these two algorithms and make a hybrid algorithm. Proposed FWW algorithm has been tested in standard IEEE 30 Bus test system and simulation results reveal the better performance of the proposed algorithm in reducing the real power loss and voltage profiles were found to be within the limits.

Embellished Particle Swarm Optimization Algorithm for Solving Reactive Power Problem

This paper proposes Embellished Particle Swarm Optimization (EPSO) algorithm for solving reactive power problem .The main concept of Embellished Particle Swarm Optimization is to extend the single population PSO to the interacting multi-swarm model. Through this multi-swarm cooperative approach, diversity in the whole swarm community can be upheld. Concurrently, the swarm-to-swarm mechanism drastically speeds up the swarm community to converge to the global near optimum. In order to evaluate the performance of the proposed algorithm, it has been tested in standard IEEE 57,118 bus systems and results show that Embellished Particle Swarm Optimization (EPSO) is more efficient in reducing the Real power losses when compared to other standard reported algorithms.

Propagation Algorithm for Solving Optimal Reactive Power Problem

This paper presents a nature inspired heuristic optimization algorithm based on lightning progression called the propagation algorithm (PA) to solve optimal reactive power problem. It is from the imitated natural phenomenon of lightning and the procedure of step frontrunner propagation using the theory of fast particles. Three particle kinds are established to distinguish the transition particles that produce the first step frontrunner population, the space particles that attempt to turn out to be the frontrunner, and the prime particle that epitomize the particle thrilled from best positioned step frontrunner. The proposed PA has been tested in 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 with control variables within the limits.

Decline of Real Power Loss by the Combination of Ant Colony Optimization and Simulated Annealing Algorithm

Combination of ant colony optimization (ACO) algorithm and simulated annealing (SA) algorithm has been done to solve the reactive power problem.In this proposed combined algorithm (CA), the leads of parallel, collaborative and positive feedback of the ACO algorithm has been used to apply the global exploration in the current temperature. An adaptive modification threshold approach is used to progress the space exploration and balance the local exploitation. When the calculation process of the ACO algorithm falls into the inactivity, immediately SA algorithm is used to get a local optimal solution. Obtained finest solution of the ACO algorithm is considered as primary solution for SA algorithm, and then a fine exploration is executed in the neighborhood. Very importantly the probabilistic jumping property of the SA algorithm is used effectively to avoid solution falling into local optimum. The proposed combined algorithm (CA) approach has been tested in standard IEEE 30 bus test system and simulation results show obviously about the better performance of the proposed algorithm in reducing the real power loss with control variables within the limits.

Hybridisation of backtracking search optimisation algorithm with differential evolution algorithm for solving reactive power problem

Backtracking search optimisation BSA algorithm is a new-fangled methodology which takes into account the previous experiences to guide the process to reach the global optimum. But BSA converges slowly and exploitation character is also below par the level. In order to improve the performance of the BSA algorithm, hybridisation has been done with differential evolution DE algorithm in iteration level. DE algorithm has fast convergence speed and good in exploit the solution. In this paper, we propose a new methodology-hybridisation of backtracking search optimisation algorithm with differential evolution, called as HBSDE for solving reactive power problem. In HBSDE, at each iteration process, DE-exploitive strategy is used to accelerate the speed of convergence. The proposed HBSDE has been tested in standard IEEE 30 bus test system and simulation results show clearly about the better performance of the proposed algorithm in reducing the real power loss with control variables within the limits.

Enhanced differential evolution algorithm for solving reactive power problem

Differential evolution (DE) is one of the efficient evolutionary computing techniques that seem to be effective to handle optimization problems in many practical applications. Conversely, the performance of DE is not always flawless to guarantee fast convergence to the global optimum. It can certainly get inaction resulting in low accuracy of acquired results. An enhanced differential evolution (EDE) algorithm by integrating excited arbitrary confined search (EACS) to augment the performance of a basic DE algorithm have been proposed in this paper. EACS is a local search method that is excited to swap the present solution by a superior candidate in the neighbourhood. Only a small subset of arbitrarily selected variables is used in each step of the local exploration for randomly deciding the subsequent provisional solution. The proposed EDE has been tested in standard IEEE 30 bus test system. The simulation results show clearly about the better performance of the proposed algorithm in reducing the real power loss with control variables within the limits.