scholarly journals A hybrid bacterial foraging-particle swarm optimization technique for solving optimal reactive power dispatch problem

2020 ◽  
Vol 9 (2) ◽  
pp. 127
Author(s):  
P. Lokender Reddy ◽  
Yesuratnam Guduri
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Computation ◽  
Reactive Power ◽  
Particle Swarm ◽  
Test System ◽  
Swarm Optimization ◽  
Bacterial Foraging ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Reactive Power Dispatch

<div data-canvas-width="397.27351844386203">This paper presents a hybrid evolutionary computation algorithm termed as hybrid bacterial foraging-particle swarm optimization (HBFPSO) algorithm, to optimal reactive power dispatch (ORPD) problem. HBFPSO algorithm merges velocity and position updating strategy of particle swarm optimization (PSO) algorithm and reproduction and elimination dispersal of bacterial foraging algorithm (BFA). The ORPD is solved for minimization of two objective functions; system real power loss and voltage stability L-index. The objective is minimized by optimally choosing the control variables; generator excitations, tap positions of on-load tap changing transformers and switched var compensators while satisfying their constraints and also the constraints of dependent variabl</div><div data-canvas-width="98.30049385204596">es; voltages of all load buses and reactive power generation of all generators. The proposed approach has been evaluated on a standard IEEE 30 bus test system and 24 bus EHV southern region equivalent Indian power system. The results offered by the proposed algorithm are compared with those offered by other evolutionary computation algorithms reported in the recent state of the art literature and the superiority of the proposed algorithm is demonstrated.</div>

scholarly journals Hybrid Bacteria Foraging Particle Swarm Optimization based Optimal Reactive Power Dispatch for the Alleviation of Voltage Deviations

2019 ◽  
Vol 9 (2) ◽  
pp. 5350-5354
Keyword(s):  
Particle Swarm Optimization ◽  
Hybrid Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Bacterial Foraging ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Bacterial Foraging Algorithm ◽  
Reactive Power Dispatch

This paper presents a hybrid algorithm for optimal reactive power dispatch by combining two popular evolutionary computation algorithms; Bacterial Foraging algorithm and Particle Swarm Optimization. The Hybrid algorithm combines velocity and position updating strategy of Particle swarm optimization algorithm and reproduction and elimination dispersal of Bacterial foraging algorithm. The proposed algorithm is applied to solve optimal power flow with the objective of minimization of Sum of squares of voltage deviations of all load buses. The proposed approach has been evaluated on a standard IEEE 30 bus test system and 24 bus EHV southern region equivalent Indian power system. The results obtained by the proposed Hybrid algorithm are compared with their basic counter parts and superiority of the proposed hybrid algorithm is demonstrated

scholarly journals CROWDING DISTANCE BASED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER DISPATCH PROBLEM

2018 ◽  
Vol 6 (6) ◽  
pp. 226-237
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Test System ◽  
Swarm Optimization ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Crowding Distance ◽  
Reactive Power Dispatch

In this paper, Crowding Distance based Particle Swarm Optimization (CDPSO) algorithm has been proposed to solve the optimal reactive power dispatch problem. Particle Swarm Optimization (PSO) is swarm intelligence-based exploration and optimization algorithm which is used to solve global optimization problems. In PSO, the population is referred as a swarm and the individuals are called particles. Like other evolutionary algorithms, PSO performs searches using a population of individuals that are updated from iteration to iteration. The crowding distance is introduced as the index to judge the distance between the particle and the adjacent particle, and it reflects the congestion degree of no dominated solutions. In the population, the larger the crowding distance, the sparser and more uniform. In the feasible solution space, we uniformly and randomly initialize the particle swarms and select the no dominated solution particles consisting of the elite set. After that by the methods of congestion degree choosing (the congestion degree can make the particles distribution more sparse) and the dynamic e infeasibility dominating the constraints, we remove the no dominated particles in the elite set. Then, the objectives can be approximated. Proposed crowding distance based Particle Swarm Optimization (CDPSO) algorithm has been tested in standard IEEE 30 bus test system and simulation results shows clearly the improved performance of the projected algorithm in reducing the real power loss and static voltage stability margin has been enhanced.

scholarly journals AMELIORATED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER DISPATCH PROBLEM

2018 ◽  
Vol 6 (2) ◽  
pp. 202-213
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Test System ◽  
Crossover Operator ◽  
Swarm Optimization ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Reactive Power Dispatch

In this paper, an Ameliorated Particle Swarm Optimization (APSO) algorithm has been proposed to solve the optimal reactive power dispatch problem. Particle Swarm Optimization (PSO) is swarm intelligence-based exploration and optimization algorithm which is used to solve global optimization problems. But due to deficiency of population diversity and early convergence it is often stuck into local optima. Diversity upsurges and avoids premature convergence by using evolutionary operators in PSO. In this paper the intermingling crossover operator is used to upsurge the exploration capability of the swarm in the exploration space. Particle Swarm Optimization uses this crossover method to converge optimum solution in quick manner. Thus the intermingling crossover operator is united with particle swarm optimization to augment the performance and possess the diversity which guides the particles to the global optimum powerfully. Proposed Ameliorated Particle Swarm Optimization (APSO) algorithm has been tested in standard IEEE 30 bus test system and simulation results shows clearly the improved performance of the projected algorithm in reducing the real power loss and static voltage stability margin has been enhanced.

scholarly journals Single and Multiobjective Optimal Reactive Power Dispatch Based on Hybrid Artificial Physics–Particle Swarm Optimization

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2333 ◽  
Author(s):  
Tawfiq M. Aljohani ◽  
Ahmed F. Ebrahim ◽  
Osama Mohammed
Keyword(s):  
Particle Swarm Optimization ◽  
Reactive Power ◽  
Particle Swarm ◽  
Hybridization Technique ◽  
Swarm Optimization ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Voltage Deviation ◽  
Reactive Power Dispatch ◽  
Artificial Physics

The optimal reactive power dispatch (ORPD) problem represents a noncontinuous, nonlinear, highly constrained optimization problem that has recently attracted wide research investigation. This paper presents a new hybridization technique for solving the ORPD problem based on the integration of particle swarm optimization (PSO) with artificial physics optimization (APO). This hybridized algorithm is tested and verified on the IEEE 30, IEEE 57, and IEEE 118 bus test systems to solve both single and multiobjective ORPD problems, considering three main aspects. These aspects include active power loss minimization, voltage deviation minimization, and voltage stability improvement. The results prove that the algorithm is effective and displays great consistency and robustness in solving both the single and multiobjective functions while improving the convergence performance of the PSO. It also shows superiority when compared with results obtained from previously reported literature for solving the ORPD problem.

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