scholarly journals ACTIVE POWER LOSS REDUCTION BY ASSORTED ALGORITHMS

2018 ◽  
Vol 6 (5) ◽  
pp. 263-275
Author(s):  
K. Lenin
Keyword(s):  
Convergence Rate ◽  
Power Loss ◽  
Reactive Power ◽  
Pso Algorithm ◽  
Reactive Power Optimization ◽  
Swarm Optimization ◽  
Power Loss Reduction ◽  
Power Problem ◽  
Species Hybridization ◽  
Conventional Algorithm

This paper presents assorted algorithms for solving optimal reactive power problem. Symbiosis modeling (SM), which extends the dynamics of the canonical PSO algorithm by adding a significant ingredient that takes into account the symbiotic co evolution between species, Hybridization of  Evolutionary  algorithm with Conventional Algorithm (HCA) that uses the abilities of evolutionary and conventional algorithm and Genetical Swarm Optimization (GS), which combines Genetic Algorithms (GA) and Particle Swarm Optimization (PSO).All the above said  SM, HCA,GS algorithms are used to  augment the convergence rate with good Exploration & Exploitation. All the three SM, HCA, GS is applied to Reactive Power optimization problem and has been evaluated in standard IEEE 30 System. The results shows that all the three algorithms perform well in solving the reactive power problem with rapid convergence rate .Of all the three  algorithms SM has the slight edge in reducing the real power loss over  HCA&GS.

scholarly journals ACTIVE POWER LOSS REDUCTION BY SYNTHESIZED ALGORITHM

2018 ◽  
Vol 6 (5) ◽  
pp. 149-156
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Harmony Search ◽  
Pso Algorithm ◽  
Loss Reduction ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
De Algorithm ◽  
Power Loss Reduction ◽  
Power Problem

In this paper, Synthesized Algorithm (SA) proposed to solve the optimal reactive power problem. Proposed Synthesized Algorithm (SA) is a combination of three well known evolutionary algorithms, namely Differential Evolution (DE) algorithm, Particle Swarm Optimization (PSO) algorithm, and Harmony Search (HS) algorithm. It merges the general operators of each algorithm recursively. This achieves both good exploration and exploitation in SA without altering their individual properties. In order to evaluate the performance of the proposed SA, it has been tested in Standard IEEE 57,118 bus systems and compared to other standard reported algorithms. Simulation results show’s that Synthesized Algorithm (SA) successfully reduces the real power loss and voltage profiles are within the limits.

scholarly journals A REDUCTION OF REAL POWER LOSS BY ENRICHED GENETIC ALGORITHM

2018 ◽  
Vol 6 (5) ◽  
pp. 167-176
Author(s):  
K. Lenin
Keyword(s):  
Genetic Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Pso Algorithm ◽  
Test System ◽  
Swarm Optimization ◽  
Real Power ◽  
Dynamical Parameters ◽  
Power Problem ◽  
Standard Genetic Algorithm

In this paper Enriched Genetic Algorithm (EGA) is proposed to solve the optimal reactive power problem. In order to overcome the drawbacks of standard genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, some improved mechanisms based on non-linear ranking selection, competition and selection among several crossover offspring and adaptive change of mutation scaling are adopted in the genetic algorithm, and dynamical parameters are adopted in PSO. The new population is produced through three approaches to improve the global optimization performance. Proposed algorithm has been tested in standard IEEE 57 bus test system and simulation results reveal the better performance of the proposed algorithm in reducing the real power loss

scholarly journals REAL POWER LOSS REDUCTION BY ENHANCED ACCLIMATIZED BACTERIAL EXPLORATION ALGORITHM

2018 ◽  
Vol 6 (3) ◽  
pp. 182-190
Author(s):  
K. Lenin
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Loss Reduction ◽  
Swarm Optimization ◽  
E Coli ◽  
Search Optimization ◽  
Power Loss Reduction ◽  
Speed Up ◽  
Power Problem

This paper presents Enhanced Acclimatized Bacterial Exploration (EBE) algorithm to solve reactive power problem. Bacterial Search Optimization Algorithm has recently emerged as a very powerful technique based on the behaviour of E-coli bacteria. In order to speed up the convergence of Bacterial search Optimization Algorithm, this paper proposed a new hybridization between Bacterial Search Optimization Algorithm (BSO) and Particle Swarm Optimization (PSO). In order to evaluate the proposed Enhanced Acclimatized Bacterial Exploration (EBE) algorithm, it has been tested in standard IEEE 118 & practical 191 bus test systems and compared to other standard algorithms.

scholarly journals TRUE POWER LOSS REDUCTION BY WOLF OPTIMIZATION ALGORITHM

2018 ◽  
Vol 6 (11) ◽  
pp. 323-329
Author(s):  
K. Lenin
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Test System ◽  
Loss Reduction ◽  
Power Losses ◽  
Swarm Optimization ◽  
Power Loss Reduction ◽  
Solution Efficiency ◽  
Power Problem

In this paper wolf optimization algorithm (WOA) has been applied for solving reactive power problem. In order to enhance the search procedure the basic qualities of particle swarm optimization has been intermingled to improve the capability of the search to reach a global solution. Efficiency of the projected wolf optimization algorithm (WOA) is tested in standard IEEE 30 bus test system. Simulation study indicates wolf optimization algorithm (WOA) performs well in tumbling the actual power losses& particularly voltage stability has been enriched.

scholarly journals REDUCTION OF ACTIVE POWER LOSS BY VOLITION PARTICLE SWARM OPTIMIZATION

2018 ◽  
Vol 6 (6) ◽  
pp. 346-356
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Reactive Power ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test System ◽  
Swarm Optimization ◽  
Fish School ◽  
Exploration Exploitation ◽  
Power Problem

This paper projects Volition Particle Swarm Optimization (VP) algorithm for solving optimal reactive power problem. Particle Swarm Optimization algorithm (PSO) has been hybridized with the Fish School Search (FSS) algorithm to improve the capability of the algorithm. FSS presents an operator, called as collective volition operator, which is capable to auto-regulate the exploration-exploitation trade-off during the algorithm execution. Since the PSO algorithm converges faster than FSS but cannot auto-adapt the granularity of the search, we believe the FSS volition operator can be applied to the PSO in order to mitigate this PSO weakness and improve the performance of the PSO for dynamic optimization problems. In order to evaluate the efficiency of the proposed Volition Particle Swarm Optimization (VP) algorithm, it has been tested in standard IEEE 30 bus test system and compared to other reported standard algorithms.  Simulation results show that Volition Particle Swarm Optimization (VP) algorithm is more efficient then other algorithms in reducing the real power losses with control variables are within the limits.

scholarly journals Real power loss reduction by hyena optimizer algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 186
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Mathematical Modelling ◽  
Convergent Evolution ◽  
Power Loss ◽  
Social Relationship ◽  
Reactive Power ◽  
Scent Marking ◽  
Loss Reduction ◽  
Real Power ◽  
Power Loss Reduction ◽  
Power Problem

<p><span>To solve optimal reactive power problem this paper projects Hyena Optimizer (HO) algorithm and it inspired from the behaviour of Hyena. Collaborative behaviour &amp; Social relationship between Hyenas is the key conception in this algorithm. Hyenas a form of carnivoran mammal &amp; deeds are analogous to canines in several elements of convergent evolution. Hyenas catch the prey with their teeth rather than claws – possess hardened skin feet with large, blunt, no retractable claws are adapted for running and make sharp turns. However, the hyenas' grooming, scent marking, defecating habits, mating and parental behaviour are constant with the deeds of other feliforms. Mathematical modelling is formulated for the basic attributes of Hyena. Standard IEEE 14,300 bus test systems used to analyze the performance of Hyena Optimizer (HO) algorithm. Loss has been reduced with control variables are within the limits.</span></p>

Reactive Power Optimization in Distribution Network with PV Station Based on Intelligence Algorithm

2013 ◽  
Vol 753-755 ◽  
pp. 2429-2432
Author(s):  
Xin Wei Ren ◽  
Jian Zheng Xu
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Optimal Solution ◽  
Active Power ◽  
Reactive Power Optimization ◽  
Pv System ◽  
Voltage Level ◽  
Bacterial Colony ◽  
Calculation Results ◽  
Power Problem

Reactive power problem of PV station in distribution power system is discussed. Probability theory is introduced to calculate the expectation of active power, which is approximately used to replace the randomly changing output. Reactive output can be adjusted by changing some related parameters of the grid-connected PV system. Considering reactive power of PV station as control variables, a model with voltage level constraints of minimizing the active power loss is established and its optimal solution is figured out with IBCC (Improved Bacterial Colony Chemotaxis). Case calculation results show the validity of above-mentioned model and algorithm.

scholarly journals REAL POWER LOSS REDUCTION BY ENRICHED GENETIC ALGORITHM

2017 ◽  
Vol 5 (8) ◽  
pp. 18-25
Author(s):  
K. Lenin
Keyword(s):  
Genetic Algorithm ◽  
Power Loss ◽  
Optimization Problem ◽  
Reactive Power ◽  
Selection Procedure ◽  
Selection Method ◽  
Loss Reduction ◽  
Reactive Power Optimization ◽  
Real Power ◽  
Power Loss Reduction

In this paper, Enriched Genetic Algorithm (EGA) utilized to solve reactive power optimization problem. In the proposed algorithm Stochastic Universal Selection (SS) is utilized to improve the selection procedure. The selection method in Genetic algorithm (GA) plays a significant role in the runtime to get the optimized solution as well as in the superiority of the solution. In this work, an enriched selection technique is presented which uphold both fast runtime and elevated quality solution. Proposed EGA algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the advanced performance of the proposed algorithm in reducing the real power loss.

scholarly journals Active power loss reduction by opposition based kidney search algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 217
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Search Algorithm ◽  
Optimization Techniques ◽  
Loss Reduction ◽  
Opposition Based Learning ◽  
Candidate Solution ◽  
Power Loss Reduction ◽  
Power Problem

In this work Opposition based Kidney Search Algorithm (OKS) is used to solve the optimal reactive power problem. Kidney search algorithm imitates the various sequences of functions done by biological kidney. Opposition based learning (OBL) stratagem is engaged to commence the algorithm. This is to make certain high-quality of preliminary population and to expand the exploration steps in case of stagnation of the most excellent solutions. Opposition based learning (OBL) is one of the influential optimization tools to boost the convergence speed of different optimization techniques. The thriving implementation of the OBL engages evaluation of opposite population and existing population in the similar generation to discover the superior candidate solution of a given reactive power problem.  Proposed Opposition based Kidney Search Algorithm (OKS) has been tested in standard IEEE 14, 30, 57,118,300 bus test systems and simulation results show that the proposed algorithm reduced the real power loss efficiently.

scholarly journals Particle Swarm Optimization Algorithm Based Reactive Power Optimization in Distribution Network

2019 ◽  
Vol 8 (10) ◽  
pp. 3792-3796
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Distributed Generation ◽  
Power Loss ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Fitness Function ◽  
Reactive Power Optimization ◽  
Swarm Optimization ◽  
Loss Index

Minimization of power loss is the first priority of the power companies. Generally power loss is directly proportional to the reactive power demand and minimization of this is known as reactive power optimization (RPO). In this paper we are trying to minimize the reactive power loss with help of distributed generation. Distributed generation provides active as well as reactive power locally so, there is no need of taking the reactive power from the generator consequently reactive power loss minimizes. Now problem arises that where to place the distributed generation to have minimum power loss. To find the optimal location of the distributed generation, we have used particle swarm optimization algorithm (PSO). For that we have defined the fitness function as well as constraints. Constraints limits the value of variable within the defined range. Fitness function is sum of real power loss index, reactive power loss index and voltage deviation index. We have also used genetic algorithm just to compare the results and to find which one is better out of genetic algorithm and PSO. RPO increases the power transfer capability, reduces the line loss and boost the system stability therefore it can be applied in the distribution network.

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