scholarly journals REDUCTION OF ACTIVE POWER LOSS BY PIONEERING POLL ALGORITHM

2017 ◽  
Vol 5 (11) ◽  
pp. 139-148
Author(s):  
K. Lenin
Keyword(s):  
Political Parties ◽  
Power Loss ◽  
Reactive Power ◽  
Solution Space ◽  
Active Power ◽  
The People ◽  
The World ◽  
Simulation Results ◽  
Set Up ◽  
Power Problem

This paper projects Pioneering Poll (PP) algorithm, which inspired by poll around the world is used to solve optimal reactive power problem. In Pioneering Poll (PP) algorithm population is general people and each person may be a candidate or a voter. Definite number of people will form dissimilar groups to set up political parties in the solution space. Advertising movement is the fundamental of this Pioneering Poll (PP) algorithm and it contains three core phases: sanguine campaign, disparate campaign and union campaign. During sanguine campaign, the nominee proclaims themselves through accentuate their positive descriptions and potentials. In the disparate campaign, candidates challenge with each other to raise their status and malign their contender. In extraordinary cases, the candidates that have equivalent information can united together in order to increase the possibility of success of the joint party. Campaign positively grounds the people to congregate to a state of solution space that is the comprehensive optimum. All these determinations lead up to poll day (end condition). On poll day, the candidate who is acquiring maximum votes is proclaimed as the conqueror and it matches to the supreme solution that is found for the reactive power problem. The proposed Pioneering Poll (PP) algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulation results show clearly about the enhanced performance of the proposed Pioneering Poll (PP) algorithm in reducing the real power loss with voltage profiles are within the limits.

scholarly journals ACTIVE POWER LOSS REDUCTION BY AMPLIFIED ANT COLONY ALGORITHM

2018 ◽  
Vol 6 (7) ◽  
pp. 132-141
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Ant Colony Algorithm ◽  
Reactive Power ◽  
Active Power ◽  
Ant Colony ◽  
Superior Performance ◽  
Loss Reduction ◽  
Power Loss Reduction ◽  
Simulation Results ◽  
Power Problem

In this paper, Amplified Ant Colony (AAC) algorithm has been proposed for solving optimal reactive power problem. Mutation of Genetic algorithm (GA) is used in Ant Colony Algorithm (ACA) and the output of the GA is given as an input to the ACA. The proposed Amplified Ant Colony (AAC) algorithm has been tested on standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the superior performance of the proposed Amplified Ant Colony (AAC) algorithm in reducing the real power loss & voltage profiles are within the limits.

scholarly journals REDUCTION OF ACTIVE POWER LOSS BY COYOTE SEARCH ALGORITHM

2018 ◽  
Vol 6 (10) ◽  
pp. 130-138
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Search Algorithm ◽  
Special Property ◽  
Test System ◽  
Active Power ◽  
Real Power ◽  
Simulation Results ◽  
Searching Ability ◽  
Power Problem

This paper presents Coyote Search Algorithm (CSA) for solving optimal reactive power problem. Coyote Search Algorithm is a new bio – inspired heuristic algorithm which based on coyote preying behaviour. The way coyote search for food and survive by avoiding their enemies has been imitated to formulate the algorithm for solving the reactive power problem. And the specialty of coyote is possessing both individual local searching ability & autonomous flocking movement and this special property has been utilized to formulate the search algorithm. The proposed Coyote Search Algorithm (CSA) has been tested on standard IEEE 57 bus test system and simulation results shows clearly about the good performance of the proposed algorithm in reducing the real power loss.

scholarly journals REDUCTION OF ACTIVE POWER LOSS BY IMPROVED FROG LEAPING ALGORITHM

2017 ◽  
Vol 5 (9) ◽  
pp. 44-51
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Power Loss ◽  
Reactive Power ◽  
Active Power ◽  
Swarm Optimization ◽  
Real Power ◽  
The Real ◽  
Simulation Results ◽  
Power Problem

This paper presents Improved Frog Leaping (IFL) algorithm for solving optimal reactive power problem.  Comprehensive exploration capability of Particle Swarm Optimization (PSO) and   good local search ability of Frog Leaping Algorithm (FLA) has been hybridized to solve the reactive power problem and it overcomes the shortcomings of premature convergence. In order to evaluate the validity of the proposed Improved Frog Leaping (IFL) algorithm, it has been tested in Standard IEEE 57,118 bus systems and compared to other standard algorithms. Simulation results show that proposed Improved Frog Leaping (IFL) algorithm has reduced the real power loss considerably and voltage profiles are within the limits.

scholarly journals Integrated Algorithm for Decreasing Active Power Loss

2018 ◽  
Vol 7 (1) ◽  
pp. 33
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Power Loss ◽  
Firefly Algorithm ◽  
Reactive Power ◽  
Cuckoo Search ◽  
Active Power ◽  
Voltage Profile ◽  
Swarm Optimization ◽  
Real Power ◽  
Simulation Results ◽  
Power Problem

This paper projects an Integrated Algorithm (IA) for solving optimal reactive power problem. Quick convergence of the Cuckoo Search (CS), the vibrant root change of the Firefly Algorithm (FA), and the incessant position modernization of the Particle Swarm Optimization (PSO) has been combined to form the Integrated Algorithm (IA).  In order to evaluate the efficiency of the proposed Integrated Algorithm (IA), it has been tested in standard IEEE 57,118 bus systems and compared to other standard reported algorithms. Simulation results show that Integrated Algorithm (IA) is considerably reduced the real power loss and voltage profile within the limits.

scholarly journals REDUCTION OF ACTIVE POWER LOSS BY GROUP COMPETITION ALGORITHM

2017 ◽  
Vol 5 (11) ◽  
pp. 260-270
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Active Power ◽  
Match Analysis ◽  
Group Competition ◽  
Sport Teams ◽  
Group A ◽  
Simulation Results ◽  
Number Of Individuals ◽  
Power Problem

This paper proposes Group Competition (GC) algorithm for solving optimal reactive power problem. Group Competition (GC) algorithm stimulated from the contest of sport teams in a sport group. A number of individuals as sport teams contend in a simulated group for numerous weeks. Based on the group schedule in every week, teams play in pairs and their game result is determined in terms of win or loss, given known the playing strength along with the teams’ planned formations. Modeling an artificial match analysis, each team devises a new playing strategy for the subsequent week competition and this procedure is repetitive for number of seasons. In order to evaluate the validity of the proposed Group Competition (GC) algorithm, it has been tested on Standard IEEE 57,118 bus systems and simulation results reveal about the good performance of the proposed algorithm in reducing real power loss and voltage profiles are within the limits.

Factual Power Loss Diminution by Enhanced Frog Leaping Algorithm

2020 ◽  
Vol 3 (2) ◽  
pp. 114-118
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Local Search ◽  
Power Loss ◽  
Reactive Power ◽  
Test System ◽  
Premature Convergence ◽  
Speed Of Convergence ◽  
Real Power ◽  
The Real ◽  
Simulation Results ◽  
Power Problem

This paper proposes Enhanced Frog Leaping Algorithm (EFLA) to solve the optimal reactive power problem. Frog leaping algorithm (FLA) replicates the procedure of frogs passing though the wetland and foraging deeds. Set of virtual frogs alienated into numerous groups known as “memeplexes”. Frog’s position’s turn out to be closer in every memeplex after few optimization runs and certainly, this crisis direct to premature convergence. In the proposed Enhanced Frog Leaping Algorithm (EFLA) the most excellent frog information is used to augment the local search in each memeplex and initiate to the exploration bound acceleration. To advance the speed of convergence two acceleration factors are introduced in the exploration plan formulation. Proposed Enhanced Frog Leaping Algorithm (EFLA) has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss considerably.

scholarly journals AMPLIFIED NELDER-MEAD ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER PROBLEM

2018 ◽  
Vol 6 (11) ◽  
pp. 299-306
Author(s):  
K. Lenin
Keyword(s):  
Simulated Annealing ◽  
Power Loss ◽  
Reactive Power ◽  
Superior Performance ◽  
Real Power ◽  
Last Stage ◽  
Simulation Results ◽  
Power Problem ◽  
Nelder Mead Algorithm ◽  
Prime Solution

This paper presents Hybridization of Simulated Annealing with Nelder-Mead algorithm (SN) is proposed to solve optimal reactive power problem. The proposed Hybridized - Simulated Annealing, Nelder-Mead algorithm starts with a prime solution, which is produced arbitrarily and then the solution is disturbed into partitions. The vicinity zone is created, arbitrary numbers of partitions are selected and variables modernizing procedure is started in order to create a trail of neighbour solutions. This procedure helps the SN algorithm to explore the region around an existing iterate solution. The Nelder- Mead algorithm is used in the last stage in order to progress the most excellent solution found so far and hasten the convergence in the closing stage. The proposed Hybridization of Simulated Annealing with Nelder-Mead algorithm (SN) has been tested in standard IEEE 57,118 bus systems and simulation results show the superior performance of the proposed SN algorithm in reducing the real power loss and voltage profiles are within the limits.

scholarly journals ENHANCED SPIDER ALGORITHM FOR MINIMIZATION OF REAL POWER LOSS

2018 ◽  
Vol 6 (4) ◽  
pp. 301-311
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
High Quality ◽  
Quality Performance ◽  
Real Power ◽  
Social Spiders ◽  
The Real ◽  
Spider Web ◽  
Simulation Results ◽  
Power Problem

In this paper Enhanced Spider (ES) algorithm is proposed to solve reactive power Problem. Enthused by the spiders, a new Enhanced Spider (ES) algorithm is utilized to solve reactive power problem. The composition is primarily based on the foraging approach of social spiders, which make use of of the vibrations spread over the spider web to choose the position of prey. The simulation results demonstrate high-quality performance of Enhanced Spider (ES) algorithm in solving reactive power problem.  The projected Enhanced Spider (ES) algorithm has been tested in standard IEEE 57,118 bus systems and compared to other reported standard algorithms. Results show that Enhanced Spider (ES) algorithm is more efficient than other algorithms in reducing the real power loss.

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 Shrinkage of Real Power Loss by Enriched Brain Storm Optimization Algorithm

2019 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Human Being ◽  
New Approach ◽  
Real Power ◽  
Brain Storm Optimization ◽  
Course Of Action ◽  
Simulation Results ◽  
Power Problem

<p class="Author">This paper proposes Enriched Brain Storm Optimization (EBSO) algorithm is used for soving reactive power problem. Human being are the most intellectual creature in this world. Unsurprisingly, optimization algorithm stimulated by human being inspired problem solving procedure should be advanced than the optimization algorithms enthused by collective deeds of ants, bee, etc. In this paper, we commence a new Enriched brain storm optimization algorithm, which was enthused by the human brainstorming course of action. In the projected Enriched Brain Storm Optimization (EBSO) algorithm, the vibrant clustering strategy is used to perk up the k-means clustering process. The most important view of the vibrant clustering strategy is that; regularly execute the k-means clustering after a definite number of generations, so that the swapping of information wrap all ideas in the clusters to accomplish suitable searching capability. This new approach leads to wonderful results with little computational efforts. In order to evaluate the efficiency of the proposed Enriched Brain Storm Optimization (EBSO) algorithm, has been tested standard IEEE 118 &amp; practical 191 bus test systems and compared to other standard reported algorithms. Simulation results show that Enriched Brain Storm Optimization (EBSO) algorithm is superior to other algorithms in reducing the real power loss.</p>

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