Using of Genetic Algorithms (GAs) to find the optimal location and sizing of static VAR compensator (SVC) to minimize real power loss

2021 ◽  
Author(s):  
Mohamed Bashir Jannat ◽  
Alexandar Savic
Keyword(s):  
Genetic Algorithms ◽  
Power Loss ◽  
Optimal Location ◽  
Static Var Compensator ◽  
Real Power

scholarly journals Embedded adaptive mutation evolutionary programming for distributed generation management

2019 ◽  
Vol 16 (1) ◽  
pp. 364
Author(s):  
Muhammad Fathi Mohd Zulkefli ◽  
Ismail Musirin ◽  
Shahrizal Jelani ◽  
Mohd Helmi Mansor ◽  
Naeem M. S. Honnoon
Keyword(s):  
Distribution System ◽  
Power Loss ◽  
Renewable Energy Sources ◽  
Distribution Network ◽  
Evolutionary Programming ◽  
Test System ◽  
Optimal Location ◽  
Adaptive Mutation ◽  
Optimal Size ◽  
Real Power

<span>Distribution generation (DG) is a widely used term to describe additional supply to a power system network. Normally, DG is installed in distribution network because of its small capacity of power. Number of DGs connected to distribution system has been increasing rapidly as the world heading to increase their dependency on renewable energy sources. In order to handle this high penetration of DGs into distribution network, it is crucial to place the DGs at optimal location with optimal size of output. This paper presents the implementation of Embedded Adaptive Mutation Evolutionary Programming technique to find optimal location and sizing of DGs in distribution network with the objective of minimizing real power loss. 69-Bus distribution system is used as the test system for this implementation. From the presented case studies, it is found that the proposed embedded optimization technique successfully determined the optimal location and size of DG units to be installed in the distribution network so that the real power loss is reduced.</span>

scholarly journals Optimal Allocation of UPFC to Minimize Real Power Losses using NSPSO Algorithm

2019 ◽  
Vol 8 (12) ◽  
pp. 924-931
Keyword(s):  
Power Loss ◽  
Optimal Allocation ◽  
Transmission System ◽  
Optimal Location ◽  
The Other ◽  
System Stability ◽  
Power Losses ◽  
Real Power ◽  
Optimal Technique ◽  
Bus System

This paper presents the optimal location of UPFC in Transmission system by implementing a new methodology called NSPSO. With this we can achieve two objectives one is reduction of Real Power loss (RPL) and the other one is improving the bus voltages. In order to identify the optimal location of the UPFC, L-Index strategy is utilized. Moreover the effectiveness of the method is tested on the IEEE 14 bus & IEEE 30 bus system by considering 125%, 150%, 175% and 200% overloading cases. Finally, we can prove that the NSPSO algorithm is the optimal technique for finding the rating and location of UPFC and also improving the system stability.

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 ACTIVE POWER LOSS REDUCTION BY FLOWER POLLINATION ALGORITHM

2020 ◽  
Vol 5 (12) ◽  
pp. 223-231
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Biological Evolution ◽  
Point Of View ◽  
Flower Pollination Algorithm ◽  
Real Power ◽  
Flower Pollination ◽  
Power Loss Reduction ◽  
Nature Inspired Algorithm ◽  
Survival Of The Fittest

This paper presents Flower Pollination (FP) algorithm for solving the optimal reactive power problem. Minimization of real power loss is taken as key intent. Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants. The biological evolution point of view, the objective of the flower pollination is the survival of the fittest and the optimal reproduction of plants in terms of numbers as well as the largely fittest. In order to evaluate the performance of the proposed Flower Pollination (FP) algorithm, it has been tested on IEEE 57 bus system and compared to other standard reported algorithms. Simulation results show that FP algorithm is better than other algorithms in reducing the real power loss and voltage profiles are within the limits.

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