scholarly journals Real Power Loss Minimization of AC/DC Hybrid Systems with Reactive Power Compensation by using Teaching Learning based Optimization Algorithm

2020 ◽  
Vol 7 (4) ◽  
pp. 23-33
Author(s):  
Dr.B.Suresh Babu
Keyword(s):  
Hybrid Systems ◽  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Reactive Power Compensation ◽  
Loss Minimization ◽  
Real Power ◽  
Power Loss Minimization ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

scholarly journals Advanced Teaching-Learning-Based Optimization Algorithm for Actual Power Loss Reduction

2019 ◽  
Vol 9 (1) ◽  
pp. 46
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Learning Procedure ◽  
Power Loss Reduction ◽  
Non Linear ◽  
Teaching Learning Based Optimization ◽  
Weighted Factor ◽  
Teaching Learning ◽  
Advanced Teaching

In this work Advanced Teaching-Learning-Based Optimization algorithm (ATLBO) is proposed to solve the optimal reactive power problem. Teaching-Learning-Based Optimization (TLBO) optimization algorithm has been framed on teaching learning methodology happening in classroom. Algorithm consists of “Teacher Phase”, “Learner Phase”. In the proposed Advanced Teaching-Learning-Based Optimization algorithm non-linear inertia weighted factor is introduced into the fundamental TLBO algorithm to manage the memory rate of learners.  In order to control the learner’s mutation arbitrarily during the learning procedure a non-linear mutation factor has been applied. Proposed Advanced Teaching-Learning-Based Optimization algorithm (ATLBO) has been tested in standard IEEE 14, 30 bus test systems and simulation results show the proposed algorithm reduced the real power loss effectively.

Reactive power control for real power-loss minimization

1988 ◽  
Vol 1 (3) ◽  
pp. 16-21 ◽  
Author(s):  
M.A.H. El-Sayed ◽  
T.M. Abdel-Rahman ◽  
M.O. Mansour
Keyword(s):  
Power Control ◽  
Power Loss ◽  
Reactive Power ◽  
Loss Minimization ◽  
Reactive Power Control ◽  
Real Power ◽  
Power Loss Minimization

scholarly journals Minimization of real power loss by enhanced teaching learning based optimization algorithm

2020 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Power Flow ◽  
Reactive Power ◽  
Optimization Problems ◽  
High Concentration ◽  
Real Power ◽  
Reactive Power Flow ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

<p class="Abstract">This paper presents an Enhanced Teaching-Learning-Based Optimization (ETLBO) algorithm for solving reactive power flow problem. Basic Teaching-Learning-Based Optimization (TLBO) is reliable, accurate and vigorous for solving the optimization problems. Also it has been found that TLBO algorithm slow in convergence due to its high concentration in the accuracy. This paper presents an, enhanced version of TLBO algorithm, called as enhanced Teaching-Learning-Based Optimization (ETLBO). A parameter called as “weight” has been included in the fundamental TLBO equations &amp; subsequently it increases the rate of convergence. In order to evaluate the proposed algorithm, it has been tested in Standard IEEE 57,118 bus systems and compared to other standard reported algorithms. Simulation results reveal about the better performance of the proposed algorithm in reducing the real power loss &amp; voltage profiles are within the limits.</p><p> </p>

Real Power Loss Reduction by Enhanced Trailblazer Optimization Algorithm

2021 ◽  
pp. 77-93
Author(s):  
K. Lenin
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Local Optimum ◽  
Loss Reduction ◽  
Exploration And Exploitation ◽  
Real Power ◽  
Tlbo Algorithm ◽  
Power Loss Reduction ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

In this paper Teaching learning based Trailblazer optimization algorithm (TLBOTO) is used for solving the power loss lessening problem. Trailblazer optimization algorithm (TOA) is alienated into dual phases for exploration: trailblazer phase and adherent phase. Both phases epitomize the exploration and exploitation phase of TOA correspondingly. Nevertheless, in order to avoid the solution falling in local optimum in this paper Teaching-learning-based optimization (TLBO) is integrated with TOA approach. Learning segment of the TLBO algorithm is added to the adherent phase. Proposed Teaching learning based Trailblazer optimization algorithm (TLBOTO) augment exploration capability of the algorithm and upsurge the convergence speed. Algorithm's exploration competences enhanced by linking the teaching phase and learning. Exploration segment of the trailblazer algorithm identifies the zone with the pre-eminent solution. Subsequently inducing the teaching process, the trailblazer performs as a teacher to teach additional entities and engender a new-fangled entity. The new-fangled unit is equated with the trailblazer, and with reference to the greedy selection norm, the optimal one is designated as the trailblazer to endure exploration. The location of trailblazer is modernized. Legitimacy of the Teaching learning based Trailblazer optimization algorithm (TLBOTO) is substantiated in IEEE 30 bus system (with and devoid of L-index). Actual power loss lessening is reached. Proportion of actual power loss lessening is augmented

Reactive Power Loss Minimization on an Interconnected Electric Power Network

2018 ◽  
pp. 207-228
Author(s):  
Uche Chinweoke Ogbuefi ◽  
Boniface Onyemaechi Anyaka ◽  
Muncho Josephine Mbunwe
Keyword(s):  
Electric Power ◽  
Power Loss ◽  
Reactive Power ◽  
Power Network ◽  
Loss Minimization ◽  
Power Loss Minimization
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