Optimal Control of Reactive Power Flow for Improvements in Voltage Profiles and for Real Power Loss Minimization

<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 & 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 & voltage profiles are within the limits.</p><p> </p>

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Reactive power control using dynamic Particle Swarm Optimization for real power loss minimization

International Journal of Electrical Power & Energy Systems ◽

10.1016/j.ijepes.2012.03.030 ◽

2012 ◽

Vol 41 (1) ◽

pp. 133-136 ◽

Cited By ~ 79

Author(s):

Altaf Q.H. Badar ◽

B.S. Umre ◽

A.S. Junghare

Keyword(s):

Particle Swarm Optimization ◽

Power Control ◽

Power Loss ◽

Reactive Power ◽

Particle Swarm ◽

Loss Minimization ◽

Reactive Power Control ◽

Swarm Optimization ◽

Real Power ◽

Power Loss Minimization

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

International Journal of Electrical and Electronics Engineering ◽

10.14445/23488379/ijeee-v7i4p106 ◽

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

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ENRICHED BLACK HOLE ALGORITHM FOR DIMINUTION OF REAL POWER LOSS

International Journal of Research -GRANTHAALAYAH ◽

10.29121/granthaalayah.v5.i9.2017.2228 ◽

2017 ◽

Vol 5 (9) ◽

pp. 186-194

Author(s):

K. Lenin

Keyword(s):

Black Hole ◽

Power Loss ◽

Power Flow ◽

Reactive Power ◽

Solution Space ◽

Real Power ◽

Large Size ◽

Candidate Solution ◽

Reactive Power Flow ◽

Black Hole Algorithm

This paper presents an Enriched Black Hole (EBH) algorithm for solving reactive power flow problem. The Black Hole Algorithm starts with a preliminary population of contestant and for all iteration of the black hole algorithm, the most excellent candidate is favored to be the black hole, which followed by pulling further candidates around it, called stars. If a star move very close to the black hole, it will be consumed by the black hole and is vanished undyingly. In such a case, a new star - candidate solution is arbitrarily created and placed in the exploration space and starts a new search. Black hole algorithm is feeble to carry out global search completely in the large size problem spaces. So the enhancement in the amalgamation process in black hole algorithm has to be done. In this work, black hole algorithm will be enhanced, using stars gravities information. For this aim, a kind of gravitational force between stars is defined and the movement of stars to the black hole is adjusted during the penetration of solution space. In order to evaluate the projected Enriched Black Hole (EBH) algorithm, it has been tested in Standard IEEE 57,118 bus systems and compared to other standard reported algorithms. Simulation results reveal about the Enriched performance of the projected algorithm in plummeting the real power loss.

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