scholarly journals Optimal Placement of TCSC for Congestion Management and Power Loss Reduction Using Multi-Objective Genetic Algorithm

2020 ◽  
Vol 12 (7) ◽  
pp. 2813 ◽  
Author(s):  
Thang Trung Nguyen ◽  
Fazel Mohammadi
Keyword(s):  
Genetic Algorithm ◽  
Power Loss ◽  
Congestion Management ◽  
Mixed Integer ◽  
Total Power ◽  
World Population ◽  
Loss Reduction ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Power Loss Reduction

Electricity demand has been growing due to the increase in the world population and higher energy usage per capita as compared to the past. As a result, various methods have been proposed to increase the efficiency of power systems in terms of mitigating congestion and minimizing power losses. Power grids operating limitations result in congestion that specifies the final capacity of the system, which decreases the conventional power capabilities between coverage areas. Flexible AC Transmission Systems (FACTS) can help to decrease flows in heavily loaded lines and lead to lines loadability improvements and cost reduction. In this paper, total power loss reduction and line congestion improvement are assessed by determining the optimal locations and compensation rates of Thyristor-Controlled Series Compensator (TCSC) devices using the Multi-Objective Genetic Algorithm (MOGA). The results of applying the proposed method on the IEEE 30-bus test system confirmed the efficiency of the proposed procedure. In addition, to check the performance, applicability, and effectiveness of the proposed method, different heuristic algorithms, such as the multi-objective Particle Swarm Optimization (PSO) algorithm, Differential Evolution (DE) algorithm, and Mixed-Integer Non-Linear Program (MINLP) technique, are used for comparison. The obtained results show the accuracy and fast convergence of the proposed method over the other heuristic techniques.

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 Multi-Objective Planning for Optimal Allocation of DG and RPC Units in Radial Distribution System Using Genetic Algorithm

10.29007/bngk ◽  
2018 ◽  
Author(s):  
Jaydeepsinh Sarvaiya ◽  
Mahipalsinh Chudasama
Keyword(s):  
Genetic Algorithm ◽  
Distribution System ◽  
Power Loss ◽  
Distribution Network ◽  
Cost Effective ◽  
Loss Reduction ◽  
Effective Solution ◽  
Radial Distribution System ◽  
Real Power ◽  
Power Loss Reduction

DG penetration is continuously increased across distribution network not only to reduce carbon emission, but also to enhance the performance of the distribution network. In a restructured environment any distribution utility need to address DG placement and sizing problem to find a cost effective solution for the specific investment. Most of the authors have attempted to solve the problem based on real power loss reduction across the network. Some authors consider voltage stability based analysis for increased loadability of network with real power loss. However, optimal reactive power compensation also need to be incorporated for a cost effective solution. In this paper an attempt has been made to address various types of DG and RPC units citing and sizing problem with multi-objectives consists real power loss reduction and VSI improvement. A new approach includes development of cost function to find cost-effective solution for distribution network. Evolutionary based Genetic Algorithm used to optimize the objective function. Proposed algorithm is tested onIEEE-33 bus radial distribution system.

>1200 V, >50A SILICON CARBIDE SUPER JUNCTION TRANSISTOR

2011 ◽  
Vol 2011 (HITEN) ◽  
pp. 000104-000107
Author(s):  
Ranbir Singh ◽  
Siddarth Sundaresan ◽  
Stoyan Jeliazkov ◽  
Deepak Veereddy ◽  
Eric Lieser
Keyword(s):  
Power Loss ◽  
Electrical Performance ◽  
Total Power ◽  
Common Source ◽  
Loss Reduction ◽  
Low Leakage ◽  
Power Loss Reduction ◽  
Source Current ◽  
Junction Transistor ◽  
Schottky Rectifier

The electrical performance of GeneSiC's 1200 V/7 A SiC Super Junction Transistor (SJT) is compared with three best-in-class commercial Si IGBTs in this paper. Low leakage currents of < 100 μA at 325 °C operating temperature, switching transients < 15 ns at 250 °C, Common Source current gains of 63 and on-resistance as low as 220 mΩ were measured on the SiC SJTs. For switching 7 A, 800 V at 100 kHz, the SiC SJT+GeneSiC SiC Schottky rectifier as Free Wheeling Diode (FWD) achieved a total power loss reduction of about 64% when compared to the best all-Si IGBT+FWD configuration and a power loss reduction of about 47 %, when compared to the best Si IGBT + SiC Schottky FWD.

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