Constriction factor based particle swarm optimization applied to reactive power dispatch in transmission system
Reactive power dispatch (RPD) is one of the major issues in the transmission and distribution domain of the power system. RPD being a fundamental part of optimal power flow problem, controls balanced voltage profile and minimises active power loss. As of its objective function, RPD problem is treate...
Saved in:
| Published in | International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2011) pp. 335 - 339 |
|---|---|
| Main Authors | , , |
| Format | Conference Proceeding |
| Language | English |
| Published |
Stevenage
IET
2011
The Institution of Engineering & Technology |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9789380430003 9380430000 |
| DOI | 10.1049/cp.2011.0385 |
Cover
| Summary: | Reactive power dispatch (RPD) is one of the major issues in the transmission and distribution domain of the power system. RPD being a fundamental part of optimal power flow problem, controls balanced voltage profile and minimises active power loss. As of its objective function, RPD problem is treated as mixed integer non linear function where metaheuristics algorithm may be fitted properly as solving tool. In this paper such an algorithm named particle swarm optimization (PSO) have been applied for solving reactive power dispatch problem. The special feature of PSO technique is its local as well as global optimization of the objective function. In this paper constriction factor is included in generalised PSO algorithm to enhance the velocity of the particle used in particle swarm optimization technique which is applied to the IEEE 14-bus system. In the test system control variable such as generator terminal voltages, transformer tap positions and the shunt capacitor placements are varied to solve the RPD problem. The obtained result is compared with previously used different optimization algorithm including generalised PSO. The comparison shows the effectiveness of the constriction factor and the practicability of the applied optimization technique in this paper. |
|---|---|
| Bibliography: | ObjectType-Article-1 ObjectType-Feature-2 SourceType-Conference Papers & Proceedings-1 content type line 22 |
| ISBN: | 9789380430003 9380430000 |
| DOI: | 10.1049/cp.2011.0385 |