Back to Search
Start Over
Multi-objective optimization of optimal placement and sizing of multiple DG placements in radial distribution system using stud krill herd algorithm
- Source :
- Neural Computing and Applications. 33:13619-13634
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- This paper presents the Stud krill herd algorithm (SKHA) implementation to find the optimal location and sizing of DG units in a radial distribution system. In recent years, distributed generation (DG) plays an important role in the electric power industry. DG units are connected to the system mainly for improving voltage stability, real power loss reduction, reliability, grid strengthening, and reduction of SO2, CO2 gas emission. The problem formulation is based on the multi-objective function. This work focuses on real power loss reduction, cost minimization, and improvement of voltage stability and formulates a single objective function with suitable weightage preference subject to various constraints like voltage limit, DG real power limit, power balance constraint, and DG location limit. The algorithm has been tested on IEEE 33 bus, 69 bus, and 94 bus radial systems at different load levels with DG placement. The obtained simulation results are compared with other methods. The comparison reveals that the proposed algorithm offers a better solution for the optimal placement of DG in the radial distribution system with faster convergence.
- Subjects :
- 0209 industrial biotechnology
Mathematical optimization
business.industry
Computer science
Reliability (computer networking)
02 engineering and technology
Grid
Multi-objective optimization
Sizing
Reduction (complexity)
020901 industrial engineering & automation
Artificial Intelligence
Distributed generation
Limit (music)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Electric power industry
business
Software
Subjects
Details
- ISSN :
- 14333058 and 09410643
- Volume :
- 33
- Database :
- OpenAIRE
- Journal :
- Neural Computing and Applications
- Accession number :
- edsair.doi...........f7cc6f31508cefc51bc23a3b09d3b42c