Dispersed generator placement using fuzzy-GA in distribution systems
By Kyu-Ho Kim, Yu-Jeong Lee, S. Rhee, Sang-keun Lee, Seok-Ku You. IEEE Power Engineering Society Summer Meeting,, 2002
315 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/ce8910bd07f6d5ff18224fc8d5354b0ed6b74bf5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- Jianhui Wang
- Zhaoyu Wang
- Bokan Chen
- Miroslav M. Begovic
- Nadarajah Mithulananthan
- Rabih A. Jabr
- Duong Quoc Hung
- Bikash Chandra Pal
In Electrical and Electronic Engineering
- Jianhui Wang
- Zhaoyu Wang
- Bokan Chen
- Miroslav M. Begovic
- Rabih A. Jabr
- Bikash Chandra Pal
- Nadarajah Mithulananthan
- Duong Quoc Hung
Citations in the corpus
- Stochastic DG Placement for Conservation Voltage Reduction Based on Multiple Replications Procedure (2015, methodology): “Several works [18], [19] claimed that IAs were suitable for multiobjective problems and could achieve a near-optimal solution.”
- Multiple Distributed Generator Placement in Primary Distribution Networks for Loss Reduction (2013, methodology): “The genetic algorithm (GA)based method has been presented to determine the size and location of DG [25], [26].”
- Ordinal optimisation approach for locating and sizing of distributed generation (2009, methodology): “The search-based methods comprise various artificial intelligence techniques such as genetic algorithms (GA) [6, 7], combined fuzzy-GA [8], multi-objective evolutionary approaches [9] and tabu search [10].”