A self-regulating optimization algorithm for locating and sizing a local power generation source for a radial structured distribution system in deregulated environment
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.3.19Keywords:
Deregulations, LS-Local source, MPFO-Modified pathfinder, RSDS-Radial Structure distribution system, RFO-Red fox optimization, GA–GeneticDimensions Badge
Issue
Section
License
Copyright (c) 2024 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The Indian power sector is a large and complex network. Maintaining that complex network with the present regulatory format is very difficult for the government as well as transco and discom companies in terms of cost, efficiency, and reliability. That is why the government encourages deregulation in the power sector. One of the deregulation concepts is the integration of local sources into the distribution network. While integrating local sources into the system, several challenges come up, like voltage fluctuations and losses, safety and stability, protection coordination, and mitigation strategies. From those problems, one of the problems is deciding ‘the right place with the right size’ for the local source in RSDS. This work proposes a modified pathfinder optimization algorithm that has a fast convergence rate and the best balance between exploration and mining ability compared to other methods and previous PFOs. Applying MPFO to the IEEE-12 and IEEE-33 test systems to find the optimal place and size of the local source with the help of VSI and LSF. Compare other traditional methods.Abstract
How to Cite
Downloads
Similar Articles
- V. Karthikeyan, C. Jayanthi, Improving image quality assessment with enhanced denoising autoencoders and optimization methods , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Amanda Quist Okronipa, Isaac Asampana, Jones Yeboah Nyame, Exploring e-learning system loyalty: The role of system quality and satisfaction , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- R. Selvakumar, A. Manimaran, Janani G, K.R. Shanthy, Design and development of artificial intelligence assisted railway gate controlling system using internet of things , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Pavithra M, Dr. R. Neelaveni, Muthuraman K. R , Kamalesh G, Design of an interactive smart band for intellectually disabled person , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- V. Baby Deepa, R. Jeya, Dynamic resource allocation with otpimization techniques for qos in cloud computing , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Pravin P. Adivarekar1, Amarnath Prabhakaran A, Sukhwinder Sharma, Divya P, Muniyandy Elangovan, Ravi Rastogi, Automated machine learning and neural architecture optimization , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Anurag Tripathi, Histoenzymological Distribution of Acetylcholinesterase in the Rostral Mesencephalic Torus Semicircularis and Tegmental Nuclei of an Indian air Breathing Teleost Heteropneustes fossilis , The Scientific Temper: Vol. 12 No. 1&2 (2021): The Scientific Temper
- Dhulasi Priya S, Saranya K G, Significance of artificial intelligence in the development of sustainable transportation , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Gourav Kalra, Arun Kumar Gupta, Multi-response Optimization of Machining Parameters in Inconel 718 End Milling Process Through RSM-MOGA , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- C. S. Manikandababu, V. Rukkumani, Advanced VLSI-based digital image contrast enhancement: A novel approach with modified image pixel evaluation logic , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
<< < 2 3 4 5 6 7 8 9 10 11 > >>
You may also start an advanced similarity search for this article.