OVERVIEW ON RELIABILITY ASSESSMENT AND ASSETS MANAGEMENT IN POWER SYSTEM DISTRIBUTION WITH HIGH PENETRATION OF SOLAR ENERGY USING COMPUTATIONAL INTELLIGENCE
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| Publicado en: | Annals of the Faculty of Engineering Hunedoara vol. 23, no. 2 (May 2025), p. 81-92 |
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Faculty of Engineering Hunedoara
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 001 | 3254942006 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1584-2665 | ||
| 022 | |a 2601-2332 | ||
| 035 | |a 3254942006 | ||
| 045 | 2 | |b d20250501 |b d20250531 | |
| 084 | |a 148329 |2 nlm | ||
| 100 | 1 | |a Olajuyin, E A |u Bamidele Olumilua University of Education, Science and Technology, (Electrical and Electronic Engineering, School of Engineering), Ikere-Etiti, NIGERIA | |
| 245 | 1 | |a OVERVIEW ON RELIABILITY ASSESSMENT AND ASSETS MANAGEMENT IN POWER SYSTEM DISTRIBUTION WITH HIGH PENETRATION OF SOLAR ENERGY USING COMPUTATIONAL INTELLIGENCE | |
| 260 | |b Faculty of Engineering Hunedoara |c May 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The Stable and regular supply of electricity is very germane to the economic development of any nation and insufficient generation has affected the regular, constant, reliable supply of electricity and other failures in the system. To reduce and eliminate these issues, reliability assessment and assets management of distribution systems with high penetration of solar energy is proposed using a Monte Carlo-based recurrent neural network. The background of reliability assessment and assets management in power system, review of some past works on reliability and assets management, research gaps, state of art of the research work, reliability worth, reliability of power system network, the procedure for Monte Carlo based recurrent neural network for reliability assessment and conclusion were presented. The network would be modeled with high penetration solar pv, distributed generators (DG) and heavy duty generators and the reliability assessment would be carried out using a recurrent neural network under different scenario with Monte Carlo. The recurrent neural network (RNN) is chosen because it can give a predictive result in sequential data, recurrent neural network will take care of excessive use of the memory by Monte Carlo because it has internal memory itself and it can also learn from any pattern and adapt to it and give result without any functioning equation. The bulkiness of using only probabilistic methods such as Monte Carlo and Markov etc. which required making many simplifying assumptions to reduce to a manageable size is solved by this proposed method and whale optimization algorithm would be carried out. | |
| 653 | |a Reliability analysis | ||
| 653 | |a Monte Carlo simulation | ||
| 653 | |a Failure | ||
| 653 | |a Distributed generation | ||
| 653 | |a Solar energy | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Electricity | ||
| 653 | |a Memory | ||
| 653 | |a Probabilistic methods | ||
| 653 | |a Network reliability | ||
| 653 | |a Generators | ||
| 653 | |a Decision making | ||
| 653 | |a Neural networks | ||
| 653 | |a Recurrent neural networks | ||
| 653 | |a Power supply | ||
| 653 | |a Alternative energy sources | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Economic development | ||
| 653 | |a Asset management | ||
| 700 | 1 | |a Olulope, P K |u Ekiti State University, (Electrical and Electronics Engineering, Faculty of Engineering), Ado-Ekiti, (Ekiti), NIGERIA | |
| 700 | 1 | |a Fasina, E T |u Ekiti State University, (Electrical and Electronics Engineering, Faculty of Engineering), Ado-Ekiti, (Ekiti), NIGERIA | |
| 773 | 0 | |t Annals of the Faculty of Engineering Hunedoara |g vol. 23, no. 2 (May 2025), p. 81-92 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3254942006/abstract/embedded/Y2VX53961LHR7RE6?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3254942006/fulltext/embedded/Y2VX53961LHR7RE6?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3254942006/fulltextPDF/embedded/Y2VX53961LHR7RE6?source=fedsrch |