Oscillation Damping Neuro-Based Controllers Augmented Solar Energy Penetration Management of Power System Stability
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
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Oscillation Damping Neuro-Based Controllers Augmented Solar Energy Penetration Management of Power System Stability
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| Автор |
Aref, M.
Abdelaziz, A. Y. Geem, Z. W. Hong, J. Abo-Elyousr, F. K. |
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| Тематика |
FACTS
HYBRID MICROGRID OPERATION LOW FREQUENCY OSCILLATION NEURO-BASED CONTROLLERS ADAPTIVE CONTROL SYSTEMS CONTROLLERS DAMPING ELECTRIC LOADS ELECTRIC POWER SYSTEM CONTROL ELECTRIC POWER SYSTEM INTERCONNECTION ELECTRIC POWER SYSTEM STABILITY FLEXIBLE AC TRANSMISSION SYSTEMS FUZZY INFERENCE FUZZY NEURAL NETWORKS HYDROELECTRIC POWER PLANTS PARTICLE SWARM OPTIMIZATION (PSO) SMART POWER GRIDS STATIC SYNCHRONOUS COMPENSATORS SYNCHRONOUS GENERATORS FACT FREQUENCY OSCILLATIONS HYBRID MICROGRID OPERATION LOW FREQUENCY OSCILLATION LOWER FREQUENCIES MICROGRID OPERATIONS NEURO-BASED CONTROLLER OSCILLATIONS DAMPING PARTICLE SWARM SWARM OPTIMIZATION SOLAR ENERGY |
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| Описание |
The appropriate design of the power oscillation damping controllers guarantees that distributed energy resources and sustainable smart grids deliver excellent service subjected to big data for planned maintenance of renewable energy. Therefore, the main target of this study is to suppress the low-frequency oscillations due to disruptive faults and heavy load disturbance conditions. The considered power system comprises two interconnected hydroelectric areas with heavy solar energy penetrations, severely impacting the power system stabilizers. When associated with appropriate controllers, FACTs technology such as the static synchronous series compensator provides efficient dampening of the adverse power frequency oscillations. First, a two-area power system with heavy solar energy penetration is implemented. Second, two neuro-based controllers are developed. The first controller is constructed with an optimized particle swarm optimization (PSO) based neural network, while the second is created with the adaptive neuro-fuzzy. An energy management approach is developed to lessen the risky impact of the injected solar energy upon the rotor speed deviations of the synchronous generator. The obtained results are impartially compared with a lead-lag compensator. The obtained results demonstrate that the developed PSO-based neural network controller outperforms the other controllers in terms of execution time and the system performance indices. Solar energy penetrations temporarily influence the electrical power produced by the synchronous generators, which slow down for uncomfortably lengthy intervals for solar energy injection greater than 0.5 pu. © 2023 by the authors.
Ministry of Science, ICT and Future Planning, MSIP: 2019M3F2A1073164; National Research Foundation of Korea, NRF This research was supported by the Energy Cloud R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (2019M3F2A1073164). |
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| Дата |
2024-04-05T16:17:21Z
2024-04-05T16:17:21Z 2023 |
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| Тип |
Article
Journal article (info:eu-repo/semantics/article) |info:eu-repo/semantics/publishedVersion |
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| Идентификатор |
Aref, M, Abdelaziz, AY, Geem, ZW, Hong, J & Abo-Elyousr, FK 2023, 'Oscillation Damping Neuro-Based Controllers Augmented Solar Energy Penetration Management of Power System Stability', Energies, Том. 16, № 5, 2391. https://doi.org/10.3390/en16052391
Aref, M., Abdelaziz, A. Y., Geem, Z. W., Hong, J., & Abo-Elyousr, F. K. (2023). Oscillation Damping Neuro-Based Controllers Augmented Solar Energy Penetration Management of Power System Stability. Energies, 16(5), [2391]. https://doi.org/10.3390/en16052391 1996-1073 Final All Open Access, Gold https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149787739&doi=10.3390%2fen16052391&partnerID=40&md5=d7f9c1f707a742a533acf45555fa8062 https://www.mdpi.com/1996-1073/16/5/2391/pdf?version=1677751898 http://elar.urfu.ru/handle/10995/130267 10.3390/en16052391 85149787739 000947780200001 |
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| Язык |
en
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| Права |
Open access (info:eu-repo/semantics/openAccess)
cc-by https://creativecommons.org/licenses/by/4.0/ |
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| Формат |
application/pdf
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| Издатель |
MDPI
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| Источник |
Energies
Energies |
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