Online Estimation of Plant Participation Factors for Automatic Generation Control in Power Systems with Variable Energy Resources; IEEE Transactions on Industry Applications; Vol. ХХ, iss. Х
| Источник: | IEEE Transactions on Industry Applications Vol. ХХ, iss. Х.— 2022.— [10 p.] |
|---|---|
| Главный автор: | |
| Автор-организация: | |
| Другие авторы: | , |
| Примечания: | Title screen The paper discusses the limitations of existing automatic generation control systems that appear under the impact of variable energy resources. To overcome identified issues, the authors proposed an approach that advances the functional block responsible for computation of plant participation factors. This approach connects an optimizer with a component for power flow calculations and allows online estimation of plant participation factors to increase flexibility and selectivity of automatic generation control. The corresponding optimization models were established to perform conventional and advanced control strategies. To meet performance requirements imposed by variable energy sources, the machine learning model, namely the densely connected neural network, was designed for power flow calculations. Besides, Lasso regression method was proposed to select relevant features for the considered control tasks and improve the performance of the machine learning-based power flow model. Finally, the software tool was developed to implement the proposed approach and tested on a model of real 60 GW interconnection containing 464 nodes and 742 branches. The results of the software testing confirmed its feasibility and easy integration into existing automatic generation control systems. Режим доступа: по договору с организацией-держателем ресурса |
| Язык: | английский |
| Опубликовано: |
2022
|
| Предметы: | |
| Online-ссылка: | https://doi.org/10.1109/TIA.2022.3174190 |
| Формат: | Электронный ресурс Статья |
| Запись в KOHA: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=668135 |
MARC
| LEADER | 00000naa0a2200000 4500 | ||
|---|---|---|---|
| 001 | 668135 | ||
| 005 | 20250603093503.0 | ||
| 035 | |a (RuTPU)RU\TPU\network\39359 | ||
| 090 | |a 668135 | ||
| 100 | |a 20220622d2022 k||y0rusy50 ba | ||
| 101 | 0 | |a eng | |
| 102 | |a US | ||
| 135 | |a drcn ---uucaa | ||
| 181 | 0 | |a i | |
| 182 | 0 | |a b | |
| 200 | 1 | |a Online Estimation of Plant Participation Factors for Automatic Generation Control in Power Systems with Variable Energy Resources |f E. A. Tsydenov, A. V. Prokhorov, Wang Li | |
| 203 | |a Текст |c электронный | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 38 tit.] | ||
| 330 | |a The paper discusses the limitations of existing automatic generation control systems that appear under the impact of variable energy resources. To overcome identified issues, the authors proposed an approach that advances the functional block responsible for computation of plant participation factors. This approach connects an optimizer with a component for power flow calculations and allows online estimation of plant participation factors to increase flexibility and selectivity of automatic generation control. The corresponding optimization models were established to perform conventional and advanced control strategies. To meet performance requirements imposed by variable energy sources, the machine learning model, namely the densely connected neural network, was designed for power flow calculations. Besides, Lasso regression method was proposed to select relevant features for the considered control tasks and improve the performance of the machine learning-based power flow model. Finally, the software tool was developed to implement the proposed approach and tested on a model of real 60 GW interconnection containing 464 nodes and 742 branches. The results of the software testing confirmed its feasibility and easy integration into existing automatic generation control systems. | ||
| 333 | |a Режим доступа: по договору с организацией-держателем ресурса | ||
| 461 | |t IEEE Transactions on Industry Applications | ||
| 463 | |t Vol. ХХ, iss. Х |v [10 p.] |d 2022 | ||
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a automatic generation control | |
| 610 | 1 | |a load flow | |
| 610 | 1 | |a task analysis | |
| 610 | 1 | |a optimization | |
| 610 | 1 | |a power systems | |
| 610 | 1 | |a adaptation models | |
| 610 | 1 | |a databases | |
| 610 | 1 | |a автоматическое управление | |
| 610 | 1 | |a нагрузки | |
| 610 | 1 | |a оптимизация | |
| 610 | 1 | |a энергосистемы | |
| 610 | 1 | |a адаптация | |
| 700 | 1 | |a Tsydenov |b E. A. |c specialist in the field of electrical engineering |c Senior Laboratory Assistant of] Tomsk Polytechnic University |f 1996- |g Evgeny Aleksandrovich |3 (RuTPU)RU\TPU\pers\46098 |9 22044 | |
| 701 | 1 | |a Prokhorov |b A. V. |c specialist in the field of electricity |c acting head, associate Professor, Deputy Director on educational work of Tomsk Polytechnic University, candidate of technical Sciences |f 1985- |g Anton Viktorovich |3 (RuTPU)RU\TPU\pers\32985 |9 16830 | |
| 701 | 0 | |a Wang Li | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа энергетики |b Отделение электроэнергетики и электротехники (ОЭЭ) |3 (RuTPU)RU\TPU\col\23505 |
| 801 | 1 | |a RU |b 63413507 |c 20110823 | |
| 801 | 2 | |a RU |b 63413507 |c 20220622 |g RCR | |
| 850 | |a 63413507 | ||
| 856 | 4 | 0 | |u https://doi.org/10.1109/TIA.2022.3174190 |z https://doi.org/10.1109/TIA.2022.3174190 |
| 942 | |c CF | ||