ЦИФРОВОЙ ПРОРЫВ: МИРОВОЙ ОПЫТ ПРИМЕНЕНИЯ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ЭКСПЛУАТАЦИИ И КОНТРОЛЕ ЭНЕРГОСИСТЕМ

Adilov Yalkin  Tuychiyevich; Raxmonov  Komronbek Alisher ugli

В статье представлен обзор различных методов искусственного интеллекта (ИИ), которые могут быть применены в эксплуатации и контроле энергосистем. Показано, как искусственный интеллект можно использовать для учёта оптимального расхода энергии, регулирования напряжения, контроля стабильности энергосистемы, регулирования частоты нагрузки. Методы ИИ позволяют обрабатывать большие объемы данных быстрее, чем методы численной оптимизации и потому могут повысить производительность энергетических систем

Name of journalRaqamli iqtisodiyot
Number of edition8-son
View count 20

Web Address https://infocom.uz/magazine-releases

DOI

Date of creation in the UzSCI system 30-10-2024

Read count 20

Date of publication 21-10-2024

Main LanguageRus

Pages324-344

Tags

эксплуатация и управление энергосистемами

искусственная нейронная сеть

адаптивная нейро-нечеткая система

оптимизация муравьиной колонии

Русский

В статье представлен обзор различных методов искусственного интеллекта (ИИ), которые могут быть применены в эксплуатации и контроле энергосистем. Показано, как искусственный интеллект можно использовать для учёта оптимального расхода энергии, регулирования напряжения, контроля стабильности энергосистемы, регулирования частоты нагрузки. Методы ИИ позволяют обрабатывать большие объемы данных быстрее, чем методы численной оптимизации и потому могут повысить производительность энергетических систем

Tags

эксплуатация и управление энергосистемами

искусственная нейронная сеть

адаптивная нейро-нечеткая система

оптимизация муравьиной колонии

№ Author name position Name of organisation

1 Adilov Y.T. PhD, доцент Научно-Исследовательский Университет « Ташкентский институт инженеров ирригации и механизации сельского хозяйства»

2 Raxmonov K.A. магистр Научно-Исследовательский Университет « Ташкентский институт инженеров ирригации и механизации сельского хозяйства»

№ Name of reference

1 Utkarsh Pandey, Anshumaan Pathak, Adesh Kumar and Surajit Mondal Applications of artificial intelligence in power system operation, control and planning: a review Clean Energy, 2023, Vol. 7, No. 6, 1199–1218).

2 Panta S, Premrudeepreechacharn S. Economic dispatch for power generation using artificial neural network ICPE’07 conference in Daegu, Korea. In: 2007 7th International Conference on Power Electronics, Daegu, South Korea, 22–26 October 2007,558–562.

3 Rahmat NA, Musirin I. Differential evolution ant colony optimization (DEACO) technique in solving economic load dispatch problem. In: 2012 IEEE International Power Engineering and Optimization Conference, Melaka, Malaysia, 6–7 June 2012, 263–268.

4 Reyes DM, de Souza RM, de Oliveira AL. A three-stage approach for modeling multiple time series applied to symbolic quartile data. Expert Syst Appl, 2022, 187:115884.

5 Almaghrebi A, Aljuheshi F, Rafaie M, et al. Data-driven charging demand prediction at public charging stations using supervised machine learning regression methods. Energies, 2020, 13:4231.

6 Huang P, Copertaro B, Zhang X, et al. A review of data centers as prosumers in district energy systems: renewable energy integration and waste heat reuse for district heating. Appl Energy, 2020, 258:114109

7 Dommel HW, Tinney WF. Optimal power flow solutions. IEEE Transactions on Power Apparatus and Systems, 1968, PAS-87:1866–1876.

8 Holen AT, Botnen A, Stoa P, et al. Coupling between knowledge based and algorithmic methods. Proc IEEE, 1992, 80:745–757.

9 Borges AF, Laurindo FJ, Spínola MM, et al. The strategic use of artificial intelligence in the digital era: systematic literature review and future research directions. Int J Inf Manage, 2021, 57:102225.

10 Bordin C, Skjelbred HI, Kong J, et al. Machine learning for hydropower scheduling: state of the art and future research directions. Procedia Comput Sci, 2020, 176:1659–1668

11 Bibri SE, Krogstie J. Environmentally data-driven smart sustainable cities: applied innovative solutions for energy efficiency, pollution reduction, and urban metabolism. Energy Informatics, 2020, 3:1–59.

12 Strielkowski W. Social Impacts of Smart Grids: The Future of Smart Grids and Energy Market Design. Amsterdam, Netherlands: Elsevier, 2019.

13 Moleda M, Momot A, Mrozek D. Predictive maintenance of boiler feed water pumps using SCADA data. Sensors, 2020, 20:571.

14 Ren C, Xu Y. A fully data-driven method based on generative adversarial networks for power system dynamic security assessment with missing data. IEEE Trans Power Syst, 2019, 34:5044–5052.

15 Wood AJ, Wollenberg BF, Sheblé GB. Power Generation, Operation, and Control. Hoboken, NJ, USA: John Wiley & Sons, 2013.

16 Kumar N, Mishra VM, Kumar A. Smart grid and nuclear power plant security by integrating cryptographic hardware chip. Nuclear Engineering and Technology, 2021, 53:3327–3334.

17 Kumar N, Mishra VM, Kumar A. Smart grid security by embedding s-box advanced encryption standard. Intelligent Automation &. Soft Comput, 2022, 34:623–638.

Waiting

№	Author name	position	Name of organisation
1	Adilov Y.T.	PhD, доцент	Научно-Исследовательский Университет « Ташкентский институт инженеров ирригации и механизации сельского хозяйства»
2	Raxmonov K.A.	магистр	Научно-Исследовательский Университет « Ташкентский институт инженеров ирригации и механизации сельского хозяйства»

№	Name of reference
1	Utkarsh Pandey, Anshumaan Pathak, Adesh Kumar and Surajit Mondal Applications of artificial intelligence in power system operation, control and planning: a review Clean Energy, 2023, Vol. 7, No. 6, 1199–1218).
2	Panta S, Premrudeepreechacharn S. Economic dispatch for power generation using artificial neural network ICPE’07 conference in Daegu, Korea. In: 2007 7th International Conference on Power Electronics, Daegu, South Korea, 22–26 October 2007,558–562.
3	Rahmat NA, Musirin I. Differential evolution ant colony optimization (DEACO) technique in solving economic load dispatch problem. In: 2012 IEEE International Power Engineering and Optimization Conference, Melaka, Malaysia, 6–7 June 2012, 263–268.
4	Reyes DM, de Souza RM, de Oliveira AL. A three-stage approach for modeling multiple time series applied to symbolic quartile data. Expert Syst Appl, 2022, 187:115884.
5	Almaghrebi A, Aljuheshi F, Rafaie M, et al. Data-driven charging demand prediction at public charging stations using supervised machine learning regression methods. Energies, 2020, 13:4231.
6	Huang P, Copertaro B, Zhang X, et al. A review of data centers as prosumers in district energy systems: renewable energy integration and waste heat reuse for district heating. Appl Energy, 2020, 258:114109
7	Dommel HW, Tinney WF. Optimal power flow solutions. IEEE Transactions on Power Apparatus and Systems, 1968, PAS-87:1866–1876.
8	Holen AT, Botnen A, Stoa P, et al. Coupling between knowledge based and algorithmic methods. Proc IEEE, 1992, 80:745–757.
9	Borges AF, Laurindo FJ, Spínola MM, et al. The strategic use of artificial intelligence in the digital era: systematic literature review and future research directions. Int J Inf Manage, 2021, 57:102225.
10	Bordin C, Skjelbred HI, Kong J, et al. Machine learning for hydropower scheduling: state of the art and future research directions. Procedia Comput Sci, 2020, 176:1659–1668
11	Bibri SE, Krogstie J. Environmentally data-driven smart sustainable cities: applied innovative solutions for energy efficiency, pollution reduction, and urban metabolism. Energy Informatics, 2020, 3:1–59.
12	Strielkowski W. Social Impacts of Smart Grids: The Future of Smart Grids and Energy Market Design. Amsterdam, Netherlands: Elsevier, 2019.
13	Moleda M, Momot A, Mrozek D. Predictive maintenance of boiler feed water pumps using SCADA data. Sensors, 2020, 20:571.
14	Ren C, Xu Y. A fully data-driven method based on generative adversarial networks for power system dynamic security assessment with missing data. IEEE Trans Power Syst, 2019, 34:5044–5052.
15	Wood AJ, Wollenberg BF, Sheblé GB. Power Generation, Operation, and Control. Hoboken, NJ, USA: John Wiley & Sons, 2013.
16	Kumar N, Mishra VM, Kumar A. Smart grid and nuclear power plant security by integrating cryptographic hardware chip. Nuclear Engineering and Technology, 2021, 53:3327–3334.
17	Kumar N, Mishra VM, Kumar A. Smart grid security by embedding s-box advanced encryption standard. Intelligent Automation &. Soft Comput, 2022, 34:623–638.