A segmented optimal PID method to consider both regulation performance and damping characteristic of hydroelectric power system. (May 2023)
- Record Type:
- Journal Article
- Title:
- A segmented optimal PID method to consider both regulation performance and damping characteristic of hydroelectric power system. (May 2023)
- Main Title:
- A segmented optimal PID method to consider both regulation performance and damping characteristic of hydroelectric power system
- Authors:
- Dong, Wenhui
Cao, Zezhou
Zhao, Pengchong
Yang, Zhenbiao
Yuan, Yichen
Zhao, Ziwen
Chen, Diyi
Wu, Yajun
Xu, Beibei
Venkateshkumar, M. - Abstract:
- Abstract: Hydropower has become the main force of grid frequency regulation due to its regulation flexibility and rapid response characteristics. However, when the operating conditions are changed, the continued pursuit of a faster response speed deteriorates the damping characteristics of the system with wider water-head, causing low-frequency oscillations, threatening the power station's safety and stability and the power grid. In this study, an improved quantized damping method is applied. The settling time and damping coefficient variation under different PID parameters and operating conditions are recorded, revealing the contradiction between regulation performance and damping characteristics. Then the segmented optimal PID controller is proposed to balance this contradiction. The twin-delayed deep deterministic policy gradient learning algorithm enables the controller to find the optimal policy online. With the settling time-damping threshold control strategy, the controller optimizes parameters according to operating conditions, and changes parameters when reaching the threshold. The results show that, compared with using a set of PID parameters, the damping of the hydropower system is increased by 0.35 from negative to positive. In contrast, the settling time increases by 11.63s within limits. The proposed controller ensures the safe and coordinated operation of the power grid and the power station of the hydroelectric power system with a wide water-head.
- Is Part Of:
- Renewable energy. Volume 207(2023)
- Journal:
- Renewable energy
- Issue:
- Volume 207(2023)
- Issue Display:
- Volume 207, Issue 2023 (2023)
- Year:
- 2023
- Volume:
- 207
- Issue:
- 2023
- Issue Sort Value:
- 2023-0207-2023-0000
- Page Start:
- 1
- Page End:
- 12
- Publication Date:
- 2023-05
- Subjects:
- Hydroelectric power system -- Damping quantization -- Regulation performance -- Deep reinforcement learning -- Segmented optimal PID controller
Renewable energy sources -- Periodicals
Power resources -- Periodicals
Énergies renouvelables -- Périodiques
Ressources énergétiques -- Périodiques
333.794 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09601481 ↗
http://www.elsevier.com/journals ↗
http://www.journals.elsevier.com/renewable-energy/ ↗ - DOI:
- 10.1016/j.renene.2023.02.091 ↗
- Languages:
- English
- ISSNs:
- 0960-1481
- Deposit Type:
- Legaldeposit
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 7364.187000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 26729.xml