Real-time out-of-step prediction control to prevent emerging blackouts in power systems: A reinforcement learning approach

Hossein Hassani, Roozbeh Razavi-Far and Mehrdad Saif

Applied Energy, 2022, vol. 314, issue C, No S0306261922002951

Abstract: Blackouts impose undesired economical impacts and integrity issues on electric power systems. Early out-of-step prediction of generating units is of paramount importance for blackout prevention and power management. This work is concerned with the design of novel real-time mechanisms based on reinforcement learning for the early out-of-step prediction in small-scale and large-scale power systems while mitigating the rate of false and miss alarms. These mechanisms are enabled by formulating the out-of-step prediction problem as a partially observable Markov decision process, for which a reward shaping strategy is devised based upon deep Q-networks to support the learning process of the agent. The proposed prediction mechanisms are real-time and capable of dealing with the dynamic changes of loads. Various scenarios in the form of three separate experiments are simulated on Kundur’s two-area and IEEE 39-bus systems. The attained results verify the effectiveness of the proposed mechanisms in early out-of-step prediction when the received observations by the agent are noisy and the active power of loads is subject to dynamical changes.

Keywords: Reinforcement learning; Deep learning; Power systems; Out-of-step prediction; Real-time systems (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261922002951
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:314:y:2022:i:c:s0306261922002951

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2022.118861

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().