Risk detection method for power grid equipment operation based on deep Q-learning algorithm

Weichao Ou

doi:10.1117/12.3024676

3 April 2024 Risk detection method for power grid equipment operation based on deep Q-learning algorithm

Weichao Ou

Proceedings Volume 13078, Second International Conference on Informatics, Networking, and Computing (ICINC 2023); 130780T (2024) https://doi.org/10.1117/12.3024676
Event: Second International Conference on Informatics, Networking, and Computing (ICINC 2023), 2023, Wuhan, China

Abstract

The slow real-time and historical data processing speed and insufficient data storage capacity of power grid equipment operation have led to an increase in risk detection time and a decrease in accuracy., A risk detection method for power grid equipment operation based on deep q learning algorithm is proposed for this purpose. Utilize big data mining technology to deeply explore the potential correlation between operational risks and early warning of power grid equipment, and establish a strong correlation model for risk early warning data. Use deep Q-learning algorithm to automatically identify abnormal situations in the operation of power grid equipment and achieve risk detection of power grid equipment operation. The experimental results show that the proposed method can accurately identify and predict potential risks that may occur during the operation of power grid equipment, and greatly shorten the detection response time.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Weichao Ou "Risk detection method for power grid equipment operation based on deep Q-learning algorithm", Proc. SPIE 13078, Second International Conference on Informatics, Networking, and Computing (ICINC 2023), 130780T (3 April 2024); https://doi.org/10.1117/12.3024676

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available