Abnormal power consumption detection based on EEMD-RMS

Jianfeng Jiang; Wenjun Zhu; Xingang Wang; Yechen Han

doi:10.1117/12.3014820

4 March 2024 Abnormal power consumption detection based on EEMD-RMS

Jianfeng Jiang, Wenjun Zhu, Xingang Wang, Yechen Han

Proceedings Volume 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023); 129811H (2024) https://doi.org/10.1117/12.3014820
Event: 9th International Symposium on Sensors, Mechatronics, and Automation (ISSMAS 2023), 2023, Nanjing, China

Abstract

Based on EEMD and RMS, a method of abnormal power consumption detection by using a high-dimensional statistical index was proposed. Firstly, power users were divided into typical load types based on the density space clustering algorithm. For each load type, EEMD was used to transform the energy consumption curve into the high-dimensional empirical mode function group, and then Chebyshev polynomial index in the random matrix theory was used to monitor the abnormal behavior. The high-dimensional statistical index was constructed to judge abnormal power consumption. The algorithm in this paper has general applicability to multi-source load and a good discriminating effect.

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

Citation Download Citation

Jianfeng Jiang, Wenjun Zhu, Xingang Wang, and Yechen Han "Abnormal power consumption detection based on EEMD-RMS", Proc. SPIE 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023), 129811H (4 March 2024); https://doi.org/10.1117/12.3014820

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