Adaptive harmonic prediction algorithm for power system based on LSSVM

Lingkang Zhang; Zhongwen Shi; Hui Wang; Decai Gao; Jian Shen; Yong Li

doi:10.1117/12.2690127

10 August 2023 Adaptive harmonic prediction algorithm for power system based on LSSVM

Lingkang Zhang, Zhongwen Shi, Hui Wang, Decai Gao, Jian Shen, Yong Li

Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 127481Y (2023) https://doi.org/10.1117/12.2690127
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China

Abstract

With the development of various new technologies and the continuous transformation of the power system, the system safety and power quality of the new power system have become the primary focus of attention. Three-phase imbalance and harmonic, as the main factors affecting power quality, have become the focus of current research. Based on the research of traditional prediction model, this paper establishes a combined prediction model based on adaptive EEMD and LSSVM to predict the content of each harmonic in the power grid. First, the adaptive EEMD is used to separate the signals of the power grid to be measured, and the harmonics of similar frequencies are effectively separated into each corresponding IMF. Then the optimal LSSVM prediction model is established separately according to the characteristics of each IMF. Finally, the prediction results are analyzed to achieve the prediction of each harmonic content in the power grid. The experiment shows that the prediction model can effectively predict the harmonic content of different load signals in the power system with high accuracy, and the prediction effect is also relatively higher than the traditional prediction method.

Citation Download Citation

Lingkang Zhang, Zhongwen Shi, Hui Wang, Decai Gao, Jian Shen, and Yong Li "Adaptive harmonic prediction algorithm for power system based on LSSVM", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 127481Y (10 August 2023); https://doi.org/10.1117/12.2690127

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