Paper
31 May 2023 Research on enterprise comprehensive load modeling based on large motor group
Hengxiang Gou
Author Affiliations +
Proceedings Volume 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023); 1270420 (2023) https://doi.org/10.1117/12.2680032
Event: 8th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2023), 2023, Hangzhou, China
Abstract
With the development of load modeling technology, the accuracy of load model is increasingly required. In addition, as the end user of power, the accuracy of load model has a great impact on the simulation of power system. In the load modeling of large power grids, 220KV or 110KV substations are generally used as load nodes, and enterprise load modeling is rarely carried out. In view of the shortcomings of the current comprehensive load model in the enterprise load grid structure, due to the large proportion of the enterprise motor load, this paper uses the comprehensive statistical method based on statistical data to divide the motors with different load characteristics into multiple motor groups, and then aggregate the motor clusters into equivalent motors. Then, according to the characteristics of motors and the influence of distribution network impedance, the motors in the enterprise are aggregated into two or more motor groups to realize the integrated load modeling of the enterprise load grid.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hengxiang Gou "Research on enterprise comprehensive load modeling based on large motor group", Proc. SPIE 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023), 1270420 (31 May 2023); https://doi.org/10.1117/12.2680032
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Modeling

Data modeling

Statistical modeling

Polymerization

Motion models

Error analysis

Integrated modeling

Back to Top