Recognition of the control parameters for the grid-forming converter based on an improved particle swarm algorithm
Authors: 
Liuan Cheng, Wei Liu, Wei Gu, Wenjun Zi, Dehu ZouAuthors Info & Claims
Liuan Cheng, Wei Liu, Wei Gu, Wenjun Zi, Dehu ZouAuthors Info & ClaimsPages 35 - 41
Abstract
To address the issue of converter control parameter influence on the simulation accuracy of photovoltaic power system, this study proposes a grid-forming converter control parameter recognition method based on an improved particle swarm algorithm. The converter employs virtual synchronous generator control. The weights and cognitive factors of the particle swarm algorithm are adjusted to enhance its local optimization ability in the early stage and global search ability in the later stage. Firstly, the study recognizes control parameters under active command disturbance and active load disturbance conditions. The converter output curves of the recognized and actual parameters demonstrate that the improved particle swarm algorithm is more accurate and less likely to fall into local optimality than the particle swarm algorithm. Finally, in order to test the effectiveness of the improved particle swarm algorithm for the grid-forming converter control parameter recognition method proposed in this study, the short-circuit condition between phases is used to compare the converter output curves of the recognized parameters and the actual parameters, and the accuracy and applicability of the identified parameters are verified.
References
[1]
J. Xu, W. Liu, S. Liu, F. Chang, X. Xie, “Current status and development trend of power system converter structure network control technology,” Power system technology, 2022, vol.46(09), pp.3586-95.
[2]
Y. Chi, B. Jiang, J. Hu, W. Lin, H. Liu, et al., “Grid-Forming converters: physical nature and characterization,” High Voltage Engineering, 2024.
[3]
C. Zhan, H. Wu, X. Wang, J. Tian, X. Wang, Y. Lu, “A review of stability studies of grid-forming converters,” Proceedings of the CSEE, 2023, vol.43(06), pp.2339-59.
[4]
J. Zhao, S. Shi, S. Xie, X. Dong, Y. Teng, “Research on control technology of grid-forming converter,” Hebei Electric Power, 2023, vol.42(04), pp.1-9.
[5]
B. Zhao, B. Zhao, F. Zhang, J. Chen, “Overview of grid-forming inverter technology,” Journal of Beijing Information Science & Technology University (Science and Technology Edition), 2023, vol.37(04), pp. 57-67.
[6]
Y. Gong, Z. Zhou, X. Huang, Y. Zhao, Y. Zhao, et al., “Cooperative control strategy for shared energy storage considering autonomous support characteristics of grid-forming converters,” Journal of North China Electric Power University (Natural Science Edition), 2024.
[7]
X. Jiang, H. Yi, F. Zhuo, Y. Li, Z. Wang, “Analysis and damping control of low-frequency oscillations in a grid-forming converter based on DC voltage synchronization,” Automation of Electric Power Systems, 2023.
[8]
H. Xu, S. Zhu, Y. Huang, F. Xue, X. Zhang, “Control parameter identification of doubly-fed wind turbine based on GRU-IPSO algorithm,” Journal of North China Electric Power University (Natural Science Edition), 2023.
[9]
Y. Jin, P. Ju, X. Pan, L. Sun, Y. Zhang, “A step-by-step identification method for photovoltaic inverter control parameters,” Power System Technology, 2015, vol.39(03), pp.594-600.
[10]
M. Wang, A. Yang, H. Li, “Research on inertia lifting of new power system based on VSG double sagging optimization control,” Journal of Physics: Conference Series, 2588(1).
[11]
J. Liu, S. Liu, F. Qian, J. Liu, X. Peng, M. Liu, et al., “Wind turbine parameter identification based on Bayesian neural network,” Technology Innovation and Application, 2020, vol.12, pp.21-23.
[12]
Y. Xiao, Y. Zhao, “Research on MPPT control strategy of improved particle swarm algorithm based on chaotic mapping and Gaussian perturbation,” Journal of Power Supply, 2023.
[13]
Z. Ming, Q. Lv, Y. Ming, W. Zeng, H. Lv, et al., “LED light source array optimization based on improved particle swarm algorithm,” Journal of Applied Optics, 2022, vol.43(03), pp.524-31+43.
Index Terms
- Recognition of the control parameters for the grid-forming converter based on an improved particle swarm algorithm
Recommendations
An Improved Particle Swarm Algorithm for Search Optimization
GCIS '09: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 01To address the problem of space locus searching, a slowdown particle swarm optimization (SPSO) is proposed to improve the convergence performance of particle swarm from the position viewpoint. The particle swarm in SPSO is divided into many independent ...
An improved cooperative quantum-behaved particle swarm optimization
Particle swarm optimization (PSO) is a population-based stochastic optimization. Its parameters are easy to control, and it operates easily. But, the particle swarm optimization is a local convergence algorithm. Quantum-behaved particle swarm ...
A new and improved version of particle swarm optimization algorithm with global---local best parameters
This paper presents a new and improved version of particle swarm optimization algorithm (PSO) combining the global best and local best model, termed GLBest-PSO. The GLBest-PSO incorporates global---local best inertia weight (GLBest IW) with global---...
Comments
Information & Contributors
Information
Published In
July 2024
55 pages
ISBN:9798400716881
DOI:10.1145/3705956
Copyright © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 28 December 2024
Check for updates
Qualifiers
- Research-article
Conference
HPCCT 2024
HPCCT 2024: 2024 8th High Performance Computing and Cluster Technologies Conference (HPCCT)
July 5 - 7, 2024
Beijing, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 10Total Downloads
- Downloads (Last 12 months)10
- Downloads (Last 6 weeks)2
Reflects downloads up to 02 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in