Optimal Allocation of Distributed Synchronous Condensers Considering Voltage Support Capability Enhancement in HVDC Sending-End AC Power System
CSEE Journal of Power and Energy Systems(2025)SCI 2区SCI 3区
School of Electrical Engineering and Automation
Abstract
This paper introduces an innovative optimal allocation method for distributed synchronous condensers (DSCs) in the high voltage direct current (HVDC) sending-end AC power system, designed to significantly enhance the system voltage support capability and effectively suppress the overvoltage issues. With the increasing integration of large-scale renewable energy sources (RESs) into the HVDC sending-end AC power system, the system short-circuit ratio and voltage support capability are compromised, which may easily cause the prominent overvoltage problem after the HVDC fault. Our method addresses these challenges by not only enhancing the system voltage support capability but also suppressing the overvoltage problems. The impact of DSCs on multiple renewable energy stations short circuit ratio (MRSCR) of the system is analyzed, and a comprehensive quantitative evaluation index of voltage support capability that can quantitatively represent the system voltage support capability enhancement after installing DSCs is defined. A multi-objective optimization model for DSCs allocation is proposed with the objectives of simultaneously minimizing the investment cost and maximizing the system voltage support capability enhancement. The optimal installation positions and configuration quantities (capacities) of DSCs are then obtained by solving the optimization model. Simulation studies conducted on a practical power system demonstrate the method's effectiveness in maximizing system voltage support capability enhancement, suppressing overvoltage issues, and offering superior economic performance compared to the centralized synchronous condensers (CSCs) configuration method.
MoreTranslated text
Key words
distributed synchronous condenser,optimal allocation,HVDC sending-end AC power system,MRSCR,voltage support capability enhancement
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper