Control Method for Overvoltage During Power Supply Loss in Medium-Voltage Flexible DC Sections Based on Guided Strategy Adaptive Particle Swarm Optimization

To address the challenge of overvoltage control during power loss in medium-voltage flexible DC sections under complex and variable operating conditions, a control method based on guided-strategy adaptive particle swarm optimization (GS-APSO) is proposed to ensure voltage stability. This method analyzes the overvoltage formation mechanism during faults according to the topology of the medium-voltage flexible DC section. Building on this analysis, an adaptive nearest-level modulation (ANLM) strategy with voltage-limiting functionality is employed. The strategy adjusts the DC voltage based on the changes in the average submodule capacitor voltage, constraining the voltage near the target value. Furthermore, to enhance the overvoltage suppression performance, the guided-strategy adaptive particle swarm algorithm optimizes the voltage-limiting coefficient, deriving the coefficient that minimizes the overvoltage decay ratio, thereby improving the overvoltage loss control in the medium-voltage flexible DC section. The experimental results demonstrate that under different unbalanced power conditions, the proposed method achieves overvoltage control within 1.5 s, enabling rapid voltage recovery and stabilization. Even under significant power fluctuations, it effectively restricts the peak voltage variation range. After control, the overvoltage decay ratio remains below 0.015 on average.

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Publication Details

Journal
EAI Endorsed Transactions on Energy Web
Published
2026-04-15
DOI
https://doi.org/10.4108/ew.12157
Primary Topic
HVDC Systems and Fault Protection
Type
article
Field-Weighted Citation Impact
0.00
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article

Control Method for Overvoltage During Power Supply Loss in Medium-Voltage Flexible DC Sections Based on Guided Strategy Adaptive Particle Swarm Optimization

Ran Lu, Jiadong Zhao, Peng Wang, Asleng et al.
EAI Endorsed Transactions on Energy Web
HVDC Systems and Fault Protection
article

Control Method for Overvoltage During Power Supply Loss in Medium-Voltage Flexible DC Sections Based on Guided Strategy Adaptive Particle Swarm Optimization

Ran Lu, Jiadong Zhao, Peng Wang, Asleng, Yang Liu, Jun Guo
article en

Abstract

To address the challenge of overvoltage control during power loss in medium-voltage flexible DC sections under complex and variable operating conditions, a control method based on guided-strategy adaptive particle swarm optimization (GS-APSO) is proposed to ensure voltage stability. This method analyzes the overvoltage formation mechanism during faults according to the topology of the medium-voltage flexible DC section. Building on this analysis, an adaptive nearest-level modulation (ANLM) strategy with voltage-limiting functionality is employed. The strategy adjusts the DC voltage based on the changes in the average submodule capacitor voltage, constraining the voltage near the target value. Furthermore, to enhance the overvoltage suppression performance, the guided-strategy adaptive particle swarm algorithm optimizes the voltage-limiting coefficient, deriving the coefficient that minimizes the overvoltage decay ratio, thereby improving the overvoltage loss control in the medium-voltage flexible DC section. The experimental results demonstrate that under different unbalanced power conditions, the proposed method achieves overvoltage control within 1.5 s, enabling rapid voltage recovery and stabilization. Even under significant power fluctuations, it effectively restricts the peak voltage variation range. After control, the overvoltage decay ratio remains below 0.015 on average.

EAI Endorsed Transactions on Energy WebVol. 12
Inner Mongolia Electric Power (China) (CN)
Affordable and clean energy
Openalex Percentile: Top 27%
HVDC Systems and Fault Protection
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Control Method for Overvoltage During Power Supply Loss in Medium-Voltage Flexible DC Sections Based on Guided Strategy Adaptive Particle Swarm Optimization — Ran Lu, Jiadong Zhao, et al. · EAI Endorsed Transactions on Energy Web (2026) | TGRS Research Map | TGRS