Damping-enhanced virtual inertia control and parameter optimization for full-sized converter pumped storage units

QI Zheng; WANG Mingrui; ZHENG Le

doi:10.19783/j.cnki.pspc.251211

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Damping-enhanced virtual inertia control and parameter optimization for full-sized converter pumped storage units

更新时间：2026-05-14

- Damping-enhanced virtual inertia control and parameter optimization for full-sized converter pumped storage units
- Power System Protection and Control Vol. 54, Issue 10, (2026)
- 作者机构：
  
  华北电力大学电气与电子工程学院,北京,102206
- 作者简介：
- 基金信息：
- DOI：10.19783/j.cnki.pspc.251211
  CLC：
- Published：2026
- 稿件说明：
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QI Zheng, WANG Mingrui, ZHENG Le. Damping-enhanced virtual inertia control and parameter optimization for full-sized converter pumped storage units[J]. Power System Protection and Control, 2026, 54(10).
DOI：

QI Zheng, WANG Mingrui, ZHENG Le. Damping-enhanced virtual inertia control and parameter optimization for full-sized converter pumped storage units[J]. Power System Protection and Control, 2026, 54(10). DOI： 10.19783/j.cnki.pspc.251211.

摘要

全功率抽水蓄能机组是未来抽水蓄能机组发展的主要方向之一，但是全功率抽水蓄能机组无法向系统提供惯量支撑。传统虚拟惯量控制存在弱阻尼、频率过渡不平滑、无法有效消除稳态频差等问题。为提高系统的惯量裕度和频率安全，提出了一种全功率抽水蓄能机组改进的虚拟惯量控制策略。该策略通过引入 Lead 模块改善传统控制中的负阻尼，通过平滑因子代替传统控制中的死区模块使得频率过渡更平滑，并通过 Anti-Windup 积分模块配合 Washout 滤波器改善传统控制存在稳态频差的问题。同时研究了控制策略基础参数的整定方法，并基于设计实验 (design of experiments

DOE) 下的 ParEGO 多目标优化算法对比例积分微分 (proportional-integral-derivative

PID) 参数进行优化。最后，通过对含有全功率抽水蓄能机组的系统进行仿真分析，结果表明所提控制策略具有良好的频率支撑能力，有助于提高系统的频率稳定性。

Abstract

Full-sized converter pumped storage units represent one of the main development directions for future pumped storage systems. However

such units are unable to provide inherent inertia support to the power system. Conventional virtual inertia control methods suffer from issues such as weak damping

unsmooth frequency transients

and limited capability in eliminating steady-state frequency deviation. To enhance system inertia margin and frequency security

an improved virtual inertia control strategy for full-sized converter pumped storage units is proposed. The proposed approach introduces a Lead module to mitigate the negative damping inherent in conventional control schemes. A smoothing factor is adopted to replace the conventional deadband

resulting in smoother frequency transitions. Additionally

an Anti-Windup integral module combined with a Washout filter is employed to suppress steady-state frequency deviation issues present in conventional methods. Moreover

the tuning methods for the baseline control parameters are investigated

and the PID gains are optimized using a DOE-based ParEGO multi-objective algorithm. Finally

simulation studies on a system incorporating full-sized converter pumped storage units demonstrate that the proposed strategy provides effective frequency support and improves system frequency stability.

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references

NOBILE E, SARI G, SCHWERY A. Variable speed hydro pumped storage as flexible enabler of intermittent renewable penetration[C] // 2018 IEEE Power Energy Society General Meeting (PESGM), August 5-10, 2018, Portland, OR, USA: 1-5.

WEN Yunfeng, CHUNG C Y, LIU Xuan, et al. Microgrid dispatch with frequency-aware islanding constraints[J]. IEEE Transactions on Power Systems, 2019, 34(3): 2465-2468.

LIANG J, HARLEY R G. Pumped storage hydro-plant models for system transient and long-term dynamic studies[C] // IEEE PES General Meetings, July 25-29, 2010, Minneapolis, MN, USA: 1-8.

DENG Z, WANG H, QIN Y, et al. Enhanced inertia response control of grid-forming PMSG-based wind turbines considering safe operation boundary[J]. Protection and Control of Modern Power Systems, 2025, 10(5): 56-67.

DUCKWITZ D, FISCHER B. Modeling and design of df/dt-based inertia control for power converters[J]. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2017, 5(4): 1553-1564.

KNOWLES Y. ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems[J]. IEEE Transactions on Evolutionary Computation, 2006, 10(1): 50-66.

LUO J, FENG J, JIN R. A new approach to building the gaussian process model for expensive multi-objective optimization[C] // International Conference on Information Science and Technology, August 2-5, 2019, Hulunbuir, China: 374-379.

LOEPPKY J L, JEROME S, WELCH W J. Choosing the sample size of a computer experiment: a practical guide[J]. Technometrics, 2009, 51(4): 366-376.

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