基于改进WAA算法的多机PSS参数优化设计

兰飞; 陈振华; 黎静华; 田伟

doi:10.19783/j.cnki.pspc.251304

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基于改进WAA算法的多机PSS参数优化设计

工程应用 | 更新时间：2026-07-19

- 基于改进WAA算法的多机PSS参数优化设计
- Optimal design of multi-machine PSS parameters based on improved weighted average algorithm
- 电力系统保护与控制 2026年54卷第14期
- 作者机构：
  
  广西电力系统最优化与节能技术重点实验室(广西大学),广西,南宁,530004
- 作者简介：
- 基金信息：
- DOI：10.19783/j.cnki.pspc.251304
  中图分类号：
- 纸质出版：2026
- 稿件说明：
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兰飞, 陈振华, 黎静华, 等. 基于改进WAA算法的多机PSS参数优化设计[J]. 电力系统保护与控制, 2026,54(14).

LAN Fei, CHEN Zhenhua, LI Jinghua, et al. Optimal design of multi-machine PSS parameters based on improved weighted average algorithm[J]. Power System Protection and Control, 2026, 54(14).
兰飞, 陈振华, 黎静华, 等. 基于改进WAA算法的多机PSS参数优化设计[J]. 电力系统保护与控制, 2026,54(14). DOI： 10.19783/j.cnki.pspc.251304.

LAN Fei, CHEN Zhenhua, LI Jinghua, et al. Optimal design of multi-machine PSS parameters based on improved weighted average algorithm[J]. Power System Protection and Control, 2026, 54(14). DOI： 10.19783/j.cnki.pspc.251304.

摘要

随着新能源高比例接入与电网规模扩张，电力系统低频振荡问题日益复杂。为克服传统电力系统稳定器(power system stabilizers

PSS)参数整定耗时、适应性差，及常规智能算法在高维多机寻优中效率不足的局限，提出一种基于改进加权平均算法(improved weighted average algorithm

IWAA)的PSS参数协调优化策略。该策略以加权平均算法(weighted average algorithm

WAA)在处理非线性约束问题时的优势为基础，针对其搜索策略固化与高维空间收敛慢的局限进行适应性改进。首先，引入二阶导数法(second-derivative method

SDM)-Prony算法辨识关键振荡模态以构建多维目标函数，并设计基于模态辨识的自适应“探索-开发”平衡策略。其次，融合Weibull飞行与准对立学习(quasi-oppositional based learning

QOBL)机制，以增强算法在高维空间中的全局搜索效率与初始种群多样性。IEEE四机两区域系统的仿真对比结果表明，经IWAA整定优化的PSS，能更有效地提升系统在多种运行工况下的阻尼水平与动态稳定性。

Abstract

With the increasing penetration of renewable energy and the continuous expansion of power grids

low-frequency oscillations in power systems have become increasingly complex. To overcome the limitations of conventional power system stabilizer (PSS) parameter tuning methods

including high computational burden

poor adaptability

and the inadequate optimization efficiency of traditional intelligent algorithms in high-dimensional multi-machine systems

a coordinated PSS parameter optimization strategy based on the improved weighted average algorithm (IWAA) is proposed. Building upon the advantages of the weighted average algorithm (WAA) in handling nonlinear constrained optimization problems

the strategy makes adaptive improvements to address its limitations of rigid search behavior and slow convergence in high-dimensional spaces. First

the second-derivative method (SDM)-Prony algorithm is employed to identify critical oscillation modes and construct a multi-dimensional objective function. An adaptive exploration-exploitation balancing strategy based on modal identification is then developed. Second

Weibull flight and quasi-oppositional based learning (QOBL) are integrated to enhance global search efficiency and the diversity of the initial population in high-dimensional search spaces. Simulation studies conducted on the IEEE four-machine two-area system demonstrate that the PSS parameters optimized by the proposed IWAA can more effectively improve system damping and dynamic stability under various operating conditions.

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Keywords

references

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