| 引用本文: | 张晓英,何 蓉,史冬雪,张 琎,王进花.基于组合模型的风电高渗透电力系统区域惯量辨识[J].电力系统保护与控制,2025,53(22):100-110.[点击复制] |
| ZHANG Xiaoying,HE Rong,SHI Dongxue,ZHANG Jin,WANG Jinhua.Regional inertia identification of high wind power penetration power systems based on a combination model[J].Power System Protection and Control,2025,53(22):100-110[点击复制] |
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| 摘要: |
| 风电机组的大规模接入导致电力系统惯量明显下降，并对系统频率安全稳定造成威胁。针对风电高渗透电力系统区域惯量辨识中频率最优测点选择困难和惯量估计误差较大等问题，提出了一种基于组合模型的风电高渗透电力系统区域惯量辨识方法。首先，采用基于形状距离(shape-based distance, SBD)指标的K-shape聚类算法对频率响应曲线进行聚类，并确定各区域内频率动态响应曲线的最优测量路径。其次，结合最小二乘支持向量机(least squares support vector machine, LSSVM)和受控自回归滑动平均模型(autoregressive moving average with exogenous input, ARMAX)对不同风电渗透率下各区域惯量水平进行辨识，并与传统ARMAX模型的惯量辨识结果进行对比分析。最后，通过改进的IEEE10机39节点系统对所提方法的有效性进行仿真验证。结果表明，所提方法有效提高了区域和全系统惯量辨识精度。 |
| 关键词: 风电高渗透电力系统 频率动态响应 K-shape聚类 组合模型 区域惯量辨识 |
| DOI：10.19783/j.cnki.pspc.250030 |
| 投稿时间：2025-01-09修订日期：2025-06-09 |
| 基金项目:国家自然科学基金项目资助(62063020)；甘肃省教育厅：产业支撑计划项目资助(2025CYZC-029) |
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| Regional inertia identification of high wind power penetration power systems based on a combination model |
| ZHANG Xiaoying1,HE Rong1,SHI Dongxue2,ZHANG Jin2,WANG Jinhua1 |
| (1. College of Automation and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, China;
2. Longnan Power Supply Company, State Grid Gansu Electric Power Company, Longnan 746000, China) |
| Abstract: |
| The large-scale integration of wind turbine generators has led to a significant decrease in power system inertia, posing threats to system frequency security and stability. To address the challenges of optimal frequency measurement point selection and high estimation error in regional inertia identification of high wind power penetration systems, a regional inertia identification method based on a combined model is proposed. First, the K-shape clustering algorithm based on the shape-based distance (SBD) index is adopted to cluster frequency response curves, and the optimal path of frequency dynamic responses within each region is determined. Second, the least squares support vector machine (LSSVM) and the autoregressive moving average with exogenous input (ARMAX) model are combined and used to identify regional inertia levels under different wind power penetration rates. The results are compared with those obtained using the traditional ARMAX-based inertia identification method. Finally, the effectiveness of the proposed method is verified through simulations using the modified IEEE 10-generator 39-bus system. The results show that the proposed method effectively improves the identification accuracy of both regional and system-level inertia. |
| Key words: high wind power penetration power system frequency dynamic response K-shape clustering combined model regional inertia identification |
