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基于MMPF-KF方法的3型风力发电机组集成电力系统环境数据驱动的SSO在线监测

Ambient data-driven SSO online monitoring of type-3 wind turbine generator integrated power systems based on MMPF-KF method.

作者信息

Chen Xi, Wu Xi, Zhou Jinyu, Li Qingfeng, Wu Chenyu, Li Qiang, Ren Bixing, Xu Ke

机构信息

School of Electrical Engineering, Southeast University, Nanjing, 210096, China.

The State Grid Jiangsu Electric Power Co., Ltd. Research Institute, Nanjing, 211103, China.

出版信息

Sci Rep. 2023 Sep 22;13(1):15813. doi: 10.1038/s41598-023-42729-3.

DOI:10.1038/s41598-023-42729-3
PMID:37740022
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10517174/
Abstract

Series compensation grids connected with type-3 wind turbine generator (WTG)-based wind farms have suffered numerous subsynchronous oscillation (SSO) events worldwide. For early alerting of SSO and effective development of protection and control strategies, it is critical to monitor and identify SSO accurately and quickly. Ambient data is continuously available, which is useful for online monitoring. This paper proposes an ambient data-driven SSO online monitoring method based on the Kalman filter (KF) combined with the multi-model partitioning filter (MMPF). The KF is utilized to fit the measured ambient data with an auto regressive (AR) model. Then, the damping factor (or damping ratio) and frequency in the SSO mode can be acquired by solving the roots of the characteristic polynomial corresponding to the AR model. Moreover, the MMPF is an effective model order selection method applied to the KF for better identification. The performance of the MMPF-KF method is demonstrated by simulations and real-time experiments. The results of case studies validate the effectiveness of the proposed method under various conditions.

摘要

与基于3型风力发电机组（WTG）的风电场相连的串联补偿电网在全球范围内遭遇了众多次同步振荡（SSO）事件。为了对SSO进行早期预警并有效制定保护和控制策略，准确、快速地监测和识别SSO至关重要。环境数据持续可得，这对于在线监测很有用。本文提出了一种基于卡尔曼滤波器（KF）并结合多模型划分滤波器（MMPF）的环境数据驱动的SSO在线监测方法。利用KF将测量的环境数据与自回归（AR）模型进行拟合。然后，通过求解与AR模型对应的特征多项式的根，可以获取SSO模式下的阻尼因子（或阻尼比）和频率。此外，MMPF是一种应用于KF的有效模型阶次选择方法，用于更好地进行识别。通过仿真和实时实验证明了MMPF-KF方法的性能。案例研究结果验证了所提方法在各种条件下的有效性。

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