基于MMPF-KF方法的3型风力轮发电机集成电力系统的环境数据驱动SSO在线监控
Xi Chen1, Xi Wu2, Jinyu Zhou1
1School of Electrical Engineering, Southeast University, Nanjing, 210096, China.
Scientific reports
|September 22, 2023
概括
本研究引入了一种新方法,用于使用环境数据检测风电场中的次同步振荡 (SSO). 该方法将卡尔曼波器 (KF) 与多模型分区波器 (MMPF) 结合起来,用于准确和快速的在线监控.
科学领域:
- 电气工程 电气工程
- 电力系统 电力系统
- 可再生能源可再生能源是可再生能源.
背景情况:
- 在具有串行补偿的风电场中,次同步振荡 (SSO) 是一个重大问题.
- 准确和快速的在线监控对于制定有效的SSO保护和控制策略至关重要.
- 环境数据为实时SSO监控提供了持续和有价值的资源.
研究的目的:
- 提出一种基于环境数据的在线监测方法,用于基于3型风力轮发电机 (WTG) 的风力发电场的次同步振荡 (SSO).
- 提高SSO识别的准确性和速度,以提高电网稳定性和保护.
主要方法:
- 使用卡尔曼波器 (KF) 适应使用自动回归 (AR) 模型的环境数据.
- 在KF框架内使用多模型分区过器 (MMPF) 进行有效的模型订单选择.
- 通过解决 AR 模型的特征多项式来计算 SSO 模式的阻尼系数和频率.
主要成果:
- 拟议的多模型分区过器-卡尔曼过器 (MMPF-KF) 方法证明了有效的SSO在线监控.
- 模拟和实时实验证实了该方法在各种操作条件下的性能.
- 案例研究验证了拟议方法在识别SSO事件方面的有效性.
结论:
- MMPF-KF方法为系列补偿风电场的SSO在线监控提供了可靠和高效的解决方案.
- 这种方法有助于提高电网稳定性,并为风力发电集成制定强有力的保护方案.
- 环境数据驱动的监控为实时评估电力系统动态提供了一个实用的途径.
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