论文标题
电力系统动态状态估计使用扩展和无流感的卡尔曼过滤器
Power System Dynamic State Estimation Using Extended and Unscented Kalman Filters
论文作者
论文摘要
电力系统动态的准确估算对于增强电力系统的可靠性，弹性，安全性和电源系统稳定性非常重要。随着基于逆变器的分布式能源的越来越多的整合，电力系统动态的知识比以往任何时候都变得更加必要和关键，以适当控制和操作电力系统。尽管最近的测量设备和传输技术的进步已大大降低了测量和传输误差，但这些测量值仍然没有完全摆脱测量噪声。因此，需要过滤嘈杂的测量值，以获得准确的功率系统操作动力学。在这项工作中，使用扩展的Kalman滤波器（EKF）和无气味的Kalman Filter（UKF）估算了电力系统动态状态。我们已经对西方电力协调委员会（WECC）的案例研究（WECC）的$ 3 $ -Machine $ 9 $ -BUS系统和新英格兰$ 10 $ -Machine $ 39 $ -BUS。结果表明，UKF和EKF可以准确估计功率系统动力学。还提供了EKF和UKF的比较性能。其他卡尔曼过滤技术与基于机器学习的估计器将在此报告中进行更新。所有来源代码，包括牛顿·拉夫森·鲍尔（Newton Raphsonpower）流，录取矩阵计算，EKF计算，ANDUKF计算，在GitHub中公开可用
Accurate estimation of power system dynamics is very important for the enhancement of power system reliability, resilience, security, and stability of power system. With the increasing integration of inverter-based distributed energy resources, the knowledge of power system dynamics has become more necessary and critical than ever before for proper control and operation of the power system. Although recent advancement of measurement devices and the transmission technologies have reduced the measurement and transmission error significantly, these measurements are still not completely free from the measurement noises. Therefore, the noisy measurements need to be filtered to obtain the accurate power system operating dynamics. In this work, the power system dynamic states are estimated using extended Kalman filter (EKF) and unscented Kalman filter (UKF). We have performed case studies on Western Electricity Coordinating Council (WECC)'s $3$-machine $9$-bus system and New England $10$-machine $39$-bus. The results show that the UKF and EKF can accurately estimate the power system dynamics. The comparative performance of EKF and UKF for the tested case is also provided. Other Kalman filtering techniques alongwith the machine learning-based estimator will be updated inthis report soon.All the sources code including Newton Raphsonpower flow, admittance matrix calculation, EKF calculation, andUKF calculation are publicly available in Github
