School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel.
Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA.
Sensors (Basel). 2023 Jan 26;23(3):1387. doi: 10.3390/s23031387.
This paper considers the problem of estimating the states in an unobservable power system, where the number of measurements is not sufficiently large for conventional state estimation. Existing methods are either based on pseudo-data that is inaccurate or depends on a large amount of data that is unavailable in current systems. This study proposes novel graph signal processing (GSP) methods to overcome the lack of information. To this end, first, the graph smoothness property of the states (i.e., voltages) is validated through empirical and theoretical analysis. Then, the regularized GSP weighted least squares (GSP-WLS) state estimator is developed by utilizing the state smoothness. In addition, a sensor placement strategy that aims to optimize the estimation performance of the GSP-WLS estimator is proposed. Simulation results on the IEEE 118-bus system show that the GSP methods reduce the estimation error magnitude by up to two orders of magnitude compared to existing methods, using only 70 sampled buses, and increase of up to 30% in the probability of bad data detection for the same probability of false alarms in unobservable systems The results conclude that the proposed methods enable an accurate state estimation, even when the system is unobservable, and significantly reduce the required measurement sensors.
本文考虑了在不可观测的电力系统中估计状态的问题，其中测量的数量对于传统的状态估计来说不够大。现有的方法要么基于不准确的伪数据，要么依赖于当前系统中不可用的大量数据。本研究提出了新的图信号处理 (GSP) 方法来克服信息不足的问题。为此，首先通过经验和理论分析验证了状态（即电压）的图平滑性。然后，利用状态平滑性开发了正则化 GSP 加权最小二乘 (GSP-WLS) 状态估计器。此外，还提出了一种传感器放置策略，旨在优化 GSP-WLS 估计器的估计性能。在 IEEE 118 母线系统上的仿真结果表明，与现有的方法相比，GSP 方法使用仅 70 个采样母线将估计误差幅度降低了两个数量级，并且在不可观测系统中相同的误报概率下，坏数据检测的概率提高了 30%。结果表明，即使在系统不可观测的情况下，所提出的方法也能够实现准确的状态估计，并显著减少所需的测量传感器。
