Qiao Lina, Gao Wengen, Li Yunfei, Guo Xinxin, Hu Pengfei, Hua Feng
College of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China.
Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Chinese Ministry of Education, Wuhu 241000, China.
Sensors (Basel). 2023 Sep 24;23(19):8053. doi: 10.3390/s23198053.
Outliers can be generated in the power system due to aging system equipment, faulty sensors, incorrect line connections, etc. The existence of these outliers will pose a threat to the safe operation of the power system, reduce the quality of the data, affect the completeness and accuracy of the data, and thus affect the monitoring analysis and control of the power system. Therefore, timely identification and treatment of outliers are essential to ensure stable and reliable operation of the power system. In this paper, we consider the problem of detecting and localizing outliers in power systems. The paper proposes a Minorization-Maximization (MM) algorithm for outlier detection and localization and an estimation of unknown parameters of the Gaussian mixture model (GMM). To verify the performance of the method, we conduct simulation experiments by simulating different test scenarios in the IEEE 14-bus system. Numerical examples show that in the presence of outliers, the MM algorithm can detect outliers better than the traditional algorithm and can accurately locate outliers with a probability of more than 95%. Therefore, the algorithm provides an effective method for the handling of outliers in the power system, which helps to improve the monitoring analyzing and controlling ability of the power system and to ensure the stable and reliable operation of the power system.
由于系统设备老化、传感器故障、线路连接错误等原因，电力系统中可能会产生异常值。这些异常值的存在会对电力系统的安全运行构成威胁，降低数据质量，影响数据的完整性和准确性，进而影响电力系统的监测分析与控制。因此，及时识别和处理异常值对于确保电力系统稳定可靠运行至关重要。在本文中，我们考虑电力系统中异常值的检测与定位问题。本文提出了一种用于异常值检测与定位以及高斯混合模型（GMM）未知参数估计的极小化-极大化（MM）算法。为验证该方法的性能，我们通过在IEEE 14节点系统中模拟不同测试场景进行了仿真实验。数值例子表明，在存在异常值的情况下，MM算法比传统算法能更好地检测异常值，并且能够以超过95%的概率准确地定位异常值。因此，该算法为电力系统中异常值的处理提供了一种有效方法，有助于提高电力系统的监测分析与控制能力，确保电力系统稳定可靠运行。
