2024 : 5 : 24
Mohammad Bayat

Mohammad Bayat

Academic rank: Assistant Professor
ORCID: https://orcid.org/0000-0003-1465-0015
Education: PhD.
ScopusId: 56681445600
Faculty: Engineering
Address: Arak University


A Fast Outlier-robust Fusion Estimator for Local Bus Frequency Estimation in Power Systems
Robust Unscented Kalman Filter, Power system state estimation, Sensor Fusion, Outlier detection, Wide-Area power system stabilizers.
Researchers Ali Farahani ، Amirhossein Abolmasoumi ، Mohammad Bayat ، Lamine Mili


A new robust fusion Unscented Kalman filter (UKF) is proposed and applied to the problem of local bus frequency estimation in power systems. The presented UKF has two main features. Firstly, it fuses the local bus frequency data obtained from two different methods, i.e. SRF-PLLs and Frequency divider (FD) formula. Secondly, the detection and outweighing outliers in state estimation process is addressed using projection statistics. It is shown that the proposed robust fusion UKF (RFUKF) increases the reliability of local bus frequency estimation against data loss and cyber-attacks affecting one of data sources due to the fusion method. More importantly, it is robust against observation outliers. To verify the effectiveness of the presented method, the impact of the robust fusion state estimation method is studied on the bus frequency estimation for providing feedback signal to Wide Area Power System Stabilizer (WAPSS) which aims to damp the inter-area oscillation in power system. The results of the transient stability analysis are discussed through non-linear time domain simulations with PST toolbox.