Phasor Estimation for Grid Power Monitoring: Least Square vs. Linear Kalman Filter
Yassine Amirat, Zakarya Oubrahim, Hafiz Ahmed, Mohamed Benbouzid, Tianzhen Wang
Abstract
This paper deals with a comparative study of two phasor estimators based on the least square (LS) and the linear Kalman filter (KF) methods, while assuming that the fundamental frequency is unknown. To solve this issue, the maximum likelihood technique is used with an iterative Newton–Raphson-based algorithm that allows minimizing the likelihood function. Both least square (LSE) and Kalman filter estimators (KFE) are evaluated using simulated and real power system events data. The obtained results clearly show that the LS-based technique yields the highest statistical performance and has a lower computation complexity.
Topics & Concepts
Kalman filterPhasorEstimatorComputationMathematicsMinimum mean square errorControl theory (sociology)Fast Kalman filterFilter (signal processing)AlgorithmSquare (algebra)Power (physics)Computer scienceExtended Kalman filterMathematical optimizationStatisticsElectric power systemPhysicsArtificial intelligenceControl (management)Quantum mechanicsComputer visionGeometryPower System Optimization and StabilityOptimal Power Flow DistributionPower Systems Fault Detection