COMPARATIVE STUDY OF DIFFERENT KALMAN FILTER IMPLEMENTATIONS IN POWER SYSTEM STABILITY

Voltage stability and voltage collapse issues have in recent years begun to constitute an unpleasant w arning to the operational security of power systems. Many techniques have been investigated in order to predi ct the point of voltage collapse. However, there are still several restrictions due to the insufficiency of c urrent system state information. Accompanied by the commencement of the Phasor Measurement Units (PMUs) evolving technology, it donates a solution to enhan ce the existing power system state estimation. In c onsequence, the significances to develop preferable methods tha t would provide a preliminary warning before the vo ltage collapse had grabbed the attention. This study cove rs the forming of real-time system monitoring methods that able to provide a timely warning in the power syste m. The algorithms used to estimate the points of co llapse are according to the theory that voltage instability is approximately linked to the maximum load ability o f a transmission network. As a result, the critical ope rating conditions (peak of maximum deliverable power) come when the system Thevenin impedance is equal to the load impedance. This study focuses specifically on research about the motivation and the application of differe nt Kalman filter implementations such as Discrete K alman Filter (DKF), Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are used to track the The venin parameters. Therefore, the implications of this res earch paper are to determine the robustness and rel iability of the proposed tracking methods. As compared to previous studies, the tracking process is just mainly focuse d on DKF method only, while the novelty throughout this stud y is to compare the performances and efficiencies o f different Kalman filters in determining the maximum load ability on the 2 different types of test systems. Accom panying, the parameters are utilized in real-time voltage in stability estimator to discover the current system’ s condition. In this study, the effectiveness of the proposed algor ithms is assessed under a large number of random operating conditions on the Malaysia’s power system 132 kV, 2-bus and 10-bus systems. Eventually, the results ar e differentiated by using the early-warning index of voltage collapse. All through the test cases, EKF m ethod shows the best ability to track the Thevenin parameters a s compared to DKF and UKF. Last but not least, the earlywarning index acted as a pioneer implication in est imating the maximum load power ability of the power system right before load shedding methods are being execut ed.