Kalman filter based impedance parameter estimation for transmission line and distribution line

• Main Author: Siti Nur Aishah, Mohd Amin
• Format: Thesis
• Language: English
• Published: 2019
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://umpir.ump.edu.my/id/eprint/30097/1/Kalman%20filter%20based%20impedance%20parameter%20estimation%20for%20transmission%20line%20and%20distribution%20line.wm.pdf; http://umpir.ump.edu.my/id/eprint/30097/

Accurate knowledge of impedance parameters in transmission line can helps to improve the performance of a system. Currently, methods for parameter identification in transmission line are various but limited due to the insufficient technology and finance. Most of the current algorithms for parameter identification are based on the assumption that transmission line is fully transposed and the corresponding components are symmetric. However, this is not true for certain low voltage systems or systems with short lines. In these cases, for transmission lines that are not fully transposed, the three sequence networks will be mutually coupled. The positive sequence measurement is use to estimate the positive sequence parameters which will generate inaccurate parameter estimates. Therefore, a detailed study on developing and evaluating the new algorithms for transmission line parameter estimation is considered in this thesis. The estimation of impedance parameters in transmission line has become possible with the availability of computational and prediction method. Therefore, this research presents the development of Kalman Filter model by using MATLAB simulink in order to estimate the accurate values of RXB and RLC parameters in transmission line. This research also reports the performance of Kalman Filter method in transmission line compared with the other methods such as Linear Least Square method and Synchronous Phasor Measurement method. To demonstrate the efficiency of the new proposed method, a case study of simulated distribution line in UMP is also considered. Firstly, the data set from the research areas such as voltage, current, power factor and design of Kalman Filter are presented. The modeling of Kalman Filter was evaluated by using MATLAB simulink to ensure there is no technical problem with the Kalman Filter itself. Then, the data set values such as voltage, current and power factor are used to estimates the new values of RXB and RLC parameters. The results showed that the higher accuracy with 98.64% in the estimation of RXB and RLC parameter values which are better than what was obtained. From this, it is shown that Kalman Filter is possible to use in the estimation areas especially for RXB and RLC parameters.