Open AccessThe Extended Kalman Filter in the Dynamic State Estimation of Electrical Power Systems
Author(s) -
Holger Cevallos,
Gabriel Intriago,
Douglas Plaza,
Roger M. Idrovo
Publication year - 2018
Publication title -
enfoque ute
Language(s) - English
Resource type - Journals
eISSN - 1390-9363
pISSN - 1390-6542
DOI - 10.29019/enfoqueute.v9n4.407
Subject(s) - kalman filter , extended kalman filter , control theory (sociology) , invariant extended kalman filter , unscented transform , ensemble kalman filter , electric power system , computer science , alpha beta filter , state variable , matlab , monte carlo method , power (physics) , mathematics , statistics , artificial intelligence , moving horizon estimation , physics , operating system , control (management) , quantum mechanics , thermodynamics
The state estimation and the analysis of load flow are very important subjects in the analysis and management of Electrical Power Systems (EPS). This article describes the state estimation in EPS using the Extended Kalman Filter (EKF) and the method of Holt to linearize the process model and then calculates a performance error index as indicators of its accuracy. Besides, this error index can be used as a reference for further comparison between methodologies for state estimation in EPS such as the Unscented Kalman Filter, the Ensemble Kalman Filter, Monte Carlo methods, and others. Results of error indices obtained in the simulation process agree with the order of magnitude expected and the behavior of the filter is appropriate due to follows adequately the true value of the state variables. The simulation was done using Matlab and the electrical system used corresponds to the IEEE 14 and 30 bus test case systems. State Variables to consider in this study are the voltage and angle magnitudes.