Abstract
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.
Highlights
State estimation is an important topic which contributes to the proper management of electrical energy
State estimation is mainly used in the tracking of the dynamics of the power system through the assimilation of real-world information obtained from smart measurement devices
The Static State Estimation provides the system operating point in a determined time reflected through the state variables for each measurement data set and has no memory of the evolution of the state variables, it was introduced by Schweppe and Wildes (1970), based on Weighted Least Square (WLS)
Summary
State estimation is an important topic which contributes to the proper management of electrical energy. In this sense, fields where the application of state estimation methods is crucial such as modeling, optimizing power flow, bad data detection, contingencies analysis and corrective actions against possible failures in the power systems improve the monitoring capability of the grid to take control actions in real-time raising the system reliability and stability, (Huang et al, 2012). State estimation is mainly used in the tracking of the dynamics of the power system through the assimilation of real-world information obtained from smart measurement devices. Kalman filters are widely used for the analysis of dynamic systems with normally distributed noise: the EKF applied for non-linear systems, the unscented Kalman filter, and the ensemble Kalman filter; researchers are studying the particle filter and other stochastic methods
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