L_pState Estimators for Power Systems

Abstract

The widely used method of least squares for state estimation is revisited. The commonly used least squares philosophy is based on the L 2 Hölder norm. The L 1 and L 8 norms are considered for applications in power engineering. The effects of outliers in measurements and multicolinearity on state estimation are studied. An application in parameter estimation for synchronous generators is given as an example.

Generalized State Estimation of Flexible AC Power Systems Considering Wind Generators and Primary Frequency Control

“…State estimation is the statistical process in which the best estimate of the state of the system is obtained at a given point in time based on a set of redundant real-time measurements and a predetermined system model [19]. In the context of this paper, the power system statex is defined by the estimated values of the nodal voltage angles and magnitudes, the state variables of FACTS controllers and WTGs as well as the frequency deviation from its nominal value:…”

“…In our proposal the current measurements are represented in the rectangular system of coordinates to avoid the convergence problems reported in [19,20]. The uncertainty propagation theory must be applied, however, to compute the variances of this new set of measurements in terms of variance on the magnitude and phase angle of currents [15,20,21].…”

On-Line Estimation of Synchronous Generator Parameters Using a Damper Current Observer and a Graphic User Interface

“…Least squares, infinite-norm and 1-norm estimators are some of these methods. Reference [10] offers an overview and some examples of these methods. In [11], a new approach is presented in which the authors use a Park's transformation model and synthetic data to estimate synchronous machine parameters employing least squares minimization techniques.…”

Robust state estimator based on maximum constraints satisfaction of uncertain measurements

“…The proposed estimator aims at searching for a particular state variable vector that maximizes the number of satisfied uncertain measurements (double inequality constraints) described in equation (1). Formally the objective function of the proposed estimator is to maximize the number of satisfied constraints for a given potential solution x .…”

“…Hence, robust estimators have been introduced as an alternative, to eliminate or down-weigh the effects of faulty measurements (or outliers). Normally, outliers occur quite frequently, as a consequence of high amplitude noise in measurements (e.g., due to induction during large transients), brief loss of measurement data and noise occurring due to unintended signal paths and measurements [1]. Furthermore, many AC applications result in frequent measurement errors and "periodic noise".…”