T. Van Cutsem

Bio: T. Van Cutsem is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 49 citations.

Real-time processing of transformer tap positions

Abstract: Reviews the methods for network parameter estimation and correction in power system state estimation and proposes a sequential-type method for real-time processing of transformer tap positions. The method exploits the information contained in the measurement residuals to estimate a better tap position; it uses a linearized sensitivity model to relate the measurement residuals to the tap position error. Based on part of the Belgium HV system, several testing results are reported.

Generalized state estimation

Abstract: Power system state estimation derives a real-time network model by extracting information from a redundant data set consisting of telemetered, predicted and static data items. This paper describes a generalized, fully developed, estimation approach that fundamentally improves the information extraction process. Its main contribution is the successful inclusion of topology and parameters in the estimation and bad data analysis processes. This is valuable both in the initial commissioning of a state estimator, and in its routine real-time and study mode application. The approach involves a variety of novel concepts and methods. It is usable in weighted least squares (WLS) and other estimation approaches.

Power system parameter estimation: a survey

Abstract: This paper deals with the problem of network parameter errors in state estimation. First of all, some experimental results are presented showing the influence of these errors on the performance of weighted least squares state estimators. Secondly, the preliminary step of identifying suspicious network parameters is briefly discussed. A classification of the techniques proposed in the literature to estimate parameter errors is then suggested, followed by a description of the main ideas behind each method. Finally, a discussion is included on the possibilities and limitations of every class of methods.

Estimation of parameter errors from measurement residuals in state estimation (power systems)

Abstract: Any error of network parameters affects the value of the measurement residuals calculated in state estimation. Explicit mathematical expressions relating the residuals to the parameter errors are derived. A two-step approach is proposed for parameter error estimation. The first step is to estimate a bias vector which combines the effects of parameter errors and the state of the system. A least-square approach using the measurement residuals calculated in each state estimation run is proposed for the first step. After several state estimation runs, a sequence of such bias vectors is obtained. The second step is to estimate the parameter errors from the sequence of bias vectors. A recursive least-square estimation method is proposed for this step. Theoretical and computational issues of the proposed method are addressed. Test results are presented. >

Identification of network parameter errors

Abstract: This paper describes a simple yet effective method for identifying incorrect parameters associated with the power network model. The proposed method has the desired property of distinguishing between bad analog measurements and incorrect network parameters, even when they appear simultaneously. This is accomplished without expanding the state or the measurement vectors. There is also no need to a priori specify a suspect parameter set. All these features are verified via simulations that are carried out using different-size test systems for various possible cases. Implementation of the method involves minor changes in the weighted least-squares state estimation code; hence, it can be easily integrated into existing state estimators as an added feature.

Real time recursive parameter estimation in energy management systems

Abstract: A new method of recursive parameter estimation using a Kalman filter is presented. The method is capable of estimating impedance parameters of network branches in both online and offline modes. It provides accurate estimation of branch parameters in the presence of noise in measurements and has the ability to identify and reject gross measurement errors. The method can track impedance parameters as they fluctuate due to changes in load and ambient conditions. Test results on a 100-bus network as well as results of method's implementation in a real life EMS are reported in the paper.