Abstract
State estimation in middle- (MV) and low-voltage (LV) electrical grids poses a number of challenges for the estimation method employed. A significant difference to high-voltage grids is the lack of measurements as the instrumentation with measurement equipment in MV and LV grids is very sparse due to economical reasons. Typically, pseudo-measurements are used as a replacement for actual measurements to this end. A recently proposed disturbance observer based on the extended Kalman filter uses a simplified dynamic model for the errors in the pseudo-measurements of bus power. The aim is then to estimate the errors in the pseudo-measurements and thereby improving the overall estimation result. Despite initial promising results of this so-called nodal load observer (NLO), the main disadvantage of this method is the need for a suitable dynamic model for the error of the pseudo-measurements. Therefore, we here propose a versatile dynamic model for the disturbance observer based on autoregressive processes (AR). We consider a recently proposed online learning algorithm for the prediction of the AR model parameters together with the extended Kalman filter disturbance observer. We demonstrate that this approach results in an efficient method for the dynamic state estimation for MV and LV grids than the original NLO method.
Highlights
Static state estimation is a standard procedure in power network analysis to obtain information at all system buses at a given time point [1]
3.2 NLO with online learning technique we present the final algorithm, which is obtained by combining the basic procedure of the extended Kalman filter, the nodal load observer for power distribution grids, and the considered online learning technique for autoregressive processes (AR) process parameters
5 Conclusions and outlook It has been shown previously that the original nodal load observer (NLO) approach to state estimation in LV and MV grids has the principle advantage of improving over the pseudo-measurements by estimating the remaining
Summary
Static state estimation is a standard procedure in power network analysis to obtain information at all system buses at a given time point [1]. A single set of measurements is used to estimate the system state at one snapshot in time, traditionally by using the weighted least squares method [2] With such an approach, the information contained in the evolution of the system state over consecutive time instants is not taken into account. To this end, [3] and others proposed following the changes of the system by means of quasi-dynamic state estimation. Since this method utilizes forecasting of future values, it is known as forecasting-aided state estimation (FASE) [4,5,6]. The extended Kalman filter for this model is given by [16–18]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call