Power system tracking and dynamic state estimation

Abstract

Abstract--State estimation is a key Energy Management System(EMS) function, responsible for estimating the state of the power system. Since state estimation is computationally expensive, it is not easy to execute it repetitively at short intervals, which is a requirement for real time monitoring and control. Hence in order to obtain a computationally inexpensive real time update of the state vector, tracking state estimation algorithms have been proposed and discussed in various research papers available in the literature. Tracking state estimation provides a fast real time update on the state of the power system with out any physical modeling of the time varying nature of the system. Dynamic state estimation models the time varying nature of the system, which allows it to predict the state vector in advance. Hence proves to be a major advantage in security analysis and other control functions. Various techniques for tracking and dynamic state estimation are available in the literature. This paper presents a bird's view on different methodologies and developments in both of these techniques based on our comprehensive survey of the available literature.Index Terms --Dynamic state estimation, kalman filter, phasor measurement unit, power systems, square root filter, state estimation, tracking state estimation.

Performance Assessment of Linear State Estimators Using Synchrophasor Measurements

“…In [18] and [33], the LSE is based on the DKF algorithm with a refresh rate of 50 frames-per-second and is applied to the distribution networks of the Swiss Federal Institute of Technology of Lausanne campus and the BML 2.10 feeder of Alliander in the Netherlands, respectively. Nuqui and Phadke [34] have proposed a hybrid state estimator with a measurement model that combines the estimated state from the classical state estimator with the direct state measured by the PMUs, whereas [35] is a review paper on tracking and dynamic SE techniques. Bian et al [36] have presented an approach defined as state tracking with correlated predictionmeasurement errors that can account for possible abrupt state change.…”

“…In the literature dealing with power systems SE using DKF, the values of Q k are, usually, arbitrarily selected although, in principle, they can be computed if the process is known [35], [48]. Since it can significantly influence the DKF accuracy, an appropriate assessment of this matrix is of fundamental importance for the maximization of the DKF-SE accuracy.…”

Identification of requirements for distribution management systems in the smart grid context

“…As the distribution grid is becoming active due mainly to the integration of distributed renewable generators directly connected to the low and medium voltage grid, the state estimation started to be a key function in the DMS. The state estimation recreates values for different distribution system variables using the available data present in the SCADA system [3].…”

Power System Dynamic State Estimation based on Kalman Filter

“…As a result,, the power flows and injections at the buses were changed; therefore, the power system is dynamic not static and the static state estimation fails to capture the nature state of power system [5].This lead to development Dynamic state estimation [6]. Dynamic state estimation (DSE) computes the state of the system by forming the time changing the behavior of power system the cater to dynamic variation of the power system and predict the state of the system at the next instant of time (k + 1) [7]. Various techniques are available for state estimation which can be applied to the power system.…”