Estimating Dynamic Model Parameters for Adaptive Protection and Control in Power System

Abstract

This paper presents a new approach in estimating important parameters of power system transient stability model such as inertia constant and direct axis transient reactance in real time. It uses a variation of unscented Kalman filter (UKF) on the phasor measurement unit (PMU) data. The accurate estimation of these parameters is very important for assessing the stability and tuning the adaptive protection system on power swing relays. The effectiveness of the method is demonstrated in a simulated data from 16-machine 68-bus system model. The paper also presents the performance comparison between the UKF and EKF method in estimating the parameters. The robustness of method is further validated in the presence of noise that is likely to be in the PMU data in reality.

Wide-Area Measurement—Based Model-Free Approach for Online Power System Transient Stability Assessment

“…However, preserving full network observability may not be practically possible in large power systems [29]. Since the proposed approach performs online stability assessment only using the rotor angle and rotor speed data of generators, the rotor angle and rotor speed of the unobserved generators can be estimated by the dynamic state estimation algorithms, which can provide accurate and real-time estimation with respect to PMU measurements [30,31]. In our cases, the optimal PMU placement can be fulfilled based on the maximization of the determinant of the empirical observability, per Gramian in [32], where the obtained optimal PMU placements for generators can still guarantee good observability under a small or large disturbance.…”

Controlling the renewable microgrid using semidefinite programming technique

“…Interestingly, in order to avoid the calculation of Jacobians, the UKF based power system state estimation is explored in [16,22,23] and it shows that the UKF preserves high-order estimation accuracy compared with the EKF. This improvement is due to the fact that the UKF calculates the mean and covariance of state variables accurately that undergo a nonlinear transformation [24,23].…”

“…The process noise n d ðkÞ is the zero mean Gaussian distribution [16,17,24] whose covariance matrix is Q n .…”

“…where yðkÞ is the measurements, C is the sensing matrix and wðkÞ is the zero mean Gaussian distribution [16,17,24] whose covariance matrix is R w . The observation information is transmitted to the nearby base station.…”

Estimation of Inherent Governor Dead-Band and Regulation Using Unscented Kalman Filter

“…The mechanical power is not directly observable and may be obtained using the electrical power measurements and the accelerating power (which is calculated using the speed signal). The choice of UKF is guided by its superior ability to handle nonlinearities efficiently [13], when compared to other parameter estimation techniques used with power system measurements, such as least square [14], extended Kalman filter (EKF) [15], particle filter [16], trajectory sensitivity [17].…”