PMU-based line and transformer parameter estimation

A Method for Accurate Transmission Line Impedance Parameter Estimation

“…where Both (17) and (19) are nonlinear constrained optimization problems, for which minima can be obtained with a range of algorithms. In this instance the interior-point method was chosen [25].…”

“…A number of approaches have been proposed to reduce the impact of random errors in synchrophasor measurements on impedance parameter determination: unbiased linear least squares estimation [15], [16], non-linear least squares algorithms [17]- [19], total least squares estimation [20] as well as optimization procedures [7], [21]. On the other hand, the impact and reduction of systematic errors in the synchrophasor measurements with regards to impedance parameter estimation has received less attention.…”

Application of modal‐phase domain relations to estimate transmission line parameters

“…Several approaches aiming to accurately obtain online TLP have been developed using the voltage and current (V&I) signals at the line terminals obtained through PMU, SCADA, and WAMS devices. Early methodologies are focused in solving the TLPE problem in order to include digital computers in distance protection [10][11][12][13][14][15] and others implement online state estimation (SE) in the utilities' energy control centres (later known as Energy Management System) [16][17][18][19][20][21][22][23].…”

“…least squares (LS) [10], weighted least squares (WLS) [13], and recursive least squares (RLS) [15]). On the other hand, TLPE algorithms established to enhance SE, prefer power and voltage samples as input data; for cases where V&I samples are preferred, current signals are calculated from the available SCADA data [22]. These TLPE methods can be classified into two types: the methods based on residual sensitivity analysis (post SE methods) and methods augmenting state vector (simultaneous SE methods) [16].…”

“…Meanwhile, the simultaneous SE methods are TLPE procedures that include the TLP as variables in the state variable vector. These TLPE techniques modify the SE objective function to include the TLP in the variable vector [22]. In either approach, the parameters are estimated from redundant data and the estimated parameters are obtained by methods such as Kalman filter [20,24], Newton-Raphson (NR) method [21], LS, and meta-heuristic methods [19,23].…”