Kalman-filter based recursive regression for three-phase line parameter estimation using synchrophasor measurements

Phasor Measurement Unit measurement data have been widely used in nowadays power system applications both in steady state and dynamic analysis. The performance of these applications running in utilities' energy management system depends heavily on an accurate positive sequence power system model. However, it is impractical to find this accurate model with transmission line parameters calculated directly with the PMU measurements due to ratio errors brought by instrument transformers and communication errors brought by PMUs. Therefore, a methodology is proposed in this paper to estimate the actual transmission lines parameters throughout the whole system and, at the same time, calibrate the corresponding instrument transformers. A PMU positive sequence measurement error model is proposed targeting at the aforementioned errors, which is applicable to both transposed and un-transposed transmission lines. A single line parameters estimation method is designed based on Least Squares Estimation and this error model. This method requires only one set of reference measurements and the accuracy can be propagated throughout the whole network along with the topology acquired by the introduced Edge-based Breadth-first Search algorithm. The IEEE 118-bus system and the Texas 2000-bus system are used to demonstrate the effectiveness and efficiency of the proposed method. The potential for deployment in reality is also discussed. P REV PS ratio error on voltage measurement , P ut REV PS ratio error on voltage measurement of untransposed TL ph  Complex value indicating single-phase deviation from PS Z Impedance matrix of one single TL i KV Correction factor of i REV i KI Correction factor of i REI iM V  PS quantization error on iM V , iM q V Actual quantized PS voltage measurement on bus i output by PMU , iM q I Actual quantized PS current measurement on bus i output by PMU real K Real part of correction factors imag K Imaginary part of correction factors   true real

show abstract

“…Thus, with adequate measurements to eliminate the quantization errors, the linear estimation problem can be formed as shown in (14).…”

Section: A Single Transmission Line Parameters Estimation Modelmentioning

confidence: 99%

“…where, Using LSE and the single line parameters model formed in (14), one can get the estimated impedance matrix:…”

Section: A Single Transmission Line Parameters Estimation Modelmentioning

confidence: 99%

Transmission Lines Positive Sequence Parameters Estimation and Instrument Transformers Calibration Based on PMU Measurement Error Model

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…An accurate understanding of transmission line parameters is necessary for accurate power flow modelling, fault location, transient stability and etc. Previously, calculations based in conductor dimensions, sag and line length were used to estimates these parameters [13]. However, due to the assumptions and approximation involved, the estimation was not reliable for real -time application [13].…”

Section: Introductionmentioning

confidence: 99%

“…Previously, calculations based in conductor dimensions, sag and line length were used to estimates these parameters [13]. However, due to the assumptions and approximation involved, the estimation was not reliable for real -time application [13]. Therefore, this situation has inspired this research to examine UMP transmission line as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Kalman filter estimation of RLC parameters for UMP transmission line

Amin

Ahmad

Mohamed

2018

MATEC Web of Conferences

View full text Add to dashboard Cite

This paper present the development of Kalman filter that allows evaluation in the estimation of resistance (R), inductance (L), and capacitance (C) values for Universiti Malaysia Pahang (UMP) short transmission line. To overcome the weaknesses of existing system such as power losses in the transmission line, Kalman Filter can be a better solution to estimate the parameters. The aim of this paper is to estimate RLC values by using Kalman filter that in the end can increase the system efficiency in UMP. In this research, matlab simulink model is developed to analyse the UMP short transmission line by considering different noise conditions to reprint certain unknown parameters which are difficult to predict. The data is then used for comparison purposes between calculated and estimated values. The results have illustrated that the Kalman Filter estimate accurately the RLC parameters with less error. The comparison of accuracy between Kalman Filter and Least Square method is also presented to evaluate their performances.

show abstract

“…Concurrently, PMU based parameters estimations have been proposed for distribution networks [16], the estimation problem in this context can be considered as easier due to the higher variability in the data of an operating distribution network compared to the one observed in the operating transmission networks. The use of Kalman filtering is proposed in [26]. It improves the estimation accuracy in comparison to the least-squares based methods.…”

mentioning

confidence: 99%

Parameter Estimation of Three-Phase Untransposed Short Transmission Lines From Synchrophasor Measurements

Wehenkel

Mukhopadhyay

Boudec

et al. 2020

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

We present a new approach for estimating the parameters of three-phase untransposed electrically short transmission lines using voltage/current synchrophasor measurements obtained from phasor measurement units. The parameters to be estimated are the entries of the longitudinal impedance matrix and the shunt admittance matrix at the rated system frequency. Conventional approaches relying on the admittance matrix of the line cannot accurately estimate these parameters for short lines, due to their high sensitivity to measurement noise. Our approach differs from the conventional ones in the following ways: First, we model the line by the three-phase transmittance matrix that is observed to be less sensitive to measurement noise than the admittance matrix. Second, we compute an accurate noise covariance matrix using the realistic specifications of noise introduced by instrument transformers and phasor measurement units. This noise covariance matrix is then used in least-squares-based estimation methods. Third, we derive different least-squares-based estimation methods based on a statistical model of estimation and show that the weighted leastsquares and the maximum likelihood methods, which make use of the noise covariance matrix produce the best estimates of the line parameters. Finally, we apply the proposed methods to a real dataset and show that our approach significantly outperforms existing ones. I. INTRODUCTION Fundamental functionalities used in the operation of power grids, e.g., state estimation (SE) [1], [2], [3], optimal power flow (OPF)-based control [4], [5], [6], [7], , Model Predictive Control [8], [9] and optimal relay tuning [10], [11], require the knowledge of transmission line (TL) parameters at the rated system frequency. Conventionally, TL parameters are obtained either by using the physical properties of the line (such as conductor dimensions, types of wires, tower geometries, ground electrical parameters) [12], [13] or by making measurements on the line when it is off-grid [14]. The first method is applicable only when accurate conductor characteristics are known, whereas the second method, although reliable, is time consuming and difficult to implement in practice. With the availability of highly accurate measurement devices, e.g., phasor measurement units (PMUs), instrument transformers (ITs), estimation methods based on measurements from these devices have gained significant research

show abstract

Kalman-filter based recursive regression for three-phase line parameter estimation using synchrophasor measurements

Cited by 21 publications

References 9 publications

Transmission Lines Positive Sequence Parameters Estimation and Instrument Transformers Calibration Based on PMU Measurement Error Model

Transmission Lines Positive Sequence Parameters Estimation and Instrument Transformers Calibration Based on PMU Measurement Error Model

Kalman filter estimation of RLC parameters for UMP transmission line

Parameter Estimation of Three-Phase Untransposed Short Transmission Lines From Synchrophasor Measurements

Contact Info

Product

Resources

About