Mutually Coupled Transmission Line Parameter Estimation and Voltage Profile Calculation Using One Terminal Data Sampling and Virtual Black-Box

Abstract

In this paper, an accurate parameter identification algorithm is proposed for transient voltage profile calculation of the unknown transmission lines. This method is based on the virtual black-box method and uses single-ended data sampling, where the input data include voltage and current samples obtained by measuring at one end of the line in transient mode. A mathematical formulation is proposed to separate the sampled data and form a virtual black-box system with virtual inputs and outputs. The virtual black-box is designed, such that the system coefficients relating the virtual outputs and inputs are the known functions of transmission line parameters in the real world. The coefficient values are then calculated by employing the recursive least squares estimation method, which minimizes the sum of squared errors of observations. Using this method, transmission line parameters and transient voltage profile are calculated from only one terminal data with no need of measuring devices, data synchronization, and communication devices at both sides. The efficiency of the proposed method is tested and proved through the EMTP simulations. INDEX TERMS One terminal sampling, power system transients, recursive estimation, time domain analysis, transmission lines.

Estimation of the Frequency-Dependent Parameters of Transmission Lines by Using Synchronized Measurements

“…Such measurements can be obtained through synchronized measurements via PSCAD simulations. In real cases, these values can be obtained through fault records by digital relays and subsequent Reference Year Focus of the paper [10] 1999 TLPE at fundamental frequency via LS for transmission line remote protection [23] 2001 TLPE at fundamental frequency via Generalized Equation Error [24] 2003 TLPE at fundamental frequency together with fault/detection location technique [12] 2008 TLPE at fundamental frequency via Newton-Raphson algorithms using measurements at both ends [22] 2008 TLPE at fundamental frequency via Newton-Raphson algorithms using synchronized measurements at both ends [16] 2009 TLPE at fundamental frequency via LS using re-synchronized measurements [18] 2011 TLPE at fundamental frequency via Extended-Kalman Filter (EKF) using Phasor Measurement Units (PMU) measurements [25] 2011 TLPE at fundamental frequency via an optimization model using long-term synchronized phasor measurements [26] 2012 TLPE at fundamental frequency via LS for evaluation of the influence of different transmission line lengths [27] 2013 TLPE at fundamental frequency via LS and Kirchoff's Law using PMU measurements [28] 2014 TLPE at fundamental frequency via weighted least squares (WLS) and PMU measurements [17] 2015 TLPE at fundamental frequency via LS considering a hybrid domain model (phase and modal) using synchronised fault records [29] 2017 TLPE at fundamental frequency via Nonlinear Weighted Least Squares (NWLS) using two devices to acquire measurements, PMU and Supervisory Control and Data Acquisition (SCADA) [19] 2017 TLPE at fundamental frequency via an optimization model solved through a derived Newton method [30] 2018 TLPE at fundamental frequency via Cloud Computing using PMU and SCADA measurements [13] 2018 TLPE at fundamental frequency via a M-estimator using PMU measurements [31] 2019 TLPE at fundamental frequency via recursive least-squares (RLS) using one terminal data sampling [32] 2020 TLPE at fundamental frequency via Kalman filter methods using synchronized measurements [15] 2021 TLPE at fundamental frequency via two LS methods using synchronized measurements [33] 2021 TLPE at fundamental frequency via an optimization model solved through Firefly Algorithm (FA) [34] 2021 TLPE at fundamental frequency via an optimization model solved through Whale Optimization Algorithm (WOA) using voltage and current measurements frequency decomposition [39].…”

Estimation of the electrical parameters of overhead transmission lines using Kalman Filtering with particle swarm optimization

“…These can be identified and corrected by using an appropriate data validation method [13]. The parameter estimation can be performed in the time domain, nevertheless, using this approach, the estimation is possible only for transient conditions such as a fault [6,14,15]. On the other hand, there are interesting proposals based on estimation methods in the frequency domain by using phasor measurements, which can be applied in steady state [10,[16][17][18].…”

Transmission Lines Impedance Fitting Using Analytical Impedance Equation and Frequency Response Analysis

“…An estimation algorithm for mutually coupled transmission line parameters is developed in [17]. The presented model deals with the mutually coupled transmission line as a black box and measures the voltages and the currents at the sending end during a transient event.…”