Medium Voltage Line Parameter Estimation Using Synchrophasor Data: A Step Towards Dynamic Line Rating

Singh, Ravi Shankar; Cobben, Sjef; Gibescu, Madeleine; Brom, Helko E. van den; Colangelo, Daniele; Rietveld, G.

doi:10.1109/pesgm.2018.8586111

Cited by 10 publications

(8 citation statements)

References 6 publications

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“…This paper presents a new method for estimating the resistance, reactance, and susceptance (R, X and B) parameters for a 3-phase cable system. The new method is developed to facilitate accurate monitoring of cable temperature in real time to establish flexible loading levels of the cables [3]. The real-time cable temperature could be accurately estimated by tracking the real-time resistance of the cable.…”

Section: Discussionmentioning

confidence: 99%

“…It is known that accurate pre-fault line impedance and knowledge about impedance of loads on the line could be utilized to calculate the location of the faults more precisely [2]. A novel idea of utilizing real-time resistance estimates to monitor the temperature of an overhead line or a cable and facilitate dynamic line rating (DLR) was first mentioned in [3]. It was presented that the relationship between the conductor resistance and temperature could be utilized to estimate the real-time temperature of the conductor.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Impedance and Susceptance Parameters of a 3-Phase Cable System Using PMU Data

et al. 2019

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This paper proposes a new regression-based method to estimate resistance, reactance, and susceptance parameters of a 3-phase cable segment using phasor measurement unit (PMU) data. The novelty of this method is that it gives accurate parameter estimates in the presence of unknown bias errors in the measurements. Bias errors are fixed errors present in the measurement equipment and have been neglected in previous such attempts of estimating parameters of a 3-phase line or cable segment. In power system networks, the sensors used for current and voltage measurements have inherent magnitude and phase errors whose measurements need to be corrected using calibrated correction coefficients. Neglecting or using wrong error correction coefficients causes fixed bias errors in the measured current and voltage signals. Measured current and voltage signals at different time instances are the variables in the regression model used to estimate the cable parameters. Thus, the bias errors in the sensors become fixed errors in the variables. This error in variables leads to inaccuracy in the estimated parameters. To avoid this, the proposed method uses a new regression model using extra parameters which facilitate the modeling of present but unknown bias errors in the measurement system. These added parameters account for the errors present in the non- or wrongly calibrated sensors. Apart from the measurement bias, random measurement errors also contribute to the total uncertainty of the estimated parameters. This paper also presents and compares methods to estimate the total uncertainty in the estimated parameters caused by the bias and random errors present in the measurement system. Results from simulation-based and laboratory experiments are presented to show the efficacy of the proposed method. A discussion about analyzing the obtained results is also presented.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Impedance and Susceptance Parameters of a 3-Phase Cable System Using PMU Data

et al. 2019

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“…To improve the model accuracy, skin-effect, proximity effect, and temperature dependency should be represented since they affect the electrical resistance values, which have an impact on losses [63]. Another way to estimate the electrical resistance of MV cables requires the availability of synchrophasor data given by phasor measurement units (PMUs) [64]. By modelling heat flow with a current in the electric analogy, the different node voltages inherently model the temperatures.…”

Section: Power Cable Modellingmentioning

confidence: 99%

Concepts and Methods to Assess the Dynamic Thermal Rating of Underground Power Cables

et al. 2021

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With the increase in the electrical load and the progressive introduction of power generation from intermittent renewable energy sources, the power line operating conditions are approaching the thermal limits. The definition of thermal limits variable in time has been addressed under the concept of dynamic thermal rating (DTR), with which it is possible to provide a more detailed assessment of the line rating and exploit the electrical system more flexibly. Most of the literature on DTR has addressed overhead lines exposed to different weather conditions. The interest in the dynamic thermal rating of power cables is increasing, considering the evolution of computational methods and advanced systems for cable monitoring. This paper contains an overview of the concepts and methods referring to dynamic cable rating (DCR). Starting from the analytical formulations developed many years ago for determining the power cable rating in steady-state conditions, also reported in International Standards, this paper considers the improvements of these formulations proposed during the years. These improvements are leading to include more specific details in the models used for DCR analysis and the computational methods used to assess the power cable’s thermal conditions buried in soil. This paper is focused on highlighting the path from the initial theories and models to the latest literature contributions. Attention is paid to thermal modelling with different levels of detail, applications of 2D and 3D solvers and simplified models, and their validation based on experimental measurements. A salient point of the overview is considering the DCR impact on reliability aspects, risk estimation, real-time calculations, forecasting, and planning with different time horizons.

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“…A graphical model-based approach employing D-PMU is proposed in [16,17] to estimate distribution line impedance with a linear-coupled power flow (LCPF) model. Exploring the application of D-PMU, the authors in reference [18] proposed a least-squares (LS) algorithm based impedance estimation for dynamic line rating (DLR) monitoring. In [19,20], a data-driven joint estimation method with the inclusion of D-PMU for parameters and topology is proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

Distribution line parameter estimation driven by probabilistic data fusion of D‐PMU and AMI

Xiao

Xie

Fang

et al. 2021

IET Generation Trans & Dist

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This paper proposes a novel distribution line parameter estimation method, driven by the probabilistic data fusion of the distributed phasor measurement unit (D-PMU) and the advanced measurement infrastructure. The synchronized and high-precision D-PMU is utilized to tackle the challenge risen by the a-synchronization of smart meters. Correspondingly, a time-alignment algorithm is proposed to obtain the time-synchronous error (TSE) dataset for the up-stream smart meter. The non-parametric estimation method is performed then to evaluate the probabilistic density curve of TSE. Furthermore, TSE data of down-stream smart meters are generated by implementing the acceptance-rejection process based on the obtained probabilistic density curve. Leveraging the generated TSE dataset, a new time-shifted D-PMU curve is probabilistically aligned or fused with the down-stream advanced measurement infrastructure curves. According to the complete voltage drop model, the line parameter estimation of resistance and reactance is formulated as a quadratic programming problem and solved by Optimal Toolbox in MATLAB by conducting multi-run Monte-Carlo simulations under various scenarios. Simulation results demonstrate the effectiveness and robustness of the proposed methodology.

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Medium Voltage Line Parameter Estimation Using Synchrophasor Data: A Step Towards Dynamic Line Rating

Cited by 10 publications

References 6 publications

Estimation of Impedance and Susceptance Parameters of a 3-Phase Cable System Using PMU Data

Estimation of Impedance and Susceptance Parameters of a 3-Phase Cable System Using PMU Data

Concepts and Methods to Assess the Dynamic Thermal Rating of Underground Power Cables

Distribution line parameter estimation driven by probabilistic data fusion of D‐PMU and AMI

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