Estimating zero-sequence impedance of three-terminal transmission line and Thevenin impedance using relay measurement data

Das, Swagata; Ananthan, Sundaravaradan Navalpakkam; Santoso, Surya

doi:10.1186/s41601-018-0108-y

Cited by 12 publications

(18 citation statements)

References 16 publications

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“…Equation (30) has two more new unknown variables (I 6-4 and I 3-2 ) in comparison to (17) or one additional unknown variable compared to (28). The presence of additional fault current contributors between the relay and the fault introduces further unknowns in the impedance equation.…”

Section: Figure 11 Case 3: Simplified Circuit Representation To Demonmentioning

confidence: 99%

Novel system model‐based fault location approach using dynamic search technique

Ananthan

Bastos

Santoso

2021

IET Generation Trans & Dist

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Impedance-based fault location (IBFL) approaches are the most commonly used fault location methods in digital relays. However, each IBFL approach is designed specific to a line or network configuration and thus is not universal. For example, complex line configurations such as lines with mutual coupling between them and three-terminal lines have to employ individual IBFL algorithms derived specifically for them. Furthermore, they suffer from several sources of errors such as non-homogeneous system, and CT saturation. Hence, this paper presents a novel fault location approach that utilises a system model to overcome these limitations. The proposed model-based fault location (MBFL) approach estimates the fault location by identifying the closest match among various anticipated fault scenarios obtained using the system model and the actual fault scenario. It uses a dynamic search technique to implement the MBFL efficiently. A key highlight of the proposed approach is identifying the location of a fault on a neighbouring line using limited measurements, as few as only the through fault current flowing in a neighbouring line. The advantages of the approach and its practical applicability have been demonstrated by implementing it in complex network configurations as well as field data.

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Section: Figure 11 Case 3: Simplified Circuit Representation To Demonmentioning

confidence: 99%

Novel system model‐based fault location approach using dynamic search technique

Ananthan

Bastos

Santoso

2021

IET Generation Trans & Dist

Self Cite

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“…Different online methods have been proposed in [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] to estimate line parameters considering the line length, specific line configurations, data availability, etc. The methods for estimating zero-sequence line parameters are proposed in [11][12][13]. The author of [14] proposed a method for identifying and estimating erroneous transmission line parameters.…”

Section: Introductionmentioning

confidence: 99%

Kalman filter based method for tracking dynamic transmission line parameters

Zhang

Liao

2021

SN Appl. Sci.

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Transmission line parameters are required by various power system applications. Thus, it is important to develop accurate transmission line parameter estimation methods. This paper puts forward Kalman filter based methods to track the dynamically changing line parameters. The proposed methods can overcome the estimation inaccuracy problem caused by the effect of the noise in the measurements. The proposed methods are based upon the equivalent- circuit of long line and utilize voltage and current data recorded by the Phasor Measurement Units at both ends of the line. Non-compensated and differently configured series compensated lines have been considered in this paper. The case studies demonstrate that the proposed methods have the capability of dynamically tracking changing line parameters and yield accurate estimates with the presence of measurement noises.

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“…It is very common that the entire power system model is available to protection engineers for performing protection coordination studies. Researchers have also developed several algorithms to estimate power system parameters to form the power system model even when these parameters are not available [3][4][5]. Most commonly, only the line parameters of the faulted line and the estimated source impedance values are used.…”

Section: Introductionmentioning

confidence: 99%

Application technique for model‐based approach to estimate fault location

Ananthan

Santoso

2020

IET Smart Grid

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Impedance-based algorithms commonly used for determining the fault location in transmission lines are prone to several sources of error and are specific to the line and system configuration. Furthermore, these algorithms do not utilise available valuable information about the power system surrounding the faulted line. These issues can be overcome using a model-based fault location (MBFL) approach. It uses a circuit model to simulate possible fault scenarios and compares the simulated fault currents with the measured currents recorded by the relay to identify the fault location. However, there are several difficulties and limitations while applying MBFL. There is a loss in accuracy and precision based on the number of simulated scenarios and a requirement to store voluminous simulation results. Hence, this study presents a novel application technique for implementing model-based approach efficiently to estimate the fault location and fault resistance using artificial neural networks-based approach. A key highlight of the proposed approach is the ability to identify the location of a fault present on neighbouring lines using the measured through fault current. The study also presents representative scenarios to demonstrate the capability and potential of the proposed approach.

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Estimating zero-sequence impedance of three-terminal transmission line and Thevenin impedance using relay measurement data

Cited by 12 publications

References 16 publications

Novel system model‐based fault location approach using dynamic search technique

Novel system model‐based fault location approach using dynamic search technique

Kalman filter based method for tracking dynamic transmission line parameters

Application technique for model‐based approach to estimate fault location

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