Optimal placement of faulted circuit indicators in power distribution systems

Almeida, Madson C. de; Costa, Fabiano F.; Xavier‐de‐Souza, Samuel; Santana, F. de

doi:10.1016/j.epsr.2010.10.037

Cited by 44 publications

(33 citation statements)

References 18 publications

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“…The most common of them is the multiple fault location estimation problem, when a solution of d corresponds with different network points (each one in a different branch). A traditional solution for this problem is to combine these fault location methods with additional instrumentation [64,65], or a complementary analysis of signal information (e.g., voltage sag, currents fluctuation, etc.). This complementary analysis uses common base-knowledge classifiers to distinguish the real affected zone (e.g., Learning Algorithm for Multivariate Data Analysis [66], ANN [67], k-Nearest Neighbors [68], etc.).…”

Section: One-end Methodsmentioning

confidence: 99%

A Comparison of Impedance-Based Fault Location Methods for Power Underground Distribution Systems

et al. 2016

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Abstract:In the last few decades, the Smart Grid paradigm presence has increased within power systems. These new kinds of networks demand new Operations and Planning approaches, following improvements in the quality of service. In this sense, the role of the Distribution Management System, through its Outage Management System, is essential to guarantee the network reliability. This system is responsible for minimizing the consequences arising from a fault event (or network failure). Obviously, knowing where the fault appears is critical for a good reaction of this system. Therefore, several fault location techniques have been proposed. However, most of them provide individual results, associated with specific testbeds, which make the comparison between them difficult. Due to this, a review of fault location methods has been done in this paper, analyzing them for their use on underground distribution lines. Specifically, this study is focused on an impedance-based method because their requirements are in line with the typical instrumentation deployed in distribution networks. This work is completed with an exhaustive analysis of these methods over a PSCAD TM X4 implementation of the standard IEEE Node Test Feeder, which truly allows us to consistently compare the results of these location methods and to determine the advantages and drawbacks of each of them.

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Section: One-end Methodsmentioning

confidence: 99%

A Comparison of Impedance-Based Fault Location Methods for Power Underground Distribution Systems

et al. 2016

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“…Possible fault locations are ranked and compared with each other to determine the actual fault location. Methods to reduce and eliminate the uncertainty about the fault location in distribution systems are discussed in Refs [10][11][12][13]. de Almeida et al [10] present a way to optimally place faulted-circuit indicators along the feeder.…”

Section: Introductionmentioning

confidence: 99%

Fault Location Methods for Ungrounded Distribution Systems Using Local Measurements

Xiu

Liao

2013

International Journal of Emerging Electric Power Systems

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This article presents novel fault location algorithms for ungrounded distribution systems. The proposed methods are capable of locating faults by using obtained voltage and current measurements at the local substation. Two types of fault location algorithms, using line to neutral and line to line measurements, are presented. The network structure and parameters are assumed to be known. The network structure needs to be updated based on information obtained from utility telemetry system. With the help of bus impedance matrix, local voltage changes due to the fault can be expressed as a function of fault currents. Since the bus impedance matrix contains information about fault location, superimposed voltages at local substation can be expressed as a function of fault location, through which fault location can be solved. Simulation studies have been carried out based on a sample distribution power system. From the evaluation study, it is evinced that very accurate fault location estimates are obtained from both types of methods.

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“…The multi-objective optimization approach used herein is based on an objective function that contains two weighted indices representing sensor cost and information entropy as described in Equation (16).…”

Section: Problem Formulationmentioning

confidence: 99%

“…High-speed rail [9] Multi Nonlinear Not considered Greedy algorithm Water network [10] Single Nonlinear Considered Bayesian approach Framework [11] Single Nonlinear Considered Bayesian approach Framework [12] Single Nonlinear Considered Stochastic decomposition algorithm Water network [13] Single Nonlinear Considered Bayesian approach, genetic algorithm Framework [14] Single Nonlinear Considered Gradient search method Transport-reaction process [15] Single Nonlinear Considered Genetic algorithm Power distribution system [16] Multi Nonlinear Considered Annealing algorithm Shell structure [17] Multi Nonlinear Considered Hybrid greedy randomized adaptive search procedure Vehicular network [18] Multi Nonlinear Considered Genetic algorithm Framework [19] Most of the studies have a single objective function and do not consider system uncertainties. The highlighted cells in Table 1 identify optimization methodology characteristics that are common with the methodology developed herein.…”

Section: Introductionmentioning

confidence: 99%

An Entropy Based Bayesian Network Framework for System Health Monitoring

Parhizkar

Balali

Mosleh

2018

Entropy

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Oil pipeline network system health monitoring is important primarily due to the high cost of failure consequences. Optimal sensor selection helps provide more effective system health information from the perspective of economic and technical constraints. Optimization models confront different issues. For instance, many oil pipeline system performance models are inherently nonlinear, requiring nonlinear modelling. Optimization also confronts modeling uncertainties. Oil pipeline systems are among the most complicated and uncertain dynamic systems, as they include human elements, complex failure mechanisms, control systems, and most importantly component interactions. In this paper, an entropy-based Bayesian network optimization methodology for sensor selection and placement under uncertainty is developed. Entropy is a commonly used measure of information often been used to characterize uncertainty, particularly to quantify the effectiveness of measured signals of sensors in system health monitoring contexts. The entropy based Bayesian network optimization outlined herein also incorporates the effect that sensor reliability has on system information entropy content, which can also be related to the sensor cost. This approach is developed further by incorporating system information entropy and sensor costs in order to evaluate the performance of sensor combinations. The paper illustrates the approach using a simple oil pipeline network example. The so-called particle swarm optimization algorithm is used to solve the multi-objective optimization model, establishing the Pareto frontier.

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Optimal placement of faulted circuit indicators in power distribution systems

Cited by 44 publications

References 18 publications

A Comparison of Impedance-Based Fault Location Methods for Power Underground Distribution Systems

A Comparison of Impedance-Based Fault Location Methods for Power Underground Distribution Systems

Fault Location Methods for Ungrounded Distribution Systems Using Local Measurements

An Entropy Based Bayesian Network Framework for System Health Monitoring

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