Importance of artificial neural networks for location of faults in transmission systems: A survey

Prasad, Avagaddi; Edward, J. Belwin

doi:10.1109/isco.2017.7856016

Cited by 20 publications

(15 citation statements)

References 31 publications

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“…Different mathematical tools have been used to propose algorithms for fault location over the years [13,[15][16][17] but recent researchers have encouraged and predicted that machine learningbased fault location techniques would play a significant role in future fault location research [13,18]. A very effective tool to tackle the issues discussed previously is by the use of artificial neural networks (ANNs).…”

Section: Motivation and Objectivementioning

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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Section: Motivation and Objectivementioning

confidence: 99%

Application technique for model‐based approach to estimate fault location

Ananthan

Santoso

2020

IET Smart Grid

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“…The second algorithm presented in this paper is based on the Artificial Neural Network (ANN) theory. The importance of ANN-based algorithms is growing in the scientific community, as well as for power system analysis and fault location, as witnessed by several papers [12][13][14][15][16][17][18][19][20][21]. A certain number of ANN-based algorithms have been developed in recent years.…”

Section: Technical Literature Reviewmentioning

confidence: 99%

“…A certain number of ANN-based algorithms have been developed in recent years. In [12], a BPNN architecture has been developed to estimate the fault position in various locations; RMS values of current and voltage samples are the input data for this network. In [13], the authors propose an impedance-type estimator by using a phase or amplitude comparison of signals.…”

Section: Technical Literature Reviewmentioning

confidence: 99%

“…During the training phase, the inter-unit connections are optimized until the error in prediction is minimized and the network reaches the specified level of accuracy. Once the network is trained and tested, it can receive new input information to predict the desired output (the fault position in this case) [12]. In terms of model specification, ANNs can combine both simulation-based and experimental data.…”

Section: Artificial Neural Network-based Algorithmmentioning

confidence: 99%

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Overcoming the Limits of the Charge Transient Fault Location Algorithm by the Artificial Neural Network

2019

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In this paper, two algorithms for single-ended fault location are presented with reference to the unearthed sub-transmission Italian grid (with a voltage level of 60 kV). Both algorithms deal with the correlation between the ground capacitance charging frequency of sound phases and the fault position. In the former, the frequency response of a lumped parameter circuit in the Laplace domain is linked to the fault distance. With such a simplified lumped parameter circuit, the average error in locating a phase-to-ground (PtG) short circuit is 5.18% (total overhead line length equal to 60 km). Since this error is too high, another approach is presented. In this second algorithm, the frequency spectra of the transient current waveforms are used as a database for the training of an Artificial Neural Network (ANN). With this approach, the average error decreases significantly up to 0.36%. The fault location accuracies of the two proposed methods are compared in order to reveal their strengths and weaknesses. The developed procedures are applied to a single-circuit overhead line and to a double-circuit one, both modelled in the EMTP-rv environment, whereas the fault location algorithms are implemented in the MATLAB environment (for the ANN-based algorithm, the Deep Learning toolbox is used).

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“…Huge research efforts have been undertaken worldwide to develop incipient fault (before the actual occurrence of faults) diagnostic techniques. Neural network (NN) or known as artificial neural network (ANN) is a tool that plays an important role in developing online and offline diagnostic tools for motors, generators, transmission lines, cables, and transformers [32][33][34][35][36][37]. A mathematical model of an induction machine with stator inter-turn fault has been derived based on winding function theory [8].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors

2018

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Induction motors constitute the largest proportion of motors in industry. This type of motor experiences different types of failures, such as broken bars, eccentricity, and inter-turn failure. Stator winding faults account for approximately 36% of these failures. As such, condition monitoring is used to protect motors from sudden breakdowns. This paper proposes the use of neural networks as an efficient diagnostic tool for estimating the percentage of stator winding shorted turns in three-phase induction motors. A MATLAB-based model was developed and simulated under different fault-load combination cases for different sizes of motors. The motor's developed electromechanical torque was selected as a fault indicator. For the design and training of the neural network, the mean, variance, max, min, and F120 time based on statistical and frequency-related features were found to be very distinct for correlating the captured electromechanical torque with its corresponding percentage of shorted turns. In the training phase of the neural network, five different motors were used and are referred to as seen motors. On the other hand, for testing the efficiency of the developed diagnostic tool, the electromechanical torque under different fault-load combination cases, previously never seen from the first five motors and those of two new motors (referred to as unseen), was used. Testing results revealed accuracy in the range of 88-99%.

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Importance of artificial neural networks for location of faults in transmission systems: A survey

Cited by 20 publications

References 31 publications

Application technique for model‐based approach to estimate fault location

Application technique for model‐based approach to estimate fault location

Overcoming the Limits of the Charge Transient Fault Location Algorithm by the Artificial Neural Network

An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors

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