Wavelet Entropy and Neural Network Based Fault Detection on A Non Radial Power System Network

Jana, Soumyadip

doi:10.9790/1676-0232631

Cited by 13 publications

(7 citation statements)

References 13 publications

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“…Reference Classification method Feature extraction method [1] Learning vector quantization (LVQ) Bispectra [2] Rule-based method Wavelet analysis [3] Hidden markov models (HMM) Wavelet analysis [4] Adaptive network-based fuzzy inference system (ANFIS) Root-mean-square values [5] Fuzzy-expert system and Artificial neural network (ANN) Fourier analysis and wavelet analysis [6] Artificial neural network (ANN) Butterworth filters and Finite impulse response (FIR) digital filters [7] Artificial neural network (ANN) Wavelet analysis [8] Artificial neural network (ANN) Wavelet analysis [9] Artificial neural network (ANN) Wavelet analysis…”

Section: K-nearest Neighbor (K-nn)mentioning

confidence: 99%

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Data-Based Ground Fault Diagnosis of Power Cable Systems

Ahmad

Mabuchi

Kano

et al. 2013

SICE Journal of Control, Measurement, and System Integration

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: Ground faults are major problems of power cable systems. Time-series data of voltage and current are available for diagnosis when a ground fault occurs. In the present work, a data-based fault diagnosis system of power cable systems was developed. In order to achieve the high fault diagnosis performance, new feature variables were generated by using wavelet analysis and cepstrum analysis. In addition, six classification techniques, i.e., k-nearest neighbor (k-NN), artificial neural network (ANN), boosted ANN (B-ANN), random forest (RF), classification and regression trees (CART), and boosted CART (B-CART), were compared. B-ANN and B-CART were combined with the naive Bayes classifier to cope with multiclass problems. The results of applying the proposed methods to real ground fault data show that B-ANN and B-CART with the naive Bayes classifier can achieve the best diagnosis performance, which satisfies the requirement for its industrial application.

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Section: K-nearest Neighbor (K-nn)mentioning

confidence: 99%

“…Approaches for fault diagnosis of power cable systems have been mostly based on wavelet analysis for feature extraction and ANN for classification as summarized in Table 1 [1]- [9]. Wavelet analysis has many advantages over the conventional Fourier analysis [10].…”

Section: Introductionmentioning

confidence: 99%

Data-Based Ground Fault Diagnosis of Power Cable Systems

Ahmad

Mabuchi

Kano

et al. 2013

SICE Journal of Control, Measurement, and System Integration

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“…Due to the structural challenges of distribution grids, such as non-homogeneity and the presence of laterals, fault identification methodologies employed in transmission grids cannot be applied directly to distribution grids [1]. Numerous studies [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] have accounted for the unpredictability of distribution systems by employing machine learning-based fault diagnosis methods employing data-based knowledge corresponding to varying conditions. Object recognition, visual object/speech recognition, and other fields such as genomics have all benefited significantly from deep learning [21].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Transfer Learning Techniques for Fault Classification in Radial Distribution Systems: A Comparative Study

Tiwari,

Saini

2023

IJRITCC

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Transfer learning has recently had a detectable impact on the state of the art in a wide variety of applications, and this trend is expected to continue in the near future. Both transfer learning and deep learning algorithms make use of a number of network layers, each of which may be intellectually learned and typically represents the data in a hierarchical fashion with increasing levels of abstraction. Convolutional neural networks have been proven to be exceptionally successful machine learning and deep learning techniques for a number of computer vision problems. These networks were developed by companies such as Alexa, Google, and Squeeze. Fault diagnostic strategies that are based on deep learning techniques are currently a topic of intense investigation due to their higher performance. Using transfer learning technology to carry out fault categorization in a power distribution system in a manner that is both accurate and efficient The work at hand employs a fault classification model for a radial power distribution system that is based on transfer learning and deep learning. Images of time series of three-phase fault currents are acquired via simulation with the assistance of PSCAD software as part of the proposed approach for doing so. In the next step, CNN models that are based on Alex Net, Google Net, and Squeeze Net are utilized to extract fault features from defective photos in order to categorize eleven distinct defects (using the MATLAB platform). For the categorization of defects in a radial distribution system, Alex Net, Google Net, and SqueezNet each offer accuracy of approximately 98.92%, 97.48%, and 99.82%, respectively. In this study, the classification of faults in a distribution system is accomplished with the help of AlexNet, GoogleNet, and SqueezNet. According to the findings of the simulations, the test accuracy for SqueezeNet is the highest it can be, coming in at 99.82%. Because of this, selecting it as the solution to the issue of fault classification in the test distribution system is your best option.

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“…Method in [8] locates fault by combining the travelling waves methodology with the network topology to isolate the faulty link first and then locate the fault distance. The faultlocation methods described in [9] employ Wavelet Entropy and Neural Network. The authors of [10] proposed new technique based on measurements provided by intelligent electronic devices with built-in oscillography function, installed only at the substation level and on a database that stores information about the network topology and its electrical parameters.…”

Section: Introductionmentioning

confidence: 99%

Fault location in distribution network using cumulative approach

et al. 2013

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In distribution system, the subject of fault location has been of considerable interest for a long time. The objective of this paper is to develop Cumulative approach for fault location based on statuses of Fault Indicators (FI), Circuit Breakers (CB), Fuses and Fault Impedances from distance protection devices. This approach is using mainly graph theory algorithms and Area Marking Strategy to resolve fault diagnosis problem in electric power networks. Unlike neighborhood approach where one link only affects the marking of neighbor areas, cumulative approach marks all areas down the link. In many situations, cumulative approach may out-perform neighbor approach.

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Wavelet Entropy and Neural Network Based Fault Detection on A Non Radial Power System Network

Cited by 13 publications

References 13 publications

Data-Based Ground Fault Diagnosis of Power Cable Systems

Data-Based Ground Fault Diagnosis of Power Cable Systems

Evaluation of Transfer Learning Techniques for Fault Classification in Radial Distribution Systems: A Comparative Study

Fault location in distribution network using cumulative approach

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