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1

Detection and classification of short-circuit faults on a transmission line using current signal

Coban Melih, Tezcan Suleyman S.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2021

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Vol. 69, nr 4

art. no. e137630

EN

This study offers two Support Vector Machine (SVM) models for fault detection and fault classification, respectively. Different short circuit events were generated using a 154 kV transmission line modeled in MATLAB/Simulink software. Discrete Wavelet Transform (DWT) is performed to the measured single terminal current signals before fault detection stage. Three level wavelet energies obtained for each of three-phase currents were used as input features for the detector. After fault detection, half cycle (10 ms) of three-phase current signals was recorded by 20 kHz sampling rate. The recorded currents signals were used as input parameters for the multi class SVM classifier. The results of the validation tests have demonstrated that a quite reliable, fault detection and classification system can be developed using SVM. Generated faults were used to training and testing of the SVM classifiers. SVM based classification and detection model was fully implemented in MATLAB software. These models were comprehensively tested under different conditions. The effects of the fault impedance, fault inception angle, mother wavelet, and fault location were investigated. Finally, simulation results verify that the offered study can be used for fault detection and classification on the transmission line.

2

Faults detection and classification on parallel transmission lines using modified Clarke’s transformation-ANN approach

Saini Makmur, Yunus Shiddiq, Sulthan Ahmed Rizal, Djalal Muh.Ruswandi, Mustafa Mohd. Wasir, Rahimuddin Rahimuddin, Kitta Ikhlas

Przegląd Elektrotechniczny

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2020

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R. 96, nr 4

23--27

EN

This paper introduces a comparative study for fault detection and classification on parallel transmission line using cascade forward and feed forward back propagation. Both calculations were based on discrete wavelet transform (DWT) and Clarke’s transformation. Daubechies4 mother wavelet (Db4) was applied to decompose coefficients of wavelet transforms coefficients (WTC) and wavelet energy coefficients (WEC) of high frequency signals. The coefficients were inputs for training of neural network back-propagation (BPNN). The results showed that the feed forward back propagation algorithm of Artificial Neural Network (ANN) models responded better than Cascade forward back propagation algorithm models, particularly in fault detection and classification on parallel transmission. The results showed that the proposed method for fault analysis was able to classify all the faults on the parallel transmission line rapidly and correctly.

PL

W pracy przedstawiono badanie porównawcze wykrywania i klasyfikacji uszkodzeń równoległej linii przesyłowej z wykorzystaniem propagacji kaskadowej do przodu i do tyłu. Oba obliczenia oparto na dyskretnej transformacie falkowej (DWT) i transformacji Clarke'a. Falkę macierzystą Daubechies4 (Db4) zastosowano do dekompozycji współczynników przekształceń falkowych (WTC) i współczynników energii falkowej (WEC) sygnałów wysokiej częstotliwości. Współczynniki stanowiły dane wejściowe do szkolenia propagacji wstecznej sieci neuronowej (BPNN). Wyniki pokazały, że algorytm propagacji wstecznego sprzężenia zwrotnego modeli sztucznej sieci neuronowej (ANN) zareagował lepiej niż modele algorytmu kaskadowego propagacji wstecznej, szczególnie w wykrywaniu błędów i klasyfikacji w transmisji równoległej. Wyniki pokazały, że zaproponowana metoda analizy uszkodzeń była w stanie szybko i poprawnie sklasyfikować wszystkie uszkodzenia na równoległej linii przesyłowej.

3

A High-Accuracy of Transmission Line Faults (TLFs) Classification Based on Convolutional Neural Network

Fuada S., Shiddieqy H. A., Adiono T.

International Journal of Electronics and Telecommunications

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2020

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Vol. 66, No. 4

655--664

EN

To improve power system reliability, a protection mechanism is highly needed. Early detection can be used to prevent failures in the power transmission line (TL). A classification system method is widely used to protect against false detection as well as assist the decision analysis. Each TL signal has a continuous pattern in which it can be detected and classified by the conventional methods, i.e., wavelet feature extraction and artificial neural network (ANN). However, the accuracy resulting from these mentioned models is relatively low. To overcome this issue, we propose a machine learning-based on Convolutional Neural Network (CNN) for the transmission line faults (TLFs) application. CNN is more suitable for pattern recognition compared to conventional ANN and ANN with Discrete Wavelet Transform (DWT) feature extraction. In this work, we first simulate our proposed model by using Simulink® and Matlab®. This simulation generates a fault signal dataset, which is divided into 45.738 data training and 4.752 data tests. Later, we design the number of machine learning classifiers. Each model classifier is trained by exposing it to the same dataset. The CNN design, with raw input, is determined as an optimal output model from the training process with 100% accuracy.

4

A fuzzy logic system to detect and classify faults for laboratory prototype model of TCSC compensated transmission line

Bhupendra Kumar, Anamika Yadav

Journal of Power Technologies

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2019

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Vol. 99, nr 1

49--57

EN

In this paper, an expert system-based fault detection and classification scheme is developed for a laboratory prototype model of TCSC compensated long transmission line (thyristor controlled series compensator). The equivalent model of laboratory prototype system is simulated in MATLAB Simulink. An expert system based on fuzzy logic is developed by using threephase voltage and current signals from single end measurements. Obtained voltage and current signals are pre-processed with Discrete Fourier Transform (DFT) to obtain the fundamental component of these signals. Further zero sequence current and obtained fundamental voltage and current signals are used to develop a fuzzy inference system (FIS) for shunt fault detection and classification task. There are three different FISs developed for three individual phases of the transmission system and one FIS is developed for zero sequence current signal, which provides ground involvement information. The combined binary output of the developed four FISs provides fault classification. The performance of the developed FISs is rigorously tested with the variation of different fault parameters, and different location of the TCSC. The simulated results indicate that the proposed scheme performance is reliable in its zone of protection.

5

Communication assisted fuzzy based adaptive protective relaying scheme for microgrid

Chaitanya B. K., Soni A. K., Yadav A.

Journal of Power Technologies

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2018

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Vol. 98, nr 1

57--69

EN

This study proposes a communication assisted fuzzy based adaptive protective relaying scheme for fault detection, fault classification and faulty phase identification of microgrid along with a solution to isolate the microgrid from the utility grid by disconnecting the static-switch. Any fault in the utility grid causes the microgrid to be isolated from the utility grid whereas if there is a fault in the microgrid it continues to operate with the utility grid. An adaptive fuzzy inference system has been developed using a separate fuzzy rule base for the two modes of operation of microgrid, i.e. islanded mode or grid connected mode. The Central Grid Status Communication System (CGSCU) is considered which monitors the status of PCC and sends a command signal to the relays so that the relay settings are updated with new rules for any transition in the mode of the microgrid. The fundamental phasor amplitude and zero sequence component of current signals are used as input features, fault detection, fault classification and faulty phase identification. A standard microgrid model IEC 61850-7-420 was simulated using MATLAB/SIMULINK. The proposed method is tested for all types of faults by varying fault parameters and also for dynamic situations such as connection/disconnection of DGs and loads. The test results substantiate the effectiveness of the method.

6

A relaying scheme for detection and classification of shunt faults in six-phase transmission system based on DFT-FIS approach

Ashok V., Yadav A.

Journal of Power Technologies

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2018

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Vol. 98, nr 2

202--211

EN

Hitherto many schemes based on the fuzzy system have been protected by a three-phase transmission system, but not by a six-phase transmission system. This paper sets out a novel protection scheme based on DFT-FIS approach for detection/classification of shunt faults in a six-phase transmission system. In this scheme, two separate DFT-FIS modules have been designed to detect the presence of fault in any of the six-phase(s) and to identify the presence of ground in the fault loop, thus classifying all 120 types of fault in a six-phase transmission line. The six-phase voltage and current signals are collected at one end of the transmission line only, thus circumvent dependence on a communication link for remote end data. A widerange of fault simulation studies were carried out in MATLAB/Simulink environment for all possible shunt fault combinations by varying fault locations, fault inception angle, fault resistance, short circuit capacity (SCC) of the source and at various fault conditions such as: close-in faults, remote-end faults, high resistance faults, including CT saturation. Furthermore, the relay operation time in fault detection/classification is less than one-cycle (<16.67ms) and since the scheme does not experience any malfunction it is deemed reliable and adaptable.

7

Automatic Fault Classification for Journal Bearings Using ANN and DNN

Narendiranath Babu T., Aravind A., Rakesh A., Jahzan M., Rama Prabha D., Ramalinga Viswanathan M.

Archives of Acoustics

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2018

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Vol. 43, No. 4

727--738

EN

Journal bearings are the most common type of bearings in which a shaft freely rotates in a metallic sleeve. They find a lot of applications in industry, especially where extremely high loads are involved. Proper analysis of the various bearing faults and predicting the modes of failure beforehand are Essentials to increase the working life of the bearing. In the current study, the vibration data of a journal Bering in the healthy condition and in five different fault conditions are collected. A feature extraction metod is employed to classify the different fault conditions. Automatic fault classification is performed using artificial neural networks (ANN). As the probability of a correct prediction goes down for a higher number of faults in ANN, the method is made more robust by incorporating deep neural networks (DNN) with the help of autoencoders. Training was done using the scaled conjugate gradient algorithm and the performance was calculated by the cross entropy method. Due to the increased number of hidden layers in DNN, it is possible to achieve a high efficiency of 100% with the feature extraction method.

8

Application of EMD ANN and DNN for Self-Aligning Bearing Fault Diagnosis

Narendiranath B. T., Aravind A., Rakesh A., Jahzan M., Rama P. D.

Archives of Acoustics

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2018

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Vol. 43, No. 2

163--175

EN

Self-aligning roller bearings are an integral part of the industrial machinery. The proper analysis and prediction of the various faults that may happen to the bearing beforehand contributes to an increase in the working life of the bearing. This study aims at developing a novel method for the analysis of the various faults in self-aligning bearings as well as the automatic classification of faults using artificial neural network (ANN) and deep neural network (DNN). The vibration data is collected for six different faults as well as for the healthy bearing. Empirical mode decomposition (EMD) followed by Hilbert Huang transform is used to extract instantaneous frequency peaks which are used for fault analysis. Time domain and time-frequency domain features are then extracted which are used to implement the neural networks through the pattern recognition tool in MATLAB. A comparative study of the outputs from the two neural networks is also performed. From the confusion matrix, the efficiency of the ANN has been found to be 95.7% and using DNN has been found to be 100%.

9

Supervised and unsupervised learning process in damage classification of rolling element bearings

Strączkiewicz M., Czop P., Barszcz T.

Diagnostyka

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2016

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Vol. 17, No. 2

71--80

EN

Damage classification plays a crucial role in the process of management in nearly every branch of industry. In fact, is becomes equally important as damage detection, since it can provide information of malfunction severity and hence lead to improvement of a production or manufacturing process. Within this paper selected supervised and unsupervised pattern recognition methods are employed for this purpose. The attention of the authors is given to assessment of selection, performance benchmarking and applicability of selected pattern recognition methods. The investigation is performed on the data collected using an experimental test grid and rolling element bearing with deteriorating condition of an outer race.

PL

Klasyfikacja uszkodzeń odgrywa ważną rolę w procesie zarządzania w niemalże każdej gałęzi przemysłu. W rzeczywistości staje się ona równie istotna co samo wykrywanie uszkodzenia ponieważ pozwala określić stopień uszkodzenia, a co za tym idzie, poprawić efektywność zarządzania zakładem przemysłowym. W tym celu wykorzystano wybrane nadzorowane i nienadzorowane metody rozpoznawania wzorców. W artykule zwrócono uwagę na ocenę wyboru, porównanie wydajności oraz możliwości wykorzystania tych metod. Analiza przeprowadzona została na danych zgromadzonyh na eksperymentalnym stanowisku testowym, gdzie obserwowany jest stan łożyska tocznego z pogłębiającym się uszkodzeniem bieżni zewnętrznej.

10

Faults Classification Of Power Electronic Circuits Based On A Support Vector Data Description Method

Cui J.

Metrology and Measurement Systems

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2015

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Vol. 22, nr 2

205--220

EN

Power electronic circuits (PECs) are prone to various failures, whose classification is of paramount importance. This paper presents a data-driven based fault diagnosis technique, which employs a support vector data description (SVDD) method to perform fault classification of PECs. In the presented method, fault signals (e.g. currents, voltages, etc.) are collected from accessible nodes of circuits, and then signal processing techniques (e.g. Fourier analysis, wavelet transform, etc.) are adopted to extract feature samples, which are subsequently used to perform offline machine learning. Finally, the SVDD classifier is used to implement fault classification task. However, in some cases, the conventional SVDD cannot achieve good classification performance, because this classifier may generate some so-called refusal areas (RAs), and in our design these RAs are resolved with the one-against-one support vector machine (SVM) classifier. The obtained experiment results from simulated and actual circuits demonstrate that the improved SVDD has a classification performance close to the conventional one-against-one SVM, and can be applied to fault classification of PECs in practice.

11

A real-valued genetic algorithm to optimize the parameters of support vector machine for classification of multiple faults in NPP

Amer F. Z., El-Garhy A. M., Awadalla M. H., Rashad S. M., Abdien A. K.

Nukleonika

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2011

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Vol. 56, No. 4

323-332

EN

Two parameters, regularization parameter c, which determines the trade off cost between minimizing the training error and minimizing the complexity of the model and parameter sigma (σ) of the kernel function which defines the non-linear mapping from the input space to some high-dimensional feature space, which constructs a non-linear decision hyper surface in an input space, must be carefully predetermined in establishing an efficient support vector machine (SVM) model. Therefore, the purpose of this study is to develop a genetic-based SVM (GASVM) model that can automatically determine the optimal parameters, c and sigma, of SVM with the highest predictive accuracy and generalization ability simultaneously. The GASVM scheme is applied on observed monitored data of a pressurized water reactor nuclear power plant (PWRNPP) to classify its associated faults. Compared to the standard SVM model, simulation of GASVM indicates its superiority when applied on the dataset with unbalanced classes. GASVM scheme can gain higher classification with accurate and faster learning speed.

12

Analog Circuit Fault Classification Using Improved One-Against-One Support Vector Machines

Cui J., Wang Y.

Metrology and Measurement Systems

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2011

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Vol. 18, nr 4

569-582

EN

This paper presents a novel strategy of fault classification for the analog circuit under test (CUT). The proposed classification strategy is implemented with the one-against-one Support Vector Machines Classifier (SVC), which is improved by employing a fault dictionary to accelerate the testing procedure. In our investigations, the support vectors and other relevant parameters are obtained by training the standard binary support vector machines. In addition, a technique of radial-basis-function (RBF) kernel parameter evaluation and selection is invented. This technique can find a good and proper kernel parameter for the SVC prior to the machine learning. Two typical analog circuits are demonstrated to validate the effectiveness of the proposed method.

13

A novel approach of analog fault classification using a support vector machines classifier

Cui J., Wang Y.

Metrology and Measurement Systems

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2010

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Vol. 17, nr 4

561-581

EN

In order to make the analog fault classification more accurate, we present a method based on the Support Vector Machines Classifier (SVC) with wavelet packet decomposition (WPD) as a preprocessor. In this paper, the conventional one-against-rest SVC is resorted to perform a multi-class classification task because this classifier is simple in terms of training and testing. However, this SVC needs all decision functions to classify the query sample. In our study, this classifier is improved to make the fault classification task more fast and efficient. Also, in order to reduce the size of the feature samples, the wavelet packet analysis is employed. In our investigations, the wavelet analysis can be used as a tool of feature extractor or noise filter and this preprocessor can improve the fault classification resolution of the analog circuits. Moreover, our investigation illustrates that the SVC can be applicable to the domain of analog fault classification and this novel classifier can be viewed as an alternative for the back-propagation (BP) neural network classifier.

14

Optimising a fuzzy fault classification tree by a single-objective genetic algorithm

Zio E., Baraldi P., Popescu I. C.

Journal of Polish Safety and Reliability Association

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2007

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Vol. 2

391--400

EN

In this paper a single-objective Genetic Algorithm is exploited to optimise a Fuzzy Decision Tree for fault classification. The optimisation procedure is presented with respect to an ancillary classification problem built with artificial data. Work is in progress for the application of the proposed approach to a real fault classification problem.

15

Wnioskowanie rozmyte w zastosowaniu do klasyfikacji zwarć dla potrzeb zabezpieczeniowych

Lisik I., Rosołowski E.

Przegląd Elektrotechniczny

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2003

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R. 79, nr 9(1)

544-548

PL

W artykule przedstawiona jest metoda klasyfikacji zwarć w sieciach elektroenergetycznych, w której wykorzystuje się technikę wnioskowania rozmytego. Decyzja układu klasyfikującego rodzaj zwarcia jest podejmowana na podstawie analizy rozmytych relacji kątowych pomiędzy wektorami składowych symetrycznych prądów mierzonych w stacji. Zalety proponowanej procedury identyfikacji zwarcia potwierdzone zostały przeprowadzonymi obszernymi badaniami symulacyjnymi, które są zamieszczone w pracy. Pokazano główne korzyści wynikające ze stosowania tego rozwiązania w automatyce zabezpieczeniowej.

EN

Fuzzy logic based fault type classification algorithm for transmission lines is presented in the paper. The fuzzy relations between angles of 3-phase currents symmetrical components are used to detect of a faulty phase. Two different criterion values are used: an angle between negative- and positive-sequence currents, and angle between zero- and negative-sequence currents. It was shown that the proposed method has good accuracy within satisfactory decision period. Attached examples illustrate basic characteristics of the proposed method. It is specially suited for high fault resistance.