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Development of overcurrent relay based on wavelet transform for fault detection in transmission line
(2024-12-01)
Ananwattanaporn, Santipont
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Lertwanitrot, Praikanok
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Ngaopitakkul, Atthapol
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Pothisarn, Chaichan
This study proposes a protection relay using a microcontroller to detect and classify faults in transmission lines based on the wavelet transform. An experimental model was constructed from an actual 115 kV transmission system prototype. The current signal was observed based on the fault type, phase, and position. Clark’s transform and the discrete wavelet transform (DWT) were applied to transform signals for analysis. Moreover, the performance of fault detection based on the output signals of Clark’s transform (alpha sequence, beta sequence, and zero sequence current) was compared to the performance of the alternative proposed fault detection method, which is based on the combining factor between alpha and beta sequence current. In addition, the influence of DWT level on fault analysis is also considered and is used to confirm the accuracy of fault detection. Results show that the proposed method is efficient for fault detection and classification. This finding allows the researcher to choose the appropriate analytical method. Moreover, it can also be used as the basis for overcurrent relay algorithm design in the effort to develop more advanced technologies.
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Artificial Intelligence based Faults Identification, Classification, and Localization Techniques in Transmission Lines-A Review
(2023-12-01)
Kanwal, Shazia
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Jiriwibhakorn, Somchat
An overview of the many methods used for fault detection, classification and location in the power system, particularly in transmission lines, is provided in this review, it also includes an experimental result of adaptive neuro-fuzzy inference system -based fault detection , fault classification and fault location. Being in operation outdoor environment, transmission lines are more vulnerable to various faults which may lead to system collapse in severe cases. Therefore, to ensure the reliable and safe operation of power system it is imperative to critically monitor the faults in transmission lines. In this regard, researchers around the globe have developed several techniques and constantly putting efforts to further improve the protection efficacy. The brief yet thorough analysis and comparison of the artificial intelligence-based techniques, hybrid methodologies and most recent approaches in the context of power system faults have been discussed and presented. In addition, the research work and the experimental results of an adaptive neuro-fuzzy inference system-based techniques have also been discussed for IEEE-9 bus system. The mean square error for testing data of ANFIS-based fault detection, classification, is zero and for fault location Mean square error is 5.32km. This piece of work could be helpful in the development of a comprehensive understanding of various artificial intelligence-based techniques within the realm of fault detection, classification and localization in transmission lines.
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Comparison of various mother wavelets for fault classification in electrical systems
(2020-02-01)
Pothisarn, Chaichan
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Klomjit, Jittiphong
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Ngaopitakkul, Atthapol
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Jettanasen, Chaiyan
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Asfani, Dimas Anton
This paper presents a comparative study on mother wavelets using a fault type classification algorithm in a power system. The study aims to evaluate the performance of the protection algorithm by implementing different mother wavelets for signal analysis and determines a suitable mother wavelet for power system protection applications. The factors that influence the fault signal, such as the fault location, fault type, and inception angle, have been considered during testing. The algorithm operates by applying the discrete wavelet transform (DWT) to the three-phase current and zero-sequence signal obtained from the experimental setup. The DWT extracts high-frequency components from the signals during both the normal and fault states. The coefficients at scales 1-3 have been decomposed using different mother wavelets, such as Daubechies (db), symlets (sym), biorthogonal (bior), and Coiflets (coif). The results reveal different coefficient values for the different mother wavelets even though the behaviors are similar. The coefficient for any mother wavelet has the same behavior but does not have the same value. Therefore, this finding has shown that the mother wavelet has a significant impact on the accuracy of the fault classification algorithm.
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An experimental setup investigation to study characteristics of fault on transmission system
(2015-01-01)
Yindeesap, P.
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Ngaopitakkul, A.
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Pothisarn, C.
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Jettanasen, C.
This paper proposes the experimental setup for studying the characteristics of fault caused by balance and unbalance on a transmission system. The parameters of transmission system (inductance and capacitance) are calculated based on forms of transmission tower, size of conductor, types of conductor and arrangement of transmission line and, they normalized to obtain the values in the π- equivalent circuit model at voltage level of 400 V. In addition, the ATP/EMTP is used to compare the simulated results with the experimental setup in order to show the advantage of the experimental setup. The obtained results show that the similarity between the two waveforms. The experimental setup will be useful in the development of short-circuit protection system in laboratory.
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Characteristics and behaviour of transient current during lightning strikes on transmission tower
(2015-01-01)
Lertwanitrot, P.
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Kettranan, P.
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Itthisathienkul, P.
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Ngaopitakkul, A.
This paper investigates on the characteristics of current when lightning strikes on the conductor of transmission lines. The impedance of multistory model based on the 115 kV transmission tower is employed to simulate in EMTP/ATPDraw program. The location of lightning strikes on the conductor on transmission lines and the ground resistance is varied. The characteristics of the peak amplitudes and the first peak time of current waveform are observed. The obtained results show that when lightning striking occurs near the substation, the peak amplitudes of current tend to increase with decreasing distance between the substation and the lightning location. In addition, the first peak time tends to decrease with decreasing distance between the substation and the lightning location, and its change will be very useful in the development of lightning location scheme.
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Identifying types of simultaneous fault in transmission line using discrete wavelet transform and fuzzy logic algorithm
(2013-07-17)
Ngaopitakkul, Atthapol
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Apisit, Chaowat
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Bunjongjit, Sulee
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Pothisarn, Chaichan
In the literature for fault classification, several decision algorithms have different solutions and techniques. These research works have been rarely mentioned about simultaneous faults in transmission systems. This paper presents the decision algorithm for identifying types of simultaneous fault along the transmission line. Decision algorithms based on discrete wavelet transform (DWT) and fuzzy logic are investigated. The analysis of fault signals is performed using DWT. The DWT is used in order to detect the high frequency components. The coefficient details (phase A, B, C and zero sequence of post-fault current signals) of DWT at the first peak time that positive sequence current can detect fault, are performed as an input for the fuzzy logic. The result shows that the accuracy of the proposed algorithm is highly satisfactory. © 2013 ICIC International.
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Selection of proper artificial neural networks for fault classification on single circuit transmission line
(2012-01-01)
Bunjongjit, Sulee
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Ngaopitakkul, Atthapol
This paper proposes a new technique using discrete wavelet transform (DWT) and artificial neural networks for fault classification on single circuit transmission line. Simulation and the training process for the artificial neural networks are performed using ATP/EMTP and MATLAB respectively. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these current signals. Positive sequence current signals are employed in faults detection decision algorithm. The variations of first scale high frequency component detecting faults are employed as an input for the training process. Back-propagation (BP) neural network, Radial basis function (RBF) neural network and Probabilistic neural network (PNN) are compared in this paper. The results are shown that average accuracy values obtained from PNN give satisfactory results with less training time. © 2012 ICIC International.
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Identification of the fault location for three-terminal transmission lines using discrete wavelet transforms
(2009-12-16)
Chiradeja, P.
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Pothisarn, C.
This paper proposes a technique to detect fault locations in a three-bus transmission system using discrete wavelet transform (DWT). The comparison among the first peak time in first scale of each terminal (buses) that can detect fault is performed and the two fastest first peak time obtained from comparison are used as an input data for traveling wave equation later. A comparison of results obtained from three different types of mother wavelet is discussed in order to identify the fault locations with an application of traveling wave theory. It is shown that the db4 mother wavelet produces better results than those from 'sym4' and 'coif4', with a mean error of less than 400 m.
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Discrete wavelet transform and back-propagation neural networks algorithm for fault classification on transmission line
(2009-12-16)
Pothisarn, C.
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Ngaopitakkul, A.
This paper proposes a technique using Discrete Wavelet Transform (DWT) and Back-Propagation Neural Network (BPNN) to identify the fault types on single circuit transmission lines. The ATP/EMTP is used to simulate fault signals. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these signals. The variations of first scale high frequency component that detect fault are used as an input for the training pattern. The result has shown that the proposed technique gives satisfactory results.
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Discrete wavelet transform and back-propagation neural networks algorithm for fault location on single-circuit transmission line
(2008-01-01)
Ngaopitakkul, Atthapol
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Pothisarn, Chaichan
This paper proposes a technique using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) for locating of fault location on single circuit transmission lines. The ATP/EMTP was used to simulated fault signals. The mother wavelet daubechies4 (db4) is employed to decompose, high frequency component from these signals. The first peak time in first scale of each bus that can detect fault are used as input pattern for the training pattern. It is shown that the proposed technique gives satisfactory. © 2008 IEEE.

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