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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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Discrete wavelet transform for improving the accuracy of an unbalance current protection relay due to transient fault and inrush current signals
(2019-05-01)
Patcharoen, Theerasak
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Ngaopitakkul, Atthapol
An unbalance current protection relay (60C, 51NC) cannot operate fast enough to avoid catastrophic failure against high system fault currents within capacitor units because traditional unbalance current protection relays are not capable of distinguishing and classifying transient fault current and switching inrush current. This paper proposes a new algorithm for the detection and classification of such current signals. The program used for the simulation was PSCAD/EMTDC. The current signals output was used as discrete wavelet transform (DWT) input. The wavelet-based fault and inrush current detection and classification unit received the various captured transient current signals, and used DWT to detect and analyse various cases studies. The actual inrush current signals from both the experimental setup and testing on an actual 115 kV HV shunt capacitor bank were used to verify the proposed algorithm. Results show that the proposed algorithm is effective at accurately identifying fault and inrush currents.
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Application of discrete wavelet transform and back-propagation neural network for internal and external fault classification in transformer
(2019-01-01)
Ngaopitakkul, Atthapol
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Jettanasen, Chaiyan
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Asfani, Dimas Anton
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Negara, Yulistya
This paper proposes an algorithm for internal and external fault discrimination in the three-phase two-winding power transformer based on a combination of discrete wavelet transform (DWT) and back-propagation neural network (BPNN). The maximum ratio obtained from division algorithm between DWT coefficient value of differential current and zero sequence component in post-fault condition differential current signals is employed as an input for the training pattern for BPNN in order to discriminate between internal fault and external short circuit. The proposed algorithm performance has been test using various cases studies based on Thailand electricity transmission and distribution systems data. Results show that the proposed technique can achieved satisfy accuracy for internal and external fault detection and discrimination in the considered system. This methodology and result can be used to further improve protection system of power transformer in the future.
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Application of support vector machines algorithm for discriminating between external fault and internal winding fault in power transformer
(2014-02-04)
Ngaopitakkul, Atthapol
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Jettanasen, Chaiyan
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Leelajindakrairerk, Monthon
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Pothisarn, Chaichan
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Suechoey, Boonlert
The differential relaying principle is used for protection of medium and large power transformers. In the past decade, several decision algorithms have different solutions and techniques. This paper proposes a new technique using discrete wavelet transform (DWT) and support vector machines (SVM) to classify and discriminate between external fault and internal fault in power transformer. The DWT is used to detect the high frequency components from fault signals. The variations of first scale high frequency component that detects fault are used as input for the SVM. The proposed method gives satisfactory accuracy, and will be very useful in the development of a modern protection scheme for electrical power transmission systems. © 2014 ICIC International.
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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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The combination of discrete wavelet transform and fuzzy logic algorithm for fault classification on transmission system
(2012-10-01)
Ngaopitakkul, Atthapol
In the literature for fault classification, several decision algorithms have different solutions and techniques. The most research works have only considered the fault diagnosis for single bus systems and two-bus systems. In fact, transmission lines are connected to each other and become a large grid connected system. During faults, it is necessary for the protection system to deal with a complicated transmission network. This paper proposes a new technique using discrete wavelet transform (DWT) and Fuzzy Logic in order to identify the fault types on transmission systems. The DWT is used to detect the high frequency components from these signals. Positive sequence current signals are used in fault detection decision algorithm. The variations of first scale high frequency component that detects fault are used as an input for the fuzzy logic. Various cases studies based on Thailand electricity transmission systems have been investigated so that the algorithm can be implemented. Fuzzy logic is also compared with the comparison of the coefficients DWT technique. The proposed method gives satisfactory accuracy, and will be very useful in the development of a modern protection scheme for electrical power transmission systems. ICIC International © 2012.
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The protection of transmission network systems using Discrete Wavelet transforms
(2012-09-01)
Ngaopitakkul, Atthapol
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Pothisarn, Chaichan
This paper proposes a novel protective relay algorithm to protect transmission systems using Discrete Wavelet Transforms (D WT). Fault conditions are simulated using PSCAD/EMTDC in order to obtain current signals. Various cases based on Thailand electricity transmission systems are carried out to verify the validity of the proposed technique. The maximum coefficients of the positive sequence current obtained from all buses are compared in order to detect the faulty bus on a three-bus transmission network with a loop structure. The first peak time of positive sequence current obtained from the faulty bus is used as input data for travelling wave equation. The coefficient ratio between buses that the fault occurs is calculated so that the proper protective relay sequence can be selected. The result is shown that the proposed algorithm is capable of locating the fault position as well as arranging the protective relay sequence, with satisfactory accu¬racy results, and is suitable for all types of fault that occurs in different sections on the transmission lines. © 2012 ICIC International.
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Selection of proper activation functions in back-propagation neural networks algorithm for identifying the phase with fault appearance in transformer windings
(2012-06-01)
Ngaopitakkul, Atthapol
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Jettanasen, Chaiyan
This paper presents an algorithm based on a combination of Discrete Wavelet Transforms and back-propagation neural networks for identifying the types of fault including the phase with fault appearance of a two-winding three-phase power transformer. Fault conditions of the transformer are simulated using ATP/EMTP in order to obtain current signals. The training process for the neural network and fault diagnosis decision are implemented using toolboxes on MATLAB. Various cases and fault types based on Thailand electricity transmission and distribution systems are studied to verify the validity of the algorithm. Various activation functions in each hidden layer and the output layer are compared in order to select the best activation function for identifying the types of internal fault of the transformer winding. It is found that average accuracy obtained from hyperbolic tangent-hyperbolic tangent-linear activation function gives satisfactory accuracy, and will be particularly useful in the development of a modern differential relay. © 2012 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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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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