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Comparative Study for Discrete Wavelet Transform Between Single and Double Detection Approach to Fault Classification on Transmission Line
(2024-01-01)
Lertwanitrot, Praikanok
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Ngaopitakkul, Atthapol
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Ananwattanaporn, Santipont
This study presented a novelty method for a protection relay using a microcontroller. The method able to detect and classify faults in high voltage transmission lines based on the Discrete Wavelet transform (DWT). In addition, novelty of proposed method when compared to traditional methods was a signal analysis process applied Clark's Transformation and Double Detection Technique. A performance of proposed method was verified by created experimental model in our KMITL laboratory. All fault types (SL, LL, DLG and 3P) were observed. This result of this study found that not only applied DWT method can improved the accuracy of signal analysis, applied Clark's Transformation able to filter noise of signal, without loss of information. Meanwhile, Double Detection Technique able to re-confirm the accuracy of fault detection and classify. Therefore, it results in the performance of a protection relay increase which is beneficial to the electrical system. It can increase the accuracy and reliability of the system.
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Comparison of artificial intelligence methods for fault classification of the 115-kv hybrid transmission system
(2020-06-01)
Klomjit, Jittiphong
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Ngaopitakkul, Atthapol
This research proposes a comparison study on different artificial intelligence (AI) methods for classifying faults in hybrid transmission line systems. The 115-kV hybrid transmission line in the Provincial Electricity Authority (PEA-Thailand) system, which is a single circuit single conductor transmission line, is studied. Fault signals in the transmission line were generated by the EMTP/ATPDraw software. Various factors such as fault location, type, and angle were considered. Then, fault signals were analyzed by coefficient details on the first scale of the discrete wavelet transform. Daubechies mother wavelet from MATLAB software was used to decompose the fault signal. The coefficient value of the mother wavelet behaved depending on the position, inception of fault angle, and fault type. AI methods including probabilistic neural networks (PNNs), back-propagation neural networks (BPNNs), and support vector machine (SVM) were used to identify faults. AI input used the maximum first peak coefficients of phase ABC and zero sequence. The results obtained from the study were found to be satisfactory with all AI methodologies having an average accuracy of more than 98% in the case study. However, the SVM technique can provide more accurate results than the PNN and BPNN techniques with less computation burden. Thus, it is suitable for being applied to actual protection systems.
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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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Fault classifications in distribution systems consisting of wind power as distributed generation using discrete wavelet transforms
(2019-12-01)
Patcharoen, Theerasak
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Ngaopitakkul, Atthapol
This paper proposed a fault type classification algorithm in a distribution system consisting of multiple distributed generations (DGs). The study also discussed the changing of signal characteristics in the distribution system with DGs during the occurrence of dierent fault types. Discrete Wavelet Transform (DWT)-based signal processing has been used to construct a classification algorithm and a decision tree to classify fault types. The input data for the algorithm is extracted from the three-phase current signal under normal conditions and during fault occurrence. These signals are recorded from the substation, load, and DG bus. The performance of the proposed classifying algorithm has been tested on a simulation system that was modeled after part of Thailand's 22 kV distribution system, with a 2-MW wind power generation as the DG, connected to the distribution line by PSCAD software. The parameters that were taken into consideration consisted of the fault type, location of the fault, location of DG(s), and the number of DGs, to evaluate the performance of the proposed algorithm under various conditions. The result of the simulation indicated significant changes in current signal characteristics when installing DGs. In addition, the proposed algorithm has achieved a satisfactory accuracy in terms of identifying and classifying fault types when applied to a distribution system with multiple DGs.
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Fault classification on the hybrid transmission line system between overhead line and underground cable
(2017-08-30)
Klomjit, Jittiphong
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Ngaopitakkul, Atthapol
This paper illustrates fault classification on hybrid transmission line. Current signals were analyzed by coefficients of discrete wavelet transform (DWT). Daubechies4 (db4) is employed as mother wavelet to decompose high frequency components from fault signals. In this paper, ATP/EMTP is used to simulate fault signal from current signals. Hybrid system between overhead line and underground cable of 115 kV from Provincial Electricity Authority (PEA-Thailand) system in case single circuit single conductor with overhead and underground was used as simulation case study. Various factors such as location of fault, fault type and fault angle have been taken into consideration. DWT is then applied on phase current and zero sequence signals using MATLAB software in order to obtain coefficient in scale 1 for further analysis. This value is mainly used to design algorithm for fault classification. Result obtained from the study is satisfactory.
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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 probabilistic neural network algorithm for classification of fault type in underground cable
(2012-01-01)
Ngaopitakkul, A.
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Suttisinthong, N.
This paper proposes an algorithm based on a combination of discrete wavelet transform (DWT) and probabilistic neural network (PNN) for classifying fault types on underground cable. Simulations and the training process for the PNN are performed using ATPIEMTP and MATLAB. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these signals. The maximum coefficients of DWT of phase A, B, C and zero sequence for post-fault current waveforms are used as an input for the training pattern. Various cases studies based on Thailand electricity distribution underground systems have been investigated so that the algorithm can be implemented. The coefficients of DWT are also compared with those of PNN in this paper. The results show that the proposed algorithm is capable of performing the fault classification with satisfactory accuracy. © 2012 IEEE.
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Discrete wavelet transform and back-propagation neural networks algorithm for fault classification in underground cable
(2011-07-26)
Kaitwanidvilai, S.
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Pothisarn, C.
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Jettanasen, C.
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Chiradeja, P.
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Ngaopitakkul, A.
This paper proposes a new technique using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) for fault classifications on underground cable. Simulations and the training process for the back-propagation neural network are performed using ATP/EMTP and MATLAB. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these signals. Positive sequence current signals are used in fault detection decision algorithm. The variations of first scale high frequency component that detect fault are used as an input for the training pattern. Various cases studies based on Thailand electricity distribution underground systems have been investigated so that the algorithm can be implemented. The results are shown that an average accuracy values obtained from BPNN can indicate the fault classification with satisfactory accuracy, and will be very useful in the development of a power system protection scheme.
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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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