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Subject: search.filters.subject.Fault Classification

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    Combining Fuzzy Logic and Discrete Wavelet Transform for Accurate Fault and Inrush Current Classification in High Voltage Capacitor Banks
    (2024-01-01)
    Songsukthawan, Panapong
    ;
    Patcharoen, Theerasak
    ;
    Ananwattanaporn, Santipont
    ;
    Yoomak, Suntiti
    ;
    Pothisarn, Chaichan
    The high voltage capacitor bank is a critical component in substations, essential for maintaining power quality and system stability. However, these banks are susceptible to faults and inrush currents, posing significant operational challenges. This paper presents a method for accurately classifying fault and inrush currents in high voltage capacitor banks using Fuzzy Logic and Discrete Wavelet Transform (DWT). The DWT decomposes current waveforms into frequency components, enabling the extraction of features that characterize faults and inrush currents. These features are processed by a Fuzzy Inference System (FIS), which classifies the events based on predefined rules and membership functions. The integration of DWT and FIS provides a robust framework for distinguishing between different types of faults and inrush currents with high accuracy. Simulation results demonstrate the proposed method's efficacy, showing improved performance in classification accuracy and noise robustness compared to traditional techniques. This research enhances monitoring and protection systems in power networks, ensuring more reliable operation of high voltage capacitor banks. Implementing this method allows for better fault management and minimized downtime, leading to improved overall system efficiency and stability.
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    Classification of Fault Type on Loop-Configuration Transmission System Using Support Vector Machine
    (2017-11-15)
    Sreewirote, Bancha
    ;
    Ngaopitakkul, Atthapol
    This paper proposed to applied Support vector machine (SVM) algorithm for classified the fault type on the 500 kV transmission systems with connected in loop configuration. The fault signal was simulated using ATPDraw/EMTP program at frequency 200 kHz. The fault detection was analyzing the high frequency component by discrete wavelet transform (DWT). For the first stage, the coefficient of DWT was used for the fault detection. After the fault can be detected, the fault classification will be identified using SVM algorithm. The maximum coefficient from wavelet transform was used as input pattern of SVM to classify the type of fault. The input pattern of SVM consists of 4 input; maximum coefficient of DWT in all phase current and zero sequence current. For the SVM process, the fault classification used the five model of SVM because each model is working in parallel to avoid mistake (or error). In addition, the same input in five model were simultaneously used while the output of each models is differently according to specification of model. The overall result of 2160 case studies data can be summarized that the fault classification using SVM algorithm is highly satisfactory.
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    An application of discrete wavelet transform and support vector machines algorithm for classification of fault types on underground cable
    (2012-12-12)
    Ngaopitakkul, A.
    ;
    Pothisarn, C.
    ;
    Bunjongjit, S.
    ;
    Suechoey, B.
    This paper proposes a new technique using discrete wavelet transform (DWT) and support vector machines (SVM) to classify the fault types in underground distribution systems. The DWT is used to detect the high frequency components from fault 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 SVM. Various cases studies based on Thailand electricity underground distribution systems have been investigated so that the algorithm can be implemented. SVM is also compared with the coefficients DWT comparison technique. The proposed method gives satisfactory accuracy, and will be very useful in the development of a modern protection scheme for electrical power transmission and distribution systems. © 2012 IEEE.
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    Discrete wavelet transform and support vector machines algorithm for classification of fault types on transmission line
    (2012-01-01)
    Kunadumrongrath, K.
    ;
    Ngaopitakkul, A.
    This paper proposes a new technique using discrete wavelet transform (DWT) and support vector machines (SVM) to classify the fault types on transmission systems. The DWT is used to detect the high frequency components from fault 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 SVM. Various cases studies based on Thailand electricity transmission systems have been investigated so that the algorithm can be implemented. SVM is also compared with the comparison of the coefficients DWT technique as well as back-propagation neural network algorithm. The proposed method gives satisfactory accuracy, and will be very useful in the development of a modern protection scheme for electrical power transmission systems.