KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Jettanasen, ChaiyanSubject: search.filters.subject.Discrete wavelet transform

Search Results

Now showing 1 - 3 of 3
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Comparison of various mother wavelets for fault classification in electrical systems
    (2020-02-01)
    Pothisarn, Chaichan
    ;
    Klomjit, Jittiphong
    ;
    Ngaopitakkul, Atthapol
    ;
    Jettanasen, Chaiyan
    ;
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    The comparisons technique of coefficient DWT for identifying simultaneous fault types on transmission system
    (2011-10-01)
    Ngaopitakkul, Atthapol
    ;
    Jettanasen, Chaiyan
    Several decision algorithms have been reported in the literature for fault classification, but the effects of simultaneous faults have been neglected. This paper is focused on the decision algorithm for identifying the types of simultaneous fault along the transmission systems using discrete wavelet transform (DWT). The comparison of coefficients DWT has been carried out. DWT is used in order to detect high frequency components of the fault current signals. 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 comparison indicator in order to classify fault types. Various cases based on Thailand electricity transmission systems are studied to verify the validity of the proposed algorithm. It is found that the technique proposed in this paper gives satisfactory results to precisely identify simultaneous fault types on transmission systems. © ICIC International 2011.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Combination of discrete wavelet transform and probabilistic neural network algorithm for detecting fault location on transmission system
    (2011-04-01)
    Ngaopitakkul, Atthapol
    ;
    Jettanasen, Chaiyan
    This paper proposes a new algorithm for detecting faults in an electrical power transmission system, using discrete wavelet transform (DWT) and probabilistic neural network (PNN). Fault conditions are simulated using ATP/EMTP to obtain current signals. The algorithm used to analyze fault locations is developed on MATLAB. Fault detection is processed using the positive sequence current signals. The comparison among the maximum coefficients in first scale of each bus which can detect fault is performed in order to detect the faulty bus. The first peak time obtained from the faulty bus is used as an input for training pattern. Various cases based on Thailand electricity transmission systems are studied to verify the validity of the proposed technique. The result shows that the algorithm is capable of performing the fault locations with accuracy. ICIC International © 2011.