Pothisarn, Chaichan
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Preferred name
Pothisarn, Chaichan
Alternative Name
Pothisarn, C.
Main Affiliation
Email
chaichan.po@kmitl.ac.th
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Item type:Publication, Classification of Capacitor Bank Switching Using Fuzzy Interference Systems in 230 kV Substation(2024-01-01); ; ; Lertwanitrot, PraikanokFlexible AC transmission systems are used for enhancing the stability, transmission efficiency, and reliability of AC grids. Additionally, the most cost-effective devices for compensating reactive power are Mechanically Switched Capacitors (MSCs). This study proposes a novel algorithm for detection and capacitor bank switching transient signals in MSC, to prevent the protective relay maloperation by these transients. The Discrete wavelet transform (DWT) is used for effective time-frequency analysis and detection of measured three-phase current signals. DWT extracts the detailed wavelet coefficients of current signals at levels 1 to 30. In addition, the fuzzy inference system (FIS) has been used to determine the type of switching transient. The proposed combination of FIS and DWT has been tested on 230 kV substation and the result demonstrated precision for the identification and classification of both transient signals in MSC with 88% accuracy rate. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparison of various mother wavelets for fault classification in electrical systems(2020-02-01); ; ; Asfani, Dimas AntonThis 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.