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Classification of Capacitor Bank Switching Using Fuzzy Interference Systems in 230 kV Substation
(2024-01-01)
Patcharoen, Theerasak
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Pothisarn, Chaichan
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Ngaopitakkul, Atthapol
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Ananwattanaporn, Santipont
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Lertwanitrot, Praikanok
Flexible 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.
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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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The comparisons technique of coefficient DWT for identifying simultaneous fault types on transmission system
(2011-10-01)
Ngaopitakkul, Atthapol
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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.
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Combination of discrete wavelet transform and probabilistic neural network algorithm for detecting fault location on transmission system
(2011-04-01)
Ngaopitakkul, Atthapol
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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.

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