KMITL

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

Browse

Search Results

Now showing 1 - 8 of 8

Some of the metrics are blocked by your
consent settings
An experimental setup investigation to study characteristics of fault on transmission system
(2015-01-01)
Yindeesap, P.
;
Ngaopitakkul, A.
;
Pothisarn, C.
;
Jettanasen, C.
This paper proposes the experimental setup for studying the characteristics of fault caused by balance and unbalance on a transmission system. The parameters of transmission system (inductance and capacitance) are calculated based on forms of transmission tower, size of conductor, types of conductor and arrangement of transmission line and, they normalized to obtain the values in the π- equivalent circuit model at voltage level of 400 V. In addition, the ATP/EMTP is used to compare the simulated results with the experimental setup in order to show the advantage of the experimental setup. The obtained results show that the similarity between the two waveforms. The experimental setup will be useful in the development of short-circuit protection system in laboratory.
Some of the metrics are blocked by your
consent settings
Application of DWT and fuzzy logic algorithm for classifying simultaneous fault types
(2011-12-01)
Pothisarn, C.
;
Ngaopitakkul, A.
This paper proposes a technique using discrete wavelet transform (DWT) and fuzzy logic for identifying types of simultaneous fault along the transmission systems. The PSCAD/EMTDC is used to simulate fault signals. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these signals. The coefficients detail (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, is performed as input variables for the proposed algorithm. The result shows that the proposed technique gives satisfactory accuracy, and will be very useful in the development of a power system protection scheme. © 2011 IEEE.
Some of the metrics are blocked by your
consent settings
DWT and RBF neural networks algorithm for identifying the fault types in underground cable
(2011-12-01)
Ngaopitakkul, A.
;
Pothisarn, C.
;
Bunjongjit, S.
;
Suechoey, B.
A new technique for classifying fault type in underground distribution system has been proposed. Discrete wavelet transform (DWT) and Radial basis function (RBF) neural network are investigated. Simulations and the training process for the RBF 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 output pattern of RBF is divided into two case studies training for comparison between classifying of the fault types and identifying the phase with fault appearance. The variations of first scale high frequency component that detect fault are used as an input for the training pattern. The comparison of the coefficients DWT is also compared with the RBF neural network in this paper. The result is shown that an average accuracy values obtained from RBF gives satisfactory results. © 2011 IEEE.
Some of the metrics are blocked by your
consent settings
Study of characteristics for simultaneous faults in distribution underground cable using DWT
(2011-12-01)
Ngaopitakkul, A.
;
Pothisarn, C.
;
Leelajindakrairerk, M.
In the literature for fault detection, most of research works have never been mentioned about effects of simultaneous faults. This paper presents behavior of characteristics for simultaneous fault signals in an electrical distribution underground cable using wavelet transform. The fault signal is simulated using ATP/EMTP, and the behavior analysis of signals is performed using discrete wavelet transform (DWT). The DWT is used to detect the high frequency components. The results obtained from the analysis will be useful in the development of a detect fault scheme for electrical distribution underground cable in the future due to an effect of the other fault that occurs at the other side of the system; this leads to the malfunction of the protective relays. © 2011 IEEE.
Some of the metrics are blocked by your
consent settings
Discrete wavelet transform and back-propagation neural networks algorithm for fault classification in underground cable
(2011-07-26)
Kaitwanidvilai, S.
;
Pothisarn, C.
;
Jettanasen, C.
;
Chiradeja, P.
;
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.
Some of the metrics are blocked by your
consent settings
Identification of fault locations in underground distribution system using Discrete Wavelet Transform
(2010-12-01)
Ngaopitakkul, A.
;
Apisit, C.
;
Pothisarn, C.
;
Jettanasen, C.
;
Jaikhan, S.
In this paper, a technique for detecting faults in underground distribution system is presented. Discrete Wavelet Transform (DWT) based on traveling wave is employed in order to detect the high frequency components and to identify fault locations in the underground distribution system. The first peak time obtained from the faulty bus is employed for calculating the distance of fault from sending end. The validity of the proposed technique is tested with various fault inception angles, fault locations and faulty phases. The result is found that the proposed technique provides satisfactory result and will be very useful in the development of power systems protection scheme.
Some of the metrics are blocked by your
consent settings
Discrete wavelet transform and back-propagation neural networks algorithm for fault classification on transmission line
(2009-12-16)
Pothisarn, C.
;
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.
Some of the metrics are blocked by your
consent settings
Identification of the fault location for three-terminal transmission lines using discrete wavelet transforms
(2009-12-16)
Chiradeja, P.
;
Pothisarn, C.
This paper proposes a technique to detect fault locations in a three-bus transmission system using discrete wavelet transform (DWT). The comparison among the first peak time in first scale of each terminal (buses) that can detect fault is performed and the two fastest first peak time obtained from comparison are used as an input data for traveling wave equation later. A comparison of results obtained from three different types of mother wavelet is discussed in order to identify the fault locations with an application of traveling wave theory. It is shown that the db4 mother wavelet produces better results than those from 'sym4' and 'coif4', with a mean error of less than 400 m.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results