Pothisarn, Chaichan
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Preferred name
Pothisarn, Chaichan
Alternative Name
Pothisarn, C.
Main Affiliation
Email
chaichan.po@kmitl.ac.th
5 results
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Item type:Publication, Identification of the fault location for three-terminal transmission lines using discrete wavelet transforms(2009-12-16) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Discrete wavelet transform and back-propagation neural networks algorithm for fault classification on transmission line(2009-12-16); 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 yourconsent settings
Item type:Publication, Application of nearest neighbor algorithm for critical clearing time (CCT) approximation(2002-12-01); The nearest neighbor algorithm (NNA) has been applied for calculating the critical clearing time (CCT) of a 4-machine 11 bus system under variations in load, fault location and network structure. The CCT is one significant factor for the operator to set the protective relays and circuit breakers to maintain the transient stability of the power systems during the large disturbances, e.g. three-phase-to-ground faults. In this paper, Sobol sequences (Sob) [1] was applied to the selection of the training patterns of the CCT. The results when compared with time domain simulation methods show less mean absolute errors than using a pseudo random choice of inputs. The nearest neighbor algorithm has been successfully applied for approximating the CCT of the system under those variations. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Critical Clearing Time Determination of EGAT System Using Artificial Neural Networks(2003-12-01); Currently, with the continuous interconnection and size increasing, the power systems become larger and more complex. Therefore, the study of transient stability for protection system design and planning is more difficult and takes more time due to system size and complexity. This paper proposes an application of Artificial Neural Networks (ANNs) in transient stability study, with fast access to the answer of the power system stability. ANNs was used to determine the critical clearing time (CCT) of the Electricity Generating Authority of Thailand (EGAT) system. The results show that designed ANNs can estimate the CCT correctly. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Discrete wavelet transform and back-propagation neural networks algorithm for fault location on single-circuit transmission line(2008-01-01); This paper proposes a technique using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) for locating of fault location on single circuit transmission lines. The ATP/EMTP was used to simulated fault signals. The mother wavelet daubechies4 (db4) is employed to decompose, high frequency component from these signals. The first peak time in first scale of each bus that can detect fault are used as input pattern for the training pattern. It is shown that the proposed technique gives satisfactory. © 2008 IEEE.