Jiriwibhakorn, Somchat
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Jiriwibhakorn, Somchat
Alternative Name
Jiriwibhakorn, S.
Jiriwibhakorn, Somchart
Main Affiliation
Email
somchat.ji@kmitl.ac.th
9 results
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Item type:Publication, Network observability determination using artificial neural networks(2005-12-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, PEA distribution reliability (SAIFI, SAIDI) determination using artificial neural networks(2005-12-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Critical generator and maximum power limit determination using neural networks(2002-12-01)The critical clearing time (CCT) often determines the maximum power output of a critical generator (CG) in a multi-machine network if transient stability is to be maintained under fault conditions. Using a time domain simulation method the CG can be identified and its maximum power limit (MPL) established. This paper proposes a novel method of using a neural network to predict the MPL of the CG. Both weighted and weightless neural networks are used and compared using Sobol sequences (Sob) to select the training patterns. The methods are tested on a 4 machine, 11 bus and a 10 machine, 39 bus New England system under variation of loading, fault location and network structures. It is shown that the permissible MPL of the CG can be established to within 5% of those obtained by time simulation. - 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, Neural networks for constrained transient stability flows(2002-01-01)A weighted neural network (WNN) and a weightless neural network (WLNN) were compared for output accuracy dependent on the number of training data and distribution. If the number of training inputs is limited, having an appropriate distribution is important. Sobol's method was used to generate a quasi-random sequence of training inputs, providing good coverage over a specified range. These Sobol sequences (Sob) were employed to select the training patterns for WNN and WLNN designed to determine the limiting power flows over critical lines under transient stability conditions of a 4-machine 11 bus and a 10-machine 39 bus New England system with variations in load level and fault location. The results indicate that the constrained flows to maintain given transient stability margins in operation can be efficiently estimated to better than 5% using both WNN and WLNN, but WLNN is recommended for its ease and speed of training. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Solving the economic dispatch problem by using differential evolution(2009-10-22) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fault diagnosis in transmission lines using wavelet transform analysis(2002-12-01); ; Kando, M.This paper presents a new method to diagnose faults in a transmission system, This is based on detecting high frequency components contained in a fault signal spectrum. The Discrete Wavelet Transform (DWT) is used in the analysis in order to classify fault types and to locate fault positions. Simulations are performed using ATP/EMTP. It is found that the proposed method gives satisfactory results, and will be very useful in the development of a power system protection scheme. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, PEA feeder losses calculation by using artificial neural networks(2005-12-01)