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Author: search.filters.author.Jiriwibhakorn, SomchatSubject: search.filters.subject.Artificial neural networks (ANNs)

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    PEA distribution reliability (SAIFI, SAIDI) determination using artificial neural networks
    (2005-12-01)
    Kaewmanee, Keattisak
    ;
    Jiriwibhakorn, Somchat
    This paper purposes the methodology of SAIFI and SAIDI determination of Provincial Electricity Authority (PEA) distribution network in Thailand using Artificial Neural Networks (ANNs). Data used in this study was obtained from the Reliability Program[1]. The data of feeders 2 and 7 of Pattananikom substation was used as the examples in this research from January to July 2004. The three inputs of ANNs for reliability indices (SAIFI, SAIDI) consist of the number of customers behind the protective equipment, interruption frequency of protective equipment per month and total time interruption per month. Referring to the results obtained after inputting ANNs and SAIFI to be trained in neural networks, the mean absolute percentage error (mape) of feeders 2 and 7 are 0.0042%, 0.0168% respectively, while inputting SAIDI into the network, the mean absolute percentage error (mape) of feeders 2 and 7 are 0.6377%, 3.2942% respectively.
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    Network observability determination using artificial neural networks
    (2005-12-01)
    Tanprasert, Pornthep
    ;
    Jiriwibhakorn, Somchat
    This paper purposes a method for the determination of network observability of the Provincial Electricity Authority (PEA)'s power system in Thailand using the artificial neural networks (ANNs). The network observability problem related to the power system configuration or network topology, called the topological observability, is studied to solve the topological observability problem. The artificial neural networks (ANNs) based on back propagation learning are used as a tool to solve this problem.