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Adaptive DOCR Coordination in Loop Electrical Distribution System With DG Using Artificial Neural Network LMBP

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dc.date.accessioned 2019-02-15T03:44:35Z
dc.date.available 2019-02-15T03:44:35Z
dc.date.issued 2018-11-22
dc.identifier.uri http://dspace.hangtuah.ac.id/xmlui/handle/dx/765
dc.description.abstract Abstract - To design the coordination protection for passive distribution system is not the tough work, while active or mesh distribution system which consists many distributed generators is quite more challenge for protection engineers. Additionally, the short circuit current will also vary if any DG in the system is offline, which causes to re-coordinate the relay protection in the system. To reset the relay protection, the engineers need more time. However In order to reduce the time of relay setting calculation, the adaptive protection coordination is proposed in this study by using artificial neural network. The study bases on the combinations of DGs’ state and the current short circuit levels as the input data and low setting of the directional overcurrent relays (DOCR) as the output data training. This research is conducted on modified IEEE 9-bus system equipped with distributed generators. After reaching convergence of Levenberg-Marquardt Back Propagation (LMBP) learning process, the results of weights and biases are compiled into the master controller to control all of the relays in the whole system. It will generate the relay setting automatically base on the results of ANN training. The results of this research have been testified in Etap simulation successfully and it is obvious that LMBP neural network is a robust method to model adaptive relay coordination system. en_US
dc.language.iso en_US en_US
dc.publisher 2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) en_US
dc.subject Neural Network, Directional Over Current Relay, Distributed Generation, Loop system en_US
dc.title Adaptive DOCR Coordination in Loop Electrical Distribution System With DG Using Artificial Neural Network LMBP en_US
dc.type Article en_US

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