WCSE 2017
ISBN: 978-981-11-3671-9 DOI: 10.18178/wcse.2017.06.082

An Intuitive Approach in Scada Systems by Using Artificial Neural Networks

Cem SEN, Ozhan OZKAN, Burcu ÇARKLI YAVUZ, Cenk YAVUZ

Abstract— In the light of technological development, monitoring and control of remote point workstations from a single control center can be an important place in all business areas. SCADA systems can be remote point control and monitoring jobs in business are quite busy and brought convenience and innovation are looking forward is playing an important role in the development of the projection. But in the process of the SCADA systems business problems occurring and that causes disruption of the handle. One of the most important problem is the data communication is dropped. Data transmission in remote point workstation are not intelligent devices (RTU), taking advantage of the learning method of historical data can be found in the system's intuitive approach to business process can continue and the solution to this problem. Intuitive approaches in recent years and use different application areas as well as control technology has been especially used in scada technology new. In this study, the energy sector, scada applications, and especially drinking water distribution networks unmanned operational operations (SCADA systems), see the heuristics approach can be applied to the note were asked to resign.

Index Terms— an intuitive approach, system, SCADA, artificial neural networks, Matlab, potable water, Network Management

Cem SEN
Kocaeli Water and Sewerage Administration, TURKEY
Ozhan OZKAN
Dept. of Electrical&Electronics Eng., Eng. Faculty, Sakarya University, TURKEY
Burcu ÇARKLI YAVUZ, Cenk YAVUZ
Dept. of Information Sys. Eng., Faculty of Comp. & Inf. Sci., Sakarya University, TURKEY

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Cite: Cem SEN, Ozhan OZKAN, Burcu ÇARKLI YAVUZ, Cenk YAVUZ, "An Intuitive Approach in Scada Systems by Using Artificial Neural Networks," Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering, pp. 476-480, Beijing, 25-27 June, 2017.