Abstract
There has been multiple research in the energy distribution sector over the last years because of the significant impact in societies. However, the use of aerial high voltage power lines involves some risks that may be avoided with periodic reviews. The objective of this work is to reduce the number of these reviews to reduce the maintenance cost of power lines. So the work is focused on the periodic review of transmission towers (TT). A virtual organization of agents in conjunction with different artificial intelligence methods and algorithms are proposed in order to reduce the number of TT to be reviewed. The proposed system is able to provide a sample of TT from a set of them, a whole line for example, to be reviewed and to ensure that the set will have similar values without needing to review all the TT. The result is a web application to manage all the review processes and all the TT of a country (Spain in this case). This allows the review companies to use the application either when they initiate a new review process for a whole line or area of TT, or when they want to place an entirely new set of TT, in which case the system would recommend the best place and the best type of structure to use.
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Acknowledgments
This work has been supported by the European Commission H2020 MSCA-RISE-2014: Marie Skłodowska-Curie project DREAM-GO Enabling Demand Response for short and real-time Efficient And Market Based Smart Grid Operation - An intelligent and real-time simulation approach ref 641794.
The research of Pablo Chamoso has been financed by the Regional Ministry of Education in Castilla y León and the European Social Fund (Operational Programme 2014-2020 for Castilla y León, EDU/310/2015 BOCYL).
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Chamoso, P., De Paz, J.F., Bajo, J., Villarrubia, G., Corchado, J.M. (2016). Predictive Analysis Tool for Energy Distribution Networks. In: Luaces , O., et al. Advances in Artificial Intelligence. CAEPIA 2016. Lecture Notes in Computer Science(), vol 9868. Springer, Cham. https://doi.org/10.1007/978-3-319-44636-3_25
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DOI: https://doi.org/10.1007/978-3-319-44636-3_25
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