There has been continuous research in the energy distribution sector because of its huge impact on modern societies. Nonetheless, aerial high voltage power lines are still supported by old transmission towers which involve some serious risks. Those risks may be avoided with periodic and expensive reviews. The main objective of this work is to reduce the number of these periodic reviews so that the maintenance cost of power lines is also reduced. More specifically, the work is focused on reducing the number of periodic reviews of transmission towers to avoid step and touch potentials, which are very dangerous for humans. A virtual organization-based multi-agent system is proposed in conjunction with different artificial intelligence methods and algorithms. The developed system is able to propose a sample of transmission towers from a selected set to be reviewed. The system ensures that the whole set will have similar values without needing to review all the transmission towers. As a result of this work, a website application is provided to manage all the review processes and all the transmission towers of Spain. It allows the companies that review the transmission towers to initiate a new review process for a whole line or area, while the system indicates the transmission towers to review. The system is also able to recommend the best place to locate a new transmission tower or the best type of structure to use when a new transmission tower must be used. ; This work has been supported by the European Commission H2020 MSCARISE-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 realtime 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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    Title :

    Agent-based tool to reduce the maintenance cost of energy distribution networks


    Contributors:

    Publication date :

    2017-10-23


    Remarks:

    oai:zenodo.org:1158002
    Knowledge and Information Systems 1-17



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629





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