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Classification of distribution power grid structures using inception v3 deep neural network

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Abstract

To maintain the supply of electrical energy, it is necessary that failures in the distribution grid are identified during inspections of the electrical power system before shutdowns occur. To automate the inspections, artificial intelligence techniques based on computer vision are proposed. Due to the low number of visible faults, it is difficult to train deep learning models based on images of electrical power system inspections. In this paper, it is proposed to use segmentation and edge detection techniques to increase the database, making classification possible using the Inception v3 deep neural network model. From a pre-processing using the Gaussian filter to smooth the image, the techniques of the threshold with binarization, adaptive binarization, and Otsu and riddler-calvard are used for segmentation; and for edge detection, the sobel and canny techniques are used. The Inception v3 had better results than VGG-16 and ResNet50, considering mean squared error, root mean square error, accuracy, precision, recall, F-measure, and speed to convergence in this application.

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  1. https://github.com/ElectricalPowerGrid/InspectionDataSet.

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Acknowledgements

We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), funding reference number DDG-2020-00034. Cette recherche a été financée par le Conseil de recherches en sciences naturelles et en génie du Canada (CRSNG), numéro de référence DDG-2020-00034.

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Authors and Affiliations

  1. Fondazione Bruno Kessler, Via Sommarive 18, Trento, Italy

    Stefano Frizzo Stefenon

  2. University of Udine, Via delle Scienze 206, Udine, Italy

    Stefano Frizzo Stefenon

  3. Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Pkwy, Regina, SK, S4S 0A2, Canada

    Kin-Choong Yow

  4. Electrical Engineering Department, Santa Catarina State University, R. Paulo Malschitzki 200, Joinville, SC, 89219-710, Brazil

    Ademir Nied

  5. Electrical Engineering Department, Regional University of Blumenau, Rua São Paulo 3250, Blumenau, SC, 89030-903, Brazil

    Luiz Henrique Meyer

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Stefenon, S.F., Yow, KC., Nied, A. et al. Classification of distribution power grid structures using inception v3 deep neural network. Electr Eng 104, 4557–4569 (2022). https://doi.org/10.1007/s00202-022-01641-1

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