ON THE SEMANTIC SEGMENTATION AND VALIDATION OF ELECTRICAL SUBSTATIONS

Abstract. Converting deep learning methods from benchmark testing to real applications is highly sought after both in academia and the industry. Key challenges that remain are the performance of the methods on new datasets, the preprocessing of the data and the integration of the results into application pipelines. Specifically for the implementation of semantic segmentation procedures, each of these challenges are still very much the subject of research. In this paper, we present a testcase to digitally twin and validate an electrical substation. Concretely, we discuss the data processing, training and the follow up integration of the results in the validation pipeline. In the experiments, we show that 86% initial F1-score can be achieved using the proper transfer learning on 14 classes and that this results in a 97% recall on the validation and 80% recall on the digitization of the substation. Overall, we show that the segmentation significantly contributes to these processes and that they are absolutely necessary for the automation of the digital twinning.