An Ontology-Based Predictive Maintenance Tool for Power Substation Faults in Distribution Grid

Recent advances in Power Grid (PG) technology pose an important problem of measuring the effectiveness of power grid configurations. Current assessment models are not adequate to mitigate the setup issues due to the absence of a high-fidelity evaluation framework that can consider diverse scenarios based on the market interest. Consequently, we develop a highly flexible Ontology-based Evaluation System that can accommodate and assess different scenarios. The use of ontology as middleware is the best approach to produce an efficient, semantically aware, and operationally accurate system environment for managing flexibility in evaluation. The evaluation is made by predicting the failure intensity and subsequently generate a maintenance report of a particular configuration. The selection of the best configuration is made by comparing the maintenance report of different configurations. The developed evaluation system consists of three main components which are Configuration Generator Tool (GCT), Failure Prediction Model (FDM), and Hybrid Simulation Platform (HSP). The GCT is a knowledge-based system that provides a powerful tool for engineers to generate alternative configurations. The GCT data were collected from literature, validated by experts, and modeled using Web Ontology Language (OWL). While the HSP was developed using several modelings and ontology-based tools such as blender 3D modeling, unity 3d, asp.net, my sql, and apache Jena fuseki. Finally, the FDM was developed based on the impact and relationship of odd events to power grid components and the impact of a failed component to other components, the prediction is modeled using two methods Poisson Model and Likelihood Estimation Method.

@article{Mahmoud2020,
  title     = {An Ontology-Based Predictive Maintenance Tool for Power Substation Faults in Distribution Grid},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {11},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Moamin A. Mahmoud and Alicia Y.C. Tang and Kuganesan Kumar and Nur Liyana Law Mohd Firdaus Law and Mathuri Gurunathan and Durkasiny Ramachandran},
  doi       = {10.14569/IJACSA.2020.0111151},
  url       = {https://doi.org/10.14569/IJACSA.2020.0111151}
}