Health Index Prediction of Overhead Transmission Lines A Machine Learning Approach

Abstract : This paper presents an asset health index (HI) prediction methodology for high voltage transmission overhead lines (OHLs) using supervised machine learning and structured, unambiguous visual inspections. We propose a framework for asset HI predictions to determine the technical condition of individual OHL towers to improve grid reliability in a cost-effective manner. The paper focuses on asset HI prediction and the selection of the most parsimonious model. Based on the technical specifications and HI data, our methodology allows for the prediction of a HI for OHLs without HI data, and models asset aging behaviour. Technical specifications and the HI as defined in this paper are taken from the Estonian TSO periodical visual inspections implemented in 2018. The case study successfully demonstrates that the proposed methodology can predict tower HI values for a single OHL with nearly 80 percent accuracy without the need for additional measurements.
 ? Our framework learns from the examination data a transformer fleet, i.e. it is able to adapt to different maintenance policies. This results in a solution that may be adopted by already-existing maintenance strategies without disruption. ? Most are either focused on a specific aspect of PT maintenance like DGA data or take a more holistic approach to quantify the health index for the transformer. ? However, existing solutions are based on standard methods that do not take into consideration the specificities of a certain transformer fleet, such as their power load, weather exposure and maintenance policies. Furthermore, maintenance policies may be improved by leveraging more data sources.
 ? There are still some remaining problems that need to be investigated such as how to deal with new datasets to avoid overfitting problems or how to increase the accuracy for learning algorithms. ? One of the weaknesses of the ANN approach is the tendency to find only a local minimum in its training due to improper initial value. ? In this case, the optimization algorithms can be deployed to optimize the initial value and thus increase the accuracy of the neutral network training.
 The proposed framework allows utility companies to save considerable resources by reducing and improving the scheduling of oil analyses, as well as increasing the quality of service (QoS) standards of energy grids. Forecasting the health state of PT oil has the potential of saving utility companies considerable amounts of resources, as well as increasing the QoS standards of energy grids. We have proposed an approach that leverages diverse data sources and comprises of data aggregation and processing, modelling and interpretation. The predictive power of our approach enabled the creation of a metric that can be acted upon, thus allowing more informed maintenance policies.
 ? In recent years, the searching algorithms have been interested in by researchers to find the best subset of features in the feature selection problems . ? As mentioned above, the hybrid artificial intelligence approaches that use optimization algorithms to support leaning algorithms can overcome the weakness of single learning algorithms. ? Because the selection parameters of learning algorithms has a significant impact on its own usefulness and classification performance. ? Therefore, it is necessary to find the optimal value of these parameters to improve the accuracy of the prediction.
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