A probabilistic sequence classification approach for early fault prediction in distribution grids using long short-term memory neural networks

•A novel and real-time applicable method for fault prediction in distribution grids is proposed.•The method uses machine learning on real data from a Danish DSO•A VSG method using an adaptive Gaussian Distribution is used to improve the method.•The method does not require high resolution data and requires no new hardware deployment.•The proposed method yields satisfying results with prediction accuracy as high as 90%. As the global power grid must undergo a profound transformation in the coming decades to ensure reliable and cost-effective operation in a system with large shares of intermittent renewable energy generation, a critical element will be to leverage advanced data-driven predictive tools to optimise grid management activities. As it is expected that existing grids will be operated more to their limits, it is important to obtain better operational insights and estimations of the time to equipment failure to provide useful operational guidance and maintenance prioritisation support for grid operators. In this regard, this paper proposes a novel and real-time applicable method for fault prediction in 10 kV underground oil-insulated power cables using low-resolution data from a real case study from a Danish distribution system operator. The developed method is based on a sequence classification approach using long short-term memory neural networks where three different operational states are defined (Normal, Early warning, and Critical warning) to allow for prediction flexibility and better indication of the presence of systemic faults. Moreover, to enhance the data foundation, this paper investigates a Virtual Sample Generation method based on an adaptive Gaussian distribution. The capability of the proposed method yields satisfying results with prediction accuracy on the test set reaching as high as ~90%, hence proving the usefulness of the proposed approach and paving the way for smarter maintenance protocols.