Print Email Facebook Twitter Dynamic Incremental Learning for real-time disturbance event classification Title Dynamic Incremental Learning for real-time disturbance event classification Author Veerakumar, Nidarshan (TU Delft Intelligent Electrical Power Grids) Cremer, Jochen (TU Delft Intelligent Electrical Power Grids) Popov, M. (TU Delft Intelligent Electrical Power Grids) Date 2023 Abstract With recent telemetric advancements, the real-time availability of power grid measurements has opened challenging opportunities for the design of advanced protection and control schemes. Artificial neural networks (ANN) are promising approaches for detecting and classifying disturbance events from measurement data. Numerous offline ANN-based classification algorithms were proposed in the past, which increased the interest for their real-world deployment. However, these algorithms are inadequate due to their conventional offline training procedures, model updating, and large backend computing requirements. Besides, most ANN-based algorithms require disturbance event samples to be collectively available during training. This availability may be uncommon in practice as disturbance events are rare, non-deterministic, and uncertain. Hence, an online training procedure where the model processes the events on-the-fly is required. However, ANNs may also suffer from catastrophic forgetting where the model may unintentionally unlearn an occurred disturbance under the learning of new event types; this means ANN may not detect very similar disturbances of the same type in the future. In this paper, we propose Dynamic Incremental Learning (IL) method for ANN models, which is updated in real-time when a new disturbance is detected. Our proposed method adopts a Replay-based IL strategy for designing long-term IL, balancing the accuracy with catastrophic forgetting of disturbance events. The method is designed in a way to learn efficiently for incoming disturbance data with minimized training time and the highest classification accuracy eliminating catastrophic forgetting. The results describe the methodology’s performance regarding classification accuracy, training time, and storage memory. The findings demonstrate that the Dynamic IL method is promising for efficient learning and event classification. Subject Incremental LearningRehearsal IL strategiesReplay strategyParametric bufferReservoir samplingReal-time model trainingExperiencesEvent classificationHyper-parameter tuningCatastrophic forgetting To reference this document use: http://resolver.tudelft.nl/uuid:7b1ea5e3-b151-489e-86b0-b03601ed231b DOI https://doi.org/10.1016/j.ijepes.2023.108988 ISSN 0142-0615 Source International Journal of Electrical Power & Energy Systems, 148 Part of collection Institutional Repository Document type journal article Rights © 2023 Nidarshan Veerakumar, Jochen Cremer, M. Popov Files PDF 1_s2.0_S0142061523000455_main.pdf 1.8 MB Close viewer /islandora/object/uuid:7b1ea5e3-b151-489e-86b0-b03601ed231b/datastream/OBJ/view