Dynamic Incremental Learning for real-time disturbance event classification. (June 2023)

• Record Type: Journal Article
• Authors: Veerakumar, Nidarshan; Cremer, Jochen L.; Popov, Marjan
• Is Part Of: International journal of electrical power & energy systems. Volume 148(2023)
• Publication Date: 2023-06
• Subjects: Incremental Learning -- Rehearsal IL strategies -- Replay strategy -- Parametric buffer -- Reservoir sampling -- Real-time model training -- Experiences -- Event classification -- Hyper-parameter tuning -- Catastrophic forgetting; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2023.108988
• Languages: English
• ISSNs: 0142-0615

Abstract

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. Highlights: We use Incremental Learning (IL) in power systems for online model training. The new Dynamic IL method efficiently trains deep learning models in near real-time. Our method adapts the model to learn new event types anticipated in a modern grid. Learning accuracy, training time, and memory can be tuned during events learning.