Real Time Adaptive Corrective Control of Short-Term Voltage Stability
Estefanía A.Tapia Suárez (TU Delft - Intelligent Electrical Power Grids)
D. Graciela Colomé (National University of San Juan)
José L. Rueda Torres (TU Delft - Intelligent Electrical Power Grids)
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Abstract
Although several data-driven approaches for short-term voltage stability (STVS) assessment have been proposed, most of them do not extend to corrective control actions nor consider the joint dynamics of generation and load. To address this gap, this work introduces a real-time adaptive load shedding scheme (ALSS) driven by an integrated assessment of the short-term stability state (STSS) and the identification of critical induction motors (CIM) as the mechanism driving STVS instability. The methodology employs two recurrent convolutional neural network (RCNN) models operating in parallel: i) the STSS-RCNN, which classifies the system state as stable, unstable by transient stability (TS), or unstable by STVS; and ii) the CIM-RCNN, which identifies the critical motors responsible for instability, thereby inherently recognizing STVS-related problems. The joint operation of these models ensures that the ALSS is activated only when both responses consistently recognize an STVS event. This enables not only the correct activation of the load shedding scheme but also its accuracy and adaptive parameterization based on the identified CIMs. Validation on the IEEE 39-bus test system demonstrates that the proposed approach achieves robust real-time performance, outperforms single deep learning baselines, and significantly overcomes traditional load shedding schemes in efficiency and reliability.
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