Data-Driven SSO Identification by using 1D-ConvLSTM learning in Multi-Energy Systems

Abstract

Multi-energy systems, as emerging power system architectures integrated by multiple converter-controlled wind power plants and electrolyzer facilities, face new stability challenges, among which subsynchronous oscillations (SSOs) have emerged as a critical concern. Despite extensive research on SSO analysis, achieving reliable real-time identification remains challenging due to the nonlinear, time-varying nature, and the limitations of conventional signal-processing techniques. To address this challenge, this paper proposes a data-driven framework based on a one-dimensional convolutional and long shortterm memory neural network (1D-ConvLSTM) for the early identification of low-damping SSO in multi-energy systems. A stochastic database is generated through time-domain simulations covering a wide range of operating conditions and contingency scenarios. Oscillatory parameters are estimated using Matrix Pencil method to systematically label critical SSO events based on damping criteria. The proposed 1D-ConvLSTM model is trained using multivariate time-series measurements to enable early SSO identification. Simulation results obtained from a realistic offshore multi-energy system demonstrate that the proposed approach achieves competitive performance in identifying critical SSO events, outperforming standalone Conv1D and LSTM models. These results highlight the strong potential of datadriven artificial intelligence methods for real-time and predictive identification of SSO in multi-energy systems.