A Data-Driven Small-Signal Stable Operating Boundary Assessment Method for Grid-Connected Converter Systems

Some grid-connected converters in practice are “black-box” devices with proprietary internal structures and parameters, rendering traditional model-based stability assessment infeasible. To address this issue, this paper proposes a data-driven small-signal stable operating boundary assessment method. A continuous stability evaluation index is established based on the generalized Nyquist criterion (GNC), and then the relationship between the measurable operating variables and the quantitative stability index is established by a backpropagation (BP) neural network. This mapping allows stability evaluation to rely solely on the converter’s operating states, without requiring any internal information. By implementing the stability evaluation in predefined operating scenarios, the critical stability points that reflect the small-signal stable operating boundary of the converter can be assessed. The proposed method requires only 0.0179 s to complete a single stability assessment, with an error of less than 0.25% in predicting the stable operating boundary. Simulation verifications in MATLAB/Simulink under multiple operating conditions confirm the effectiveness and accuracy of the proposed method.