Multi-data Models Control of Grid-Connected Inverters for Enhanced Adaptive Compensation Capability

Data-driven regression compensation control effectively mitigates the inherent nonlinearities in grid-connected inverters. However, the structural limitations inherent to the single-data regression model challenge the balance between minimal data requirements and adaptive optimization, thereby constraining compensation precision and generalization capability. To overcome the generalization limitations of the single-data model, this article proposes a collaborative compensation control strategy based on multidata regression models, which employs adaptive weighting for dynamic model fusion. This method integrates techniques for operating interval and boundary feature extraction to construct multidata regression models using lightweight offline regression. Real-time matching of operating features enables dynamic model selection and weight assignment, thereby accurately reconstructing the compensation signal in extrapolation regions beyond the training data boundaries. Furthermore, by integrating with a low-order controller, a hybrid control architecture merging the mechanism-based and data-driven paradigms is established, significantly enhancing the adaptive compensation performance. Experimental results validate the effectiveness and superiority of the proposed strategy.