A Dual-Modality Collaborative Reactive Power Optimization Algorithm for Distribution Networks Integrating Load Forecasting and Morphological Perception

To address the inherent issues in traditional reactive power compensation strategies—such as compensation lag, the lack of load morphological identification capability, and bus transient shocks induced by multi-modality switching—this paper proposes a dual-modality collaborative reactive power optimization algorithm for distribution networks, integrating deep prediction and load morphological perception. First, a prediction model based on Trend-Residual decoupling and Gate-Aware factors is formulated to achieve precise load forecasting. Second, a morphological identification mechanism is designed to explicitly isolate the slowly varying intervals and the abrupt change intervals of the load. Finally, within the slowly varying intervals, the Artificial Hummingbird Algorithm is introduced for global optimization to provide steady-state anchors for the optimization of subsequent abrupt change intervals; conversely, within the abrupt change intervals, Finite Control Set Model Predictive Control integrating a terminal gravitational mechanism is employed for dynamic tracking, thereby realizing a flexible soft-start for the discrete compensation devices. Simulation results based on a modified IEEE 69-node system demonstrate that, under the strict premise of constraining the physical switching frequency of thyristor-switched capacitors, the proposed algorithm effectively curtails network losses, which is beneficial to prolonging the service lifespan of reactive power compensation equipment and improving the overall power quality.