Modeling and Dynamic Characteristics Research of Wind Solar Energy Storage DC Microgrid Based on Hybrid Energy Storage Collaborative Control

To address issues such as poor dynamic topology adaptability, insufficient small-sample generalization ability, and low real-time update efficiency of traditional frequency prediction technologies under high-proportion wind power grid integration scenarios, this paper proposes an adaptive frequency prediction method integrating high-dimensional feature engineering and transfer-incremental collaborative learning. The method uses the elastic net (EN) algorithm combined with the whale optimization algorithm (WOA) to achieve intelligent dimensionality reduction and parameter optimization of 552 initial features, selecting 30 key features. A transfer-incremental prediction model is constructed based on the random forest framework, which can complete topology adaptation with only 20 small target domain samples, reducing the update time of new data to within 1.6 seconds. Through the real-time decision support module, the minimum frequency, occurrence time, and frequency dynamic curve are output, with prediction errors controlled within 0.015 Hz and 0.05 seconds respectively. Verification results on the modified IEEE 10-machine 39-bus system show that the prediction accuracy of this method is improved by more than 75% compared with the traditional random forest. It can meet the second-level online prediction requirements of power grids and provide technical support for the safe and stable operation of new power systems.