Cheng Yang, Yanwei Wang, Kuiye Chen, Xinwei Wang, and Zhennan Yang
Power system, scheduling control model, manual sampling algorithm, deep forest algorithm
With the increasing application scope of the current power system, people’s attention to power scheduling and control strategies in the power system is constantly increasing. At present, there are problems with delayed response and low scheduling accuracy in the power dispatch and control model scheduling of the power system. To address this issue, this study utilises manual sampling algorithms to improve the deep forest algorithm and proposes a novel improved algorithm. By using this algorithm to construct a power dispatch control model, we aim to improve the accuracy and response speed of power dispatch. Comparative experiments on improved algorithms showed that the classification accuracy of this algorithm was 97.7% and the classification time was only 1.8 s. After testing the constructed model, it was found that the response accuracy of the model was as high as 94.4%. The power dispatch control strategy proposed based on this model could control the power stability in the power system and reduce the dispatch cost by 78.9%. The above results indicate that the proposed improved model can improve the accuracy and response time of power dispatching.
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