Research on Synchronous Transfer Control Technology for Distribution Network Load Based on Imprecise Probability

As the penetration rate of distributed power sources increases and distribution network structures grow increasingly complex, the uncertainty in switch action control during load transfer has become a critical issue affecting grid safety and reliability. Traditional control methods based on precise probability-based predictive control are susceptible to bias introduced by prior settings under small-sample conditions, making it difficult to meet the stringent requirements of time-synchronized control. To address this, this study proposes an imprecise probability-based synchronous load transfer control method for distribution networks. By integrating the Imprecise Dirichlet model (IDM) with a Naive Credal Classifier (NCC), it constructs an interval predictive control model for switching action timing. This approach effectively mitigates the prior dependency issue and enhances estimation robustness under small-sample conditions. Combined with a dynamic delay strategy, this approach strictly controls the interval between disconnection and reconnection actions within 20 ms, preventing circulating current risks and ensuring transfer reliability. The simulation and experimental results demonstrate that the proposed method outperforms traditional Bayesian classifiers in both time prediction control accuracy and model robustness, providing a theoretical foundation and a reference for engineering applications for secure action control in distribution networks.