基于升维映射线性化的软开关点自优化本地电压控制

The high penetration of distributed generators (DGs) deteriorates the voltage violations in active distribution networks (ADNs). Owing to the flexible adjustment capacity, the local power regulation provided by soft open point (SOP) presents a promising solution for eliminating voltage violations in ADNs. A data-driven local control method can fully excavate the potential logic from operational data without requiring precious network parameters. However, the training data may be insufficient in practical applications. In this paper, a self-optimizing local voltage control method for SOP is proposed to achieve adaptive control in label-poor conditions. First, a SOP local control model is constructed based on lift-dimension mapping linearization (LDML), which portrays the complex relationship between ADN states and SOP control strategies. Subsequently, a self-optimizing guidance mechanism is established to obtain the label data of SOP control strategy, which provides a large number of training samples for the local control model. Finally, the effectiveness of the proposed method is validated using a practical distribution network with a four-terminal SOP. Results demonstrate that efficient control strategies can be determined based on local state measurements. A rapid response to DG fluctuations can be achieved while enhancing the adaptability to variations in practical operations.