• Novel compressed sensing approach for differential protection in distribution grids. • Reduces data transmission volume by 75 percent while maintaining protection accuracy. • Enhances data security through compressed sampling and transmission techniques. • Simulation results demonstrate accurate fault detection with significantly less data. With the significant integration of distributed renewable energy sources into distribution grids, the characteristics of fault current in distribution grids have undergone substantial changes, rendering traditional current protection inadequate to meet the requirements. In theory, the differential protection scheme can effectively meet the requirements of rapid and accurate fault detection and isolation in power distribution grids. However, due to the reliance of differential protection on real-time communication, the data volume in power distribution grids has surged, leading to an increase in data acquisition and transmission costs. In order to reduce the amount of transmitted data and promote the widespread adoption and application of differential protection in power distribution grids, based on Compressive Sensing (CS), this paper proposes a novel sampled value differential protection scheme. Designing appropriate measurement matrix, sparse matrix, and reconstruction method based on the requirements of the sampled value differential protection. The scheme employs the CS algorithm to reduce the amount of transmitted data and achieves sampled value differential protection using the data generated by CS. Extensive simulation results indicate that the proposed scheme meets the constraints of communication conditions in power distribution grids and the requirements of differential protection.
Duan et al. (Sun,) studied this question.