Distributed state estimation of active distribution network considering mixed-frequency measurement data hierarchical encryption

Distributed state estimation is necessary for active distribution networks to calculate high-frequency and high-dynamic random fluctuations of distributed generations and controlled loads. Given the limitations of traditional measurements and the potential problem of false data injection attacks (FDIA), it is imperative that high accuracy phasor measurement units (PMUs) with information security are implemented in active distribution networks. A novel distributed state estimation method is proposed in this paper. This method involves merging data from the PMU and supervisory control and data acquisition (SCADA) and encrypting subsequent communications. Firstly, a PMU-centric active distribution network partition topology model is proposed based on the principles of parallel computing load balancing and local communication efficiency. Secondly, to improve the observability of the system, a data fusion strategy for mixed-frequency data measured by SCADA and PMU is proposed. Thirdly, a distributed state estimation method is proposed that considers an encryption model and a distributed square root cubature Kalman–Gaussian mixture probability hypothesis density attacks (DSRCK-GMPHD) algorithm. The proposed state estimation method can improve the speed and accuracy of multi-subdomain state tracking and minimize the mismatch between the measured and actual states of the active distribution network. Finally, extensive tests were performed on the improved PG&E69 system. The test results show that the method proposed in this paper is able to accurately capture the real-time state of multiple subareas of an active distribution network and improve the security of the multi-source measurement data transmission process.