A novel voltage sag state estimation method based on complex network analysis. (September 2022) - Hu, Wen-xi; Ruan, Zi-hang; Xiao, Xian-yong; Xiong, Xiao-yi; Wang, Jun-qi
Highlights: This method provides a voltage sag state estimation method to describe the voltage sags performance in the power system. Several indices are proposed to effectively evaluate the impacts of power network structure on voltage sags. There is no need to ensure the observability of the monitoring scheme. Without the requirement of random fault simulation, the proposed method is much simpler. The proposed method is accurate and robust under different fault distributions and monitoring schemes. Abstract: Voltage sag state estimation focuses on estimating the number of voltage sags at unmonitored sites. Existing methods require power quality monitors having to be allocated optimally, which are generally unpractical. To this end, an alternative estimation method is proposed for the scenario where the monitoring scheme is not optimal. Since there is a general rule in a rather long span that some network sites always record more sags than others even when short-circuit fault distribution varies, how power network structure impacts voltage sag is discussed firstly. The strength of interaction among sites, proved to be related to the number of sags, is quantified based on complex network theory analysis. The stronger the interaction between any two sites, the more possible sag events propagate from one site to another. Hence, according to the interaction field between monitored and unmonitored sites, the estimation model is built as the under-determined system of equations. Proposed method has been validated by different IEEE test systems, where the influences of fault distribution and monitoring scheme on estimation results are discussed. The results show that proposed method has better performance especially when the observability of monitoring scheme cannot be ensured. Moreover, the drawback of computation for traditional methods is overcome also, since there is no need to build estimation equations repeatedly.