Real-time static voltage stability assessment in large-scale power systems based on spectrum estimation of phasor measurement unit data

Real-time static voltage stability assessment is crucial to a stressed modern grid. This paper, based on the moment-based spectrum estimation method, proposes a data-driven approach to gain insight into changes of voltage magnitudes via synchronous phasor measurement unit (PMU) data, such that proper actions can be taken against voltage collapse. Built upon random matrix theory, an inverse Jacobian related indicator, i.e., spectrum estimation based stability indicator (SESI), is introduced in this paper to track the motion of the system. After considering the relationship between moments and distributions, this method allows for the regime where the sample size is significantly smaller than the dimensionality. It is adaptive to the real-time static voltage stability assessment in large-scale power systems where the sample size in a sliding window is extremely smaller than the dimensionality of measurement variables, hence delivering a recent status with a low computational cost. In this way, the static voltage stability assessment of a power system with massive amounts of measurement variables can be more sensitively and efficiently implemented. Case studies with the IEEE 118-bus system, the IEEE 300bus system, and a Polish 2383-bus system verify the effectiveness of the proposed approach.

Automated summary

This paper proposes a data-driven approach based on moment-based spectrum estimation method for real-time static voltage stability assessment in a stressed modern grid. The method is capable of analyzing cases where the sample size is significantly smaller than the dimensionality of measurement variables in large-scale power systems, providing a recent status with a low computational cost.