Power System Coherency Detection From Wide-Area Measurements by Typicality-Based Data Analysis

This paper presents a new data-driven methodology for power system coherency identification of generator and non-generator buses. This methodology is exclusively based on intrinsic statistical properties extracted directly from observations, without any prior assumption of the probability distribution function (PDF) for the data. The main advances of this proposal are: (i) gathering of statistical information from the data itself despite scenarios where the PDF may change (different inverter-based load and generation scenarios, load levels of the system, and changes in topology); and (ii) assignment of buses into coherent areas without any tuning of parameters, nor manually labeling of huge amounts of training data. This new method, called typicality-based data analysis (TDA), is applied to the correlation metric of the distance between dynamic responses of buses, either voltage angles or frequencies. Simulated signals from a benchmark power system with cases considering the presence of non-synchronous generation and islanding conditions, and real data associated with generation trips in the U.S. Eastern Interconnection are used to corroborate the methodology effectiveness.

Automated summary

This paper presents a new data-driven methodology for power system coherency identification of generator and non-generator buses. The methodology gathers statistical information from the data itself and does not require manual labeling of training data or parameter tuning.