Data set from 'Sequential Feature Selection for Power System Event Classification Utilizing Wide-Area PMU Data'

The increasing penetration of intermittent, nonsynchronous generation has led to a reduction in total power system inertia. Low inertia systems are more sensitive to sudden changes, and more susceptible to secondary issues that can result in large scale events. Due to the short time frames involved, automatic methods for power system event detection and diagnosis are required. Wide-area monitoring systems can provide the data required to detect and diagnose events; however due to the increasing quantity of data it is next to impossible for power system operators to manually process raw data. The important information is required to be extracted and presented to system operators for real/near-time decision making and control. This paper demonstrates an approach for the wide-area classification of a number of power system events. A mixture of sequential feature selection and linear discriminant analysis is adopted to reduce the dimensionality of PMU data. Successful event classification is obtained by employing quadratic discriminant analysis on wide-area synchronized frequency, phase angle and voltage measurements. The reliability of the proposed method is evaluated using simulated case studies and benchmarked against other classification methods.