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

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