Machine Learning of Factors Influencing Damping and Frequency of Dominant Inter-area Modes in the WECC Interconnect

The stability of inter-area electromechanical oscillations are critical to power system reliability. Due to the complexities of power systems, relationships between system conditions and oscillation characteristics, such as damping and frequency, tend to be expressed only in generalities. In this study, a list of influential factors on Western Electricity Coordinating Council interconnect modal characteristics are identified and evaluated with advanced machine learning techniques including principal component analysis, analysis of variance, classification and regression trees, and support vector machine approaches. The predictive relationships between the influential factors and modal characteristics could be used in the future to develop alert thresholds earlier than measurement-based mode estimation approaches.