Data-driven Feature Analysis in Control Design for Series-Compensated Transmission Systems

One challenge in power-system control designs is the gap between numerical model-based analysis and complex real-world power systems. With increased data and measurements being collected from power systems, data-driven analysis (e.g., machine learning) may provide an alternative approach to reveal hidden information through learning from the real system data, and provide insights for better control scheme design during the utility planning process. In this study, data-driven feature analysis is performed to evaluate the relationships between series compensation, power generation, and path flows in a real transmission system, as well as temporal patterns. The main data-driven analysis methods, including statistical cross correlation, multinomial logistical regression, and classification and regression trees, are integrated for feature selection and developing predictive models of series compensation. Analysis results demonstrates the effectiveness of the proposed methodology in feature analysis and the potential to help improve power-system control scheme design.