A Method for Extracting Generalized Typical Operating Modes of High-Proportion Renewable Energy Power Systems Driven by a Combination of Data and Knowledge

Typical operation modes serve as an important basis for guiding the operation of power systems and identifying potential safety vulnerabilities. In renewable energy power systems, however, the operation modes exhibit a trend of de-typification, making it difficult for typical modes selected based on manual experience to fully capture the impact of complex operating conditions of renewable energy on grid security. To address this issue, this paper proposes a data-knowledge hybrid-driven method for extracting generalized typical operation modes in high-proportion renewable energy power systems. Firstly, the key operational characteristic sequences of the system are selected, and the basic dataset of operation modes is divided using a hierarchical clustering method, forming a classification sample set of operation modes represented by various cluster centers. Then, considering differences in renewable energy penetration rates, load levels, and other factors, the operational point samples within each category with boundary operational characteristics are further subdivided to form a set of samples for verification. Finally, N-1 security checks are conducted on the operation modes in the verification set, and operation modes with similar safety issues are reduced to form a generalized typical operation mode set. Additionally, safety risk indicators are used to visually represent and evaluate the generalized typical operation modes in the risk characteristic space. The effectiveness of this method is validated using the actual topology and historical operational data of a provincial power grid, providing strong support for the operational departments in formulating grid operation rules and ensuring grid safety.