An optimized assessment method for grid-forming capability of new energy stations fusing voltage–frequency indicators and FCM

To accurately assess the comprehensive grid-forming capability of new energy stations in weak-inertia power systems and address the deficiencies of existing assessment methods in terms of indicator dimensionality, dynamic characterization, and uncertainty quantification, this paper proposes a new comprehensive assessment framework that integrates voltage–frequency dual dimensions with an optimized Fuzzy C-Means (FCM) clustering. First, a comprehensive evaluation system, including 15 dynamic and static indicators, is constructed. Second, a game theory-based analytic hierarchy process-entropy weight method combined weighting model based on Nash equilibrium is established to achieve an optimal balance between subjective prior knowledge and objective data distribution. To address the uncertainty of the assessment results, the “multiple repetitions and selection of the best” mechanism is used to overcome the sensitivity of standard FCM to initial values. A quantitative diagnostic model based on “assessment confidence” and “boundary fuzziness” is established, providing a hierarchical scientific basis for operation and maintenance decisions and risk warnings. Finally, an empirical analysis of ten typical new energy stations shows that the proposed framework can not only accurately assess the comprehensive grid-forming capability level of each station but also identify the balance of voltage–frequency support and conduct in-depth diagnostics for stations with low confidence and boundary ambiguity.