Primary frequency regulation performance evaluation of thermal power units based on frequency regulation data segment identification using improved swinging door algorithm

As a key power system primal frequency regulation (PFR) resource, the PFR performance evaluation of thermal power units has a significant meaning. However, most PFR performance evaluation depends on the unit performance test which leads to a result different from the actual operation performance. In this paper, a PFR performance evaluation based on frequency regulation data segment identification in daily operation is proposed. Firstly, the ramping data segments from daily operating data are selected by the improved swinging door algorithm. Secondly, PFR data segments are identified by three rules. Thirdly, three evaluation indicators including the response time, the adjustment coefficient, and the stable time of the units are calculated and a comprehensive index is formulated to evaluate the PFR performance of thermal power units by the entropy-osculating value method. Finally, the effectiveness of the proposed method is verified by a practical case.