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

2022 Asian Conference on Frontiers of Power and Energy (ACFPE) Pub Date : 2022-10-01 DOI:10.1109/ACFPE56003.2022.9952309

Zhenyi Wang, Bin Hu, Xuegang Lu, Ziyu Zhang, Changbin Ju, Xiaosheng Zhang, Tao Ding

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Abstract

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.

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基于改进摆门算法的调频数据段识别的火电机组一次调频性能评价

火电机组作为电力系统重要的原频调节资源，其性能评价具有重要意义。然而，大多数PFR性能评估依赖于单元性能测试，导致结果与实际运行性能不同。提出了一种基于日常运行中调频数据段识别的PFR性能评价方法。首先，利用改进的摆动门算法从日常运行数据中选取斜坡数据段;其次，采用三条规则对PFR数据段进行识别。再次，计算了机组的响应时间、调节系数和稳定时间三个评价指标，并采用熵值法制定了评价火电机组PFR性能的综合指标。最后，通过实例验证了所提方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2022 Asian Conference on Frontiers of Power and Energy (ACFPE)

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