基于改进群优化算法的深度调峰机组一次调频建模

在深度调峰状态下，火电机组的一次调频性能已发生较大变化。为了解机组的一次调频出力变化情况以及为电网对深度调峰机组一次调频考核提供依据，基于一次调频模型的先验知识确定模型结构，结合一种全局搜索能力更优的改进群优化算法求取模型中未知参数。为了消除人为选取稳态值造成的建模误差，将稳态值融入辨识参数中，解决了常规模型辨识要求初始与结束状态必须达到稳态的问题。模型参数计算结果表明，在深度调峰状态下一次调频能力与常规负荷条件下相比调频裕度变大，为更好地发挥深度调峰机组一次调频能力，需对相关参数进行调整。

Primary Frequency Regulation Modeling of Deep Peak Regulation Unit Based on Improved Group Optimization Algorithm

Under deep peak regulation state, the performance of primary frequency regulation of thermal power units has undergone significant changes. In order to figure out the changes of generator primary frequency regulation output and provide reference for the power grid to assess the performance of primary frequency regulation of deep peak regulation unit, the model structure is determined based on the prior knowledge of the primary frequency regulation model. Then the unknown parameter values in the model are derived by using an improved group optimization algorithm with better global search capability. Steady state values are incorporated into the identification parameters in order to eliminate modeling error which was introduced from manually selected steady state values. In this way, both the initial state and the end state converge to the steady state as required in conventional model identification process. The model parameter calculation results show that under deep peak regulation state, the capability margin of primary frequency regulation is higher than that under the normal load conditions. Therefore, relevant parameters need to be adjusted to better utilize the primary frequency regulation capability of the deep peak regulation unit.