论文部分内容阅读
针对调峰背景下火电机组非稳态工况增多, 以及常见运行工况偏离设计工况等问题, 提出了基于历史运行数据聚类的工况划分模型.首先, 考虑到运行数据中非稳态工况与稳态工况并存的情况, 以功率作为特征变量, 提出基于功率差值期望区间估计的稳态判别算法, 筛选出历史数据中的非稳态工况;其次, 由于稳态工况下外部边界条件变量的分布差异性, 提出改进的多步K-均值聚类算法进行稳态工况的划分, 并利用silhouette评价准则确定每步条件下的最佳聚类数;最后, 采用某实际发电用重型燃气轮机的历史运行数据进行模型验证.通过与传统K-均值聚类算法比较, 所提出的模型能够有效解决工况分类数目较少以及样本分布不均的问题.“,”Thermal power units have been widely put into operation for the electrical peak-shaving, which results in the increase of unsteady state operating conditions and the deviation of common operating conditions from design conditions. Thus, the operating condition classification model based on the historical data clustering is proposed in this work. Firstly, considering the co-existence of unsteady and steady state operating conditions, the output power is applied as the key indicator between the steady state and unsteady state. The interval estimation of expectation of the output power difference value is used to classify the historical data into the steady and unsteady samples. Then, due to the distribution difference among external boundary variables under the steady-state operating conditions, the improved multi-step K-means clustering algorithm is proposed. The optimal clustering number for each step is determined by using the silhouette evaluation criterion. Finally, a real heavy gas turbine is used to validate the established model. Compared with the traditional K-means clustering, the results prove that the proposed operating condition classification model can effectively solve the problems of less classifications of operating condition and uneven distribution of samples.