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作 者:闫庚 YAN Geng(SPIC&GCL Binhai Electric Power Generation Co.,Ltd.,Yancheng,Jiangsu 224500,China)
机构地区:[1]国家电投集团协鑫滨海发电有限公司,江苏盐城224500
出 处:《自动化应用》2024年第14期175-177,共3页Automation Application
摘 要:在浆液循环泵运行阶段,受客观应用需求波动的影响,其功耗相对较高。为此,提出基于模糊C均值聚类算法的浆液循环泵节能运行优化方法。在浆液循环泵运行数据特征提取阶段,采用基于无监督的深度学习模型,借助随机初始化的卷积核,对输入的数据进行卷积计算,获取低维空间的特征映射,随后通过反卷积确定浆液循环泵运行参数特征;在节能运行优化阶段,引入模糊C均值聚类算法,通过聚类具有相同特征的数据,将相同聚类内功耗最小的参数作为同类运行工况下的优化结果。结果显示,测试循环泵的功耗虽然会随着通过的最大颗粒粒度的增加而呈稳定增大的趋势,但对应的增幅较小,与对照组相比,其分别在节能程度和节能适应性方面表现出了明显优势。During the operation phase of the slurry circulation pump,its power consumption is relatively high due to fluctuations in objective application requirements.Therefore,an energy-saving operation optimization method for slurry circulation pumps based on fuzzy C-means clustering algorithm is proposed.In the feature extraction stage of the operation data of the slurry circulation pump,an unsupervised deep learning model is adopted,and a randomly initialized convolution kernel is used to perform convolution calculation on the input data to obtain a low dimensional feature map.Then,the operating parameter features of the slurry circulation pump are determined through deconvolution.In the energy-saving operation optimization stage,the fuzzy C-means clustering algorithm is introduced to cluster data with the same characteristics,and the parameter with the lowest power consumption within the same cluster is selected as the optimization result under the same operating conditions.The results showed that although the power consumption of the test circulation pump showed a stable increase trend with the increase of the maximum particle size passed,the corresponding increase was relatively small.Compared with the control group,it showed significant advantages in terms of energy-saving degree and energy-saving adaptability.
关 键 词:模糊C均值聚类算法 浆液循环泵 深度学习模型 特征提取
分 类 号:TP391[自动化与计算机技术—计算机应用技术]
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