基于混合自适应选择模型的燃气轮机建模  

Gas Turbine Modeling Based on Hybrid-form Adaptive Selection Model

作　　者：张宝凯 ZHANG Baokai(East China Electric Power Test&Research Institute of China Datang Group Science and Technology Research Institute Co.,Ltd,Hefei 230031,Anhui Province,China)

机构地区：[1]中国大唐集团科学技术研究总院有限公司华东电力试验研究院,安徽省合肥市230031

出　　处：《中国电机工程学报》2024年第7期2737-2747,I0019,共12页Proceedings of the CSEE

摘　　要：单轴燃气轮机联合循环机组作为参与电网调峰谷、调频的重要装备,其运行性能直接关系到电网安全。为提高电网可靠性,该文提出一种基于混合形式自适应选择算法模型(hybrid-form adaptive selection algorithm model,HASAM)。首先,结合厂家控制策略组态逻辑与偏互信息确定与燃机计算功率相关的变量作为建模对象;然后,利用量子粒子群QPSO辨识模型、深度置信网络DBN算法以及ELMAN算法构建子模型;最后,针对不同工况,结合QPSO优化的DBN分类模型自适应选择子模型进行燃机计算功率预测。基于9E燃气轮机工况数据验证结果表明,提出的算法能够准确的预测燃气轮机计算功率。Gas turbine combined cycle unit is an important equipment involved in peak and valley regulation and frequency regulation of power grid,and its operation performance is directly related to the security of the power grid.Aiming at improving the reliability of power grid,a hybrid-form adaptive selection algorithm model(HASAM)is proposed in this paper.First,combined with the configuration logic of the manufacturer’s control strategy and partial mutual information,variables related to the gas turbine calculation power are selected as modeling objects.Then,the modeling sub-model is constructed by the quantum particle swarm(QPSO)identification model,the deep neural network(DNN)algorithm and the simple recurrent neural network(ELMAN)algorithm.Finally,according to different working conditions,the DBN classification model optimized by QPSO adaptively selects the sub-model to predict the calculated power of the gas turbine.The results based on 9E gas turbine operating data illustrate that the proposed algorithm could accurately predict the calculated power of gas turbine.

关 键 词：数据驱动 燃气轮机 自适应选择 预测 

分 类 号：X773[环境科学与工程—环境工程]