基于改进模糊支持向量机的汽轮机热耗率预测模型
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摘要
针对现有方法难以准确预测具有复杂非线性特征的汽轮机热耗率问题,本文提出一种改进模糊支持向量机(FSVM)的汽轮机热耗率预测模型。首先采用间隔统计算法计算热耗率数据最佳聚类个数,防止出现聚类数目的不确定性,然后利用模糊C均值聚类(KFCM)算法将热耗率数据划分,生成聚类子样本,将聚类子样本代入经粒子群算法优化的FSVM中,建立基于FSVM的汽轮机热耗率预测模型。将现场采集的某超超临界660 MW机组汽轮机热耗率数据输入模型进行预测,并与传统支持向量机的预测结果进行比较。结果表明,改进的FSVM方法具有更高的预测精度和更强的泛化能力。
To the problem that the existing methods are difficult to accurately predict and analyze the heat consumption of steam turbines with complex non-linear characteristics, this paper presents an improved prediction model of steam turbine heat consumption based on fuzzy support vector machine(FSVM). Firstly, the gap statistic algorithm is used to calculate the optimal number of clusters to avoid the uncertainty of the number of clusters.Then, the kernel fuzzy C mean clustering(KFCM) algorithm is applied to divide the heat consumption data,generate cluster subsamples, and replace it into the FSVM optimized by particle swarm optimization(PSO), and establish a thermal consumption rate prediction model based on the FSVM. Finally, this model is employed to predict the heat consumption rate of an ultra-supercritical 660 MW unit steam turbine based on the data collected in the field, and the results are compared with that of the conventional support vector machine. The research results show that the improved FSVM method has higher prediction accuracy and stronger generalization ability.
To the problem that the existing methods are difficult to accurately predict and analyze the heat consumption of steam turbines with complex non-linear characteristics, this paper presents an improved prediction model of steam turbine heat consumption based on fuzzy support vector machine(FSVM). Firstly, the gap statistic algorithm is used to calculate the optimal number of clusters to avoid the uncertainty of the number of clusters.Then, the kernel fuzzy C mean clustering(KFCM) algorithm is applied to divide the heat consumption data,generate cluster subsamples, and replace it into the FSVM optimized by particle swarm optimization(PSO), and establish a thermal consumption rate prediction model based on the FSVM. Finally, this model is employed to predict the heat consumption rate of an ultra-supercritical 660 MW unit steam turbine based on the data collected in the field, and the results are compared with that of the conventional support vector machine. The research results show that the improved FSVM method has higher prediction accuracy and stronger generalization ability.
引文
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[12]牛培峰,刘超,李国强,等.基于双层聚类与GSA-LSSVM的汽轮机热耗率多模型预测[J].电机与控制学报,2016,20(3):90-95.NIU Peifeng,LIU Chao,LI Guoqiang,et al.Multi-model for turbine heat rate forecasting based on double layer clustering algorithm and GSA-LSSVM[J].Electric Machines and Control,2016,20(3):90-95.
[13]李小辉,朱莉,吴坚.基于模糊支持向量机的毫米波辐射计目标识别[J].微波学报,2017,33(增刊1):203-206.LI Xiaohui,ZHU Li,WU Jian.Target identification of millimeter wave radiometer based on fuzzy support vector machines[J].Journal of Microwaves,2017,33(Suppl.1):203-206.
[14]HANG J,ZHANG J Z,CHENG M.Application of multi-class fuzzy support vector machine classifier for fault diagnosis of wind turbine[J].Fuzzy Sets&Systems,2016,297(C):128-140.
[15]DOU D,ZHOU S.Comparison of four direct classification methods for intelligent fault diagnosis of rotating machinery[J].Applied Soft Computing,2016,46:459-468.
[16]XU H,CHEN G.An intelligent fault identification method of rolling bearings based on LSSVM optimized by improved PSO[J].Mechanical Systems&Signal Processing,2013,35(1/2):167-175.
[17]叶小岭,顾荣,邓华,等.基于WRF模式和PSO-LSSVM的风电场短期风速订正[J].电力系统保护与控制,2017,45(22):48-54.YE Xiaoling,GU Rong,DENG Hua,et al.Modification technology research of short-term wind speed in wind farm based on WRF model and PSO-LSSVM method[J].Power System Protection and Control,2017,45(22):48-54.
[2]翟兆银,王际洲,李建兰,等.基于功率的汽轮机组实时热耗率计算方法[J].热力发电,2015,44(10):15-19.ZHAI Zhaoyin,WANG Jizhou,LI Jianlan,et al.Areal-time monitoring model for heat consumption rate of steam turbine unit based on unit power[J].Thermal Power Generation,2015,44(10):15-19.
[3]牛培峰,吴志良,马云鹏,等.基于鲸鱼优化算法的汽轮机热耗率模型预测[J].化工学报,2017,68(3):1049-1057.NIU Peifeng,WU Zhiliang,MA Yunpeng,et al.Prediction of steam turbine heat consumption rate based on whale optimization algorithm[J].CIESC Journal,2017,68(3):1049-1057.
[4]韩中合,刘明浩.基于支持向量机的汽轮机振动故障诊断系统[J].汽轮机技术,2013,55(2):127-130.HAN Zhonghe,LIU Minghao.Vibration fault diagnosis of steam turbine based on SVM[J].Turbine Technology,2013,55(2):127-130.
[5]吴广宁,袁海满,宋臻杰,等.基于粗糙集与多类支持向量机的电力变压器故障诊断[J].高电压技术,2017(11):3668-3674.WU Guangning,YUAN Haiman,SONG Zhenjie,et al.Fault diagnosis for power transformer based on rough set and multi-class support vector machine[J].High Voltage Engineering,2017(11):3668-3674.
[6]李燕青,袁燕舞,郭通.基于AMD-ICSA-SVM的超短期风电功率组合预测[J].电力系统保护与控制,2017,45(14):113-120.LI Yanqing,YUAN Yanwu,GUO Tong.Combination ultra-short-term prediction of wind power based on AMD-ICSA-SVM[J].Power System Protection and Control,2017,45(14):113-120.
[7]LIN C F,WANG S D.Fuzzy support vector machines[J].IEEE Transactions on Neural Networks,2002,13(2):464-471.
[8]赵克楠,李雷,邓楠.一种构造模糊隶属度的新方法[J].计算机技术与发展,2012,22(8):75-77.ZHAO Kenan,LI Lei,DENG Nan.A new method to construct fuzzy membership[J].Computer Technology and Development,2012,22(8):75-77.
[9]张战成,王士同,邓赵红,等.一种支持向量机的快速分类算法[J].控制与决策,2012,27(3):459-463.ZHANG Zhancheng,WANG Shitong,DENG Zhaohong,et al.A fast decision algorithm of support vector machine[J].Control and Decision,2012,27(3):459-463.
[10]李状,柳亦兵,滕伟,等.基于粒子群优化KFCM的风电齿轮箱故障诊断[J].振动、测试与诊断,2017,37(3):484-488.LI Zhuang,LIU Yibing,TENG Wei,et al.Fault diagnosis of wind turbine gearbox based on KFCMoptimized by particle swarm optimization[J].Journal of Vibration,Measurement&Diagnosis,2017,37(3):484-488.
[11]朱晓东,倪秋华.基于模糊聚类的支持向量机在振动故障诊断中的运用[J].汽轮机技术,2013,55(3):232-234.ZHU Xiaodong,NI Qiuhua.Application of support vector machines based on the fuzzy clustering for fault diagnosis[J].Turbine Technology,2013,55(3):232-234.
[12]牛培峰,刘超,李国强,等.基于双层聚类与GSA-LSSVM的汽轮机热耗率多模型预测[J].电机与控制学报,2016,20(3):90-95.NIU Peifeng,LIU Chao,LI Guoqiang,et al.Multi-model for turbine heat rate forecasting based on double layer clustering algorithm and GSA-LSSVM[J].Electric Machines and Control,2016,20(3):90-95.
[13]李小辉,朱莉,吴坚.基于模糊支持向量机的毫米波辐射计目标识别[J].微波学报,2017,33(增刊1):203-206.LI Xiaohui,ZHU Li,WU Jian.Target identification of millimeter wave radiometer based on fuzzy support vector machines[J].Journal of Microwaves,2017,33(Suppl.1):203-206.
[14]HANG J,ZHANG J Z,CHENG M.Application of multi-class fuzzy support vector machine classifier for fault diagnosis of wind turbine[J].Fuzzy Sets&Systems,2016,297(C):128-140.
[15]DOU D,ZHOU S.Comparison of four direct classification methods for intelligent fault diagnosis of rotating machinery[J].Applied Soft Computing,2016,46:459-468.
[16]XU H,CHEN G.An intelligent fault identification method of rolling bearings based on LSSVM optimized by improved PSO[J].Mechanical Systems&Signal Processing,2013,35(1/2):167-175.
[17]叶小岭,顾荣,邓华,等.基于WRF模式和PSO-LSSVM的风电场短期风速订正[J].电力系统保护与控制,2017,45(22):48-54.YE Xiaoling,GU Rong,DENG Hua,et al.Modification technology research of short-term wind speed in wind farm based on WRF model and PSO-LSSVM method[J].Power System Protection and Control,2017,45(22):48-54.