基于过程数据的建模方法研究及应用
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摘要
为了保证电站的安全经济运行,需要获得一些重要热工参数的信息。由于技术和资金的限制,只利用硬件传感器很难实现这些参数的可靠、准确和快速测量。基于数据的软测量建模方法是解决此问题的途径之一,而且近年来电站信息化的发展使过程数据的获取变得越来越容易,这为构建数据模型提供了良好的研究平台。数据软测量建模已经成为热工过程检测和控制领域的一个新的研究热点。
一般构建模型采用的数据来源主要有两种：对过程进行试验设计而得到的试验数据和从历史数据库中获得的历史运行数据。两种数据的工况分布、稳态状况、均匀性、相关性以及样本量等特性均不相同。本文以基于过程数据的建模方法为研究主题,在对数据进行分析和预处理的基础上,做了以下工作：
(1)稳态工况检测
针对历史运行数据工况的稳态和动态交替特性,提出了一种基于分段曲线拟合的稳态工况检测方法。利用分段曲线将离散数据样本拟合成连续信号,并求得了各样本点对应的一阶导数和二阶导数值,同时滤除了高阶噪声。根据有关阈值判定准则,得到了数据样本的变化趋势和稳态信息,最终判定出稳态工况。以某600MW电站机组给水流量系统的稳态工况检测为例,验证了该方法的有效性。
(2)基于内部LSSVM的非线性PLS建模方法
针对热工试验数据工况特性,提出了一种新的非线性偏最小二乘(Partial Least Squares, PLS)建模方法。该方法保留了外部线性PLS框架来提取输入输出主成分特征向量,同时消除了变量间的相关性,内部采用最小二乘支持向量机(Least Squares Support Vector Machine, LSSVM)来描述主成分之间的非线性关系,而且基于误差对权值进行更新,提高了模型的预测精度。利用pH中和过程的Benchmark模型验证了本方法的有效性,并基于某电站燃煤锅炉的实际热态试验数据建立了NOx排放模型,得到了较高的预测精度。
(3)基于LSSVM集成的建模方法
针对历史运行数据局部工况分布和大样本的特点,提出了一种基于LSSVM集成的建模方法。在模糊均值聚类(Fuzzy C-Means, FCM)的基础上,提出了软聚类(Soft FCM, SFCM)算法,并基于此方法把原始样本划分成多个相互重叠的子空间；在各子空间上建立了个体LSSVM模型；基于选择性集成思想,利用PLS作为聚合策略来捕获差异度较大的个体信息得到模型输出。选取某电站660MW机组实际运行数据,建立了NOx排放的软测量模型,并与其他建模方法对比,结果表明提出的方法降低了模型的复杂度而且提高了预测精度。
(4) LSSVM集成模型的更新方法
针对过程特性的变化,提出了LSSVM集成模型的更新方法。将过程特性变化分为运行工况拓展和工况变迁,并提出了基于样本追加和样本替换的更新策略,在此基础上,利用增量式LSSVM算法来实现集成模型的更新,而且研究了模型的更新时序。基于sinc函数的数值仿真对更新策略的有效性进行了验证；结合NOx排放的集成模型,对更新前后的预测效果进行了对比,结果表明过程特性发生变化时更新后的模型仍能保持较高的预测精度。
To ensure the secure and economical operation of the power plant, some important variables are required to be measured accurately and reliably. However, it is very difficult to achieve such measurements only by using hardware-based sensors owning to economic and technical limitation. The data-driven soft sensor has been used as one of the approaches to overcome the problem. Especially the development of informatization of power plants, which makes it easier to obtain the operation data, has provided a favorable research platform for developing data-driven models. The data-driven modeling technique has become one of research focuses in the thermal process field.
Commonly there mainly exist two separate sources of data samples for the model development:experimental data samples which are gathered through specially designed experiment and plant data or historical data samples which are captured from historical normal-operation database. The two types of data samples have different characteristics in terms of the distribution, steady condition, uniformity, correlation, samples quantity and etc. Based on the analysis of data characteristics and data preprocess, the modeling methods are studied mainly on the following aspects:
(1) Steady state detection
A steady-state detecting method is proposed based on piecewise curve fitting considering the operation characteristics of historical data. Discrete sampling points are transformed to continuous signals utilizing piecewise curve fitting method. Moreover, the first and second order derivative sequences are obtained, and meanwhile, high-order noise is also suppressed. The process trend and steady information are extracted based on threshold criteria. An application is explored using the operation data of feed-water flow system from a600MW utility to validate the effectiveness of the proposed approach.
(2) Nonlinear PLS model integrated with inner LSSVM function
Considering the characteristics of the experiment data, this dissertation presents a new nonlinear partial least squares modeling method, in which outer linear partial least squares (PLS) framework is applied to extract the input and output components and eliminate the correlation. Meanwhile, least squares support vector machines (LSSVM) is deployed to describe the inner relationship between the components. Moreover, the weight-updating procedure is incorporated to enhance the accuracy of prediction. The model validity is examined based on a pH neutralization benchmark process. Then the proposed method is utilized to predict NOx emission using the experiment data of a coal-fired boiler and high accuracy is obtained.
(3) LSSVM-based ensemble modeling method
A new LSSVM-based ensemble algorithm is proposed to tackle the problem of historical operation data being concentrated in local regions and of large sample size. Based on traditional fuzzy c-means cluster (FCM) algorithm, a soft cluster (SFCM) method is proposed to divide the initial data into several overlapping subspaces, in which the LSSVM learners are trained respectively. According to the selective ensemble, PLS is deployed as the combiner to obtain the final output with capturing the diversity. The proposed method is applied to develop the NOx model based on the operating data selected from a660MW utility coal-fired boiler, and the comparison results reveal that the time complexity is reduced and the generalization is enhanced.
(4) Updating strategy of LSSVM-based ensemble
Aiming at the time-varying characteristics of industrial process, an updating strategy of LSSVM-based ensemble method is presented. Samples addition and replacement methods are proposed to tackle the extension and transition of the operation region, based on which, incremental LSSVM is employed to accomplish the update of the ensemble model. Meanwhile, the updating time and frequency are also studied. The updating strategy is validated based on the sinc function simulation. The NOx ensemble models with and without update are compared, and the results reveal that the prediction accuracy of the model with update still remains high even if the process characteristics have varied.
一般构建模型采用的数据来源主要有两种：对过程进行试验设计而得到的试验数据和从历史数据库中获得的历史运行数据。两种数据的工况分布、稳态状况、均匀性、相关性以及样本量等特性均不相同。本文以基于过程数据的建模方法为研究主题,在对数据进行分析和预处理的基础上,做了以下工作：
(1)稳态工况检测
针对历史运行数据工况的稳态和动态交替特性,提出了一种基于分段曲线拟合的稳态工况检测方法。利用分段曲线将离散数据样本拟合成连续信号,并求得了各样本点对应的一阶导数和二阶导数值,同时滤除了高阶噪声。根据有关阈值判定准则,得到了数据样本的变化趋势和稳态信息,最终判定出稳态工况。以某600MW电站机组给水流量系统的稳态工况检测为例,验证了该方法的有效性。
(2)基于内部LSSVM的非线性PLS建模方法
针对热工试验数据工况特性,提出了一种新的非线性偏最小二乘(Partial Least Squares, PLS)建模方法。该方法保留了外部线性PLS框架来提取输入输出主成分特征向量,同时消除了变量间的相关性,内部采用最小二乘支持向量机(Least Squares Support Vector Machine, LSSVM)来描述主成分之间的非线性关系,而且基于误差对权值进行更新,提高了模型的预测精度。利用pH中和过程的Benchmark模型验证了本方法的有效性,并基于某电站燃煤锅炉的实际热态试验数据建立了NOx排放模型,得到了较高的预测精度。
(3)基于LSSVM集成的建模方法
针对历史运行数据局部工况分布和大样本的特点,提出了一种基于LSSVM集成的建模方法。在模糊均值聚类(Fuzzy C-Means, FCM)的基础上,提出了软聚类(Soft FCM, SFCM)算法,并基于此方法把原始样本划分成多个相互重叠的子空间；在各子空间上建立了个体LSSVM模型；基于选择性集成思想,利用PLS作为聚合策略来捕获差异度较大的个体信息得到模型输出。选取某电站660MW机组实际运行数据,建立了NOx排放的软测量模型,并与其他建模方法对比,结果表明提出的方法降低了模型的复杂度而且提高了预测精度。
(4) LSSVM集成模型的更新方法
针对过程特性的变化,提出了LSSVM集成模型的更新方法。将过程特性变化分为运行工况拓展和工况变迁,并提出了基于样本追加和样本替换的更新策略,在此基础上,利用增量式LSSVM算法来实现集成模型的更新,而且研究了模型的更新时序。基于sinc函数的数值仿真对更新策略的有效性进行了验证；结合NOx排放的集成模型,对更新前后的预测效果进行了对比,结果表明过程特性发生变化时更新后的模型仍能保持较高的预测精度。
To ensure the secure and economical operation of the power plant, some important variables are required to be measured accurately and reliably. However, it is very difficult to achieve such measurements only by using hardware-based sensors owning to economic and technical limitation. The data-driven soft sensor has been used as one of the approaches to overcome the problem. Especially the development of informatization of power plants, which makes it easier to obtain the operation data, has provided a favorable research platform for developing data-driven models. The data-driven modeling technique has become one of research focuses in the thermal process field.
Commonly there mainly exist two separate sources of data samples for the model development:experimental data samples which are gathered through specially designed experiment and plant data or historical data samples which are captured from historical normal-operation database. The two types of data samples have different characteristics in terms of the distribution, steady condition, uniformity, correlation, samples quantity and etc. Based on the analysis of data characteristics and data preprocess, the modeling methods are studied mainly on the following aspects:
(1) Steady state detection
A steady-state detecting method is proposed based on piecewise curve fitting considering the operation characteristics of historical data. Discrete sampling points are transformed to continuous signals utilizing piecewise curve fitting method. Moreover, the first and second order derivative sequences are obtained, and meanwhile, high-order noise is also suppressed. The process trend and steady information are extracted based on threshold criteria. An application is explored using the operation data of feed-water flow system from a600MW utility to validate the effectiveness of the proposed approach.
(2) Nonlinear PLS model integrated with inner LSSVM function
Considering the characteristics of the experiment data, this dissertation presents a new nonlinear partial least squares modeling method, in which outer linear partial least squares (PLS) framework is applied to extract the input and output components and eliminate the correlation. Meanwhile, least squares support vector machines (LSSVM) is deployed to describe the inner relationship between the components. Moreover, the weight-updating procedure is incorporated to enhance the accuracy of prediction. The model validity is examined based on a pH neutralization benchmark process. Then the proposed method is utilized to predict NOx emission using the experiment data of a coal-fired boiler and high accuracy is obtained.
(3) LSSVM-based ensemble modeling method
A new LSSVM-based ensemble algorithm is proposed to tackle the problem of historical operation data being concentrated in local regions and of large sample size. Based on traditional fuzzy c-means cluster (FCM) algorithm, a soft cluster (SFCM) method is proposed to divide the initial data into several overlapping subspaces, in which the LSSVM learners are trained respectively. According to the selective ensemble, PLS is deployed as the combiner to obtain the final output with capturing the diversity. The proposed method is applied to develop the NOx model based on the operating data selected from a660MW utility coal-fired boiler, and the comparison results reveal that the time complexity is reduced and the generalization is enhanced.
(4) Updating strategy of LSSVM-based ensemble
Aiming at the time-varying characteristics of industrial process, an updating strategy of LSSVM-based ensemble method is presented. Samples addition and replacement methods are proposed to tackle the extension and transition of the operation region, based on which, incremental LSSVM is employed to accomplish the update of the ensemble model. Meanwhile, the updating time and frequency are also studied. The updating strategy is validated based on the sinc function simulation. The NOx ensemble models with and without update are compared, and the results reveal that the prediction accuracy of the model with update still remains high even if the process characteristics have varied.
引文
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