| 基于LS-SVM稀疏化算法的飞灰含碳量软测量方法 |
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| 作者姓名： | 张大海 楼锐 刘宇穗 王晓雄 张世荣 |
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| 作者单位： | 1.中国能源建设集团广东省电力设计研究院有限公司，广州 510663 |
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| 基金项目： | 中国能源广东院科技项目“大容量机组高效宽负荷率控制技术研究和应用”EV03141W |
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| 摘 要： | 目的] 为了解决经典迭代剪切稀疏化算法在飞灰含碳量软测量模型应用中计算量过大问题，提出了一种基于样本分布特征的LS-SVM稀疏化算法。 方法] 算法在计算样本特征空间距离基础上，融合密度和离散度构建全局代表性指标，并按此指标对原始样本集进行排序和剪切，完成稀疏化。为验证算法性能将提出的算法应用到某1 000 MW火电机组飞灰含碳量软测量模型，训练样本集取自机组现场试验数据。 结果] 结果表明:本算法能在适当牺牲误差性能的情况下大大消减样本容量，显著降低飞灰含碳量LS-SVM软测量模型训练及在线预测计算量。 结论] 所提LS-SVM稀疏化算法在保证误差值降低0.01%的情况下，将样本空间从90个缩小到30个，既减小了计算规模，又保证了计算精度。所提算法可在PLC等计算性能受限的工业控制器中实现飞灰含碳量在线软测量功能，并可推广至发电厂其他参数软测量系统。
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| 关 键 词： | 飞灰含碳 稀疏化 全局代表性指标 软测量 |
| 收稿时间： | 2019-01-28 |
A Soft Measurement Method for Carbon Content of Fly Ash Based on Sparseness Approach for LS-SVM |
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| Affiliation: | 1.China Energy Engineering Group Guangdong Electric Power Design Institute Co., Ltd., Guangzhou 510663, China2.School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China |
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| Abstract: | Introduction] The paper aims to establish a sparseness approach based sample distribution for LS-SVM models to solve the problem of excessive computation in the application of classical iterative shearing sparseness algorithm for the soft measurement model of the carbon content in flying ash. Method] On the basis of calculating the feature space distance between the samples, global representative indicator is constructed by mixing together the density and dispersion. The original samples were sorted and pruned and the sparsenseness was realized according to the indicator. The LS-SVM soft measurement model of the carbon content in fly ash was applied to a 1 000 MW coal-fired power plant, the original training sample set was taken from the field operation data of the unit. Result] The results show that the proposed algorithm can greatly reduce the capacity of the training set with tiny loss of the error performance and it can reduce the training and online prediction calculation work during the LS-SVM soft measurement model of the carbon content in fly ash. Conclusion] The LS-SVM sparse algorithm proposed in this paper reduces the sample space from 90 to 30, which not only reduces the calculation scale, but also guarantees the calculation accuracy, while guaranteeing that the error is reduced by 0.01%. The algorithm can realize on-line soft measurement of carbon content in fly ash in industrial controllers with limited computing performance such as PLC, and can be extended to other parameters soft measurement systems in power plants. |
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