作者：王印松, 马娉妍

作者单位：华北电力大学自动化系, 河北 保定 071003

关键词：多变量控制系统;性能评价;改进马氏距离;Hurst指数;隶属度

摘要：

为提高多变量控制系统性能评价的精度，解决传统马氏距离无法计算多个集合之间距离的问题，提出一种基于改进马氏距离的多变量控制系统性能评价方法。该方法基于多变量控制系统的在线运行数据，首先根据Hurst指数筛选出表示系统最佳性能的运行数据，以此数据建立性能评价的基准；然后对传统马氏距离的计算过程进行改进，给出Hurst指数与改进马氏距离结合的性能评价指标的具体计算方法；最后利用隶属度函数对性能指标进行性能评价等级划分。通过模型仿真验证指标的合理性，并将该方法用于某电厂二级过热喷水减温控制过程。结果表明，该评价方法有效合理且计算简单。

A performance evaluation method of MIMO system based on improved Mahalanobis distance
WANG Yinsong, MA Pingyan
Department of Automation, North China Electric Power University, Baoding 071003, China
Abstract: In order to improve the accuracy of multivariable control system performance evaluation and solve the problem that traditional Mahalanobis distance can not calculate the distance between multiple sets, a multivariable control system performance evaluation method based on improved Mahalanobis distance is proposed. This method is based on the on-line operation data of multivariable control system. Firstly, the operation data representing the best performance of the system is selected according to the Hurst index, and the benchmark of performance evaluation is established. Then the calculation process of traditional Mahalanobis distance is improved, and the specific calculation method of performance evaluation index based on the combination of Hurst index and improved Mahalanobis distance is given. Finally, the performance index is divided by the membership function. The rationality of the index is verified by model simulation, and the method is applied to the two-stage superheated water spray control process of a power plant. The results show that the evaluation method is effective and reasonable and the calculation is simple.
Keywords: multivariable control system;performance evaluation;improved Mahalanobis distance;Hurst index;membership degree
2020, 46(6):101-107  收稿日期: 2020-02-07;收到修改稿日期: 2020-03-11
基金项目: 国家自然科学基金重点项目(61533013)
作者简介: 王印松(1967-),男,河北河间市人,教授,博士,研究方向为先进控制策略和控制系统故障诊断技术
参考文献
[1] 李世博. 火电机组控制系统方差性能评价及参数优化[D]. 北京: 华北电力大学, 2018.
[2] HARRIS T J, SEPPALA C T, DESBOROUGH L D. A review of performance monitoring and assessment techniques for univariate and multivariate control systems[J]. Journal of Process Control, 1999, 9(1): 1-17
[3] HUANG B, SHAH S L, KWOK E K. Good, bad or optimal? Performance assessment of multivariable processes[J]. Automatica, 1997, 33(6): 1175-1183
[4] 周帝. 基于最小方差基准的MIMO控制系统性能评价[D]. 厦门: 厦门大学, 2014.
[5] 张庆跃. 基于最小方差基准的控制系统性能评价[D]. 厦门: 厦门大学, 2014.
[6] 王印松, 田靖雨, 李士哲, 等. 基于时域指标的火电机组负荷控制系统性能模糊综合评价[J]. 热力发电, 2016, 45(10): 99-103
[7] 张泉灵, 黄其珍. 基于用户自定义指标的多变量控制系统性能评估技术[J]. 控制与决策, 2011, 26(7): 1117-1120
[8] DAS L, SRINIVASAN B, RENGASWAMY R. Multivariate control loop performance assessment with hurst exponent and mahalanobis distance[J]. IEEE Transactions on Control Systems Technology, 2016.24(3): 1067-1074.
[9] PILLAY N, GOVENDER P. A data driven approach to performance assessment of PID controllers for setpoint tracking[J]. Procedia Engineering, 2014, 69: 1130-1137
[10] SRINIVASAN B, SPINNER T, RENGASWAMY R. Control loop performance assessment using detrended fluctuation analysis(DFA)[J]. Automatica, 2012, 48(7): 1359-1363
[11] 罗雨颋. 基于Hurst指数的火电机组控制系统性能评价方法研究[D]. 保定: 华北电力大学, 2019.
[12] 谢季坚, 刘承平. 模糊数学方法及其应用[M]. 武汉: 华中科技大学出版社, 2013: 28-29.
[13] 赵一博. 非线性四容水箱预测控制研究[J]. 自动化与仪表, 2012, 27(7): 38-41