School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China.
School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China; Colleges and Universities Key Laboratory of Intelligent Integrated Automation, Guilin University of Electronic Technology, Guangxi, 541004, China.
ISA Trans. 2019 Mar;86:48-61. doi: 10.1016/j.isatra.2018.10.042. Epub 2018 Nov 3.
In order to satisfy the growing demands of control performance and energy conservation in power generation process, a novel T-S fuzzy modeling method combined with the quantum artificial bee colony (QABC) algorithm is proposed and applied to the coordinated control system (CCS) of ultra-supercritical unit in 1000MW power plant. The T-S fuzzy modeling consists of the identifications of premise part and consequence part. In the premise part identification, the cluster number and initial cluster centers are obtained at first by using entropy-based clustering method. Secondly, the initial cluster centers are modified through QABC algorithm to guarantee the integral of data and avoid possible marginalization. Then, the consequence part is identified through exponentially-weighted least squares. Furthermore, on account of the obtained fuzzy model, an energy-saving predictive control (ESPC) algorithm based on the generalized predictive control is introduced. In the rolling optimization process of ESPC, the values of manipulated variables taken as energy consumption indicator are introduced into objective function to decrease the consumption of energy and improve the performance of control process. Meanwhile, the addition of manipulated variables constraints can obtain further improvements of energy-saving efficiency and control performance. The simulation results demonstrate the high precision of identified model and ideal performance along with energy-saving ability of ESPC.
为了满足发电过程中对控制性能和节能日益增长的需求，提出了一种新的 T-S 模糊建模方法，并结合量子人工蜂群（QABC）算法应用于 1000MW 电站超超临界机组的协调控制系统（CCS）。T-S 模糊建模由前提部分和结果部分的识别组成。在前提部分的识别中，首先使用基于熵的聚类方法获得聚类数和初始聚类中心。其次，通过 QABC 算法对初始聚类中心进行修正，以保证数据的积分并避免可能的边缘化。然后，通过指数加权最小二乘法识别结果部分。此外，基于所得到的模糊模型，引入了一种基于广义预测控制的节能预测控制（ESPC）算法。在 ESPC 的滚动优化过程中，将作为能耗指标的被控变量值引入目标函数，以降低能耗，提高控制过程的性能。同时，添加被控变量约束可以进一步提高节能效率和控制性能。仿真结果表明，所识别模型的精度高，ESPC 的性能理想，具有节能能力。
