Event-triggered variable horizon Supervisory Predictive control of hybrid power plants

The supervision of a hybrid power plant, including solar panels, a gas microturbine and a storage unit operating under varying solar power profiles is considered. The Economic Supervisory Predictive controller assigns the power references to the controlled subsystems of the hybrid cell using a financial criterion. A prediction of the renewable sources power is embedded into the supervisor. Results deteriorate when the solar power is unsteady, owing to the inaccuracy of the predictions for a long-range horizon of 10 s. The receding horizon is switched between an upper and a lower value according to the amplitude of the solar power trend. Theoretical results show the relevance of horizon switching, according to a tradeoff between performance and prediction accuracy. Experimental results, obtained in a Hardware In the Loop (HIL) framework, show the relevance of the variable horizon approach. Power amplifiers allow us to simulate virtual components, such as a gas microturbine, and to blend their powers with that of real devices (storage unit, real solar panels). In this case, fuel savings, reaching 15%, obtained under unsteady operating conditions lead to a better overall performance of the hybrid cell. The overall savings obtained in the experiments amount to 12%.     

Identification by parameter bounds in adaptive control

Approximate solutions are suggested for receding horizon dual control to guarantee acceptable control performance of a plant with large a priori parameter uncertainties under poor excitation by the output reference and to satisfy the requirement of very fast adaptation using knowledge available on sensor and performance in industrial applications of adaptive control. the aim of the paper is to present several levels of interaction between on-line identification and control performance using parameter bounds. an interesting theorem shows that parameter bounding is a necessary part of the solution of the dual control problem. Starting from the complete separation of identification and control, various approximations are presented at different levels of optimality. Finally, the exact solution of the dual control problem is found for static gain adaptation, which implicitly involves a parameter-bounding identification procedure.     

Distributed receding horizon control of large-scale nonlinear systems: Handling communication delays and disturbances

This paper studies the robust distributed receding horizon control (DRHC) problem for large-scale continuous-time nonlinear systems subject to communication delays and external disturbances. A dual-mode robust DRHC strategy is designed to deal with the communication delays and the external disturbances simultaneously. The feasibility of the proposed DRHC and the stability of the closed-loop system are analyzed, and the sufficient conditions for ensuring the feasibility and stability are developed, respectively. We show that: (1) the feasibility is affected by the bounds of external disturbances, the sampling period and the bound of communication delays; (2) the stability is related to the bounds of external disturbances, the sampling period, the bound of communication delays and the minimum eigenvalues of the cooperation matrices; (3) the closed-loop system is stabilized into a robust invariant set under the proposed conditions. A simulation study is conducted to verify the theoretical results.     

An economic receding horizon optimization approach for energy management in the chlor-alkali process with hybrid renewable energy generation

This paper presents a methodology for the application of receding horizon optimization techniques to the problem of optimally managing the energy flows in the chlor-alkali process using a hybrid renewable energy system (HRES). The HRES consists of solar and wind energy generation units and fuel cells to supply energy. The HRES is also connected to the grid and allows for buying or selling electricity from and to the grid. Initially, detailed models of each system component are introduced as the basis for the simulation study. Energy management strategies are then developed to realize the objectives of meeting production requirements while minimizing the overall operating and environmental costs. Sensitivity and uncertainty analyses are carried out to elucidate the key parameters that influence the energy management strategies. Finally, production demand response is integrated into the proposed methodology.     

Introducing robustness in model predictive control with multiple modelsand switching

Model predictive control is model-based. Therefore, the procedure is inherently not robust to modelling uncertainties. Further, a crucial design parameter is the prediction horizon. Only offline procedures to estimate an upper bound of the optimal value of this parameter are available. These procedures are computationally intensive and model-based. Besides, a single choice of this horizon is perhaps not the best option at all time instants. This is especially true when the control objective is to track desired trajectories. In this paper, we resolve the issue by a time-varying horizon achieved by switching between multiple model-predictive controllers. The stability of the overall system is discussed. In addition, an introduction of multiple models to handle modelling uncertainties makes the overall system robust. The improvement in performance is demonstrated through several examples.     

离散型随机变量的条件独立及其性质

应用条件概率引入条件独立的概念．给出了离散型随机变量条件独立的含义及其性质。     

高职毕业生薪酬期望的调查与就业指导

高校毕业生目前就业越来越难的原因主要体现在就业预期偏高,而预期偏高又以薪酬预期高为主。加强职业生涯规划和就业指导,正确引导学生就业,以便解决毕业生薪酬期望过高和用人单位给出的薪酬标准的差距过大的问题。     

确定投标报价的一种方法

文中对工程招投标中评价投标方案的方法,利用数学期望的概念,给出了一种确定投标报价的方法。     

高职毕业生就业期望的调查及分析

科学合理的就业期望,有利于毕业生顺利实现就业目标.为了了解高职毕业生就业期望问题,对河北省2013届毕业生的就业观念、期望就业地区、就业单位性质、就业薪金、基层就业意愿等方面进行了调查分析.结果表明:河北省高职毕业生在择业时过分关注个人利益,求高薪、求稳定、求舒适的心态较为普遍,存在就业期望偏高的现象,并提出了从政府政策支持、高职院校就业教育、毕业生就业观念调整及综合素质提升等方面的相应建议.     

基于深度学习的相对地质时间体估计方法

常规自动解释方法难以正确追踪三维地震数据体中被断层错断的地震反射层位。为此,提出了基于深度学习的相对地质时间体估计方法。首先,针对相对地质时间体估计需求,设计一个由编码器—解码器框架组成的相对地质时间体估计网络;其次,采用结构相似性准则为损失函数,利用生成的准确标注合成训练数据集对相对地质时间体估计网络进行训练,使其具备准确地从地震数据体中估计相对地质时间体的能力;最后,通过提取多个恒定的相对地质时间体等值面实现多个地震反射层位的自动追踪。测试结果表明,该方法不仅在验证数据集上显示出优异性能,而且在实际地震数据体上也获得了较好效果;利用估计的相对地质时间体能够一次性获得多个能够表征地层空间形态的地震反射层位。