基于EEMD方法的区域降水事件特征分析——以京津冀地区为例

区域降水事件特征影响水循环,进而对社会经济各个方面有着深远的影响。京津冀地区是我国的政治、经济、文化中心,水旱灾害频发,亟需全面了解该地区区域降水事件特征。以京津冀地区173个气象站1989—2018年间逐日降水数据为基础,共识别出552次区域降水事件,采用集合经验模态分解(EEMD)等方法,从持续时间、覆盖范围、降水强度3个要素对京津冀地区区域降水事件特征及时空分布情况展开研究。结果表明:①区域降水事件发生频率有随持续时间、覆盖范围增大而减小的趋势,减小趋势逐渐变缓,区域降水事件的降水强度在10 mm附近频率最高,区域降水事件的3个要素均与综合性强度有着明显的正相关关系;②京津冀地区区域降水事件综合性强度年际变化呈现出10 a左右的周期,但长期趋势性不明显;③京津冀地区区域降水事件沿地形呈“西北少、中部多”的空间分布规律。研究成果可为当地水资源规划、防灾减灾等提供技术支撑。     

金沙江中游梯级水电站联合优化调度实践

分析了金沙江中游区域梯级水电站联合调度的特殊性及复杂性,从电调的角度介绍了金中集控中心自成立以来对受控梯级水电站联合调度的具体实施,总结了梯级水电站联合优化调度的措施,并分析了梯级水电站联合优化调度的综合效益,发现优化调度成效明显。     

某抽水蓄能电站上水库防渗帷幕深度优选研究

针对某抽水蓄能电站上水库地质条件较为复杂、库区存在两条由库内延伸到库外的中等宽度断层、副坝区基岩弱风化层较厚的特点,以及设计防渗方案不合理的问题,根据上库工程坝址区的地质结构、地形地貌及防渗帷幕设计,建立了三维有限元渗流分析模型。首先通过参数及地下水位反演确定了库区基岩的渗透系数和水库特征点处的地下水位,然后在设计防渗方案的基础上计算分析了不同防渗帷幕深度对库区渗流场、渗透流量及各部位渗透坡降的影响,并根据分析结果对防渗帷幕深度进行了方案优选。结果表明：河床断层附近和库周断层及副坝附近的防渗帷幕深度可增大10 m,左、右坝肩帷幕深度可分别减少10 m并降低至3 Lu标准,库周非断层及副坝附近的防渗帷幕深度可减少10 m。研究结果可为类似工程防渗帷幕深度方案的优选提供参考。     

基于星敏感器的姿态优化估计算法

对于采用星敏感器的航天器姿态确定问题,提出了一种快速的姿态优化估计方法;首先,根据Rodrigues参数和观测向量之间的线性关系,引入四元数,构造了不同的非奇异的优化准则;其次,借助四元数,求解了基于该准则的姿态优化算法;误差协方差分析和数值仿真结果表明,该算法与著名的QUaternion ESTmation(QUEST)算法相比,在相同的操作系统环境下,具有相同的姿态估计精度,但计算速度更快,对两种算法选取间隔为2000次的仿真步长,统计数据长度为100个采样点,最后对结果加权求平均值,统计结果显示仅为QUEST的三分之一;这种姿态优化估计方法具有一定的工程应用价值。     

多时滞线性连续系统的最优H∞容错控制

针对状态具有多个时滞的线性连续系统,基于Lyapunov稳定性理论和线性矩阵不等式方法(LMI),采用无时滞记忆的状态反馈控制律,研究了在执行器发生故障的情况下,连续多时滞系统的最优H∞容错控制问题.首先,给出了系统没有干扰输入时存在无记忆状态反馈容错控制器的一个充分条件;进一步,给出了在H∞扰动衰减指标约束下,系统存在无记忆状态反馈H∞容错控制器的一个充分条件;最后,给出了最优无时滞记忆状态反馈H∞容错控制器的设计方法.仿真实例证明了所得最优H∞容错控制嚣设计方法的正确性和有效性.     

基于最优Oustaloup的分数阶PID参数整定

目前工程控制中大部分系统采用传统PID控制,由于分数阶PID继承了传统PID的优点,并且具有更好的控制品质及更强的鲁棒性,因此针对分数阶微积分的高精度数字实现及分数阶PID控制器在工程复杂系统中的实际应用,提出一种新的分数阶微积分高精度数字实现算法-最优Oustaloup数字实现,并建立控制系统的仿真模型,利用框图式模型结合最优ITAE性能指标来整定分数阶PID的参数。通过实例仿真验证,该方法能进一步优化控制器参数,提高控制精度及获得更好的控制效果,便于非线性系统及复杂系统的分数阶PID参数整定。     

Optimal tracking performance of MIMO discrete‐time systems with communication constraints

The optimal tracking problem for multiple‐input multiple‐output linear‐time‐invariant discrete‐time systems with communication constraints in the feedback path is studied in this paper. The tracking performance is measured by the energy of the error signal between the output of the plant and the reference signal. The objective is to obtain an optimal tracking performance, attainable by all possible stabilizing compensators. It is shown that the optimal tracking performance consists of two parts, one depends on the nonminimum phase zeros and zero direction of the given plant, as well as the reference input signal direction, and the other depends on the nonminimum phase zeros, unstable poles, and pole direction of the given plant, as well as the bandwidth and additive white Gaussian noise of the communication channel. It is also shown that, if the constraint of the communication channel does not exist, the optimal tracking performance reduces to the existing tracking performance of the control system without communication constraints. A typical example is given to illustrate the theoretical results. Copyright © 2011 John Wiley & Sons, Ltd.     

Handling multiple objectives with biogeography-based optimization

Biogeography-based optimization (BBO) is a new evolutionary optimization method inspired by biogeography. In this paper, BBO is extended to a multi-objective optimization, and a biogeography-based multi-objective optimization (BBMO) is introduced, which uses the cluster attribute of islands to naturally decompose the problem. The proposed algorithm makes use of nondominated sorting approach to improve the convergence ability effciently. It also combines the crowding distance to guarantee the diversity of Pareto optimal solutions. We compare the BBMO with two representative state-of-the-art evolutionary multi-objective optimization methods, non-dominated sorting genetic algorithm-II (NSGA-II) and archive-based micro genetic algorithm (AMGA) in terms of three metrics. Simulation results indicate that in most cases, the proposed BBMO is able to find much better spread of solutions and converge faster to true Pareto optimal fronts than NSGA-II and AMGA do.     

Optimal Performance in Tracking Stochastic Signal Under Disturbance Rejection

The analysis method of optimal tracking performance is proposed for multiple‐input multiple‐output (MIMO) linear time‐invariant (LTI) systems under disturbance rejection. An H₂ criterion of the error signal between the output of the plant and the reference signal is used as a measure for the tracking performance. Spectral factorization is applied to obtain the optimal solution of the system tracking error. The explicit expressions are derived for this minimal tracking error with respect to random reference signals under disturbance rejection. It is shown that the nonminimum phase zeros, the zero direction, the unstable poles, the pole direction of a given plant, statistical characteristics of the reference input signal, and disturbance signal have a negative effect on a feedback system's ability to reduce the system error with disturbance rejection. The results show that the optimal tracking performance will further be damaged because of disturbance rejection. Some typical examples are given to illustrate the theoretical results.