Research on the Control Strategy of Micro Wind-Hydrogen Coupled System Based on Wind Power Prediction and Hydrogen Storage System Charging/Discharging Regulation

This paper addresses the micro wind-hydrogen coupled system,aiming to improve the power tracking capability of micro wind farms,the regulation capability of hydrogen storage systems,and to mitigate the volatility of wind power generation.A predictive control strategy for the micro wind-hydrogen coupled system is proposed based on the ultra-short-term wind power prediction,the hydrogen storage state division interval,and the daily scheduled output of wind power generation.The control strategy maximizes the power tracking capability,the regulation capability of the hydrogen storage system,and the fluctuation of the joint output of the wind-hydrogen coupled system as the objective functions,and adaptively optimizes the control coefficients of the hydrogen storage interval and the output parameters of the system by the combined sigmoid function and particle swarm algorithm(sigmoid-PSO).Compared with the real-time control strategy,the proposed predictive control strategy can significantly improve the output tracking capability of the wind-hydrogen coupling system,minimize the gap between the actual output and the predicted output,significantly enhance the regulation capability of the hydrogen storage system,and mitigate the power output fluctuation of the wind-hydrogen integrated system,which has a broad practical application prospect.

Series-parallel Hybrid Vehicle Control Strategy Design and Optimization Using Real-valued Genetic Algorithm

Despite the series-parallel hybrid electric vehicle inherits the performance advantages from both series and parallel hybrid electric vehicle, few researches about the series-parallel hybrid electric vehicle have been revealed because of its complex co nstruction and control strategy. In this paper, a series-parallel hybrid electric bus as well as its control strategy is revealed, and a control parameter optimization approach using the real-valued genetic algorithm is proposed. The optimization objective is to minimize the fuel consumption while sustain the battery state of charge, a tangent penalty function of state of charge（SOC） is embodied in the objective function to recast this multi-objective nonlinear optimization problem as a single linear optimization problem. For this strategy, the vehicle operating mode is switched based on the vehicle speed, and an ＂optimal line＂ typed strategy is designed for the parallel control. The optimization parameters include the speed threshold for mode switching, the highest state of charge allowed, the lowest state of charge allowed and the scale factor of the engine optimal torque to the engine maximum torque at a rotational speed. They are optimized through numerical experiments based on real-value genes, arithmetic crossover and mutation operators. The hybrid bus has been evaluated at the Chinese Transit Bus City Driving Cycle via road test, in which a control area network-based monitor system was used to trace the driving schedule. The test result shows that this approach is feasible for the control parameter optimization. This approach can be applied to not only the novel construction presented in this paper, but also other types of hybrid electric vehicles.

The Main Biological Hazards in Animal Biosafety Level 2 Facilities and Strategies for Control

Concern about the biological hazards involved in microbiological research, especially research involving laboratory animals, has increased in recent years. Working in an animal biosafety level 2 facility （ABSL-2）, commonly used for research on infectious diseases, poses various biological hazards. Here, the regulations and standards related to laboratory biosafety in China are introduced, the potential biological hazards present in ABSL-2 facilities are analyzed, and a series of strategies to control the hazards are presented.

基于ε-level蝙蝠算法的无人机三维航迹规划

针对复杂地形环境、存在各种威胁和约束的无人机航迹规划问题,提出了一种基于ε-level改进蝙蝠算法的航迹规划算法。根据无人机目标函数和约束条件,建立无人机三维航迹规划模型;其次,针对蝙蝠算法处理高维带约束问题存在的早熟现象,设计了自适应权重系数和迭代阈值以平衡算法的探索能力和开发能力,并结合ε-level比较策略提高算法对非凸非线性约束优化问题的处理能力;设计了转弯半径可变的Dubins曲线用来对航迹进行平滑处理并解决2个航迹点连线贯穿地形的问题。通过仿真实验,并与蝙蝠算法、粒子群优化算法、ε-level粒子群优化算法和ε-level差分进化算法相比较,所提算法在开发能力、稳定性和成功率等方面都表现出较优的性能。

Optimal control strategy for energy saving in trains under the four-aspect fixed autoblock system

This paper deals with both the leading train and the following train in a train tracking under a four-aspect fixed autoblock system in order to study the optimum operating strategy for energy saving. After analyzing the working principle of the four-aspect fixed autoblock system, an energy-saving control model is created based on the dynamics equation of the Wains. In addition to safety, energy consumption and time error are the main concerns of the model. Based on this model, dynamic speed constraints of the following train are proposed, defined by the leading gain dynamically. At the same time, the static speed constraints defined by the line conditions are also taken into account. The parallel genetic algorithm is used to search the optimum operating strategy. In order to simplify the solving process, the external punishment function is adopted to transform this problem with constraints to the one without constraints. By using the real number coding and the strategy of dividing ramps into three parts, the convergence of GA is accelerated and the length of chromosomes is shortened. The simulation result from a four-aspect fixed autoblock system simulation platform shows that the method can reduce the energy consumption effectively in the premise of ensuring safety and punctuality.

Composition control and temperature inferential control of dividing wall column based on model predictive control and PI strategies

The dividing wall column(DWC) is considered as a major breakthrough in distillation technology and has good prospect of industrialization. Model predictive control(MPC) is an advanced control strategy that has acquired extensive applications in various industries. In this study, MPC is applied to the process for separating ethanol,n-propanol, and n-butanol ternary mixture in a fully thermally coupled DWC. Both composition control and temperature inferential control are considered. The multiobjective genetic algorithm function "gamultiobj" in Matlab is used for the weight tuning of MPC. Comparisons are made between the control performances of MPC and PI strategies. Simulation results show that although both MPC and PI schemes can stabilize the DWC in case of feed disturbances, MPC generally behaves better than the PI strategy for both composition control and temperature inferential control, resulting in a more stable and superior performance with lower values of integral of squared error(ISE).

Algorithm of Dynamic Operation Process of Hydraulic Automatically Operated Canals with Constant-Downstream Level Gates

On the basis of analysis the governing process of downstream water level gates AVIO and AVIS, a mathematical model for simulation of dynamic operation process of hydraulically automated irrigation canals instalIed with AVIO and AVIS gates is presented, the main point of this rnathematical model is firstly applying a set of unsteady flow equations （St. Venant equations here） and treating the condition of gate movement as its dynamic boundary, and then deeoupling this interaction of gate movement with the change of canal flow. In this process, it is necessary to give the gateg open-loop transfer function whose input is water level deviation and output is gate discharge. The result of this simulation for a practical reach has shown it has satisfactory accuracy.

A Genetic Algorithm for Optimizing Yaw Operation Control in Wind Power Plants

A genetic algorithm is proposed to optimize the yaw control system used for the stable and efficient operation of turbines in wind power plants.In particular,the factors that produce yaw static deviation are analyzed.Then,the sought optimization method for the yaw static deviation of the wind turbine is implemented by using a lidar wind meter in the engine room in order to solve the low accuracy problem caused by yaw static deviation.It is shown that fuzzy control can overcome problematic factors such as the randomness of wind direction and track the change of wind direction accurately.Power control implementation is simple,as only the voltage and current of the generator need to be measured.

An Advanced Control Strategy for Dual-Actuator Driving System in Full-Scale Fatigue Test of Wind Turbine Blades

A new dual-actuator fatigue loading system of wind turbine blades was designed.Compared with the traditional pendulum loading mode,the masses in this system only moved linearly along the loading direction to increase the exciting force.However,the two actuators and the blade constituted a complicated non-linear energy transferring system,which led to the non-synchronization of actuators.On-site test results showed that the virtual spindle synchronous strategy commonly used in synchronous control was undesirable and caused the instability of the blade’s amplitude eventually.A cross-coupled control strategy based on the active disturbance rejection algorithm was proposed.Firstly,a control system model was built according to the synchronization error and tracking error.Furthermore,based on arranging the transition process,estimating the system state and error feedback,and compensating disturbance,an active disturbance rejection controller was designed by adopting the optimal control function.Finally,on-site test results showed that the cross-coupled control strategy based on the active disturbance rejection algorithm could ensure the synchronization of two actuators.The maximum speed synchronization error of the two motors was less than 16 RPM,the displacement synchronization error of the two actuators was less than 0.25 mm and approaching zero after 4 seconds,and the peak value of vibration of the blade was less than 5 mm,which satisfied the fatigue test requirement.

Symplectic multi-level method for solving nonlinear optimal control problem

By converting an optimal control problem for nonlinear systems to a Hamiltonian system,a symplecitc-preserving method is proposed.The state and costate variables are approximated by the Lagrange polynomial.The state variables at two ends of the time interval are taken as independent variables.Based on the dual variable principle,nonlinear optimal control problems are replaced with nonlinear equations.Furthermore,in the implementation of the symplectic algorithm,based on the 2N algorithm,a multilevel method is proposed.When the time grid is refined from low level to high level,the initial state and costate variables of the nonlinear equations can be obtained from the Lagrange interpolation at the low level grid to improve efficiency.Numerical simulations show the precision and the efficiency of the proposed algorithm in this paper.

基于改进麻雀搜索算法的水质模型多参数反演

水质模型参数取值对模型的模拟精度影响很大,为提高BOD-DO水质模型参数反演精度,首先在DobbinsCamp BOD-DO水质模型的基础上,以BOD和DO浓度计算值与实测值之差的加权平方和最小为目标函数,构建了Dobbins-Camp BOD-DO水质多参数反演模型;然后针对麻雀搜索算法(Sparrow Search Algorithm,SSA)求解精度低、稳定性不足和易陷入局部最优等问题,引入Sine混沌映射和对立学习、转移概率以及差分变异3个策略,分别从提高初始种群多样性、扩大搜索空间以及增强种群跳出局部最优的能力三方面对SSA算法进行改进,提出了一种多策略改进的麻雀搜索算法(Multi-strategy Improved Sparrow Search Algorithm,MISSA),并将其应用于Dobbins-Camp BOD-DO水质多参数反演模型的求解;最后通过数值实验将得到的反演结果与SSA算法、模拟退火算法、粒子群算法、遗传算法四种优化算法进行对比,并探讨了参数初值选取和观测噪声水平对反演结果的影响。结果表明:MISSA算法的计算性能明显优于对照组中的4种算法,且能显著降低初值选取对BOD-DO水质模型参数反演结果的影响,当观测数据的噪声水平不超过5%时,MISSA算法可有效提高反演结果的稳定性。该结果验证了MISSA算法在反演Dobbins-Camp BOD-DO水质模型参数的有效性,为水质模型参数求解提供有益参考。

基于改进鼠群优化算法的起重机主梁轻量化设计

为提高元启发式算法求解桥式起重机主梁优化问题的寻优精度与效率,文中提出一种改进的鼠群优化算法(IRSO)。该算法采用Hénon混沌随机反向学习初始化种群,提高算法的初始寻优性能;在追逐行为中,引入随机反向学习和高斯变异混合策略对鼠群进行逐维学习,增强算法的全局搜索能力;在搏斗行为中,采用翻筋斗搏斗搜索策略更新鼠群位置,增强算法的局部搜索能力;在算法中引入自适应余弦控制因子,实现算法控制参数之间的动态平衡,提高算法的整体寻优能力。仿真结果表明:与其他算法相比,IRSO算法寻优能力更优、收敛精度更高、稳定性和鲁棒性更强;同时,IRSO算法可高效地解决桥式起重机主梁轻量化设计问题,减重效果可达20.72%,具有较好的工程实际应用能力。

考虑电压-无功调节的台区互联装置规划方法

伴随分布式能源广泛接入低压配电网,其对配电网运行灵活性和消纳能力的要求不断提高。利用低压柔性互联装置将独立运行的低压配电台区分区互联,避免传统电压调节和无功补偿装置频繁动作。考虑到柔性互联装置造价昂贵,协同传统电压-无功调节装置,文中提出低压柔性互联装置的选址定容规划方法。首先,分析低压柔性互联装置拓扑和运行方式,建立其潮流模型。其次,建立低压柔性互联装置优化配置的双层规划模型,上层规划以年综合费用最小为目标,下层规划考虑电压-无功协调控制时间序列模型,以运行成本和电压偏差最小为目标,基于粒子群优化算法和混合整数二阶锥规划算法交替求解,得出配电系统最优柔性互联方案和最优运行方式。最后,在IEEE 33节点系统上进行实例分析,验证该双层规划算法的有效性。结果表明,所提方法能有效减少柔性互联装置的过度布置,同时减少由分布式能源频繁波动造成的运行成本。将模型凸化并线性化的方法明显提高了求解效率。

汽车空调控制策略与算法综述

文章对汽车热管理系统中空调的控制算法进行概述,对PID、模糊PID、带前馈反馈的控制算法以及模型预测控制算法(MPC)等控制理论在汽车空调中的应用进行介绍,并对不同控制算法在汽车空调中的优点和缺点进行分析,也对控制算法在应用层面上还存在的问题进行总结。与传统的机械式元器件相比,电子化的元器件在结合智能的控制算法后,能大大提高系统的节能水平和控制的准确性。最后,结合当前新能源汽车的发展方向,得出汽车空调系统中控制算法和电子元器件协同发展对节能和智能化的重要意义。

深空探测应用微型化自适应算法控制器研究

深空探测应用控制系统中最为核心的是控制器的设计。通过采用先进的控制策略,能降低对控制器处理能力的需求。面对工作需求多样,工作内容复杂的深空探测应用,微型化控制器需要更加智能的算法和更加合理的控制策略来维持系统的稳定性。

基于改进麻雀搜索算法的PID参数整定系统设计

PID参数整定是PID控制中的一个关键步骤,常规的PID控制在一定程度上已被淘汰。为改善PID控制的效果和精度,文中将黄金正弦策略与精英反向学习策略相结合,提出一种改进的麻雀搜索算法。用23个标准函数对所提出的新方法进行了验证,实验结果证明了新方法的有效性。利用改进的麻雀搜索算法对PID控制参数进行了优化,并对该方法进行了仿真分析。结果表明,采用该方法进行PID参数整定时,其具有更好的稳定性、更高的精度和更好的性能。

改进自适应遗传算法优化的船用增压锅炉上锅筒水位滑模控制方法

[目的]船用增压锅炉的上锅筒水位由于航行中负荷的频繁变化,难以获得理想的控制效果。为了保证上锅筒水位的稳定,对大负荷扰动下的上锅筒水位控制方法进行研究。提出一种基于改进自适应遗传算法优化的增压锅炉上锅筒水位滑模控制方法。[方法]将上锅筒水位偏差的速度和速度变化率引入S函数来设计滑模控制器,采用李雅普诺夫稳定性原理证明其稳定性。在此基础上,利用改进的自适应遗传算法优化滑模控制器。[结果]将改进的自适应遗传算法优化的增压锅炉上锅筒水位滑模控制方法与传统的PID控制方法进行了对比分析。在响应斜坡扰动信号和阶跃扰动信号时,改进自适应遗传算法优化的滑模控制器均能够无差跟踪输入信号,其稳定时间比PID缩短5 s,超调量也小于PID。[结论]仿真结果表明,改进自适应遗传算法优化的滑模控制方法具有更优的控制效果。

柔性网架结构下考虑双边交易的多微网与配网协同规划

高比例风光接入下,多微网与配网间的双边交易对配电网的调控能力提出了更高要求。考虑柔性配电装置快速灵活调节网络潮流的优点,提出一种柔性网架结构下考虑多微网与配网双边交易的协同规划方法。首先,在考虑智能软开关规划的基础上构建协同规划的广义纳什议价模型。其次,采用交替优化策略将议价模型转化为易于求解的双层迭代过程,上层各主体各自优化规划方案,下层完成双边交易的结算问题。然后,将下层问题等效转化为电量结算和费用结算两个子问题,并分别采用交替方向乘子法求解。最后,在IEEE33节点配电系统中的算例表明所提规划方法可有效缩减社会总成本和各主体年综合费用。

基于行为可信的多级网络访问安全控制方法

为了提升网络安全等级,确保不同安全等级的信息仅能被有权限的用户获取,提出基于行为可信的多级网络访问安全控制方法。通过入侵检测平台、流量分析工具等渠道采集用户行为证据,将采集到的数据按照历史行为、直接行为和间接行为建立特征认证集合;为便于分析,统一数据量纲;从数据价值、脆弱性和威胁行为等方面分析风险因素,计算用户行为风险值;将计算结果和行为信任度相结合,全面评估行为可信度;利用k-means聚类算法建立初始样本矩阵,确定聚类中心,完成用户行为可信度多级划分;设置具有决策、执行和等级划分等模块的访问安全控制器,根据聚类结果,控制器会作出对应决策,实现网络访问安全控制。实验结果表明,所提方法能够准确评估用户的行为可信度,提高控制效果,增强网络安全性。