基于全鲁棒滑模控制双馈风力发电系统空载并网控制研究

为解决PI控制在双馈风力发电机并网控制中自适应和鲁棒性差等问题,文章根据定子磁场定向矢量控制方法和全鲁棒滑模控制方法,设计了双馈风力发电机的空载并网控制策略,引入带指数函数的时变因子项,消除到达阶段;并在切换控制中引入状态误差,提高状态的收敛速度和削弱抖振。通过与PI控制进行仿真比较,结果表明,所设计的全鲁棒滑模控制策略对电网电压波动和电机参数摄动具有较强的全局鲁棒性,且响应速度快,稳态误差小,保证了定子电压的稳定性,能有效实现空载并网。     

基于改进滑模观测器的永磁风力发电机无传感器控制策略

为了获得准确的发电机转速和转子位置信息,提高无机械传感器永磁风力发电机系统驱动性能,针对传统滑模观测器高频抖振与谐波误差问题,采用Sigmiod函数代替不连续符号函数的方法,建立一种基于改进滑模观测器的永磁风力发电机无传感器控制模型,并依据噪声消除原理,提出一种自适应陷波滤波器结合正交锁相环的方法。该方法能自适应补偿估计电动势中的谐波,消除相应的位置估计误差;根据所估计的位置信息,利用最小均方算法,对自适应陷波滤波器的谐波系数进行连续自整定。最后,利用1.2 MW永磁风力发电系统进行仿真,结果表明,所提方法不仅能有效抑制系统抖振与谐波,而且能提高转子位置与转速动态跟踪精度,保证观测器鲁棒性。     

提高双向调节能力的混合储能平滑风电控制策略

为有效平滑风电出力,避免电池频繁充放电,提出了基于模型预测控制-模糊控制的并网功率平滑控制策略。首先采用模型预测控制获取风电目标出力与混合储能总输出参考功率;然后,设计了基于超级电容荷电状态的模糊自适应时间常数的一阶低通滤波法,对超级电容与锂电池实现自适应功率分配;接着基于双储能系统的充放电不平衡指标设计了模糊荷电状态优化控制,同时设计了改进双储能工作模式及相应切换规则以避免荷电状态越限;最后在Matlab/Simulink平台上建模仿真,验证了该控制策略的有效性。结果表明,所提控制策略不仅可以有效平滑风电并网功率,减小储能容量与功率配置,还可以减小锂电池的充放电切换次数,提高系统的双向调节能力。     

永磁直驱风力发电机自抗扰技术及其无位置传感器控制策略

针对永磁直驱风力发电机传统矢量控制动态性能差,抗扰动能力弱的问题,提出一种双环线性自抗扰控制系统。在此基础上针对传统滑模观测器抖振等因素造成的速度和位置角观测误差较大的问题,提出改进型自适应滑模观测器。结果表明所设计的速度和电流双环线性自抗扰控制策略能有效提高转速跟踪性能和电流波形质量;在滑模观测器的基础上结合自适应算法精确观测反电动势,借鉴锁相环原理代替反正切函数估算出转子转速和位置角,相比传统滑模观测器具有更高的估算精度。     

基于EIO光伏并网逆变器非奇异快速终端滑模控制设计

为了减小外界干扰和系统参数的不确定性等因素对光伏并网逆变系统的影响,文章提出了基于非奇异快速终端滑模控制的光伏并网逆变器控制策略。设计了基于等价输入观测器的光伏并网逆变器干扰项补偿器,推导了在参数不确定和外界干扰情况下的逆变器的反馈控制律。仿真结果表明,采用扰动观测器与非奇异快速终端滑模控制相结合的策略,增强了系统的鲁棒性,提高了跟踪精确度,系统具有良好的动态性能。     

大型风电机组模型预测独立变桨控制器设计

针对恒转速运行时,大型风电机组承受的不平衡载荷问题,提出一种多输入多输出的风电机组模型预测（MPC）独立变桨控制策略。首先,建立风电机组旋转坐标系下的状态空间模型,经过坐标变换得到固定坐标系下的平均周期模型,分析表明模型在非对角存在无法忽视的耦合;然后,计算所需观测器和控制器的参数,进一步设计基于Kalman状态观测器的多输入与多输出的模型预测独立变桨控制器;最后,分析NREL 5 MW风电机组平台在湍流风作用下集中变桨、传统独立变桨以及模型预测独立变桨控制策略的载荷特性与运行特性。结果表明,与传统的PI独立变桨控制相比,多输入多输出模型预测独立变桨控制器具有更好的降载效果,且不会引起功率波动。     

无速度传感器DFIG最大风能捕获Backstepping终端滑模控制

以双馈风力发电机(DFIG)为研究对象,将两相同步旋转坐标系下的转子磁链定向数学模型看作两个子系统,分别设计Backstepping终端滑模控制器,实现最大风能捕获。设计一种基于非奇异终端滑模的DFIG转速和磁链观测器,采用高阶终端滑模面有效抑制抖振。仿真结果表明所设计的观测器和控制器均具有强鲁棒性,能实现无静差跟踪,系统响应快,稳态精度高。     

直驱式波浪发电系统无源-滑模控制策略

针对不规则波输入激励工况下的直驱式波浪发电系统,提出一类无源-滑模控制策略。利用双线性幅度法计算不规则波浪主频,结合波能捕获策略得到期望跟踪曲线,设计无源控制器实时跟踪期望曲线;为削减外部扰动影响并提高系统稳定性,在保证系统无源下引入滑模控制策略。采用扩张状态观测器代替速度传感器,辨识动子速度。仿真结果表明：双线性幅度法能预估不规则波浪主频,扩张状态观测器观测到的速度误差小,所提控制方案能快速响应期望电流变化,准确跟踪期望曲线,鲁棒性强。     

光伏LCL型并网逆变器的积分滑模容错控制策略

针对光伏LCL滤波并网逆变系统中参数的不确定性故障和外界干扰故障,对系统在不确定性和执行器故障情况下的数学模型进行重构,提出一种基于高阶滑模观测器的连续积分滑模容错控制方法,该算法设计一个高阶滑模状态观测器,可对故障状态进行有效估计,以抑制故障的不利影响。结合滑模控制原理构建一个连续积分滑模控制器,设计系统控制分配律,并推导出系统故障的稳定条件,采用Lyapunov函数证明闭环系统的稳定性。通过仿真验证所提控制策略的有效性。     

比例积分型线性超螺旋算法滑模观测器的感应电机无传感器控制

在无速度传感器感应电机调速系统中,针对滑模观测器存在滑模抖振与观测精度间的平衡问题,设计一种强鲁棒性的比例积分型线性超螺旋算法滑模观测器。首先,利用李雅普诺夫理论证明线性超螺旋算法的稳定性。然后,通过设计转子磁链观测器的中间变量,建立基于定子电流误差项的比例积分滑模面,构造出新的滑模磁链观测器。仿真与实验表明,该观测器有效抑制了滑模抖振,提升了电机的磁链观测与抗扰动能力,在电机运行时有较高的观测精度,提高了系统的动稳态性能。     

电压不平衡下无权重系数FCS-MPC的DFIG控制

针对传统多目标模型预测控制（MPC）权重系数设计复杂的问题,运用分层控制的思想,提出一种无权重系数的多目标优化模型预测控制策略。利用MPC控制算法可直接控制交流量这一优势,在两相静止坐标系下推导双馈感应发电机（DFIG）转子电流、定子电流和定子功率的参考方程和预测方程。最后进行传统多目标MPC控制策略和无权重系数多目标MPC控制策略下的对比仿真实验。仿真结果显示,相比于传统多目标控制策略,所提控制策略的动态响应速度更快,并可改善系统的稳态性能。     

基于模型预测控制的机侧变流器功率模块结温波动抑制策略

针对双馈风电机组运行在同步转速点附近时,低输出频率使机侧变流器功率模块承受较大结温波动,致使变流器可靠性降低的问题,提出一种结温波动抑制的模型预测控制（MPC）策略。首先,基于双馈感应电机电压和磁链方程,推导功率预测控制模型;其次,构建基于机组最大功率跟踪（MPPT）的目标函数;然后,搭建基于MPC的双馈风电机组电热仿真模型,时域分析机组在亚/超同步工况下往复运行时机侧变流器功率模块的动态/稳态结温波动;最后,对比现有结温波动抑制策略,验证所提控制策略的有效性。     

引入权值修正预测控制的风电叶片自适应组合抑振策略研究

针对大型风电叶片颤振开展主动控制研究,采用柔性尾缘襟翼方式,以NACA0012翼型为研究对象,建立二自由度叶片的颤振控制增广模型。引入拉盖尔函数对模型预测控制（MPC）算法中权值更新策略进行指数修正,在此基础上利用分层结构思想对叶片减振系统划分不同控制层次,设计以振动量最小化为控制目标的自适应组合控制策略。利用仿真平台对标准工况和干扰工况下所提控制策略的控制结果进行分析,结果表明：所提方法可有效抑制气弹耦合作用下的叶片振动,降低抑振能耗,与常规自适应控制方法相比,具备更优的抗干扰性能,可进一步提升复杂运行环境下风电叶片振动控制系统的适应能力。     

储能系统双向Buck-Boost变换器控制策略研究

为应对可再生能源出力波动引起储能系统功率流动方向的频繁变化,提出一种基于自抗扰控制和模型预测控制（ADRC+MPC）的储能系统双向Buck-Boost变换器控制策略。其中模型预测控制方法应用于电流内环,无需进行参数整定的同时,也提高了系统的响应速度;采用自抗扰控制策略的电压外环,通过在高频段降阶简化控制对象,达到降低自抗扰控制器复杂度的目的。仿真和样机实验显示当电感电流与输出电压参考值突变时,系统可分别在0.2与30 ms内迅速调整到给定值;当负载与电源电压突变时,系统能在20 ms内恢复稳定。实验结果证明该文提出的控制方法优于PI+MPC策略,具有响应速度快、超调量和波动幅度小的特点。     

A control architecture to coordinate energy management with trajectory tracking control for fuel cell/battery hybrid unmanned aerial vehicles

Fuel cell/battery hybrid energy storage system (HESS) powered unmanned aerial vehicle (UAV) has the outstanding advantage of long endurance time. Trajectory tracking motion is a commonly used task execution mode of UAVs, especially in autonomous UAVs. This study aims at developing a control architecture to coordinate energy management with trajectory tracking control for fuel cell/battery hybrid UAVs. Its position tracking control adopts model predictive control (MPC) and an extended state observer to eliminate the modeling errors and effect of interference. The attitude tracking control adopts an auto-disturbance rejection controller having a quick response. The obtained control parameters are given as an input to the energy management block. Energy management strategies (EMSs) based on online dynamic programming and hierarchical MPC have been proposed. The results obtained from a simulation show that the proposed trajectory tracking control architecture can track the target trajectory stably with a small tracking error. The tracking performance is stable under interference. Experimental results show that dynamic programming is solved online with good control performance. Compared to ordinary EMSs, dynamic programming and hierarchical MPC can increase endurance time by 2.69% and 1.27%, respectively. The proposed control architecture verifies the coordination of energy management and trajectory tracking control, and prospected the advantages of the combination of fuel cell and autonomous driving for long endurance UAVs in the future.     

Anti-disturbance control of oxygen feeding for vehicular fuel cell driven by feedback linearization model predictive control-based cascade scheme

The accurate control of automotive fuel cell oxygen excess ratio (OER) is necessary to improve system efficiency and service life. To this end, an anti-disturbance control driven by a feedback linearization model predictive control (MPC)-based cascade scheme is proposed. It considers strong nonlinear coupling and disturbance injection of fuel cell oxygen supply. A six-order nonlinear fuel cell oxygen feeding model is presented. It is further formulated using an extended state observer to rapidly reconstruct the OER, to overcome the slow response and interference errors of sensor measurements. In the proposed cascade control, the outer loop is the anti-disturbance control which is used to realize the optimized OER tracking and the inner loop via the feedback linearization to linearize the oxygen feeding behaviors conducts MPC to regulate the air compressor output mass flow. The feedback linearization demonstrates a robust tracking performance of nonlinear outputs, and the integral absolute error of anti-disturbance control is 0.3021 lower than that of PI control under a custom test condition. Finally, the numerical validation on a hybrid driving cycle indicates that the proposed cascade control can regulate the fuel cell OER with an average absolute error of 0.02313 in the high air compressor operation efficiency zone.     

Performance enhancement of cascaded qZS-HB based renewable energy system using Model Predictive Control

This paper presents a multi-objective Model Predictive Control (MPC) for a grid connected 2-cell 5-level quasi Z-Source (qZS) Cascaded H-Bridge (CHB) inverter. The main contribution of the proposed control approach is the design of a multi-constraint cost function to achieve multi-objective MPC strategy dealing with the complex nature of the presented qZS-CHB topology. The designed cost function takes into account three control objectives, which are the minimization of the grid current, input current, and capacitors' voltages tracking errors. The best performance scenario is realized through the fine tuning of the constraints' weighting factors based on the grid current's error minimization and the reduction of the double-line frequency ripples on the input current. As a result, the proposed scheme achieves high-quality tracking of the encompassed state variables with the elimination of the double-line frequency power flow through the qZS inductors leading to the reduction of the hysteresis losses and the increase of the overall system efficiency. The performance of the proposed MPC strategy has been investigated and compared to the state of art PI controller. Theoretical analysis and implementation results are given to show that the proposed scheme is suitable for all system configurations and has good performances even during disturbances.     

基于模型预测控制的虚拟同步发电机控制策略

为了提高惯性和促进电网恢复,提出一种基于LCL型并网逆变器的虚拟同步发电机模型预测控制（MPC-VSG）策略。该方法在模型预测控制（MPC）中引入虚拟同步发电机（VSG）控制输出电流内环的参考值,使内环电流参考值可随电网电压和频率变化。基于Matlab搭建仿真模型,和MPC控制方法相对比和分析。仿真结果表明：当逆变器输出功率发生变化时,采用VSG的模型预测控制可增加电网的惯性和稳定性,改善系统暂态过程。     

An adaptive sliding mode observer based near-optimal OER tracking control approach for PEMFC under dynamic operation condition

Tracking control of oxygen excess ratio (OER) is crucial for dynamic performance and operating efficiency of the proton exchange membrane fuel cell (PEMFC). OER tracking errors and overshoots under dynamic load limit the PEMFC output power performance, and also could lead oxygen starvation which seriously affect the life of PEMFC. To solve this problem, an adaptive sliding mode observer based near-optimal OER tracking control approach is proposed in this paper. According to real time load demand, a dynamic OER optimization strategy is designed to obtain an optimal OER. A nonlinear system model based near-optimal controller is designed to minimize the OER tracking error under variable operation condition of PEMFC. An adaptive sliding mode observer is utilized to estimate the uncertain parameters of the PEMFC air supply system and update parameters in near-optimal controller. The proposed control approach is implemented in OER tracking experiments based on air supply system of a 5 kW PEMFC test platform. The experiment results are analyzed and demonstrate the efficacy of the proposed control approach under load changes, external disturbances and parameter uncertainties of PEFMC system.