Variable Parameter Nonlinear Control for Maximum Power Point Tracking Considering Mitigation of Drive-train Load

Since mechanical loads exert a significant influence on the life span of wind turbines, the reduction of transient load on drive-train shaft has received more attention when implementing a maximum power point tracking(MPPT) controller.Moreover, a trade-off between the efficiency of wind energy extraction and the load level of drive-train shaft becomes a key issue. However, for the existing control strategies based on nonlinear model of wind turbines, the MPPT efficiencies are improved at the cost of the intensive fluctuation of generator torque and significant increase of transient load on drive train shaft. Hence, in this paper, a nonlinear controller with variable parameter is proposed for improving MPPT efficiency and mitigating transient load on drive-train simultaneously. Then,simulations on FAST(Fatigue, Aerodynamics, Structures, and Turbulence) code and experiments on the wind turbine simulator(WTS) based test bench are presented to verify the efficiency improvement of the proposed control strategy with less cost of drive-train load.

Maximum Power Point Tracking Using Fuzzy Logic Controller under Partial Conditions

This study proposes a fuzzy system for tracking the maximum power point of a PV system for solar panel. The solar panel and maximum power point tracker have been modeled using MATLAB/Simulink. A simulation model consists of PV panel, boost converter, and maximum power point tack MPPT algorithm is developed. Three different conditions are simulated: 1) Uniform irradiation;2) Sudden changing;3) Partial shading. Results showed that fuzzy controller successfully find MPP for all different weather conditions studied. FLC has excellent ability to track MPP in less than 0.01 second when PV is subjected to sudden changes and partial shading in irradiation.

Maximum Power Point Tracking in Variable Speed Wind Turbine Based on Permanent Magnet Synchronous Generator Using Maximum Torque Sliding Mode Control Strategy

The present study was carried out in order to track the maximum power point in a variable speed turbine by minimizing electromechanical torque changes using a sliding mode control strategy. In this strategy, first, the rotor speed is set at an optimal point for different wind speeds. As a result of which, the tip speed ratio reaches an optimal point, mechanical power coefficient is maximized, and wind turbine produces its maximum power and mechanical torque. Then, the maximum mechanical torque is tracked using electromechanical torque. In this technique, tracking error integral of maximum mechanical torque, the error, and the derivative of error are used as state variables. During changes in wind speed, sliding mode control is designed to absorb the maximum energy from the wind and minimize the response time of maximum power point tracking(MPPT). In this method, the actual control input signal is formed from a second order integral operation of the original sliding mode control input signal. The result of the second order integral in this model includes control signal integrity, full chattering attenuation, and prevention from large fluctuations in the power generator output. The simulation results, calculated by using MATLAB/m-file software, have shown the effectiveness of the proposed control strategy for wind energy systems based on the permanent magnet synchronous generator(PMSG).

Maximum power point tracking of a photovoltaic energy system using neural fuzzy techniques

In order to improve the output efficiency of a photovoltaic （PV） energy system, the real-time maximum power point （MPP） of the PV array should be tracked closely. The non-linear and time-variant characteristics of the photovoltaic array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP as in traditional control strategies. A neural fuzzy controller （NFC） in conjunction with the reasoning capability of fuzzy logical systems and the learning capability of neural networks is proposed to track the MPP in this paper. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the NFC. With a derived learning algorithm, the parameters of the NFC are updated adaptively. Experimental results show that, compared with the fuzzy logic control algorithm, the proposed control algorithm provides much better tracking performance.

Hybrid Power Systems Energy Controller Based on Neural Network and Fuzzy Logic

This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy storage elements in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point (MPP) for different types of photovoltaic (PV) panels. The advance fuzzy logic controller is developed to distribute the power among the hybrid system and to manage the charge and discharge current flow for performance optimization. The developed management system performance was assessed using a hybrid system comprised PV panels, wind turbine (WT), battery storage, and proton exchange membrane fuel cell (PEMFC). To improve the generating performance of the PEMFC and prolong its life, stack temperature is controlled by a fuzzy logic controller. The dynamic behavior of the proposed model is examined under different operating conditions. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for optimizing hybrid power system performance, such as that used in smart-house applications.

Power Maximization and Control of Variable-Speed Wind Turbine System Using Extremum Seeking

Maximizing the power capture is an important issue to the turbines that are installed in low wind speed area. In this paper, we focused on the modeling and control of variable speed wind turbine that is composed of two-mass drive train, a Squirrel Cage Induction Generator (SCIG), and voltage source converter control by Space Vector Pulse Width Modulation (SPVWM). To achieve Maximum Power Point Tracking (MPPT), the reference speed to the generator is searched via Extremum Seeking Control (ESC). ESC was designed for wind turbine region II operation based on dither-modulation scheme. ESC is a model-free method that has the ability to increase the captured power in real time under turbulent wind without any requirement for wind measurements. The controller is designed in two loops. In the outer loop, ESC is used to set a desired reference speed to PI controller to regulate the speed of the generator and extract the maximum electrical power. The inner control loop is based on Indirect Field Orientation Control (IFOC) to decouple the currents. Finally, Particle Swarm Optimization (PSO) is used to obtain the optimal PI parameters. Simulation and control of the system have been accomplished using MATLAB/Simulink 2014.

计及弱磁效应的直驱式波浪发电系统变系数模型预测控制策略

为提高波浪发电系统(wave energy converter,WEC)能量捕获效率,并保证其在运行过程中满足系统的各种约束条件,该文以平均捕获功率最优为目标,提出计及弱磁效应的直驱式波浪发电系统变系数模型预测控制(model predictive control,MPC)策略。针对波浪激励力过大导致的变流器输出电压过调制问题,将弱磁控制引入优化算法,在满足发电机弱磁极限的前提下增大发电系统的运行范围,提高WEC的能量捕获效率。此外,基于仿真分析,发现了规则波下MPC目标函数的正则系数与平均捕获功率的对应关系。在此基础上,引入快速傅里叶变换(fast Fourier transformation,FFT)与叠加定理,构建了适用于不规则波输入的直驱式WEC变系数MPC控制策略。仿真结果表明,在不规则波浪下,所提方案能在满足系统约束条件的同时,有效提高能量捕获效率。

基于周期性最大功率点检测的风电机组功率备用控制方法

风电机组通常运行于最大功率输出模式,无法为受扰电网提供紧急功率支撑。稳态时预留部分出力可提高风电机组主动电网支撑能力,为此提出一种基于周期性最大功率点(MPP)检测的风电机组功率备用控制(PRC)方法。通过周期性执行最大功率点跟踪程序检测风电机组实时MPP,一旦检测到MPP即可确定PRC模式参考值并切换为直接功率控制。设置伪单调转速-机械功率曲线使风电机组稳定运行在超速功率备用点,并通过储能装置平抑MPP检测产生的峰值功率波动。仿真结果表明提出的控制方法在定风速和变风速情况下均可以准确控制检测风电机组MPP并实现PRC,并且使得风电机组一次调频效果优于传统PRC。

无线电能传输系统最大效率追踪及恒压输出复合控制方法

针对无线电能传输(WPT)系统的传输效率受耦合线圈之间的互感以及负载影响的特点,该文提出一种基于阻抗匹配技术的最大效率追踪及基于前馈PI控制的恒压输出复合控制方法。首先,对双边LCC型WPT系统的参数和传输效率进行分析,通过调整系统参数优化系统的传输效率。其次,在二次侧使用DC-DC变换器采用阻抗匹配的方法实现最大效率追踪,同时在一次侧采用DC-DC变换器利用前馈PI控制器闭环控制负载端电压实现恒压输出。该方法中,效率追踪和电压控制之间相互独立,互不干扰。此外,该方法还通过系统工作时的电路参数来估算线圈间的互感值,并通过线性拟合的方法对该估算互感值进行修正,得到更精确的互感估算值。最后,通过搭建实验平台验证了该方法的可行性和有效性。与PI控制相比,前馈PI控制方法在快速性和抗扰动性上均具有明显优势。

新型双定子无刷双馈风力发电机直接功率控制

新型双定子无刷双馈发电机包括内外功率绕组和内外控制绕组,二者分别通过中间带隔磁环的背靠背笼障转子进行磁场耦合。针对该新型双定子无刷双馈风力发电机提出一种直接功率控制方法,内外功率绕组及内外控制绕组分别串联以保持各自同相绕组的电磁一致性,将功率绕组有功和无功功率滞环比较器输出值及控制绕组磁链扇区号综合,采用查找表得到控制绕组机侧变流器所需输出的电压空间矢量。通过定量对比研究12/8极50 kW样机控制系统阶跃信号作用下的特性仿真与实验结果,验证了所提双定子无刷双馈风力发电机直接功率控制在亚同步~超同步速度变化范围内变速恒频发电、最大功率跟踪、无功功率与单位功率因数控制等方面的正确性、可行性和有效性。

双定子无刷双馈风力发电机无速度传感器反步法直接功率控制

针对双定子无刷双馈发电机,将内外功率绕组及内外控制绕组分别同相串联以保证电磁一致性。基于Lyapunov和Popov稳定性理论,采用反步法和模型参考自适应系统设计一种无速度传感器的反步法直接功率控制策略,利用空间矢量脉宽调制(SVPMW)以固定开关频率驱动控制绕组机侧变流器IGBT,以减小功率绕组功率波动及电流总谐波畸变率。通过12/8极50 kW样机控制系统的特性仿真研究,结果表明:所提控制策略在亚同步~超同步的速度变化范围内,可实现变速恒频发电、最大功率跟踪、无功功率及单位功率因数控制,电流总谐波畸变率约为2%。

基于最大净功率输出的PEMFC阴极供气系统优化控制研究

通过建立质子交换膜燃料电池(PEMFC)阴极供气系统数学模型,分析空气压缩机电压和背压阀开度对系统净功率输出的影响;针对负载电流变化,研究PEMFC系统对应的最优过氧比和最优阴极压力,建立最大输出净功率-过氧比-阴极压力寻优表;考虑过氧比和阴极压力之间的耦合关系,基于辨识出的传递函数矩阵,设计针对空气压缩机电压和背压阀开度的前馈解耦控制器,优化氧气供给和压力控制,并进行相应的仿真验证。结果表明,所提解耦控制器能有效减小过氧比和阴极压力之间的耦合影响,在实现最大净功率闭环控制的同时,具有更快的响应速度和更小的超调量。

开绕组无刷双馈风力发电机直接功率控制容错策略

针对无刷双馈发电机提出了一种控制绕组采用开绕组拓扑(OW-BDFG)的馈电方案,将三相控制绕组两端打开分别连接变流器构成双两电平变流器(D-TLCs)馈电拓扑,对机侧变流器功率器件IGBT因故障切除后的容错控制策略进行深入分析,提出了功率偏差比较直接功率控制(PEC-DPC)的D-TLCs电压空间矢量优化方案。对比分析了8/4极25kW样机的仿真及实验结果,表明:当机侧变流器1个或2个IGBT被切除后,所提OW-BDFG的PEC-DPC仍可实现变速恒频、最大功率点跟踪及单位功率因数控制,进一步提高了该发电机系统的可靠性和鲁棒性。

基于直接推力控制的直驱波浪发电系统功率优化

为提升系统波能捕获能力,分析浮子受力,以状态空间法近似拟合辐射力,建立直驱式波浪发电系统数学模型。基于最优控制理论,以系统捕获波能为目标函数,在S域求解Hamilton方程,计算奇异最优控制率,经拉氏逆变换后得到理想电磁力信号。结合直接推力控制,设计二阶滑模控制方案,保证电机推力与初级磁链的跟踪效果。仿真结果表明,所提最优功率方案下系统捕获功率高,滑模控制器跟踪误差小、动态性能好,可提高波能转化率,有效降低推力与初级磁链脉动,增强系统鲁棒性。

适应电压支撑下的光伏发电机组最大功率点控制技术

常规的光伏发电机组最大功率点控制技术,在机械功率的调整上,缺少对适应电压支撑的考虑,导致最大功率点控制效果不好。基于此,提出适应电压支撑下的光伏发电机组最大功率点控制技术。使用功率信号反馈法,检测光伏发电机组的功率,将光伏发电机组的固有功率,作为主要参数。根据发电机组运行状态点的变化,对光伏发电机组功率进行跟踪,以光伏发电机组功率跟踪结果为基础,在适应电压支撑下,通过双输入单输出的结构,调节机械功率,导入调节后的机械功率,将其作为最大功率点控制的基础数据。按照隶属函数,对控制器参数进行实时调整,从而完成对光伏发电机组最大功率点控制。结果表明,在使用本文设计的方法进行控制后,光伏发电机组的最大功率振幅有效降低,控制效果较好,具有较好应用价值。

考虑功率最大输出与储能协调的光储VSG控制策略

在传统虚拟同步发电机(virtual synchronous generator, VSG)控制的光储系统中,电池通过频繁充放电处理波动的光伏功率,易出现早衰问题。为优化电池运行,提出一种考虑功率最大输出与储能协调的光储VSG控制策略。首先,建立输出频率与直流侧电容电压偏差的比例关系,实现光伏功率的最大输出与VSG的惯性支撑。其次,利用下垂特性控制电池功率输出,在实现系统一次调频功能的基础上提升电池能量管理的灵活度。然后,设计一种稳定直流侧电压的分段控制方案,确保系统正常工作时直流侧电压在合理范围之内。最后,通过仿真实验验证所提方法不仅保留了VSG的惯性支撑和一次调频功能,还实现了光伏功率的最大输出,减少了对电池的依赖。

基于萤火虫优化模糊P&O的微生物燃料电池最大功率点跟踪

针对微生物燃料电池产电量低、输出不稳定等问题,提出一种混合萤火虫优化和模糊改进扰动观测的最大功率点跟踪算法,利用双种群萤火虫算法的全局优化能力和模糊控制的逻辑推理能力产生更精确的控制信号,通过Boost变换器对等效负载进行动态调节。结果表明:基于双种群萤火虫优化-模糊扰动观测的最大功率点跟踪算法能以更快的速度追踪到并稳定在最大功率点,降低了稳态误差,抑制了功率振荡,提高了抗干扰能力,显著改善了微生物燃料电池的产电能力和供电质量。

基于MPC的直接电流控制光伏最大功率点跟踪

传统光伏电池最大功率点追踪技术(MPPT)能追踪光伏系统的最大功率点(MPP)并使系统运行在MPP点。但是,在追踪过程中由于追踪速度和追踪精度的矛盾,使得系统在MPP点附近发生振荡。提出一种模型预测直接电流控制(MPC-DCC)和改进了的电导增量最大功率点追踪技术。系统采用Boost升压型电路进行DC-DC控制,先由改进了的电导增量法得到参考电流,再由MPC环节进行直接电流控制。对所研究的系统进行了Simulink仿真和硬件在环实验验证,结果表明该方法能够使用改进电导增量法快速准确找到MPPT最大功率点,并发挥MPC-DCC的控制器带宽及控制精度高的双重优点。