Artificial Neural Network Maximum Power Point Tracker for Solar Electric Vehicle

This paper proposes an artificial neural network maximum power point tracker (MPPT) for solar electric vehicles. The MPPT is based on a highly efficient boost converter with insulated gate bipolar transis- tor (IGBT) power switch. The reference voltage for MPPT is obtained by artificial neural network (ANN) with gradient descent momentum algorithm. The tracking algorithm changes the duty-cycle of the converter so that the PV-module voltage equals the voltage corresponding to the MPPT at any given insolation, tempera- ture, and load conditions. For fast response, the system is implemented using digital signal processor (DSP). The overall system stability is improved by including a proportional-integral-derivative (PID) controller, which is also used to match the reference and battery voltage levels. The controller, based on the information sup- plied by the ANN, generates the boost converter duty-cycle. The energy obtained is used to charge the lith- ium ion battery stack for the solar vehicle. The experimental and simulation results show that the proposed scheme is highly efficient.

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 U+0028 MPPT U+0029 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 U+0028 Fatigue, Aerodynamics, Structures, and Turbulence U+0029 code and experiments on the wind turbine simulator U+0028 WTS U+0029 based test bench are presented to verify the efficiency improvement of the proposed control strategy with less cost of drive-train load. © 2017 Chinese Association of Automation.

Maximum Power Point Tracking With Fractional Order High Pass Filter for Proton Exchange Membrane Fuel Cell

Proton exchange membrane fuel cell (PEMFC) is widely recognized as a potentially renewable and green energy source based on hydrogen. Maximum power point tracking (MPPT) is one of the most important working conditions to be considered. In order to improve the performance such as convergence and robustness under disturbance and uncertainty, a fractional order high pass filter (FOHPF) is applied for the MPPT controller design based on the traditional extremum seeking control (ESC). The controller is designed with integerorder integrator (IO-I) and low pass filter (IO-LPF) together with fractional order high pass filter (FOHPF), by substituting the normal HPF in the original ESC system. With this FOHPF ESC, better convergence and smoother performance are achieved while maintaining the robust specifications. First, tracking stability is discussed under the commensurate-order condition. Then, simulation results are included to validate the proposed new FOHPF ESC scheme under disturbance. Finally, comparison results between FOHPF ESC and the traditional ESC method are also provided. © 2014 Chinese Association of Automation.

Modular Multilevel Inverter with Maximum Power Point Tracking for Grid Connected Photovoltaic Application

A single-phase modular multilevel inverter based photovoltaic system for grid connection is proposed. This photovoltaic system utilizes two conversion stages： a boost converter for tracking the maximum power point and a modular multilevel inverter used as an interfacing unit. The maximum power point tracking is achieved with a fuzzy logic controller, and the modular multilevel inverter regulates the DC link voltage and synchronizes the grid voltage and current in order to achieve unity power factor operation. The proposed system provides high dynamic performance and power quality injected into the grid. The validity of the proposed system is confirmed by simulations.

一种单级式高频隔离光伏并网逆变器及其调制方法

针对光伏并网系统中可实现双向功率流的单级式高频隔离逆变器,提出一种调制方法,结合电压钳位技术,解决了传统高频隔离光伏并网逆变器变压器二次侧的电压尖峰和振荡问题。该逆变器为两级功率变换,对其工作过程及原理进行详细分析,建立等效电路模型,并推导出该逆变器的数学模型。为弥补传统多谐振控制器的不足,研究一种针对单级式高频隔离光伏并网逆变器的相位补偿的多谐振控制器,在单级式结构中完成最大功率点跟踪(MPPT)和对并网电流的精确控制。最后对所提高频隔离光伏并网逆变器的调制方法及其控制策略进行实验验证。

Photovoltaic Powered Reverse Osmosis Plant for Brackish Water without Batteries with Self Acting Pressure Valve and MPPT

This paper presents a PV （photovoltaic） powered RO （reverse osmosis） plant for brackish water without batteries and a self-regulating pressure valve. The aim is to extract the maximum power from the PV module using an MPPT （maximum power point tracking） technique for powering a solar water pump and maintain constant the pressure in the RO membranes by using the self-operated valve. A Buck type converter using the InCond （incremental conductance） MPPT was developed for this application. The MPPT chosen was simulated, tested and validated, showing an efficiency of 86.8%. The technical feasibility of the RO plant was made by PLC （programmable logic controller） and was tested for two salinity levels （1,000 and 1,500 mg/L of TDS （total dissolved solids））. These salinity levels chosen are commonly found in most brackish water wells of the semi-arid region of Northeastern Brazil. The RO plant could permeate 175.3 L/day of drinking water with 120 mg/L of TDS and specific energy consumption of 2.56 kWh/m3.

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.

基于IP&O-ICS算法的光伏系统MPPT控制研究

传统的光伏系统MPPT算法易陷入局部最优,而基于智能优化算法的MPPT方法追踪速度慢,皆不能同时兼顾准确性和快速性。对此,该文设计一种变步长扰动观察法(IP&O)结合自适应布谷鸟搜索(ICS)算法的复合算法,先利用IP&O迅速到达对应电压最大的功率极大值点,再根据复杂光照环境下光伏阵列的输出特性,利用该点的功率值调整ICS算法的搜索范围,使其快速准确地追踪到最大功率点。通过仿真对比,验证了此算法在追踪速度和精度上的优越性。

储能型开关升压光伏并网逆变器的研究

以光伏发电并网系统为研究对象,提出一种新型储能型开关升压并网变换器。通过建立小信号模型,对系统中的储能型开关升压网络进行分析,该系统共包含3个功率单元:光伏组件、储能型开关升压网络和并网逆变器。为最大程度地利用太阳电池能量,利用扰动观测法对最大功率点进行跟踪。同时建立能量管理策略,对3个功率单元的能量流动进行控制,可提高能量利用率、增强系统的稳定性。仿真和实验验证了该拓扑结构的优越性以及储能策略的可行性。

基于改进灰狼优化算法的光伏阵列多峰MPPT研究

针对局部阴影条件下光伏阵列呈现出的非线性多峰值P-V输出特性,提出一种基于改进灰狼优化算法的最大功率点跟踪(MPPT)控制方法。该算法将传统灰狼优化算法中线性减小的收敛因子改进为按非线性规律变化,以改善算法的动态性能。结果表明:所提出的MPPT方法在局部阴影条件下能有效跟踪到光伏阵列的最大功率点,不仅具有较快的跟踪速度,且跟踪精度达到99.1%。

Assessment of high-gain quadratic boost converter with hybrid-based maximum power point tracking technique for solar photovoltaic systems

Solar photovoltaic(SPV)modules have a low output voltage and are load-dependent.Therefore,it is critical that the SPV system has an adequate DC-DC converter to regulate and improve the output voltage to get maximum output voltage.To meet load requirements,the voltage must be increased,necessitating the use of energy-efficient power electronic converters.The performance of an SPV system coupled to a high-gain quadratic boost converter(HG-QBC)with a load is investigated in this paper.The suggested HG-QBC for the SPV system at a lower value of duty ratio provides high voltage gain with a boost factor of four times.An analytical comparison is carried out with the various existing boost converters in terms of the components and the boost factor.The issue of locating the maximum power generation point from the SPV system is crucial.As a result,choosing an appropriate maximum power point tracker(MPPT)-based technique to obtain the peak power output of the SPV system under the rapidly varying atmospheric conditions is vital.To determine the highest output power of an SPV system,a hybrid-based MPPT with a neural network assisted by a perturb and observe(P&O)technique is proposed.For the HG-QBC,a comparison of the proposed MPPT with a traditional P&O-based MPPT is illustrated.The comparative analysis takes into account rise time,settling time and voltage ripples.The output voltage and power characteristics of the proposed model are analysed under constant and varying irradiation conditions using MATLAB®/Simulink®.The results of a hybrid-based MPPT show that the oscillations are minimum at the maximum power point with fewer ripples of 0.20%and a settling time of 1.2 s in comparison with the other two techniques.

基于GA-GRU神经网络的光伏MPPT算法

针对外界环境因素快速变化时,光伏发电系统难以保持在最大功率点输出的问题,提出遗传算法与GRU神经网络相结合的最大功率跟踪算法(GA-GRU-MPPT)。该算法在构建的最大功率点预测模型基础上,采用遗传算法对GRU神经网络的参数进行优化。考虑到数据的关联性,将前一时刻的太阳电池温度、太阳辐照度、最大功率点电压及当前时刻的太阳电池温度和太阳辐照度作为预测模型的输入变量,输出为当前时刻的最大功率点电压。针对3种不同气候情形的仿真结果表明,该算法跟踪精度可达99%,能显著提高光伏系统的能量转换效率。

基于图像处理的光伏局部阴影区域分割方法

为解决真实场景中光伏组件局部阴影区域分割受环境干扰的问题,该文提出基于图像处理的光伏局部阴影区域分割方法。根据光伏组件特征用改进的阈值分割方法提取组件边框,采用Hough变换检测组件边框线段,计算包围边框线段端点的最小凸包作为感兴趣区域。在感兴趣区域内再次使用改进的阈值分割方法提取局部阴影区域。用模拟仿真图像和真实场景图像验证该文所提方法的有效性,实验结果表明:所提方法可准确分割出光伏组件上的局部阴影区域且保持了遮挡区域的细节信息。与多阈值分割方法和基于Canny边缘检测的分割方法相比,该文所提方法的错误分类误差更小,类别像素准确率更高。

基于多元宇宙优化算法的光伏发电MPPT控制算法

针对局部阴影引起的光伏阵列多峰值的输出特性,传统的单峰值MPPT算法已无法适用,为寻得全局最优解,将参数设置简单、易于理解的多元宇宙优化算法原理应用于光伏发电的多峰值MPPT模块中。通过仿真分析,证明了基于多元宇宙优化算法的MPPT控制模型能较快地实现全局最大功率点的跟踪,具有更好的收敛速度和精度。该算法能有效解决光伏电站中多峰值功率点的问题,减少寻优过程的功率损耗,从而提高光电转换率,为实际工程带来重要的经济效益。

基于自适应模型预测控制的大型风电机组MPPT方法

综合叶尖速比法和自适应模型预测控制提出风电机组最大功率点跟踪策略优化方法。根据动力学方程建立适用于控制器设计的风电机组状态空间模型。基于卡尔曼滤波算法估计有效风速,进而求解出最优转速参考值,并作为预测模型自适应准则的基础。制定预测模型的风速自适应准则,解决传统模型预测控制中的模型错配问题。仿真结果表明改进后的最大功率点跟踪策略能增强系统的鲁棒性、提高风能捕获效率、降低电磁转矩波动。

太阳能电池最大功率点跟踪技术探讨

介绍了光伏发电系统太阳能电池的输出特性及最大功率点跟踪技术的基本原理。分析了多种常用的跟踪方法的优缺点。重点研究了二次插值法的最大功率点跟踪技术。并设计了一个系统,应用常规实验方法及二次插值法寻找太阳能电池的最大输出功率,试验结果表明二次插值法能快速寻找太阳能电池的最大输出功率。

基于二阶滑膜-PID控制的风力发电机最大功率跟踪策略研究

针对风电机组最大功率跟踪(MPPT)及其控制过程稳定性需求,该文提出一种二阶滑膜-PID(PSOSMC)最大功率跟踪控制策略。为了实现对发电机转矩的稳定控制,通过结合二阶滑膜控制策略与PID控制,由比例积分导数(PID)构建滑模面进而推导出转矩控制律,建立基于PID滑模面的二阶滑膜控制系统模型,并利用Lyapunov稳定性定理证明了控制系统的收敛性。该文从气动效率、发电效率、发电机转矩和低速轴转矩等方面对二阶滑膜-PID策略的有效性进行了评估。研究结果表明,相较于其他控制策略,该文提出的PSOSMC策略能有效提高风电机组的发电效率,并显著降低发电机与低速轴转矩的波动,使风电机组在获得最大功率输出的同时还兼顾有良好的运行稳定性。

基于改进滑模控制的光伏系统MPPT控制策略

针对传统滑模控制在光伏发电MPPT控制中存在响应速度慢、抖振显著等不足,提出一种改进滑模控制的光伏MPPT策略。基于采用Boost升压电路的光伏系统,设计一种采用饱和函数幂次趋近律的改进滑模控制结构,利用幂次项的快速性与饱和函数边界层内的线性反馈的特点,构建趋近过程中不同阶段的分段调节策略,稳定性理论分析证明所设计的改进滑模控制MPPT策略能进一步提高光伏系统的动态响应速度和抗抖振性能。最后,利用Matlab仿真并进行实验验证。结果表明,采用改进滑模控制策略能够实现光伏最大功率点跟踪的精确控制,有助于提升光伏系统在较大扰动下MPPT的快速跟踪和平滑控制性能,具有较好的应用前景。

基于反馈线性化的液压型风力发电机组最佳功率追踪控制

液压型风力发电机组在额定风速以下时为获得最多电能,需随风速变化追踪最佳功率点.建立并网液压型风力发电机组仿射非线性数学模型,基于反馈线性化方法,分别以风力机转速和液压系统传输功率为输出,以变量马达摆角为控制输入,线性化系统,设计最佳转速与最佳功率追踪控制器,实现机组在额定风速以下随风速变化输出最佳功率.仿真结果表明,以液压系统传输功率为控制输出的功率追踪过程动静态特性较好.理论分析表明,控制器中高压腔压力变化率是否可以规划,是影响功率追踪过程稳定性的关键因素.

基于有效风速估计与预测的风电机组自适应最大风能跟踪控制（英文）

针对如何在有效风速未知情况下实现风电机组最大风能跟踪(MPPT)的问题,本文使用支持向量回归(SVR)和自适应控制原理,提出基于有效风速估计与预测的自适应MPPT控制方案.首先,使用机组的历史运行数据,训练得到基于SVR的风速估计与预测模型,为MPPT控制提供实时参考输入.其次,结合在线学习估计器(OLA)和减小转矩增益(DTG)控制原理,设计自适应MPPT控制器,该控制器能够较好应对系统未知动态特性和干扰,且能降低传动链载荷.最后,使用李雅普诺夫原理证明闭环系统所有信号都是有界的.仿真结果表明本文提出的方法能够获得良好的MPPT效果,进而提高机组产能.