基于自适应遗传算法的PI控制在MPPT中的应用研究

光伏阵列是将太阳能转化为电能的组合装置,是光伏发电系统中的核心部分。最大限度利用光伏阵列的输出能量是光伏发电技术所考虑的重要问题之一。根据硅太阳电池的电气特性,建立了硅太阳电池的等效数学模型,介绍了传统电导增量法实现最大功率点跟踪（MPPT）的基本原理,并针对该方法电压扰动步长的不足,提出了一种基于自适应遗传算法的数字PI控制变步长跟踪方法,通过Matlab/simulink进行S函数建模和仿真,实现了光伏阵列最大功率点（MPP）的跟踪。仿真结果表明,该控制方法能够快速准确的跟踪最大功率点,使系统具有良好的动态性能和稳定性能。     

光伏并网逆变系统的MATLAB仿真研究

光伏并网发电系统是光伏发电系统发展的趋势。单极式光伏并网逆变系统具有拓扑结构简单，成本较低的有点。这种系统中只存在一个能量变换环节，太阳能最大功率点跟踪（MPPT），电网电压同步等控制目标需要考虑。本文介绍了单极式光伏系统的拓扑结构和实现最大功率的工作原理．阐述了电导增量法实现MPPT的基本思想。根据光伏系统并网发电拓扑结构，设计了一套新型的实现最大功率跟踪的单极式光伏并网逆变器，逆变器控制部分由DSP实现最大功率跟踪和输出电流跟踪控制，实现了逆变输出电流与电网同步，且高功率因数运行。仿真和实验结果表明，单极式光伏并网逆变系统能准确跟踪太阳能电池最大功率点，并具有较好的稳定性。     

基于分组粒子群的光伏最大功率点跟踪方法

针对光伏发电系统在复杂遮阴条件下,光伏输出P-V特性曲线呈现高度非线性,采用基于分组粒子群算法（particle swarm optimization, PSO）和优化的扰动观察法(perturb and observe, P&O)相结合的MPPT（maximum power point tracking）算法进行光伏发电系统输出功率的提升。提出的最大功率点算法分为两个阶段,首先通过将混合蛙跳算法(shuffled frog leaping algorithm, SFLA)的分组思想引入到传统粒子群算法,并采用改进后算法实现近似全局最大功率点的快速搜索,以加快最大功率点跟踪的收敛速度和稳定性。然后,采用优化的扰动观察法实现最大功率点附近的动态精确跟踪,同时减少后续最大功率点跟踪过程中的计算量。通过在不同阶段发挥两种MPPT算法的各自优点来提高光伏最大功率点跟踪控制的效率。最后进行光伏系统遮阴条件变化的仿真实验,与传统粒子群算法相比,提出MPPT方法具有较快的跟踪速度和稳定的功率输出。     

新型MPPT算法在光伏并网系统中的应用

光伏并网系统在天气急剧变化时,采用传统的最大功率跟踪算法会产生较大误判,不仅造成电池板功率的损失,而且还会导致并网电流的畸变。针对这一问题,对传统扰动观察法进行了改进,提出了一种新型MPPT算法,有效地防止了功率误判。将此新型算法配合电压电流双环控制应用于光伏并网系统。从仿真结果看到,在光照突变和电网电压扰动时,并网电流均能够快速跟踪电网电压的相位和频率。表明了新型算法的正确性和有效性。     

太阳能最大功率点跟踪双重寻优算法研究

针对传统太阳能最大功率点跟踪算法效率较低的缺点,在原有经典定电压跟踪方法的基础上,提出了基于扰动观察法和梯度法的双重寻优最大功率点跟踪算法.采用Matlab/Simulink工具对单相光伏逆变并网系统进行仿真.仿真试验结果表明,该算法能更稳定、更高效地对太阳能最大功率点进行跟踪,并能保持系统稳定和跟踪精度,具有很好的动态性能.     

基于DSP的光伏并网系统MPPT算法研究

介绍了以DSP为主控芯片的光伏并网系统,它能跟踪光伏阵列最大输出功率点,实现光伏阵列和负载优化匹配,使负载获得最大功率。在光伏阵列输出功率最大为跟踪目标的定步长MPPT一阶差分算法的基础上,采用以使负载获得最大功率为跟踪目标的变步长寻优MPPT算法,能够更好地实现最大功率点的跟踪。实验结果表明:该系统能够快速准确地跟踪太阳能电池最大功率点,并自动同步跟踪电网频率和相位,实现并网电流的正弦波形以便与电网电压同频同相馈入电网,提高了系统逆变效率和可靠性。     

基于扰动观测-模糊控制的单传感器MPPT算法

根据半桥升压式(half-bridge-boost)光伏并网逆变器的特点,分析了一种基于扰动观测的单传感器MPPT算法.在此基础上,将模糊控制与其结合,形成了扰动观测-模糊控制的单传感器MPPT算法,改善了系统的响应特性和稳定性.在Matlab/Simulink平台上与其他几种常用的算法进行仿真对比,结果表明基于扰动观测-模糊控制的单传感器MPPT算法跟踪速度快,在最大功率点附近振荡小,系统稳定.     

光伏电池组件工作状态监测研究

由于新能源光伏发电在当今电网中愈加重要,因此通过物联网技术实时监测光伏组件的健康状况对于保障光伏发电系统的高效运行意义重大。光伏系统无法对光伏电池最大功率点的输出进行实时监测,针对现有最大功率点跟踪算法MPPT只跟踪不监测的特点,提出以太阳能电池基本电路方程为基础,构建不依赖于变换器及MPPT算法的光伏电池功率输出曲线模型,结合电压-功率输出特性曲线的特点,采用最小二乘法拟合曲线,精准得到最大功率点附近的二次曲线数学模型,进而得到实时最大功率点。通过Matlab对光伏发电系统进行建模与仿真,系统仿真结果显示,算法准确有效,可以迅速稳定地响应环境变化,适用于各种条件下MPPT的快速、精准获取。     

光伏发电系统中最大功率跟踪控制算法的研究和控制器的设计

本文对几种常用的最大功率跟踪控制算法进行比较,分析其优缺点,在扰动观察算法的基础上,提出了"变步长"扰动观察法改进算法,避免最大功率点的"误判"。并设计制作了最大功率点跟踪控制器的硬件电路,实现跟踪控制太阳能电池的最大功率点。通过最大功率跟踪控制实验,结果说明该系统可以有效地提高响应速度和跟踪精度,提高太阳能光伏发电系统的工作效率。     

基于PI调节占空比的自适应MPPT方法

最大功率点跟踪(MPPT)技术是提高光伏发电效率的重要途径之一;扰动观测法是MPPT控制中最常用的方法,针对其无法兼顾跟踪速度与最大功率点跟踪过程的震荡问题,提出了一种基于PI调节占空比的自适应MPPT方法,该方法针对占空比采用基于PI调节的自适应策略;通过Simulink建模仿真,与其他方法进行对比分析,结果显示了该方法可显著地提高了最大功率点跟踪的速度与精度。     

A new maximum power tracking in PV system during partially shaded conditions based on shuffled frog leap algorithm

Abstract

Maximum Power Point Trackers (MPPTs) are power electronic conditioners used in photovoltaic (PV) system to ensure that PV structures feed maximum power for the given ambient temperature and sun’s irradiation. When the PV panels are shaded by a fraction due to any environment hindrances then, conventional MPPT trackers may fail in tracking the appropriate peak power as there will be multi power peaks. In this work, a shuffled frog leap algorithm (SFLA) is proposed and it successfully identifies the global maximum power point among other local maxima. The SFLA MPPT is compared with a well-entrenched conventional perturb and observe (P&O) MPPT algorithm and a global search particle swarm optimisation (PSO) MPPT. The simulation results reveal that the proposed algorithm is highly advantageous than P&O, as it tracks nearly 30% more power for a given shading pattern. The credible nature of the proposed SFLA is ensured when it outplays PSO MPPT in convergence. The whole system is realised in MATLAB/Simulink environment.     

组合模糊控制技术与扰动观察法提升光伏发电MPPT性能

组合典型MPPT算法以优化系统性能是光伏发电新的研究范式。针对传统单一MPPT算法无法兼顾动态性能和稳态性能问题,尝试将模糊控制技术和扰动观察法进行组合用于光伏发电MPPT控制,具体方法是当系统靠近功率曲线两端时采用扰动观察法跟踪,当系统位于最大功率点附近采用模糊控制技术跟踪。实验结果表明,组合控制算法能够根据系统所处状态准确地在扰动观察法和模糊控制算法之间切换,系统响应时间相对于模糊控制缩短了31%,稳态误差相对于扰动观察法减少了67%,系统性能显著提升。     

Maximum Power Extraction Control Algorithm for Hybrid Renewable Energy System

In this research, a modified fractional order proportional integral derivate (FOPID) control method is proposed for the photovoltaic (PV) and thermoelectric generator (TEG) combined hybrid renewable energy system. The faster tracking and steady-state output are aimed at the suggested maximum power point tracking (MPPT) control technique. The derivative order number (µ) value in the improved FOPID (also known as PI^λD^µ) control structure will be dynamically updated utilizing the value of change in PV array voltage output. During the transient, the value of µ is changeable; it’s one at the start and after reaching the maximum power point (MPP), allowing for strong tracking characteristics. TEG will use the freely available waste thermal energy created surrounding the PV array for additional power generation, increasing the system’s energy conversion efficiency. A high-gain DC-DC converter circuit is included in the system to maintain a high amplitude DC input voltage to the inverter circuit. The proposed approach’s performance was investigated using an extensive MATLAB software simulation and validated by comparing findings with the perturbation and observation (P&O) type MPPT control method. The study results demonstrate that the FOPID controller-based MPPT control outperforms the P&O method in harvesting the maximum power achievable from the PV-TEG hybrid source. There is also a better control action and a faster response.     

A hybrid intelligent GMPPT algorithm for partial shading PV system

Maximum power extraction for PV systems under partial shading conditions (PSCs) relies on the optimal global maximum power point tracking (GMPPT) method used. This paper proposes a novel maximum power point tracking (MPPT) control method for PV system with reduced steady-state oscillation based on improved particle swarm optimization (PSO) algorithm and variable step perturb and observe (P&O) method. Firstly, the grouping idea of shuffled frog leaping algorithm (SFLA) is introduced in the basic PSO algorithm (PSO–SFLA), ensuring the differences among particles and the searching of global extremum. Furthermore, adaptive speed factor is introduced into the improved PSO to improve the convergence of the PSO–SFLA under PSCs. And then, the variable step P&O (VSP&O) method is used to track the maximum power point (MPP) accurately with the change of environment. Finally, the superiority of the proposed method over the conventional P&O method and the standard PSO method in terms of tracking speed and steady-state oscillations is highlighted by simulation results under fast variable PSCs.     

A novel maximum power point tracking algorithms for stand-alone photovoltaic system

A novel control algorithm, namely subsection adaptive hill climbing method (SSAHC), for seeking the maximum power point (MPP) of a photovoltaic (PV) panel for any temperature and solar radiation level is proposed. The algorithm is thus a combination of the subsection and adaptive hill climbing methods. In this algorithm, the characteristic curve of power-voltage of PV panel was divided into three subsections, namely large step approximation section, adaptive hill climbing section and maximum power section. Using this method, the MPP tracker (MPPT) can tune adaptively the step to track the MPP of PV system. The main advantage of the MPPT controlled by this new algorithm, when is compared with others, is that it can draw more power at a certain weather condition, especially, in case solar radiation changes rapidly at higher radiation.     

Development and analysis of adaptive fuzzy controllers for photovoltaic system under varying atmospheric and partial shading condition

Control of power electronics converters used in PV system is very much essential for the efficient operation of the solar system. In this paper, a modified incremental conduction maximum power point tracking (MPPT) algorithm in conjunction with an adaptive fuzzy controller is proposed to control the DC–DC boost converter in the PV system under rapidly varying atmospheric and partial shading conditions. An adaptive hysteresis current controller is proposed to control the inverter. The proposed current controller provides constant switching frequency with less harmonic content compared with fixed hysteresis current control algorithm and sinusoidal PWM controller. The modeling and simulation of PV system along with the proposed controllers are done using MATLAB/SIMSCAPE software. Simulation results show that the proposed MPPT algorithm is faster in transient state and presents smoother signal with less fluctuations in steady state. The hardware implementation of proposed MPPT algorithm and inverter current control algorithms using Xilinx spartran-3 FPGA is also presented. The experimental results show satisfactory performance of the proposed approaches.     

A novel MPPT algorithm based on optimized artificial neural network by using FPSOGSA for standalone photovoltaic energy systems

Maximum power point tracking (MPPT) algorithms are used to maximize the output power of the photovoltaic (PV) panel under different temperature and irradiance conditions in photovoltaic energy sources (PVES). In this paper, a novel MPPT method based on optimized artificial neural network by using hybrid particle swarm optimization and gravitational search algorithm based on fuzzy logic (FPSOGSA) is proposed to track the operation of the PV panel in maximum power point (MPP). The performance of the proposed MPPT approach is tested by doing the simulation and experimental studies under different environmental conditions. The proposed method is compared with the conventional perturb and observation (P&O) method for standalone PVES. The results of the comparison the obtained from the simulation and experimental studies demonstrate that the proposed MPPT method provides the reduction oscillations around the MPP and the increased maximum power yield of the PV system in the steady state.

     

Maximum power tracking of a generic photovoltaic system via a fuzzy controller and a two-stage DC–DC converter

This study presents a new two-stage DC–DC converter for maximum power point tracking (MPPT) and a voltage boost of a generic photovoltaic (PV) system. An intelligent MPPT of PV system based on fuzzy logic control (FLC) is presented to adaptively design the proposed fuzzy controlled MPPT controller (FC-MPPTC) while a voltage boost controller (VBC) is used to fix the output voltage to a voltage level that is higher than the required operating voltage to the back-end grid impedance. Modeling and simulation on the PV system and the DC–DC converter circuit are achieved by state-space and the software Powersim. The PV string considered has the rated power around 600?VA under varied partial shadings. The FC-MPPTC and VBC are designed and realized by a DSP module (TMS320F2812) to adjust the duty cycle in the two-stage DC–DC converter. A special FLC algorithm is forged to render an MPPT faster and more accurate than conventional MPPT technique, perturb and observe (P&O). The simulations are intended to validate the performance of the proposed FC-MPPTC. Experiments are conducted and results show that MPPT can be achieved in a fast pace and the efficiency reaches over 90?%, even up to 96?%. It is also found that the optimized tracking speed of the proposed FC-MPPTC is in fact more stable and faster than the general P&O method with the boost voltage capable of offering a stable DC output.     

Climatic sensorless maximum power point tracking in PV generation systems

The problem of maximum power point tracking (MPPT) is addressed for photovoltaic (PV) arrays considered in a given panel position. The PV system includes a PV panel, a PWM boost power converter and a storing battery. Although the maximum power point (MPP) of PV generators varies with solar radiation and temperature, the MPPT is presently sought without resorting to solar radiation and temperature sensors in order to reduce the PV system cost. The proposed sensorless control solution is an adaptive nonlinear controller involving online estimation of uncertain parameters, i.e. those depending on radiation and temperature. The adaptive control problem at hand is not a standard one because parameter uncertainty affects, in addition to system dynamics, the output-reference trajectory (expressing the MPPT purpose). Therefore, the convergence of parameter estimates to their true values is necessary for MPPT achievement. It is formally shown, under mild assumptions, that the developed adaptive controller actually meets the MPPT objective.