Maximum Power Point Tracking Using the Incremental Conductance Algorithm for PV Systems Operating in Rapidly Changing Environmental Conditions

Maximum Power Point Tracking (MPPT) is an important process in Photovoltaic (PV) systems because of the need to extract maximum power from PV panels used in these systems. Without the ability to track and have PV panels operate at its maximum power point (MPP) entails power losses;resulting in high cost since more panels will be required to provide specified energy needs. To achieve high efficiency and low cost, MPPT has therefore become an imperative in PV systems. In this study, an MPP tracker is modeled using the IC algorithm and its behavior under rapidly changing environmental conditions of temperature and irradiation levels is investigated. This algorithm, based on knowledge of the variation of the conductance of PV cells and the operating point with respect to the voltage and current of the panel calculates the slope of the power characteristics to determine the MPP as the peak of the curve. A simple circuit model of the DC-DC boost converter connected to a PV panel is used in the simulation;and the output of the boost converter is fed through a 3-phase inverter to an electricity grid. The model was simulated and tested using MATLAB/Simulink. Simulation results show the effectiveness of the IC algorithm for tracking the MPP in PV systems operating under rapidly changing temperatures and irradiations with a settling time of 2 seconds.

基于混合算法的多峰值MPPT控制

针对光照不均匀时光伏阵列输出呈现非线性、多峰值的问题,提出一种基于自然选择粒子群(SelPSO)算法结合电导增量法(INC)的多峰值最大功率点追踪(MPPT)算法。SelPSO算法通过淘汰和替换迭代过程中适应度低的粒子,更快、更准确地完成对全局最大值点的搜索;使用INC进行局部跟踪,有效减少寻优过程中的功率振荡,达到最大功率点跟踪的目的。利用MATLAB/Simulink进行仿真试验。结果表明:与INC相比,SelPSO算法的功率振荡由12 W降至0.05 W;与PSO算法相比,跟踪时间由0.15 s缩短至0.07 s。该混合算法在复杂环境下具有较优的跟踪速度、收敛精度以及较低的功率波动。

Maximum power point tracking of photovoltaic power generation based on improved fuzzy control and conductance increment method

The National Development and Reform Commission of China announced in 2022 that it would promote the development of carbon peaking and carbon-neutrality.Therefore,gradual improvement in photovoltaic(PV)development technology would gradually show its advantages.Additionally,the development of the PV power generation industry is promoted significantly by the year-on-year increase in PV power generation.This study combines the traditional fuzzy control and incremental conductance methods by comparing the current maximum power point(MPP)intelligence with the traditional control algorithm.Furthermore,it proposes an optimization algorithm to improve the tracking speed of the MPP by using the partition variable step size.An incomplete partial differential equation is added to the judgment condition of the small step size to solve the problem of oscillation near the MPP caused by the ideal differential equation.A simulation model is established in MATLAB®/Simulink®and the method is simulated under specific conditions.The time to reach the first maximum output point is shortened by 0.21 and 0.14 s by analysing the oscillation process data of three simulation cycles of the incremental conductance,fuzzy control and fuzzy conductance methods,respectively.Additionally,the vibration loss caused by environmental mutation is reduced by 6.5%and 3.1%,respectively.The improved incremental conductance method utilizes the simulation results to optimize the problem of steady-state oscillation and improve the efficiency of the PV power generation.The feasibility and effectiveness of the improved incremental conductance method are verified by comparing the traditional control algorithm with the improved incremental conductance method.

Research on the MPPT of Photovoltaic Power Generation Based on the CSA-INC Algorithm

The existing Maximum Power Point Tracking(MPPT)method has low tracking efficiency and poor stability.It is easy to fall into the Local Maximum Power Point(LMPP)in Partial Shading Condition(PSC),resulting in the degradation of output power quality and efficiency.It was found that various bio-inspired MPPT based optimization algorithms employ different mechanisms,and their performance in tracking the Global Maximum Power Point(GMPP)varies.Thus,a Cuckoo search algorithm(CSA)combined with the Incremental conductance Algorithm(INC)is proposed(CSA-INC)is put forward for the MPPT method of photovoltaic power generation.The method can improve the tracking speed by more than 52%compared with the traditional Cuckoo Search Algorithm(CSA),and the results of the study using this algorithm are compared with the popular Particle Swarm Optimization(PSO)and the Gravitational Search Algorithm(GSA).CSA-INC has an average tracking efficiency of 99.99%and an average tracking time of 0.19 s when tracking the GMPP,which improves PV power generation’s efficiency and power quality.

基于改进变步长爬山法的潮流能发电MPPT算法

针对传统变步长爬山法在潮流能发电系统中对潮流流速突变和随机性波动的适应能力较差,易出现扰动方向判断错误和速度追踪过快的问题,在变步长爬山法的基础上提出一种改进变步长最大功率追踪(MPPT)算法。该方法能够准确判断多种最大功率跟踪的情况并采用两次采样点下的功率和转速的变化程度确定下一时刻步长的大小,自动根据获能系数大小进行分阶段调节,实现对潮流流速突变情况的应对,从而实现最大功率的跟踪。在MATLAB/Simulink环境下搭建基于PID控制与自抗扰控制的仿真系统进行仿真试验,结果表明,改进的变步长爬山法不仅能够实现最大功率的跟踪,而且对潮流流速的随机性波动和突变情况的适应能力更强,具有良好的稳态特性。

局部阴影下基于IBOA-INC的光伏复合MPPT控制

针对传统的最大功率点追踪(Maximum Power Point Tracking,MPPT)算法陷入局部极值不能找到最大功率点(Maximum Power Point,MPP)以及传统的蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)存在收敛速度慢和搜索震荡较大等问题,提出一种改进的蝴蝶优化算法(Improved Butterfly Optimization Algorithm,IBOA)结合电导增量法(Conductance Increment Method,INC)的复合MPPT追踪方法。在IBOA中,引入自适应动态转换概率来平衡算法的全局与局部搜索,然后在全局搜索阶段引入Levy飞行策略,使蝴蝶个体广泛分布于搜索空间中,提高全局寻优能力;同时在局部搜索中设置新的寻优对象,并通过贪婪算法进行筛选保留,提高局部搜索的能力。当系统位于MPP附近时,利用INC局部搜索能力强的优点快速、准确地收敛到MPP并且稳定功率的输出。仿真结果表明,在静态和动态阴影下与BOA、PSO算法进行对比,所提算法具有更快的追踪速度、更高的追踪效率和更强的鲁棒性。

基于Labview的模拟光伏MPPT研究

根据光伏电池的物理数学模型,以及基于Boost变换器稳态工作原理推导出来的光伏占空比扰动控制机理和扰动观察法控制流程,采用电压源串联电阻替代光伏电池板,并采用STC15W4K32S4单片机作为主控单元通过编程实现模拟光伏MPPT爬山法控制策略,采用Labview对模拟光伏的电压、电流的采样,通过Labview图形编程实现对模拟光伏系统工作状态和MPPT控制策略实施效果的实时直观呈现。实验结果表明,利用Labview强大的数据采集、处理、图形功能能够方便地把复杂系统状态和控制策略的实施效果实时直观地呈现出来,为光伏等系统的研究开发提供方便,对光伏控制器的研究设计具有实际意义。

Analysis and research on Maximum Power Point Tracking of Photovoltaic Array with Fuzzy Logic Control and Three-point Weight Comparison Method

The Photovoltaic Array has a best optimal operating point where the array operating can obtain the maximum power.However, the optimal operating point can be compromised by the strength of solar radiation,angle,and by the change of environment and load.Due to the constant changes in these conditions,it has become very difficult to locate the optimal operating point by following a mathematical model.Therefore,this study will focus mostly on the application of Fuzzy Logic Control theory and Three-point Weight Comparison Method in effort to locate the optimal operating point of solar panel and achieve maximum efficiency in power generation. The Three-point Weight Comparison Method is the comparison between the characteristic curves of the voltage of photovoltaic array and output power;it is a rather simple way to track the maximum power.The Fuzzy Logic Control,on the other hand,can be used to solve problems that cannot be effectively dealt with by calculation rules,such as concepts,contemplation, deductive reasoning,and identification.Therefore,this paper uses these two kinds of methods to make simulation successively. The simulation results show that,the Three-point Comparison Method is more effective under the environment with more frequent change of solar radiation;however,the Fuzzy Logic Control has better tacking efficiency under the environment with violent change of solar radiation.

基于改进电导增量法的光伏MPPT控制研究

受环境变化和光伏电池材料的影响,目前光伏发电效率普遍不高,太阳能利用率较低,因此对光伏系统进行最大功率点追踪(MPPT)控制,设计一种恒定电压法与电导增量法相结合的控制方式。通过恒定电压法快速追踪到最大功率点附近,再引进小步长的电导增量法进一步优化稳态性能。通过MATLAB/Simulink平台进行光伏发电最大功率追踪仿真,验证该方法可以大幅度提升系统的动态性能并具有良好的稳态特性。

基于类箕舌线的自适应变步长MPPT算法设计

为进一步提高光伏发电系统的稳定性和发电效率,使用最大功率点跟踪技术(MPPT)使输出功率最大化。对此提出一种基于类箕舌线的自适应变步长扰动观测法,该方法综合考虑跟踪速度、振荡幅度和计算复杂度等因素,分析了类箕舌线函数中参数α和β的不同取值对算法的影响。应用Matlab/Simulink分别在改变光照强度和温度两种情况下进行仿真,并与定步长扰动观测法进行对比。仿真结果表明,所提算法明显提高了跟踪速度,改善了靠近MPP时功率的振荡问题,相比于传统算法具有明显优越性。算法计算复杂度小,跟踪性能好,可应用于算力较小的MPPT控制器中。

IGWO-IP&O算法在光储MPPT控制系统中的应用

局部遮阴情况下光伏阵列的输出功率呈现多峰现象,导致传统MPPT控制算法失效,而基于元启发式算法的MPPT控制功率追踪速度慢,输出功率振荡大。针对上述问题,提出一种基于改进型灰狼优化算法(improved grey wolf optimization algorithm,IGWO)与改进型扰动观察法(improved perturbation and observation method,IP&O)相结合的光伏MPPT控制算法。IGWO采用非线性收敛因子调整策略提高算法适应性,并通过使用改进型莱维飞行与增强型醉汉漫步结合的搜索策略平衡全局搜索与局部寻优的关系。利用IGWO追踪至最大功率点附近,再与可调节扰动步长变化速率的IP&O结合实现最大功率的稳定输出。算法测试实验数据和仿真结果表明,所提出的MPPT控制算法具有快速的追踪速度和高输出精度,且在功率追踪过程中输出振荡小。

局部阴影下基于GWO-P&O混合算法的光伏最大功率点跟踪

针对局部遮阴环境下传统灰狼优化(Gray wolf optimization,GWO)算法在跟踪最大功率点时P-U特性曲线出现多峰值、后期收敛速度慢、稳态精度低等问题,结合灰狼优化算法和扰动观察法(Perturbation and observation,P&O)各自的优势,提出了基于GWO-P&O的混合优化最大功率点跟踪(Maximum power point tracking,MPPT)算法。首先,采用灰狼优化算法逐渐向光伏的全局最大功率点靠近。其次,在灰狼优化算法收敛后期引入P&O法,既保持了灰狼优化算法较高的稳态精度,又能以较快速度寻找到局部最大功率点。最后,在不同环境工况下,将所提出的GWO-P&O方法与传统GWO算法进行对比。结果表明,改进的GWO-P&O算法在保证良好稳态性能的同时,一定程度上提高了GWO算法后期跟踪最大功率时的收敛速度。

基于改进布谷鸟搜索算法的光伏最大功率点跟踪策略

实际工程中,光伏阵列在随机变化的环境中会出现局部遮光的情况,从而导致光伏阵列的功率-电压特性曲线会呈现多峰值状态,传统的最大功率点跟踪(maximum power point tracking, MPPT)算法易陷入局部最优解,追踪速度和精准度无法得到满足。针对这一问题,提出一种基于布谷鸟搜索算法(cuckoo search algorithm, CS)和电导增量法(conductivity increment method, CI)结合的光伏MPPT算法,在算法前期利用布谷鸟搜索算法将大步长和小步长交替使用使得全局搜索能力增强,找到全局最大功率点所处区域附近;在后期,采用步长小、控制精度高的CI进行局部寻优,快速准确地锁定到最大功率点。在MATLAB/Simulink中搭建仿真模型,并与原始布谷鸟搜索算法和粒子群优化(particle swam optimization, PSO)算法进行比较。仿真结果表明,将CS与CI结合的算法使得收敛速度更快,精度更高,稳定状态时功率曲线的波动更小。

恒电压结合牛顿法的光伏系统MPPT控制

该文以提高跟踪速度、减少最大功率点附近功率振荡和减少误判为多重目标,提出了恒电压结合牛顿法的最大功率点跟踪策略,并利用朗伯W(LambertW)函数在光伏电池实用模型的基础上对其原理进行了理论推导和证明。该方法仅依赖于当前工作点的信息计算下一工作点的参考电压,有效地减少了误判;借助于牛顿法的良好收敛性,速度和稳定性都有提高。通过三种不同光照模式算例,与传统方法进行仿真对比,验证了方法的有效性。

基于改进变步长电导增量法的MPPT控制

针对光伏系统最大功率点跟踪控制速度不稳定的问题,提出一种改进的变步长电导增量法。对电导增量法变步长的触发条件进行改进,确定变步长的使用范围,并选取适当的缩放系数,使光照强度发生变化时可稳定地跟踪最大功率点。仿真结果表明,该算法在光照强度发生大幅度变化时,具有较快的响应速度和较高的跟踪精度。

基于扰动观察法和电导增量法的光伏发电系统MPPT算法研究综述

在光伏发电系统中,为提高电池的利用率,需要实现快速精确的最大功率点跟踪(NOOT)控制。在众多的MPPT方法中,扰动观察法和电导增量法由于原理简单、易实现等优点而一直成为研究及选用较广泛的方法。为促进光伏电池的MPPT算法进步,提高传统方法性能和控制效果,在传统扰动观察法和电导增量法的基础上,对近年来这2种方法的改进算法,以及分别与其他方法的结合使用情况进行详细分析,比较出各自优缺点并对未来的MPPT方法做出展望。

电导增量法光伏发电MPPT研究综述

综述了在单峰值的情况下光伏发电系统中最大功率点跟踪使用的电导增量法以及各种变体算法,总结分析各自的优缺点,对光伏阵列最大功率点跟踪技术的跟踪速度、控制精度及将来研究和应用中需要解决的问题作了探讨。

一种基于电导增量法的MPPT实现策略

太阳能光伏阵列的输出功率随外界环境因素的变化而变化,为了能高效地利用太阳能电池,需对光伏阵列进行最大功率点跟踪(Maximum Power Point Tracking,简称MPPT)。分析了太阳能电池的工作特性和光伏系统的拓扑结构及原理,将电导增量法应用到光伏发电系统MPPT的控制中,使系统能够快速响应外界环境的变化,让光伏发电系统始终工作在最大功率点。最后通过仿真及实验证明了该方法的可行性。

基于PSIM的新型扰动观察法的MPPT仿真研究

通过分析光伏电池的物理特性,建立较为准确的数学模型,然后在PSIM仿真软件中建立能够反应外界温度和光照强度变化的光伏电池PSIM仿真模型,在此基础上建立可进行最大功率点跟踪(MPPT)算法的仿真模型。对原有的扰动观察法进行仿真分析,并在其基础上设计出一种新的三点比较式变步长扰动观察算法。将新旧两种算法在不同的环境下进行仿真比较,仿真结果表明,该方法能够更为迅速、精确地进行最大功率点跟踪,可以进一步提高光伏电池的利用率。

改进的电导增量法在光伏系统MPPT中的应用

分析了太阳能光伏阵列的输出特性和传统最大功率跟踪方法的优缺点,并在此基础上提出了一种改进的电导增量法。用Matlab/Simulink对Boost DC/DC变换器系统进行了建模与仿真,将改进的方法和传统的电导增量法分别应用于控制该系统。仿真结果表明,在外界环境变化的情况下,改进的方法在保证系统稳定性的前提下,使得光伏阵列能更快速、准确地跟踪最大功率点。