Intelligent control of photovoltaic system using BPSO-GSA-optimized neural network and fuzzy-based PID for maximum power point tracking

The maximum power point tracking (MPPT) technique is applied in the photovoltaic (PV) systems to achieve the maximum power from a PV panel in different atmospheric conditions and to optimize the efficiency of a panel. A proportional-integral-derivative (PID) controller was used in this study for tracking the maximum power point (MPP). A fuzzy gain scheduling system with optimized rules by subtractive clustering algorithm was employed for tuning the PID controller parameters based on error and error-difference in an online mode. In addition, an Elman-type recurrent neural network (RNN) was used for inverse identification of the PV system and for estimating the solar radiation intensity to determine the MPP voltage. The optimum number of neurons in the single hidden-layer of the RNN was determined by binary particle swarm optimization algorithm. The weights of this RNN were also optimized by using a hybrid method based on the Levenberg-Marquardt algorithm and gravitational search algorithm (GSA). In the proposed fitness function for optimization, both the RNN size and its convergence accuracy were considered. Thus, the algorithm for RNN optimization attempts to minimize both the structural complexity and the mean square error. Simulation results revealed superior performance of GSA in comparison with particle swarm, cuckoo, and grey wolf optimization algorithms. The performance of the proposed MPPT method was evaluated under four different ambient conditions. Our experimental results show that the proposed MPPT method is more efficient than the three competitive methods presented in recent years.     

基于无线传感器网络的光伏系统MPPT应用研究

光伏电池输出的功率随外界环境条件的变化而变化,通常采用最大功率点跟踪技术以获得最大功率输出。结合无线传感器网络(WSNs)节点的工作方式与光伏系统的特点,提出了一种基于WSNs的光伏系统最大功率点跟踪技术。针对开路电压法的不足,利用WSNs节点的测温工作方式来进行温度补偿。当系统工作在最大功率点附近时,引入阻抗匹配算法,可有效消减光伏输出功率在最大功率点处的振荡现象,从而提高系统效率。仿真结果验证了该方法的可行性和有效性。     

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.     

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.     

A novel fuzzy logic control technique tuned by particle swarm optimization for maximum power point tracking for a photovoltaic system using a current-mode boost converter with bifurcation control

This paper presents a novel fuzzy logic control technique tuned by particle swarm optimization (PSO-FLC) for maximum power point tracking (MPPT) for a photovoltaic (PV) system. The proposed PV system composes of a current-mode boost converter (CMBC) with bifurcation control. An optimal slope compensation technique is used in the CMBC to keep the system adequately remote from the first bifurcation point in spite of nonlinear characteristics and instabilities of this converter. The proposed PSO technique allows easy and more accurate tuning of FLC compared with the trial-and-error based tuning. Consequently, the proposed PSO-FLC method provides faster tracking of maximum power point (MPP) under varying light intensities and temperature conditions. The proposed MPPT technique is simple and particularly suitable for PV system equipped with CMBC. Experimental results are shown to confirm superiority of the proposed technique comparing with the conventional PVVC technique and the trial-and-error based tuning FLC.     

采用自适应扰动观察法的光伏并网系统最大功率点跟踪

提出了一种自适应扰动观察（P＆O）算法,用于在不同天气条件下太阳能光伏（PV）并网系统的最大功率点跟踪（MPPT）控制策略。该策略对于从太阳能光伏电池板中,获取最大的功率输出是十分重要的。利用一种依赖于功率变化的可变的扰动步长,提出了改进的自适应扰动观察算法。最后将通过仿真所得到的数据与传统的扰动观察算法进行了比较,结果表明所提出MPPT算法的收敛值和速度得到了改善,稳定时间缩短25%,稳态值提高20%以上,在太阳能光伏并网系统的最大功率点跟踪时是有效而实用的。     

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。     

Application of sliding mode control for maximum power point tracking of solar photovoltaic systems: A comprehensive review

A robust maximum power point tracking (MPPT) control is of paramount importance in the performance enhancement and the optimization of photovoltaic systems (PVSs). Solar panel exhibits nonlinear behavior under real climatic conditions and output power fluctuates with the variation in solar irradiance and temperature. Therefore, a control strategy is requisite to extract maximum power from solar panels under all operating conditions. Sliding mode control (SMC) is extensively used in non-linear control systems and has been implemented in PVSs to track maximum power point (MPP). The objective of this work is to classify, scrutinize and review the SMC techniques used to extract maximum power from PVSs in both off-grid and grid connected applications. The first order, perturb and observe, incremental conductance, linear expression based sliding mode control algorithms and their adaptive forms are discussed in detail. The advanced form of SMC, terminal sliding mode control (TSMC), super twisting theorem (STT) and artificial intelligent (AI) algorithm based are also presented with the focused application of MPPT of PVSs. A tabular comparison is provided at the end of each category to help the users to choose the most appropriate method for their PV application. It is anticipated that this work will serve as a reference and provides important insight into MPPT control of the PV systems.     

Unified MPPT controller for partially shaded panels in a photovoltaic array

The power output of the photovoltaic (PV) system having multiple arrays gets reduced to a great extent when it is partially shaded due to environmental hindrances. The maximum power trackers which are conventionally used may not be competent enough to find the maximum power point (MPP) during partially shaded conditions. The sensible reason for the failure of conventional trackers is during partial shaded conditions the PV arrays exhibit multi peak power curves, thereby making simple maximum power point tracking (MPPT) algorithms like perturb and observe (P&O) to get stuck with local maxima instead of capturing global maxima. Therefore, global search MPPT aided by evolutionary and swarm intelligence algorithms will be conducive to find global power point during partially shaded conditions. This work suggests a unified controller which feeds control signal to its power electronic conditioner placed at each module. The evolutionary algorithm which is taken into consideration in this work is differential evolution (DE). The performance of the proposed method is compared to the classical un-dimensional search controller and it is evident from the Matlab/Simulink results that the unified controller prevails over the distributed counterpart.     

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 Robust Single-Sensor MPPT Strategy for Shaded Photovoltaic-Battery System

A robust single-sensor global maximum power point tracking (MPPT) strategy based on modern optimization for photovoltaic systems considering shading conditions is proposed in this work. The proposed strategy is designed for battery charging applications and direct current (DC) microgrids. Under normal operation, the curve of photovoltaic (PV) output power versus PV voltage contains only a single peak point. This point can be simply captured using any traditional tracking method like perturb and observe. However, this situation is completely different during the shadowing effect where several peaks appear on the power voltage curve. Most of these peaks are local with only a single global. This condition leads to the incapability of traditional tracking approaches to extract the global peak power due to their inability to distinguish between the local and global peak points. They are trapped in the first peak point even when the point is local. Therefore, global tracking approaches based on modern optimization are highly required. A recent marine predators algorithm (MPA) has been used to solve the problem of tracking the global MPP under shadowing influence. Different shadowing scenarios are used to test and evaluate the performance of MPA based tracker. The obtained results are compared with particle swarm optimization (PSO) and ant lion optimizer (ALO). The results of the comparison confirmed the effectiveness and robustness of the proposed global MPPT-MPA based tracker over PSO and ALO.     

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.     

Maximum power point tracking （MPPT） control of a photovoltaic system based on dual carrier chaotic search

The output power of the photovoltaic (PV) array changes with the change in external environment and load.Therefore,maximum power point tracking (MPPT) technology is needed to maximize the efficiency of...     

独立光伏系统中基于参数估计的双积分滑模变结构MPPT算法研究

对于独立光伏系统,当外界温度变化或光伏面板遭受遮挡时造成系统工作不稳定,系统在短时间内无法快速精确地对外输出最大功率.针对此问题,提出一种基于参数估计的双积分滑模变结构最大功率跟踪算法并设计独立光伏系统仿真模型.在仿真过程中,利用粒子群优化算法(Particle Swarm Optimization,PSO)估计太阳能...     

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.     

基于固定电压法结合P＆O法MPPT控制策略研究

对光伏发电控制系统提出了一种新型的最大功率点跟踪（MPPT）控制方法。即在外界环境或负载突变时,先采用固定电压法将光伏阵列的工作点调整到最大功率点附近,以保证跟踪的快速性;并且引入新型的扰动观察法,对最大功率点的稳态特性进行优化,可有效减小光伏阵列的输出功率在最大功率点的振荡现象。通过仿真验证了上述方法的有效性。     

ANN-based MPPT algorithm for solar PMSM drive system fed by direct-connected PV array

In this paper, artificial neural network (ANN) based on a maximum power point tracking (MPPT) algorithm is developed for a solar permanent magnet synchronous motor (PMSM) drive system used without a boost converter and batteries. The discontinuous space vector PWM technique is used to drive two-level inverter which is directly fed by three parallel-connected Kyocera KD205GX-LP PV modules. The ANN-based MPPT algorithm estimates the voltages and currents corresponding to maximum powers produced by PV array at the maximum power point (MPP) for swiftly changing situations such as solar radiance and temperature. These maximum powers are given as input signal to vector control algorithm of PMSM. The PMSM is designed by using Infolytica/MotorSolve software so that the phase-to-phase maximum value of its operating voltage is 20 V. The use of three-phase PMSM presents more efficient solutions to the trading solar systems with dc motor or induction motor. Thus, an effective solar system is achieved. The performance of developed ANN-based MPPT algorithm, designed PMSM, vector-controlled driver and solar system is analyzed by using MATLAB/SimPowerSystems blocks under the rapidly changing environmental conditions.

     

Bond graph modeling and optimization of photovoltaic pumping system: Simulation and experimental results

Bond graphs are a promising possibility for modeling complex physical systems. This paper explores its potential by undertaking the analysis, modeling and design of a water pumping photovoltaic system. The effectiveness of photovoltaic water pumping systems depends on the sufficiency between the generated energy and the volume of pumped water. Another point developed in this paper presents the optimization of a photovoltaic (PV) water pumping system using maximum power point tracking technique (MPPT). The optimization is based on the detection of the optimal power. This optimization technique is developed to optimize the usage of power. The presented MPPT technique is used in photovoltaic water pumping system in order to increasing its efficiency. A buck–boost chopper allows an adaptation interface between the panel and the battery checked by a tracking mechanism known as the MPPT (Maximum Power Point Tracking). A new algorithm is presented to control a maximum power point tracker MPPT through a bond graph. From the chemical reactions in the batteries to the control laws of the power electronics structures, a bond graph model is proposed for every single part of the system. The model is used in simulations and the results compared to actual measurements. The model is used in simulations and the results compared to actual measurements, showing an accuracy of nearly 99%.     

基于自适应线性调节抗干扰PSO的光伏群体MPPT控制

光伏发电已成为新能源发电的主要研究方向,但当外界环境发生突变或由于遮挡使光伏阵列出现阴影时,传统的最大功率点跟踪(MPPT)算法会出现误判或因陷入局部最大功率点等问题而失效。针对这些问题,提出了一种自适应线性调节的粒子群(PSO)算法,并采用一个MPPT控制器同时实现多支路光伏阵列群体MPPT控制。最后,通过仿真验证所提控制策略的有效性。结果表明,自适应线性调节PSO群控方法振荡小,可实时精准跟踪最大功率点,控制电路较为简单,降低系统控制成本。     

分段变步长MPPT算法在风力发电系统中的应用

针对传统定步长爬山搜索(HCS)法在风力发电系统最大功率跟踪(MPPT)控制过程中的快速性和准确性矛盾,提出了一种基于爬山搜索法和模糊控制的分段变步长MPPT算法.该算法根据发电机P-ω特性曲线对最大功率点(MPP)跟踪过程进行分段,使系统能够根据工作点所在的区域选择合适的跟踪算法和步长完成最大功率跟踪.在Matlab/Simulink中分别对提出的模糊分段变步长算法和传统爬山搜索法进行了仿真.仿真结果表明:所提算法明显地改善了系统跟踪MPP的速度和稳态精度,在MPPT方面明显优于传统的爬山搜索法.