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.     

GMPP tracking based on model reference LPV control for a PV system with buck converter modelled on bond graph

A PhotoVoltaic (PV) system with a Linear Parametric Varying (LPV) average model of a buck converter modelled by bond graph methodology, is considered. It is assumed that the voltage generated by the PV panel is a smooth function of time under changes in atmospheric conditions. A model reference is proposed representing the PV system with buck converter operating at the Global Maximum Power Point (GMPP). An LPV control is designed, and an algorithm is proposed in a model reference tracking control configuration assuring that the PV system with buck converter tracks the proposed model reference and hence guaranteeing that this system operates at the GMPP. The proposed algorithm changes the voltage reference based on the power measurement of the PV system assuring that this reference represents the GMPP under shading conditions. The LPV control is designed for an average model of the buck converter solving a feasibility problem based on linear matrix inequalities that assure quadratic stability and minimize a quadratic criterion. The feedback system is tested under shading conditions, and its performance is compared with a standard Perturb and Observe method and with an ideal algorithm based on the irradiance measurement in each section of the PV system.     

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。     

局部遮阴下光伏系统MPPT研究与优化

为了解决光伏发电系统中,光伏电池在环境中被树叶、建筑物、云层等遮挡造成局部阴影,导致光伏电池出现运行不稳定和输出功率降低的问题,提出了一种基于改进自适应动态惯性权重并引入粒子寻优目标适应度评判系数的优化粒子群算法(GPPSO).将GPPSO应用于复杂自然环境条件下的最大功率点跟踪(MPPT),结果表明:双重优化后的算法有效提高了局部精确搜索和寻优空间全局收敛能力,在目标函数最优求解过程中,精度和收敛速度都明显提高,较快地适应环境遮阴变化,能够在复杂的自然环境中准确地对光伏发电系统最大功率点进行跟踪,提高光伏系统发电效率.     

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.     

A new MPPT controller based on the Ant colony optimization algorithm for Photovoltaic systems under partial shading conditions

The Maximum Power Point Tracking controller (MPPT) is a key element in Photovoltaic systems (PV). It is used to maintain the PV operating point at its maximum under different temperatures and sunlight irradiations. The goal of a MPPT controller is to satisfy the following performances criteria: accuracy, precision, speed, robustness and handling the partial shading problem when climatic changes variations occur. To achieve this goal, several techniques have been proposed ranging from conventional methods to artificial intelligence and bio-inspired methods. Each technique has its own advantage and disadvantage. In this context, we propose in this paper, a new Bio- inspired MPPT controller based on the Ant colony Optimization algorithm with a New Pheromone Updating strategy (ACO_NPU MPPT) that saves the computation time and performs an excellent tracking capability with high accuracy, zero oscillations and high robustness. First, the different steps of the design of the proposed ACO_NPU MPPT controller are developed. Then, several tests are performed under standard conditions for the selection of the appropriate ACO_NPU parameters (number of ants, coefficients of evaporation, archive size, etc.). To evaluate the performances of the obtained ACO_NPU MPPT, in terms of its tracking speed, accuracy, stability and robustness, tests are carried out under slow and rapid variations of weather conditions (Irradiance and Temperature) and under different partial shading patterns. Moreover, to demonstrate the superiority and robustness of the proposed ACO_NPU_MPPT controller, the obtained results are analyzed and compared with others obtained from the Conventional Methods (P&O_MPPT) and the Soft Computing Methods with Artificial intelligence (ANN_MPPT, FLC_MPPT, ANFIS_MPPT, FL_GA_MPPT) and with the Bio Inspired methods (PSO) and (ACO) from the literature. The obtained results show that the proposed ACO_NPU MPPT controller gives the best performances under variables atmospheric conditions. In addition, it can easily track the global maximum power point (GMPP) under partial shading conditions.     

Modeling of nonlinear dynamical systems based on deterministic learning and structural stability基于确定学习和结构稳定理论的非线性动态系统建模

Recently, a deterministic learning (DL) theory was proposed for accurate identification of system dynamics for nonlinear dynamical systems. In this paper, we further investigate the problem of modeling or identification of the partial derivative of dynamics for dynamical systems. Firstly, based on the locally accurate identification of the unknown system dynamics via deterministic learning, the modeling of its partial derivative of dynamics along the periodic or periodic-like trajectory is obtained by using the mathematical concept of directional derivative. Then, with accurately identified system dynamics and the partial derivative of dynamics, a C¹-norm modeling approach is proposed from the perspective of structural stability, which can be used for quantitatively measuring the topological similarities between different dynamical systems. This provides more incentives for further applications in the classification of dynamical systems and patterns, as well as the prediction of bifurcation and chaos. Simulation studies are included to demonstrate the effectiveness of this modeling approach.     

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.     

Modeling and simulation of a grid connected PV system based on the evaluation of main PV module parameters

In this work we present a new method for the modeling and simulation study of a photovoltaic grid connected system and its experimental validation. This method has been applied in the simulation of a grid connected PV system with a rated power of 3.2 Kw_p, composed by a photovoltaic generator and a single phase grid connected inverter. First, a PV module, forming part of the whole PV array is modeled by a single diode lumped circuit and main parameters of the PV module are evaluated. Results obtained for the PV module characteristics have been validated experimentally by carrying out outdoor I-V characteristic measurements. To take into account the power conversion efficiency, the measured AC output power against DC input power is fitted to a second order efficiency model to derive its specific parameters.The simulation results have been performed through Matlab/Simulink environment. Results has shown good agreement with experimental data, whether for the I-V characteristics or for the whole operating system. The significant error indicators are reported in order to show the effectiveness of the simulation model to predict energy generation for such PV system.     

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.     

明暗效果快速多变的CSG实本显示技术

CSG(Constructive Solid Geometry)是实体造型中一种重要表示方法.传统的CSG实 体显示方法不仅费时,而且难以控制明暗效果.本文提出的方法以一种新的CSG体素表面光 强生成模型为基础,结台深度缓冲技术,能快速有效地显示CSG实体,方便灵活地控制明暗效 果.     

基于类比推理的物体明暗描绘方法

通过源图像与目标图像的类比关系，提取源图像中的物体在不同光照条件下生成的图像中的明暗知识，并将其作用于目标图像以完成明暗描绘．在目标图像与源图像上的绘制对象是理想漫反射物体且具有可类比性的情况下，绘制结果是合理的．该方法具有原始数据量小、计算速度快、便于显示效果的控制和交互等特点．     

基于延期着色的sort-first绘制集群*

传统的图形绘制集群采用前向着色,存在光照着色功能有限、计算负载难以预测等缺陷。为此,提出基于延期着色的sort-first绘制集群架构,该系统可以实时绘制具有复杂光照效果的多边形场景,并可利用延期着色的负载可预测特性,实现绘制节点间的负载平衡。同时,提出了基于Equalizer改造现有图形绘制引擎,构造基于延期着色的sort-first绘制集群的方法。实验表明,该系统可以在静态负载平衡策略下实现高性能的实时图形绘制,具备良好的可扩展性。     

Variable-Source Shading Analysis

The shading on curved surfaces is a cue to shape. Current computer vision methods for analyzing shading use physically unrealistic models, have serious mathematical problems, cannot exploit geometric information if it is available, and are not reliable in practice. We introduce a novel method of accounting for variations in irradiance resulting from interreflections, complex sources and the like. Our approach uses a spatially varying source model with a local shading model. Fast spatial variation in the source is penalised, consistent with the rendering community’s insight that interreflections are spatially slow. This yields a physically plausible shading model. Because modern cameras can make accurate reports of observed radiance, our method compels the reconstructed surface to have shading exactly consistent with that of the image. For inference, we use a variational formulation, with a selection of regularization terms which guarantee that a solution exists. Our method is evaluated on physically accurate renderings of virtual objects, and on images of real scenes, for a variety of different kinds of boundary condition. Reconstructions for single sources compare well with photometric stereo reconstructions and with ground truth.     

Adaptive GA-based reconfiguration of photovoltaic array combating partial shading conditions

This paper concerns the study and simulation of a PV array self-organizing configuration. It introduces a new method to reconfigure the PV array using a genetic algorithm in order to maximize the output power as well as reducing the number of switching. The proposed method involves the simulation of a PV array composed of 16 panels 4 strings with 4 panels in series and associated parallel, as well as an algorithm that controls the improvement of the overall performance under different shading conditions. The obtained results using MATLAB/Simulink simulation show improvement rating varying between 106.49 and 171.03%, which is huge compared to a static configuration operating below the total available power. Another important point is the number of iterations needed to find the optimal configuration (between 6 and 132 for a population of 50 configurations tested at each generation); this means that in the worst case (132 iterations), the proposed algorithm performed 132 × 50 = 6600 configurations instead of 16¹⁶ = 1.84 × 10¹⁹ necessary in case of exhaustive search to test all possible configurations. This last point is very important in the implementation of the proposed system in auto-tuning of the system in real-time condition. Besides using genetic algorithm to track the optimal configuration, our main contribution consists of improving the output power while reducing the number of switching by keeping PV modules, if possible, in same position (0 switching) or on the same line/column (1 switching) in few iteration needing only two sensors one for the voltage and another for the current of the PV array.

     

Design and development of new control technique for standalone PV system

A Sub-Maximum Power Point Tracking (S-MPPT) algorithm improves the performance of Photo Voltaic (PV) systems. This S-MPPT is used in single-phase PV system to test the tracking accuracy and its impact on the consistency of the whole system. Single phase PV Deadbeat Scheduler is proposed in this paper. The Deadbeat scheduler is a linear system. It initializes each initial state of the system to zero in shortest time possible. A single phase PV structure configuration is proposed to decrease the partial shading effect by changing the parameters of S-MPPT control algorithm. Thus, voltage sensor based S-MPPT algorithm through voltage reference control technique with the help of controller is developed for minimizing the tracking time and steady state oscillations. Selection of the objective function to mitigate the drawbacks associated with voltage sensor based algorithm for a decrease in solar irradiance are also demonstrated. The proposed MPPT algorithm with the designed controller is tested for a step change in irradiance from 270 to 480 W/m2 with a perturbation time of 20 ms and ∆V = 0.5 V (perturbation of voltage). From the simulation results, the proposed method with S-MPPT plus deadbeat control algorithm is compared with other existing algorithms.     

融合时空特征的光伏气象因子预测模型

光伏发电极易受到天气的影响而具有波动性和不确定性,因此对气象因子的准确预测对光伏电站的运维具有重要意义。提出了一种基于深度学习的时空特征融合模型,实现对光伏气象因子的精准预测。在时间维度上,设计了一种改进的长短期记忆模块,融合注意力机制和遗传算法,得到最优注意力参数以提高预测精度;在空间维度上,将光伏电站所在区域按照经纬度划分,利用张量分解对区域内气象因子进行预测。在中国东南部某光伏系统的真实数据集上,对该模型的有效性进行了评估。结果表明,该模型在时间维度和空间维度均具有较高预测精度,同时对稀疏数据有较强的鲁棒性。     

Parameter estimation for five- and seven-parameter photovoltaic electrical models using evolutionary algorithms

Equivalent electric circuit modeling of PV devices is widely used to predict PV electrical performance. The first task in using the model to calculate the electrical characteristics of a PV device is to find the model parameters which represent the PV device. In the present work, parameter estimation for the model parameter using various evolutionary algorithms is presented and compared. The constraint set on the estimation process is that only the data directly available in module datasheets can be used for estimating the parameters. The electrical model accuracy using the estimated parameters is then compared to several electrical models reported in literature for various PV cell technologies.     

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.     

An improved power conditioning system for grid integration of solar power using ANFIS based FOPID controller

Power loss become common while integrating with common grid and in specific when power produced through Solar. This is the very lacking area which this proposal implements an Adaptive Neuro Fuzzy Inference System (ANFIS) based controller of Fractional Order Proportional Integral Derivative (FOPID) used for Tracking of Maximum PP of Grid Integrated Solar Power Conditioning System. The proposed work advances with different ambient light conditions for maximum power point traction. In this work a clear-cut Photo Voltaic (PV Cell) model has been developed and an intensive and operative training data have been extracted from the developed controller. This produced dataset have been the feeder input for the ANFIS structure in turn to locate the Tracking of Maximum PP (MPPT). Traction of MPPT is done, the FOPID controller is enforced by matching the voltage from the array of Photo Voltaic cell with attained or reference voltage produced by the ANFIS structure. In the meantime driving this PV array, DC to DC converter's duty cycle is controlled for producing maximum power from the structure. The duty cycle in FOPID controller is generated through calculating the error within the reference voltage and PV voltage. Those values are then simulated through Math Lab and the Simulation results show that this proposed work efficiency is better than the regularly employed controllers in the solar power production and conditioning system