分布式光伏发电系统最大功率点跟踪技术比较研究

基于当前光伏阵列最大功率点跟踪技术的研究现状,介绍了适用于分布式光伏发电系统最大功率点跟踪的各种常用控制方法,阐述了每一种控制方法的技术原理,分析和比较了这些常用控制方法的特点,总结了各自的优点和缺点,最后对分布式光伏发电系统最大功率点跟踪方法的选择问题进行了探讨,并指出了具体选择方法时应综合考虑的各种因素。     

Adaptive neuro-fuzzy inference system-based maximum power point tracking of solar PV modules for fast varying solar radiations

This paper analyses the operation of an adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) for solar photovoltaic (SPV) energy generation system. The MPPT works on the principle of adjusting the voltage of the SPV modules by changing the duty ratio of the boost converter. The duty ratio of the boost converter is calculated for a given solar irradiance and temperature condition by a closed-loop control scheme. The ANFIS is trained to generate maximum power corresponding to the given solar irradiance level and temperature. The response of the ANFIS-based control system is highly precise and offers an extremely fast response. The response time is seen as nearly 1 ms for fast varying cell temperature and 6 ms for fast varying solar irradiance. The simulation is done for fast-changing solar irradiance and temperature conditions. The response of the proposed controller is also presented.     

光伏系统最大功率点跟踪方法

最大功率点跟踪是光伏发电系统中重要的问题.在对太阳电池特性曲线分析的基础之上,阐述了几种常用的光伏系统最大功率点的追踪方法.文章总结并分析了各种方案的优缺点.     

Sizing of stand-alone photovoltaic power supply system based on genetic algorithm and neuro-fuzzy: application for isolated areas

This paper deals with the application of genetic algorithm (GA) and an adaptive neuro-fuzzy inference scheme (ANFIS), for the prediction of the optimal sizing coefficient of stand-alone photovoltaic supply (SAPVS) systems in remote areas. A database of total solar radiation data for 60 sites in Algeria has been used to determine the iso-reliability curves of a PVS system (C _A, C _S) for each site. Initially, the GA is used for determining the optimal coefficient (C _Aop, C _Sop) for each site by minimising the optimal cost (objective function). These coefficients allow the determination of the number of PV modules and the capacity of the battery. Subsequently, an ANFIS is used for the prediction of the optimal coefficient in remote areas based only on geographical coordinates. Therefore, 56 couples of C _Aop and C _Sop have been used for the training of the network and four couples have been used for testing and validation of the proposed technique. The simulation results have been analysed and compared with the alternative techniques. The technique has been applied and tested for Algeria locations, but it can be generalised for any location in the world.     

Adaptive neuro-fuzzy short-term wind-speed forecasting for Egypt's East-Coast

Use of wind energy as a renewable source of energy for electric utility systems is increasing around the world. The major challenges of wind energy generation are natural intermittency, unpredictability, and uncertainty due to wind variations. In this paper, five different adaptive neuro-fuzzy wind predictors are proposed and compared to forecast the speed of wind blowing in the East Coast of Egypt, a very promising location to generate more than 20 GW of wind power. The first and second proposed models are based on real wind-speed data of the selected site for the same month aided by the corresponding real wind-speed data for the same site for the past 4 and 6 years, respectively. The results that have been obtained from these models show more accuracy with respect to the previous work in the literature. Furthermore, three new models are proposed based on only 20% of the real data used for the first model obtaining similar accuracy to predict the average wind speed one day, half a day, and quarter day ahead.     

具有MPPT功能的智能户用光伏充电系统研究

光伏充电系统采用了恒流充电和du/dt恒压限流充电相结合的管理模式,在一定时间内以电压的变化量接近零,并使充电电流达到最小设定量作为判断蓄电池充电终止的条件,采用了电压自寻优算法实现了光伏电池的最大功率点跟踪.试验表明,系统除了具有智能化管理的特点外,光伏电池的最大功率点跟踪效果明显,且不用考虑日照强度和温度对光伏电池的影响,在一定程度上能够提高光伏电池的输出功率.     

光伏电池最大功率点跟踪方法的研究

在光伏发电系统中,为了提高光伏电池的利用效率,需要对光伏电池的最大功率点进行跟踪。分析了在跟踪控制中常见的扰动跟踪法和功率数学模型法,比较了它们的优缺点,并基于这两种方法提出了一种改进的跟踪方法,利用MATLAB对该方法进行了仿真研究,仿真结果验证了该方法的有效性。     

Parameter design on the multi-objectives of PEM fuel cell stack using an adaptive neuro-fuzzy inference system and genetic algorithms

This work considers a design of a PEM fuel cell (PEMFC) stack that consists of 10 cells and expects to carry out an analysis of performance. In this work, PEMFC performance as affected by different combinations of control factors, such as the cathode and anode operating pressures, the humidification temperatures, and the stoichiometric flow ratio of reaction gas, is studied. On the PEMFC stack performance, the gas supply that is expected to be the minimum and the output power that is hoped to be the maximum are a result of the demand of the multi-objectives characteristics. Due to the Taguchi orthogonal array, the screen experiment is carried out by using a fractional factorial design in order to determine main factors and interaction effects first, and then the robust design is conducted. The intelligent parameter design is developed via an Adaptive Neuro-Fuzzy Inference System combined with the definition of percentage reduction of quality loss (PRQL) in order to supply a fitness function to the genetic algorithms (GA). The best parameter design is proposed after an analysis and comparison is conducted. Finally, the adaptability of prediction for the model created by this approach is confirmed by the confirmation experiment. This work shows that the PEMFC performance is improved by 35.8% via the average PRQL.     

Maximum power point tracking using adaptive fuzzy logic control for grid-connected photovoltaic system

This paper proposes a method of maximum power point tracking using adaptive fuzzy logic control for grid-connected photovoltaic systems. The system is composed of a boost converter and a single-phase inverter connected to a utility grid. The maximum power point tracking control is based on adaptive fuzzy logic to control a switch of a boost converter. Adaptive fuzzy logic controllers provide attractive features such as fast response, good performance. In addition, adaptive fuzzy logic controllers can also change the fuzzy parameter for improving the control system. The single phase inverter uses predictive current control which provides current with sinusoidal waveform. Therefore, the system is able to deliver energy with low harmonics and high power factor. Both conventional fuzzy logic controller and adaptive fuzzy logic controller are simulated and implemented to evaluate performance. Simulation and experimental results are provided for both controllers under the same atmospheric condition. From the simulation and experimental results, the adaptive fuzzy logic controller can deliver more power than the conventional fuzzy logic controller.     

基于模糊自适应PI控制的光伏发电MPPT

介绍了光伏发电过程中最大功率点跟踪(MPPT)原理,并简要分析了常规控制算法在最大功率跟踪控制中的优缺点,提出将模糊自适应PI控制算法应用到光伏系统最大功率点跟踪的控制中,该控制方法能快速响应外界环境的变化,获得系统最大功率点,且可以有效消除系统在最大功率点附近的振荡现象,提高系统的稳定性。仿真结果表明,该方法能使系统稳定地工作在最大功率点,并且控制精度高,能灵敏反应外界环境的变化。     

Implementation of photovoltaic water pumping system with MPPT controls

To increase the output efficiency of a photovoltaic (PV) system, it is important to apply an efficient maximum power point tracking (MPPT) technique. This paper describes the analysis, the design and the experimental implementation of the tracking methods for a stand-alone PV system, using two approaches. The first one is the constant voltage (CV) MPPT method based on the optimum voltage, which was deduced experimentally, and considered as a reference value to extract the optimum power. The second one is the increment conductance (Inc-Cond) MPPT method based on the calculation of the power derivative extracted by the installation. The output controller can adjust the duty ratio to the optimum value. This optimum duty ratio is the input of a DC/DC boost converter which feeds a set of Moto-pump via a DC/AC inverter. This paper presents the details of the two approaches implemented, based on the system performance characteristics. Contributions are made in several aspects of the system, including converter design, system simulation, controller programming, and experimental setup. The MPPT control algorithms implemented extract the maximum power point (MPP), with satisfactory performance and without steady-state oscillation. MATLAB/Simulink and dSpace DS1104 are used to conduct studies and implement algorithms. The two proposed methods have been validated by implementing the performance of the PV pumping systems installed on the roof of the research laboratory in INSAT Tunisia. Experimental results verify the feasibility and the improved functionality of the system.     

基于实测数据的光伏阵列参数辨识与输出功率预测

基于光伏电池的I-U方程、基本电路理论和最大功率跟踪的输出特性,提出了运用参数辨识方法及光伏阵列的输入、输出数据,辨识光伏阵列光生电流、反向饱和电流、二极管理想因子、电池串联内阻4个待定系数,确定光伏阵列的I-U方程,再用求解得到的I-U方程来预测光伏电站的输出功率。通过算例和实测数据仿真比较,验证了所提出方法的可行性和准确性。     

自适应模糊算法在光伏系统MPPT中的应用

针对光伏电池的非线性特性,介绍了最大功率点跟踪的原理,并详细分析了常规控制算法在光伏系统最大功率跟踪控制中的优缺点,提出将自适应模糊控制算法应用到光伏系统最大功率点跟踪的控制中,自适应模糊控制能快速响应外界环境的变化,在最大功率点无明显振荡现象.实验结果表明,该方法能使系统稳定工作在最大功率点,同时能对外界环境的变化做出快速反应.     

Modeling and control of photovoltaic energy conversion connected to the grid

This paper presents modeling and control of a photovoltaic generator (PVG) connected to the grid. The parameters of the PVG have been identified in previous work (series and parallel resistance, reverse saturation current and thermal voltage) using Newton-Raphston and the gradient algorithm. The electrical energy from a PVG is transferred to the grid via two static converters (DC/DC and DC/AC). The objective of the proposed control strategy is to maximize energy captured from the PVG. The adapted control law for extracting maximum power from the PVG is based on the incremental conductance algorithm. The developed algorithm has the capability of searching the maximum photovoltaic power under variable irradiation and temperature. To control the DC/AC inverter, an intelligent system based on two structures is constructed: a current source control structure and a voltage source control structure. The system has been validated by numerical simulation using data obtained from the PVG installed in the laboratory research (INSAT, Tunisia).     

A comparison between radial basis function (RBF) and adaptive neuro-fuzzy inference system (ANFIS) to model the free convection in an open round cavity

This study demonstrates the capability of radial basis function (RBF) and adaptive neuro-fuzzy inference system (ANFIS) to model and predict the free convection heat transfer in an open round cavity. In fact, the effects of the Rayleigh number (Ra) and ratio of the nonconductor barrier distance from the bottom of the cavity to the cavity diameter (H/D), on the free convection in the cavity, are modeled via the RBF and ANFIS models. To start modeling, sufficient data are gathered. Here, data are experimentally generated using a Mach-Zehnder interferometer. In the next step, the RBF and ANFIS models are trained. According to the results, there is an optimum ratio (H/D), in which the heat transfer is maximum. This maximum value increases by increasing the Rayleigh number (Ra). Moreover, based on the results obtained by the RBF and ANFIS, the predicted results for the convection heat transfer are in good agreement with similar ones obtained experimentally. The mean relative errors of the training, testing, and checking data for the RBF model were found as 0.1348%, 1.1972%, and 2.4967%, respectively. Moreover, for the ANFIS model, the error values were 0.0731%, 0.9110%, and 1.9144%, which shows that RBF and ANFIS can predict the results precisely.     

Evaluation of performance of photovoltaic system with maximum power point (MPP)

Performance of the photovoltaic system is highly influenced by the weather, especially the irradiation and the temperature. To simplify the design of solar generator power, a mathematical model and its validity of the solar cell are required. In this work, the value of the parameters in the mathematical model is obtained by the measurement of the I–V curve of the module. To get an I–V curve, one of the modules in the system was radiated and loaded by a load simulator. A photovoltaic pumping system with maximum power point tracker was built and tested. The results were analyzed and evaluated by using the parameters and the photovoltaic system was shown to be well optimized.     

光伏并网发电系统最大功率追踪算法的稳定性研究

随着光伏发电技术及光伏产业的发展,太阳电池阵列如何在同样日照、温度的条件下输出尽可能多的电能成为人们的研究重点。然而,根据太阳电池的工作原理,当光照强度,温度等自然条件改变时,太阳电池的输出特性将随之改变,输出功率及最大工作点亦相应改变。分析了几种常见的最大功率跟踪方法（Maximum Power Point Tracking,简称MPPT）,针对扰动观测法中的误判现象进行分析,并加以改进,使系统更加平稳地输出有功功率,实现了对最大功率点的稳定追踪。     

光伏发电系统最大功率点跟踪方法研究综述

阐述和评价了目前国内外出现的几种方法的原理和特点,对这几种方法进行了分析比较,探讨了最大功率点跟踪控制方法的发展思路,并对该领域今后的研究方向做了展望。     

Enhanced control strategies for a hybrid battery/photovoltaic system using FGS-PID in grid-connected mode

This paper suggests improved control strategies using Fuzzy Gain Scheduling of Proportional-Integral-Derivative (FGS-PID) controller for a hybrid Photovoltaic (PV) and Battery Energy Storage (BES) system under different weather conditions. The proposed scheme is implemented using a two-level control system structure, combining the benefits of the PID as well as the fuzzy logic controller (FLC) for maximum power point tracking (MPPT). Ziegler-Nichols tuning method is also employed to select the initial values of PID gains. Within the period of steady-states and transients, FGS-PID adopts the gains to ensure the stability of the control scheme. It also damps out transient fluctuations and reduces settling time. Also, BES could be employed to provide a stable and reliable power from the output of PV sources to loads. It can enhance the performance of the entire power system during the grid-connected mode. The simulation results under Matlab/Simulink show that the suggested control strategies are robustness, fast transient response and proper steady-state performance in the grid-connected mode in comparison other presented methods.     

Comparative study of various artificial intelligence approaches applied to direct torque control of induction motor drives

In this paper, three intelligent approaches were proposed, applied to direct torque control (DTC) of induction motor drive to replace conventional hysteresis comparators and selection table, namely fuzzy logic, artificial neural network and adaptive neuro-fuzzy inference system (ANFIS). The simulated results obtained demonstrate the feasibility of the adaptive network-based fuzzy inference system based direct torque control (ANFIS-DTC). Compared with the classical direct torque control, fuzzy logic based direct torque control (FL-DTC), and neural networks based direct torque control (NN-DTC), the proposed ANFIS-based scheme optimizes the electromagnetic torque and stator flux ripples, and incurs much shorter execution times and hence the errors caused by control time delays are minimized. The validity of the proposed methods is confirmed by simulation results.