太阳能电池板发电效率的实验研究

为了更好的利用太阳能资源,针对南京地区太阳能发电的应用问题,设计并实验分析了太阳能电池板在不同波长光照下发电效率的规律,并进行了不同照射角度下发电效率的实验研究.文中设计了太阳能电池板发电效率的测试方案,采用滤光片分别进行了四种波长太阳光线的获取;针对太阳能电池板的组合排列,分别设计了五种组合方式,并对每一种组合方式进行了实验研究.实验结果表明,在南京地区的天气中,中午12到14点太阳能发电效率最高;在波长方面,大于600nm的光线的发电效率最大;在实用性方面,四个太阳能电池板并联方式提供的电流最强,四个串联方式提供的电压能量最大.研究结果能为更好地利用太阳资源能提供良好的借鉴.     

高效太阳能发电系统的设计

针对太阳能电池对太阳光利用率不高的问题,设计了基于太阳能电池板自动跟踪太阳的高效太阳能发电系统。系统通过无线模块,把太阳能发电系统实时状态数据发送到基站进行处理。基站实现系统密码保护、红外热释电人体检测、Flash实时数据存储、红外数据抄表、远程以太网数据传输和访问以及串口VC上位机数据曲线显示等功能。经过调试和测试表明,系统能在控制范围内最大效率地利用太阳能给锂电池充电,具有广泛的应用前景。     

积灰条件下光伏阵列最优倾角问题研究

在光伏发电系统设计中,安装倾角的选择对光伏发电效率具有重要影响.针对太阳能光伏阵列常见的表层积灰现象,改变传统的只考虑最大辐射量的倾角确定方法,提出了综合考虑表层积灰情况下的最优发电倾角计算方法,使倾角的确定更加合理与完善.建立了积灰辐射量统一发电模型,并基于Matlab对模型进行了仿真验证.以福建某光伏电站为例,搭建实验平台,通过相关数据的检验与预测,得到了光伏电池板的综合最优倾角.实验结果证明该模型所确定的倾角比传统模型可以得到更大的发电量,提高了光伏利用效率.     

智能太阳能屋顶模型的设计与实现

太阳能利用的重点是建筑,其应用方式包括利用太阳能为建筑物供热(生活热水、采暖)和供电,因此太阳能与建筑一体化是未来太阳能技术的发展方向。我国已于2009年正式启动了"太阳能屋顶计划",但是目前已实施的太阳能屋顶上的电池板均为固定安装,从而限制了太阳辐射量的吸收,减少了发电产量,降低了太阳能屋顶的工作效率。本文的智能太阳能屋顶模型将太阳跟踪技术应用于屋顶太阳能电池板上,使其能够根据太阳方位的变化自动调节角度,大大提高了太阳辐射量的吸收。     

智能太阳能电池板角度调节系统的设计

随着科技的发展以及人们思想意识的提升,节能减排、提高效率的国家政策得以进一步的落实。太阳能发电也得到了越来越广泛的认可。传统的太阳能电池板都是固定在某一方向的,这就不能保证太阳能电池板时时都有最大的发电效率。基于此,本设计应运而生。本设计包括太阳光照强度检测模块、单片机主控模块以及电机模块等组成,通过两个电机在水平以及竖直方向的转动,使得太阳能电池板时时都正对着太阳,保证太阳能电池板达到最大的发电效率。该设计在成本、功耗以及体积、结构方面都非常的具有优势,是一个非常不错的设计,非常具有推广意义。     

太阳能自动跟踪系统研究

太阳能发电具有干净、不会威胁人类和破坏环境的优点,提高太阳能板发电效率成为当今研究的热点;以单片机为控制核心,读取GPS的经纬度和当地时间等信息,根据视日运动轨迹模型精确解算太阳方位角和高度角,发送相应脉冲驱动步进电机,经过减速器减速实现对太阳能板的姿态调整,从而实现对太阳光的精确跟踪;安装于太阳能板上的姿态传感器可以将姿态信息反馈给控制器从而实现闭环控制,进一步提高了系统的精度,安装的风速传感器使太阳能跟踪器具有抗风性能;实验表明,太阳跟踪器可有效跟踪太阳角,跟踪精度可达到2°左右,大大提高了太阳能板的发电效率,具有很好的应用前景。     

关于光伏电站太阳能板清洁技术的研究

近年来,我国光伏产业发展迅速,利用太阳能进行光伏发电成为一种趋势。在光伏电站的运行维护过程中,需要对太阳能板进行清洁处理,以提高光伏电站的发电效率,有效提升经济效益。本文主要深入研究光伏电站太阳能板清洁技术的主要应用及其发展,为相关研究提供一些参考。     

太阳能路灯双轴跟踪系统设计

针对当前太阳能路灯转换效率低的弊端,介绍了一种太阳能路灯双轴跟踪系统,通过实时检测光强的变化驱动执行机构,保证太阳能电池板始终垂直于太阳光线,从而提高太阳能利用效率。实验表明,太阳能电池板在双轴跟踪情况下,发电量要比最佳角度固定安装提高34%。     

几种产业化太阳能电池板的户外实际发电性能比较

随着传统化石能源的日益消耗,各种可再生能源技术备受关注,其中,可直接将太阳能转换为电能的太阳能电池技术已成为新能源领域的主要研究方向之一。文章介绍了一种可连续测量多个太阳能电池板户外发电性能的测试设备,实际测量了目前市场上主流的太阳能电池板在不同天气条件下的发电性能,包括多晶硅、单晶硅、非晶硅、铜铟镓硒等电池,比较了单位面积和单位标称发电功率的实际发电性能,并对各种电池实际发电性能和特点进行了总结分析。     

基于多层小波分解的日前太阳辐照度预测研究

由于太阳辐照度的随机波动特性,大型光伏发电并网会给电力系统的运行带来极大困难,光伏发电功率的预测是解决此问题的关键措施之一.提出了一种基于多层小波分解的太阳辐照度预测方法,首先,根据天气状态将每日的辐照度曲线划分为不同的波动模式;然后针对不同天气下的波动模式分别建立预测模型,使用多层小波分解后的数据预测第二天连续24小时的辐照度值;最后建立基于数据驱动的融合模型,将不同天气模式下的辐照度多层小波分解预测值进行融合,以获得最终的辐照度预测结果.仿真结果表明辐照度预测结果精度与小波分解层数和天气模式高度相关,且所提算法能够有效提高短期辐照度预测精度.     

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 sliding mode control and artificial neural network based MPPT for a direct grid‐connected photovoltaic source

A robust sliding mode controller for a grid‐connected photovoltaic source is proposed in this paper. The objective of the presented control scheme is to force both the output voltage of the photovoltaic PV source and the power factor at the inverter output to follow a certain trajectory reference. The main idea is to apply the robust sliding mode controller directly to the nonlinear state model of the system composed of the PV source and the inverter with its input and output filters. In order to operate the PV system at the maximum power point and to satisfy the environmental factors, such as solar irradiance and temperature, we included a rigorous maximum power point tracker based on an artificial neural network. Simulation results are presented to illustrate the performance of the proposed control scheme. In addition, we show that the grid current satisfies the harmonic limits of the IEEE standard for interconnecting distributed energy sources with electric power systems.     

A two-level control strategy with fuzzy logic for large-scale photovoltaic farms to support grid frequency regulation

This study proposes a two-level coordinated control strategy with fuzzy logic for appropriately adjusting the total active power supplied to a grid by large-scale photovoltaic (PV) farms in order to regulate grid frequency. For a solar farm, the strategy includes a central coordinating controller and many local controllers at PV power assemblies, treated as agents. In detail, the central controller uses a frequency regulation module based on a new automatic-tuning fuzzy-logic controller scheme to compute the appropriate reference values according to the total power needed. Then, the individual reference value for each local controller is determined. Each local controller governs all power-electronic converters installed at the PV agent to inject power into the grid according to the individual reference value received. Additionally, each local controller uses an algorithm to manage the state-of-charge of the battery bank installed at the agent so that it remains in the safe range of 20–80% while operating and close to the desired idle value of 50% at the steady state. Besides, a special control mode is developed and integrated into the overall strategy to aid rapid recovery of the grid frequency under emergency conditions. Numerical simulations demonstrate that the suggested strategy has the good response in terms of injecting an appropriate amount of power into the grid to regulate the frequency deviation into acceptable ranges of ±0.2 (Hz) in the transient state and ±0.05 (Hz) at the steady state, even when the weather conditions (solar radiation, air temperature), AC system load, and important control parameters of the grid suddenly change. Furthermore, the effectiveness in improving the grid-frequency stabilization by using the proposed strategy is validated within a four-area power system, where four PV farms are connected and the operating parameters of the grids at the areas are fairly different.     

粒子群优化RBF神经网络光伏电池建模研究

本文研究神经网络在光伏电池建模优化问题。由于光伏电池具有高度非线性特性,其输出功率受到外界自然因素的影响,使得传统方法不能满足光伏控制系统动态要求。针对上述问题,本文提出一种粒子群优化的神经网络光伏电池建模算法。改进的方法以日照、温度和负载电压作为提出的RBF神经网络模型的输入值,把光伏电池的输出功率作为神经网络的输出,采用RBF神经网络对光伏电池进行建模,同时利用粒子群算法对神经网络参数进行优化,最后建立光伏电池的动态响应模型。仿真实验结果证明,所提模型更好地克服传统方法的缺点,收敛速度快,具有较高的预测精度和适合能力。     

Gaussian Kernel Based SVR Model for Short-Term Photovoltaic MPP Power Prediction

Predicting the power obtained at the output of the photovoltaic (PV) system is fundamental for the optimum use of the PV system. However, it varies at different times of the day depending on intermittent and nonlinear environmental conditions including solar irradiation, temperature and the wind speed, Short-term power prediction is vital in PV systems to reconcile generation and demand in terms of the cost and capacity of the reserve. In this study, a Gaussian kernel based Support Vector Regression (SVR) prediction model using multiple input variables is proposed for estimating the maximum power obtained from using perturb observation method in the different irradiation and the different temperatures for a short-term in the DC-DC boost converter at the PV system. The performance of the kernel-based prediction model depends on the availability of a suitable kernel function that matches the learning objective, since an unsuitable kernel function or hyper parameter tuning results in significantly poor performance. In this study for the first time in the literature both maximum power is obtained at maximum power point and short-term maximum power estimation is made. While evaluating the performance of the suggested model, the PV power data simulated at variable irradiations and variable temperatures for one day in the PV system simulated in MATLAB were used. The maximum power obtained from the simulated system at maximum irradiance was 852.6 W. The accuracy and the performance evaluation of suggested forecasting model were identified utilizing the computing error statistics such as root mean square error (RMSE) and mean square error (MSE) values. MSE and RMSE rates which obtained were 4.5566 * 10⁻⁰⁴ and 0.0213 using ANN model. MSE and RMSE rates which obtained were 13.0000 * 10⁻⁰⁴ and 0.0362 using SWD-FFNN model. Using SVR model, 1.1548 * 10⁻⁰⁵ MSE and 0.0034 RMSE rates were obtained. In the short-term maximum power prediction, SVR gave higher prediction performance according to ANN and SWD-FFNN.     

无线传感器网络节点太阳能供电系统设计

ZigBee无线传感器网络节点太阳能供电系统由太阳能电池板、充电控制电路和锂电池组成,采集光能并将其转换为电能存储在锂电池中。通过锂电池充电管理芯片CN3063组成充电控制电路对锂电池进行充电管理。利用超低功耗锂电池电压检测芯片CN301组成放电保护电路,最大限度地延长锂电池的寿命。由于电源能量来自太阳能,因此非常适合野外布置的ZigBee无线传感器网络数据采集节点使用。     

Evaluation of Grid-Connected Photovoltaic Plants Based on Clustering Methods

Photovoltaic (PV) systems are electric power systems designed to supply usable solar power by means of photovoltaics, which is the conversion of light into electricity using semiconducting materials. PV systems have gained much attention and are a very attractive energy resource nowadays. The substantial advantage of PV systems is the usage of the most abundant and free energy from the sun. PV systems play an important role in reducing feeder losses, improving voltage profiles and providing ancillary services to local loads. However, large PV grid-connected systems may have a destructive impact on the stability of the electric grid. This is due to the fluctuations of the output AC power generated from the PV systems according to the variations in the solar energy levels. Thus, the electrical distribution system with high penetration of PV systems is subject to performance degradation and instabilities. For that, this project attempts to enhance the integration process of PV systems into electrical grids by analyzing the impact of installing grid-connected PV plants. To accomplish this, an indicative representation of solar irradiation datasets is used for planning and power flow studies of the electric network prior to PV systems installation. Those datasets contain lengthy historical observations of solar energy data, that requires extensive analysis and simulations. To overcome that the lengthy historical datasets are reduced and clustered while preserving the original data characteristics. The resultant clusters can be utilized in the planning stage and simulation studies. Accordingly, studies related to PV systems integration into the electric grid are conducted in an efficient manner, avoiding computing resources and processing times with easier and practical implementation.     

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.