改进BP神经网络的光伏系统发电功率预测

为了提高光伏发电功率的预测精度,提出一种改进BP神经网络的光伏发电功率预测模型.首先采用包括室外温度、光照辐射量、风速等作为输入层节点,交流发电功率作为输出节点,引入RMSE作为衡量最优模型指标,确定了隐含层节点数,然后采用BP神经网络对其进行学习,并采用布谷鸟搜索算法对BP神经网络进行优化,最后采用仿真实验对其有效性进行测试.结果表明,改进神经网络提高了光伏发电功率预测精度,具有一定的推广价值.     

并网光伏阵列发电最大功率点孤岛检测方法

由于传统方法不能准确检测到孤岛发生的位置,存在孤岛检测精度较低的问题,为了最大程度地降低孤岛效应对光伏发电工作产生的影响,提出了并网光伏阵列发电最大功率点孤岛检测方法.建立并网光伏阵列发电模型,分析光伏阵列发电的基本原理,并从电网电压频率和电压幅值两个方面,设置孤岛检测标准.运用光伏阵列发电模型跟踪并网光伏阵列发电最大功率点,结合孤岛效应的发生机理,将收集的电压频率和电压幅值数据与设置的检测标准作比对,从而得出并网光伏阵列发电最大功率点孤岛检测结果.实验结果表明,与传统的孤岛检测方法相比,设计方法得出的检测结果更加符合发电网络的实际情况,即设计方法在检测精度方面更具优势.     

基于RBF-BP神经网络的光伏MPPT研究

针对光伏电池的输出特性受光照强度、温度等因素的影响而具有的非线性特性的问题,为了提高光伏发电系统的发电效率必须对其输出功率进行追踪,并且为了克服MPP追踪过程中收敛速度慢和精度低的缺点,提出了一种RBF-BP组合神经网络对光伏阵列最大功率点追踪的算法。首先通过对光伏电池输出特性的研究,确定了温度和光照强度是影响光伏电池最大功率点输出的主要因素。然后考虑这两个因素作为RBF-BP组合神经网络的输入来设计光伏阵列最大功率点追踪系统。最后,利用Matlab建立该系统的仿真模型,并进行仿真研究与分析。仿真结果表明,该系统具有最大功率点追踪的精度高,响应速度快等优点。从而有效地实现了对光伏最大功率点的追踪,提高了光伏发电系统的发电效率。     

CS算法优化VMD-BiLSTM-AM的光伏功率预测

针对光伏发电功率的波动性与随机性对调度部门的负荷预测以及电网安全运行带来的严峻挑战, 提出了一种基于变分模态分解(VMD)和布谷鸟搜索(CS)算法优化的双向长短期记忆网络(BiLSTM)光伏发电功率预测方法. 首先使用VMD将光伏功率序列分解成不同频率的子模态, 通过皮尔逊相关性分析确定影响各模态的关键气象因子. 其次分别构建注意力机制(AM)和BiLSTM混合的光伏发电功率预测模型, 利用CS算法获取网络最优的权重和阈值. 最后, 将不同模态的预测结果相叠加, 得到最终的预测结果. 通过对亚利桑那州地区光伏电站输出功率进行预测, 验证了所提模型的有效性.     

光伏组件最大功率点快速识别方法

光伏组件最大功率发电是实现碳达峰和碳中和的关键技术之一,为了快速识别光伏组件最大功率点,提出了一种基于Lambert W函数的光伏最大功率点识别方法.通过对检测系统的分析,构建了实现光伏最大功率点检测结构;基于Boost变换器转换特性建立了光伏全阶段检测模型,给出了实现光伏组件全范围检测的工作条件;利用Lambert W函数推导出光伏最大功率点解析模型.实验表明,该方法可以快速识别光伏组件最大功率点,与NRM算法相比输出最大功率点功率提高了1.5％.     

光伏电池的仿真及其模型的应用研究

不论是太阳能发电系统还是风光互补发电系统,熟悉光伏电池的输出特性是设计新能源发电系统的基础和前提.根据光伏电池输出特性关系式,利用MATLAB的Simulink模块搭建了参数和工况可调的光伏电池模型,并运用该模型建立了具有最大功率跟踪(MPPT)功能的光伏发电系统的仿真模型,通过仿真结果可以更好地把握光伏电池的特性,为...     

载体运动下光伏组件最大功率点动态跟踪方法

光伏发电技术是实现碳达峰和碳中和的关键技术,如何实现运动载体下光伏组件最大功率点动态跟踪是光伏高效率发电的核心问题.为此,提出一种运动状态光伏组件最大功率点动态跟踪方法,通过利用惯性测量单元获取载体运动姿态,构建坐标转化关系,实现载体运动姿态与光强之间的转换;利用光伏组件五参数模型及光强关系建立了光伏组件动态模型;基于...     

基于时空相关性的光伏发电功率爬坡预测方法

传统的光伏发电功率预测方法爬坡预测可靠性较低,准确性不高.于是提出一种时空相关性的光伏发电功率爬坡预测方法.在典型日理想光伏发电出力归一化曲线提取基础上,采用线性插值方法生成光伏发电理想出力归一化曲线.通过蒙特卡洛法生成光伏发电随机分量,结合光伏发电与随机分量生成光伏发电序列;通过偏移爬坡率及变量状态划分方法构建信度网络节点变量和各节点变量的状态集;利用贪婪搜索算法从已有变量的状态集中获取最优信度网络结构后,进行光伏发电序列学习,完成光伏发电功率爬坡事件预测.实验结果表明,上述方法可有效完成光伏发电序列生成,并且爬坡预测可靠性较高,可实现多种气象条件下的光伏发电功率爬坡预测.     

考虑柔性负荷的光伏发电网储能灵活规划模型

为了提高光伏发电网储能利用率，设计一种考虑柔性负荷的光伏发电网储能灵活规划模型。模型根据光伏发电网的特点建立。分析超级电容器的存储能量与电压数值的关系，求解光伏发电网中电荷状态；根据光伏发电转换电路计算光伏电网调频备用容量；在考虑用户柔性负荷情况下建立光伏发电规划的目标函数和约束条件并求解，完成储能灵活规划模型的设计。算例分析结果表明，考虑柔性负荷的规划方法联络线的功率曲线更加平滑，缩小了用电负荷峰谷差，减小了电网储能和投入成本。     

基于改进深度残差网络的光伏板积灰程度识别

光伏板积灰会降低光伏发电的转换效率,同时易造成光伏板的损坏;因此,对光伏板的积灰进行智能识别具有重大意义。针对以上问题,提出一种基于改进深度残差网络的光伏板积灰程度识别模型。首先,通过分解卷积和微调下采样,对次代残差网络（ResNeXt）50进行改进;然后,融合坐标注意力（CA）机制,将位置信息嵌入到通道注意力中,通过精确的位置信息对通道关系和长期依赖性进行编码,并通过二维全局池操作将特征图像分解为两个一维编码,以增强关注对象的表示;最后,用监督对比（SupCon）学习损失函数替代交叉熵损失函数,从而有效提高识别准确率。实验结果表明,在真实光伏电站4个等级的光伏板积灰程度识别中,改进后的ResNeXt50的识别准确率为90.7%,与原始ResNeXt50相比提升了7.2个百分点。所提模型可满足光伏电站智能运维的基本要求。     

基于NSGA-II的并网型风光互补发电系统协调控制

针对并网型风光互补发电系统中,系统最大输出功率大于给定功率时,风力发电子系统和光伏发电子系统功率如何协调的问题,提出了一种功率协调控制方法.在该方法中,根据系统并网收益最大和输出电流谐波最小构建目标函数,采用带精英策略的快速非支配排序遗传算法对风力发电子系统和光伏发电子系统的输出功率进行多目标优化,协调控制子系统的发电功率;并以甘肃华电阿克赛风光互补发电项目为例进行了仿真验证.仿真结果表明,与传统的光伏优先接入方式相比,基于NSGA-Ⅱ的并网型风光互补发电系统协调控制方法可以更加合理地利用风能和太阳能,提高新能源电能的电网友好性.     

基于PLC的太阳能电池板自动跟踪系统的研究

研究了基于可编程逻辑控制器(PLC)的太阳能电池板自动跟踪系统。该系统有效地提高了太阳能的利用率和光伏发电系统的效率,降低了光伏并网发电的成本,具有理论研究意义和应用推广价值。     

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。     

光伏发电的嵌入式系统电源设计

基于光伏发电的嵌入式系统电源,利用铅酸电池、太阳能电池板和相应电路,调控光能采集并进行储存。通过UC3906铅酸电池充电管理芯片及外围电路构成智能充电模块,最大限度延长了铅酸电池的使用寿命;利用Buck—Boost电路和单片机控制回路提供太阳能电池板最大功率跟踪,保证了供电效率;采用光电耦合器提供双电源切换功能,确保在...     

全方位太阳辐照度测量系统设计

设计一种以DSP为核心的全方位太阳辐照度测量系统,利用安装在云台上的太阳能电池板的转动以获取全方位的太阳辐照度数据。通过对采集到的数据进行分析,既可以测试太阳能电池板在各种安装方式（如平放、斜放和幕墙）下的发电效率,又可对光伏建筑一体化的光伏电站建设方案进行评估,还可在建成之后对光伏电站的发电状态进行管理和监测。     

Genetic algorithm-trained radial basis function neural networks for modelling photovoltaic panels

Radial basis function neural networks (RBFNs) can be applied to model the I–V characteristics and maximum power points (MPPs) of photovoltaic (PV) panels. The key issue for training an RBFN lies in determining the number of radial basis functions (RBFs) in the hidden layer. This paper presents a genetic algorithms-based RBFN training scheme to search for the optimal number of RBFs using only the input samples of a PV panel. The performance of the trained RBFN is comparable with that of the conventional model and the training algorithm is computationally efficient. The trained RBFNs have been applied to predict MPPs of two different practical PV panels. The results obtained are accurate enough for applying the models to control the PV systems for tracking the optimal power points.     

Photovoltaic Cell Panels Soiling Inspection Using Principal Component Thermal Image Processing

Intended for good productivity and perfect operation of the solar power grid a failure-free system is required. Therefore, thermal image processing with the thermal camera is the latest non-invasive (without manual contact) type fault identification technique which may give good precision in all aspects. The soiling issue, which is major productivity affecting factor may import from several reasons such as dust on the wind, bird mucks, etc. The efficient power production sufferers due to accumulated soil deposits reaching from 1%–7% in the county, such as India, to more than 25% in middle-east countries country, such as Dubai, Kuwait, etc. This research offers a solar panel soiling detection system built on thermal imaging which powers the inspection method and mitigates the requirement for physical panel inspection in a large solar production place. Hence, in this method, solar panels can be verified by working without disturbing production operation and it will save time and price of recognition. India ranks 3rd worldwide in the usage use age of Photovoltaic (PV) panels now and it is supported about 8.6% of the Nation’s electricity need in the year 2020. In the meantime, the installed PV production areas in India are aged 4–5 years old. Hence the need for inspection and maintenance of installed PV is growing fast day by day. As a result, this research focuses on finding the soiling hotspot exactly of the working solar panels with the help of Principal Components Thermal Analysis (PCTA) on MATLAB Environment.     

Deep Learning-Based FOPID Controller for Cascaded DC-DC Converters

Smart grids and their technologies transform the traditional electric grids to assure safe, secure, cost-effective, and reliable power transmission. Non-linear phenomena in power systems, such as voltage collapse and oscillatory phenomena, can be investigated by chaos theory. Recently, renewable energy resources, such as wind turbines, and solar photovoltaic (PV) arrays, have been widely used for electric power generation. The design of the controller for the direct Current (DC) converter in a PV system is performed based on the linearized model at an appropriate operating point. However, these operating points are ever-changing in a PV system, and the design of the controller is usually accomplished based on a low irradiance level. This study designs a fractional-order proportional-integrated-derivative (FOPID) controller using deep learning (DL) with quasi-oppositional Archimedes Optimization algorithm (FOPID-QOAOA) for cascaded DC-DC converters in micro-grid applications. The presented FOPID-QOAOA model is designed to enhance the overall efficiency of the cascaded DC-DC boost converter. In addition, the proposed model develops a FOPID controller using a stacked sparse autoencoder (SSAE) model to regulate the converter output voltage. To tune the hyper-parameters related to the SSAE model, the QOAOA is derived by the including of the quasi-oppositional based learning (QOBL) with traditional AOA. Moreover, an objective function with the including of the integral of time multiplied by squared error (ITSE) is considered in this study. For validating the efficiency of the FOPID-QOAOA method, a sequence of simulations was performed under distinct aspects. A comparative study on cascaded buck and boost converters is carried out to authenticate the effectiveness and performance of the designed techniques.     

Intelligent sun-tracking system based on multiple photodiode sensors for maximisation of photovoltaic energy production

To maximise the energy collected by a given photovoltaic system (PVS), it is important to track the position of the sun so that the PV panels are exposed to the maximum global radiation at any given time. Thus, it is useful to determine the direction of the maximum global radiation, which does not correspond with the condition of orthogonality between the plane of the array and the beam component of the solar radiation. Thus, it is worthwhile to investigate a low-cost solar radiation sensing system that can determine this direction. The proposed system consists of a structure of nine photodiodes (FDs); a suitable analysis of the output signals calculates the azimuth and solar height angles, which are related to the direction of maximum solar irradiance. This paper reports the development of hardware (HW) and software (SW) for a prototype system, which is called SoliSector. Outdoor experimental results regarding testing and calibration of the proposed system are also reported.     

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.