光伏电池滞环变步长MPPT算法研究

为了提高光伏电池发电效率,在传统MPPT控制方法的基础上提出了基于恒定电压法的变步长滞环控制法,通过分析光伏电池受光照强度的影响和在最大功率点附近的功率特性,确定了电压扰动步长值,并在MATLAB/Simulink仿真平台上建立了MPPT仿真模型,对该算法进行了验证。仿真结果表明,该算法在光照突变时仍能实时地跟踪光伏电池的输出功率,并能有效地抑制在MPP点附近的振荡现象,表现出很好的动态特性,证明了该算法的有效性和正确性。     

改进扰动观察法的光伏阵列最大功率点跟踪算法

为了更好地跟踪光伏阵列的最大功率点,分析单个光伏电池的物理特性,建立光伏阵列的Matlab仿真模型,分析光伏阵列随光照温度不同而变化的P-U、U-I特性.针对系统在工作工况发生变化时跟踪情况的不同,对传统的扰动观察法做了变步长的寻优算法,并结合调整策略搭建光伏系统最大功率点跟踪的仿真实验模型.结果表明该算法可以快速准确地跟踪最大功率点,稳态效果好,能够更好地提高光伏发电最大功率点跟踪系统的跟踪性能.     

基于光伏电池输出特性的MPPT算法研究

为了寻找更好的实现光伏发电系统最大功率点跟踪控制方法,基于单个光伏电池的物理特性建立了太阳能光伏电池阵列的Matlab仿真模型,分析了太阳能光伏电池阵列所具有的随着光照强度和温度不同而变化的P-U和I-U非线性特性.基于光伏电池的动态特性,在最大功率点跟踪算法的设计中增加一个电流监测回路,并结合自寻优技术对电导增量法进行改进,提出了一种自适应变步长寻优算法.仿真结果表明,该算法能够快速准确的跟踪最大功率点.     

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。     

MPPT算法与输入输出反馈线性化控制技术在光伏发电系统中的应用

针对目前光伏发电系统采用的固定电压法、增量电导法等最大功率点跟踪控制技术跟踪速度慢、精度不佳的问题,提出采用变步长电导增量法进行最大功率点跟踪控制;为了控制光伏系统中电网电流和直流母线电压,采用输入输出反馈线性化控制技术,使得系统的功率因数和直流母线电压可用相同的算法进行控制。在Matlab/Simulink环境下对基于变步长电导增量法算法与输入输出反馈线性化控制技术的光伏发电系统进行了建模仿真,结果表明,采用反馈线性化技术控制逆变器后,日照强度和温度变化不会对电网功率因数产生影响;变步长电导增量法提高了光伏发电系统的动态和稳态性能,且降低了电网电流的总谐波失真率。     

基于克隆选择算法的光伏最大功率点跟踪控制

研究太阳能电池提高光电转换率问题,由于光伏发电系统中最大功率点难以实时跟踪,使光伏阵列输出功率达不到要求。为此提出采用克隆选择算法进行优化控制。根据光伏电池等效电路模型在SIMULINK中建立仿真模型进行特性分析,并编写克隆选择算法。通过在算法进化过程中合理地选取种群规模、克隆规模和变异率等参数,从而保证算法以最佳的搜索轨迹达到最优解。通过对算法有效性的验证,仿真结果表明:在温度、光强变化情况下,能实时跟踪到最大功率点,提高系统的鲁棒性和发电效率。     

光伏电池最大功率点跟踪方法比较

根据太阳能光伏电池的工程数学模型,在Matlab环境下建立了光伏电池仿真模型,分析了光照强度和温度变化对光伏电池输出特性的影响。针对扰动观察法采用固定的扰动步长而难以获得较高跟踪精度和响应速度的问题,提出了一种基于变步长的改进的扰动观察法,并通过对光伏电池控制系统进行仿真,比较了这2种最大功率点跟踪方法的仿真曲线。结果表明,采用改进的扰动观察法的光伏电池控制系统能更快速跟踪最大功率点,且在最大功率点处稳定性较好。     

基于BP神经网络的光伏阵列温度预测

针对现有太阳能光伏阵列仿真实验中因采用环境温度代替光伏组件温度而导致的光伏阵列建模不正确问题,指出应在光伏电池仿真模型中区分环境温度和组件的实际工作温度;分析了光伏组件温度与环境温度和输出功率的关系,给出了一种基于BP神经网络的光伏阵列组件温度预测方法,并将预测结果与实测结果进行比较,得出结论:该方法可有效预测光伏阵列组件温度,且采用前一天数据和前三天数据都有较好的预测效果,因此实际应用时可采用前一天的数据来预测当天的组件温度。     

基于MATLAB的光伏电池组件的输出特性研究

在恒温、恒湿等环境因素不变的情况下,改变光伏电池组件所受光照强度,对光伏电池组件的功率点进行跟踪测试,并对测试的数据MATLAB仿真,从而得出光照变化的情况下,最大功率点的变化规律.     

一种新型光伏阵列最大功率跟踪算法仿真研究

为了克服传统自适应变步长扰动观察法在日照强度、环境温度等参数迅速变化时,存在的跟踪速度慢,跟踪易失败等缺陷,基于光伏阵列输出特性曲线,提出了一种基于自适应面积算法的MPPT方法。在Matlab/Simulink平台下建立了光伏阵列MPPT仿真模型。研究发现,即使日照强度、环境温度等参数变化迅速,该方法仍可快速、准确地跟踪光伏阵列的最大功率点,有效地克服了传统自适应变步长扰动观察法存在的上述缺陷,仿真结果验证了该方法的有效性和优越性。     

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。     

An integrated isolated inverter fed bldc motor for photovoltaic agric pumping systems

This research article proposes an isolated converter integrated with Pulse Width Modulated (PWM)Inverter fed Brushless DC motor (BLDC) for Photovoltaic agric pumping applications. The drift free Optimum Power Point Tracking (OPPT) algorithm is utilized to get maximum power from the Photo Voltaic (PV) Module.The proposed inverter has the features of high voltage gain, galvanic isolation, and better performance even in partially shaded conditions. Photovoltaic Agric Pumping System (PMPS) is aimed to support the rural development and reduce the maximum demand and burden on the distribution system. The proposed one is simulated with Matlab/Simulink software and prototype model alsobuilt with Field Programmable Gate Array (FPGA) controller.The developed systemresults demonstrate that its capability of better solar power utilization for water pumping applications.     

Performance improvement of solar powered evaporative cooler using versatile ecological balanced control technique

Solar Energy is one of the essential sources of sustainable power source. Solar Photovoltaic power (SPV) is utilized today in various applications. The mechanical load of the current evaporative cooler is the primary source of high energy consumption. This case incited us to look for better approaches to enhance the evaporative cooler regards to energy production, water utilizes proficiency, life, support, and reliance on utility power. Thus, we planned, built, and tried another computerized solar-powered evaporative cooler that significantly enhances existing outlines on every one of the regions specified above utilizing Versatile Ecological Balanced Control (VEBC) algorithm. Evaporative cooling is a notable framework to be a productive and economical means for decreasing the temperature and expanding the relative humidity in a nook. The test comes about because of the altered cooler in light of the new plan demonstrate that it conveyed air with recognizably higher humidity and lower temperature than the standard outline. The test comes about because of the changed cooler given the first model demonstrate that it furnished a climate with discernibly higher humidity and lower temperature than the traditional design. The proposed VEBC strategy decreases the storage temperature yet, also, builds the relative humidity of the storage which is essential for keeping up the freshness of the items.     

Characterization of Concentrating Photovoltaic modules by cooperative competitive Radial Basis Function Networks

Concentrating Photovoltaic (CPV) technology attempts to optimize the efficiency of solar energy production systems. As conventional Photovoltaic (PV) technology, suffers from variability in its production and needs models for determining the exact module performance. There are several problems when analyzing CPV systems performance with traditional techniques due to absence of standardization. In this sense it is remarkable the importance for the emerging CPV technology, of the existence of models which allow the prediction of modules performance from initial atmospheric conditions. In this paper, a CPV module is studied by means of atmospheric conditions obtained using an automatic test and measuring system developed by the authors. The characterization of the CPV module is carried out considering incident normal irradiance, ambient temperature, spectral irradiance distribution and wind speed. CO²RBFN, a cooperative-competitive algorithm for the design of radial basis neural networks, is adapted and applied to these data obtaining a model with a good level of accuracy on test data, improving the results obtained by other methods considered in the experimental comparison. These results are promising and the obtained model could be used to work out the maximum power at the CPV reporting conditions and to analyze the performance of the module under any conditions and at any moment.     

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.     

考虑大气折射的天空场景真实感绘制

传统的天空场景绘制方法大都是基于大气对天空光和太阳光的散射,它们没有细致考虑大气折射的影响,因此无法绘制出太阳和月亮场景在不同观测条件下形状、色泽、光晕的变化,文章提出了一种考虑大气折射的天空光模型,首先基于大气光学知识推导出大气折射率与气压、温度和水汽压的定量关系,详细计算了光线在大气中的折射轨迹,采用一种考虑折射的路径跟踪方法计算天空光强度的分布,并绘制出不同时间、不同季节、不同观测地点下更具真实感的太阳、月亮和星星的天空场景。     

基于模型辨识的BP神经网络在光伏系统MPPT中的应用

光伏电池作为光伏发电系统的重要组成部分,研究其模型的准确性并对其最大功率点进行预测与跟踪,对于光伏发电效率的提高具有重大意义;首先根据光伏电池的内部结构和伏安特性建立其数学模型,并对所建立的模型进行参数辨识,进而得到模型输出与测量信息偏差最小的参数值,验证模型的准确和有效性;根据模型所反映的规律,将温度和光照强度作为输入变量,最大功率点对应的电压作为输出变量,构建了用于MPPT的神经网络模型;神经网络经训练后对最大功率点电压进行预测与跟踪,结果表明构建的神经网络具有良好的适应性。     

基于优化BP神经网络光伏出力短期预测研究

太阳能拥有丰富的资源,而且分布广泛,现已被广泛应用到各种应用中,光伏发电已是一种可靠可行、可扩展的重要可再生能源利用的方式,因此对光伏出力进行精准的预测意义重大;从宁夏市某光伏发电站获得了一年的光伏发电数据与气象等因素,选取四月至五月的数据进行研究预测;针对BP神经网络的收敛时间长,容易陷入局部极小值等缺点;建立单一BP神经网络预测模型,基于遗传算法(GA)优化BP神经网络的GA-BP预测模型与基于狼群算法(WPA)优化的BP神经网络的WPA-BP预测模型;选择平均相对误差作为误差评估指标,结果表明,3种预测模型均能对光伏电站的发电功率进行预测,但是单一的BP神经网络模型误差较大,晴天时,误差为5.1%,经遗传算法改进后的预测误差为4.9%,较单一模型提高了0.2%的精度,而WPA-BP预测模型误差为4.4%,预测精度高于前者;同时多云天和雨天的时候,均为WPA-BP模型的预测误差小,稳定性高,具有一定的研究价值。     

Artificial neural network models for indoor temperature prediction: investigations in two buildings

The problem how to identify prediction models of the indoor climate in buildings is discussed. Identification experiments have been carried out in two buildings and different models, such as linear ARX-, ARMAX- and BJ-models as well as non-linear artificial neural network models (ANN-models) of different orders, have been identified based on these experiments. In the models, many different input signals have been used, such as the outdoor and indoor temperature, heating power, wall temperatures, ventilation flow rate, time of day and sun radiation. For both buildings, it is shown that ANN-models give more accurate temperature predictions than linear models. For the first building, it is shown that a non-linear combination of sun radiation and time of day is important when predicting the indoor temperature. For the second building, it is shown that the indoor temperature is non-linearly dependent on the ventilation flow rate.