A Modified-Simplified MPPT Technique for Three-Phase Single-State Grid-Connected PV Systems

Nowadays, the single state inverter for the grid-connected photovoltaic (PV) systems is becoming more and more popular as they can reduce circuit complexity resulting in less power losses of the inverter. This paper focuses on the use of model predictive control (MPC) to control a 3-phase and 2-level single-state grid-connected inverter in order to regulate the PV maximum power point (MPP). The algorithm of MPC scheme was done to measure the simultaneous current signal including predicting the next sampling current flow. The reference current (I_d*) was used to control the distribution of electrical power from the solar cell to the grid. To be able to control the maximum power point tracking (MPPT) when the sunlight suddenly changes, so that a developing MPPT based on estimation current perturbation and observation (ECP&O-MPPT) technique was used to control the reference current. This concept was experimented by using MATLAB/Simulink software package. The proposed technique was tested and compared with the old technique. The simulation results showed that the developed MPPT technique can track the MPP faster when the light changes rapidly under 1,000 W/m², 25°C standard climatic conditions. The MPPT time was 0.015 s. The total harmonic distortion (THD) was 2.17% and the power factor was 1.     

汽车车内噪声主动控制变步长NFB-LMS算法

为规避最小均方（Least Mean Square,LMS）算法不能同时提高收敛速度和降低稳态误差的固有缺陷,以及已有变步长LMS算法存在收敛速度慢和稳态误差估计精度差的问题,文中提出了一种基于变步长归一化频域块（Normalized Frequency-domain Block,NFB） LMS算法的汽车车内噪声主动控制方法。为了比较,应用传统的LMS算法、基于反正切函数的变步长LMS算法和变步长NFB-LMS算法分别进行实测汽车车内噪声的主动控制。结果表明,与其他两个算法相比,变步长NFB-LMS算法的收敛速度提高了70%以上,稳态误差减小了90%以上。变步长NFB-LMS算法在处理车内噪声信号时具有很高的效率,为进行汽车车内噪声主动控制提供了一种新方法。     

基于FxLMS的汽车车内噪声变步长主动均衡算法

提出了一种可用于汽车车内噪声主动均衡控制的变步长主动噪声均衡（Active Noise Equalization,ANE）算法,与传统车内噪声主动抵消控制方法所采用的滤波x最小均方（Filtered-x Least Mean Square,FxLMS）算法相比具有更好的实用性。应用固定步长主动噪声均衡（Active Noise Equalization,ANE）算法、所提出变步长ANE算法和已有变步长ANE算法分别进行汽车车内噪声主动均衡控制。结果表明,所提出变步长ANE算法具有更快的收敛速度和较低的稳态误差,并且能进一步降低汽车车内噪声响度,为汽车车内声品质主动控制提供了一种新方法。     

不均匀光照情况下太阳能最大功率追踪算法

本研究设计一种针对多峰值的最大功率追踪算法。首先通过大量光伏电池的数据模拟,发现光伏板P-I曲线具有"峰值高度以全局峰值为中心,向两侧不断递减"的重要规律,并且统计研究曲线上坡、下坡的斜率,相邻峰值间隔等多种数据,以此作为算法设计的依据。算法通过部分扫描光伏电池的输出曲线来获得全局峰值的位置,其中运用"区域扫描法"来增强扫描的精度和速度。另外对两种简单、快速的变步长P&O法进行比较,确定其适用范围并加以灵活运用。本研究设计的算法快速、有效,避免峰值的遗漏,而且具有"峰值越多,追踪越快"的特点。     

太阳能最大功率点跟踪的航模电调设计

根据太阳能光伏输出特性,采用最小二分法实现最大功率点跟踪(MPPT),对于提高太阳能电池的输出功率及太阳能的利用率,特别是在控制电机等感性负载上,意义重大.在航模飞机上,太阳能飞机的飞行受太阳能板输出功率的直接影响,而航模飞机的电调采用中颖公司的SH79F168作为主芯片,太阳能最大功率输出也需要芯片控制太阳能板的输出电压,此设计的目的就是把两者合二为一,以节省空间并提高效能.     

A Machine Learning Based Algorithm to Process Partial Shading Effects in PV Arrays

Solar energy is a widely used type of renewable energy. Photovoltaic arrays are used to harvest solar energy. The major goal, in harvesting the maximum possible power, is to operate the system at its maximum power point (MPP). If the irradiation conditions are uniform, the P-V curve of the PV array has only one peak that is called its MPP. But when the irradiation conditions are non-uniform, the P-V curve has multiple peaks. Each peak represents an MPP for a specific irradiation condition. The highest of all the peaks is called Global Maximum Power Point (GMPP). Under uniform irradiation conditions, there is zero or no partial shading. But the changing irradiance causes a shading effect which is called Partial Shading. Many conventional and soft computing techniques have been in use to harvest solar energy. These techniques perform well under uniform and weak shading conditions but fail when shading conditions are strong. In this paper, a new method is proposed which uses Machine Learning based algorithm called Opposition-Based-Learning (OBL) to deal with partial shading conditions. Simulation studies on different cases of partial shading have proven this technique effective in attaining MPP.