独立光伏系统中超级电容充电控制策略研究

针对应用在独立光伏系统中的超级电容模组,提出采用Buck变换器对其进行充电,在充电过程中,进行过压保护,未达到额定电压时采用扰动观察法对光伏阵列进行最大功率点跟踪（MPPT）控制,仿真结果表明了充电过程中能跟踪光伏阵列的最大功率点,且具有过压保护功能。     

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.     

基于光伏MPPT采样电流的自适应变步长INC算法

针对传统电导增量INC（incremental conductance）算法在跟踪最大功率点的过程中无法兼顾跟踪速度与稳态精度的问题,以及传统变步长算法在光照变化时容易发生误判的问题,提出了一种新型的自适应变步长INC算法。光照强度变化较大时,利用负载曲线与I-V特性曲线的工作原理,在暂稳态和非稳态下都可以根据最大功率点跟踪MPPT（maximum power point tracking）采样电流的变化,自适应调节跟踪速度;光照强度变化较小时,能够根据输出电压与功率的变化自适应减小步长,提高稳态精度。追踪速度是传统算法的9.3倍,是现有变步长算法的4.2倍,有效减少了光照强度变化带来的功率损失。     

Novel Lyapunov-based rapid and ripple-free MPPT using a robust model reference adaptive controller for solar PV system

The technological, economic, and environmental benefits of photovoltaic (PV) systems have led to their widespread adoption in recent years as a source of electricity generation. However, precisely identifying a PV system''s maximum power point (MPP) under normal and shaded weather conditions is crucial to conserving the maximum generated power. One of the biggest concerns with a PV system is the existence of partial shading, which produces multiple peaks in the P–V characteristic curve. In these circumstances, classical maximum power point tracking (MPPT) approaches are prone to getting stuck on local peaks and failing to follow the global maximum power point (GMPP). To overcome such obstacles, a new Lyapunov-based Robust Model Reference Adaptive Controller (LRMRAC) is designed and implemented to reach GMPP rapidly and ripple-free. The proposed controller also achieves MPP accurately under slow, abrupt and rapid changes in radiation, temperature and load profile. Simulation and OPAL-RT real-time simulators in various scenarios are performed to verify the superiority of the proposed approach over the other state-of-the-art methods, i.e., ANFIS, INC, VSPO, and P&O. MPP and GMPP are accomplished in less than 3.8 ms and 10 ms, respectively. Based on the results presented, the LRMRAC controller appears to be a promising technique for MPPT in a PV system.     

Maximum Power Extraction Control Algorithm for Hybrid Renewable Energy System

In this research, a modified fractional order proportional integral derivate (FOPID) control method is proposed for the photovoltaic (PV) and thermoelectric generator (TEG) combined hybrid renewable energy system. The faster tracking and steady-state output are aimed at the suggested maximum power point tracking (MPPT) control technique. The derivative order number (µ) value in the improved FOPID (also known as PI^λD^µ) control structure will be dynamically updated utilizing the value of change in PV array voltage output. During the transient, the value of µ is changeable; it’s one at the start and after reaching the maximum power point (MPP), allowing for strong tracking characteristics. TEG will use the freely available waste thermal energy created surrounding the PV array for additional power generation, increasing the system’s energy conversion efficiency. A high-gain DC-DC converter circuit is included in the system to maintain a high amplitude DC input voltage to the inverter circuit. The proposed approach’s performance was investigated using an extensive MATLAB software simulation and validated by comparing findings with the perturbation and observation (P&O) type MPPT control method. The study results demonstrate that the FOPID controller-based MPPT control outperforms the P&O method in harvesting the maximum power achievable from the PV-TEG hybrid source. There is also a better control action and a faster response.     

直流特高压带电线路近电安全感应预警监控系统设计

传统直流特高压带电线路中,安全感应预警监控系统存在的监测数据难以实时上传,通信不流畅、数据采集误差大,针对该问题,设计了一种基于MPPT监测的监控系统,能够将直流特高压带电线路中信息提取功率导向存储装置,并通过两个控制模块和实时监控装置,加强监控系统的监测能力,大大提高了监测效率与实时数据传输能力。同时构建了机器人学习算法的GEN算法,对预警机制进行改进,提高了预警能力,也提高了数据处理效率,增强了数据适应性,通过仿真实验,所设计的硬件在安全数据监控时,监控率最高可达94.01%。     

Power system transient stability assessment based on the multiple paralleled convolutional neural network and gated recurrent unit

In order to accurately evaluate power system stability in a timely manner after faults, and further improve the feature extraction ability of the model, this paper presents an improved transient stability assessment (TSA) method of CNN?+?GRU. This comprises a convolutional neural network (CNN) and gated recurrent unit (GRU). CNN has the feature extraction capability for a micro short-term time sequence, while GRU can extract characteristics contained in a macro long-term time sequence. The two are integrated to comprehensively extract the high-order features that are contained in a transient process. To overcome the difficulty of sample misclassification, a multiple parallel (MP) CNN?+?GRU, with multiple CNN?+?GRU connected in parallel, is created. Additionally, an improved focal loss (FL) function which can implement self-adaptive adjustment according to the neural network training is introduced to guide model training. Finally, the proposed methods are verified on the IEEE 39 and 145-bus systems. The simulation results indicate that the proposed methods have better TSA performance than other existing methods.     

双Buck太阳能LED路灯照明控制系统

利用新型能源,把太阳能和高效节能的LED路灯有机地结合在一起,开发出一款基于STC12C5410AD单片机的双Buck太阳能LED路灯照明控制系统.前级基于IR2104的同步Buck电路实现最大功率充电,后级采用同步Buck实现LED灯恒流驱动.该控制器具有驱动能力强,DC-DC转换效率高,最大功率点跟踪充电和浮充充电...     

基于光伏微网的PI-dP&O高性能MPPT控制器的设计

通过采用PI-dP&O模块、Buck-Boost电路与具有电流闭环控制的抑制积分饱和的PI控制器相结合的策略,设计出一种具有高性能的MPPT控制器。此控制器具有软件实现简单、适应性好、成本低廉等优点。在Matlab实验平台上将此高性能的MPPT控制器与传统的MPPT控制器进行对比,并应用到光伏微网中,充分显示其优越性和可行性。     

基于太阳能电源的图像采集系统

利用光伏转换与控制技术,将太阳能光伏运用于图像采集上,开发出一套基于太阳能光伏电源的图像采集系统。前级在硬件上采用BUCK型DC-DC电路,软件上提出了固定电压法结合双向扰动观察法的最大功率点跟踪算法,后级图像采集由AVR控制数字摄像头OV7620完成图像采集并传输至上位机。测试结果表明,该系统能够快速、精确、稳定地追踪到太阳能电池板的最大功率点,并且只需稍作修改便可应用于各种需要图像采集的场合。