单周控制光伏并网系统的最大功率控制研究

单周控制的单级全桥光伏并网系统具有容量大、效率高、成本低等优点.但现有基于单周控制的光伏电池最大功率跟踪存在着难以搜索最大功率点、难以稳定工作在最大功率点以及搜索精度差等缺点.针对以上不足,提出了基于增量电导的光伏电池最大功率跟踪算法,该算法将光伏电池输出功率引入到单周控制的输入量中,同时优化了光伏电池输出电压的给定量.该算法在硬件上实现简单,仿真和实验结果表明该算法能够准确跟踪光伏电池最大功率点,并具有较高的精度和稳定性.     

黄河水院光伏发电系统最大功率跟踪控制研究

分析了最大功率点跟踪的基本扰动观测法的原理和优缺点,提出了一种改进算法,即加入步长变化的环节,在工作点远离最大功率点区间时,设定扰动步长相对较大;在工作点接近最大功率点区间时,设定步长相对较小。通过仿真实验对改进算法进行了验证,得出该算法既能在稳态时减小功率损失,又能在外界条件剧烈变化时提高动态响应和系统稳定性,从而达到预定控制效果。     

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.     

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.     

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。     

光伏发电系统最大功率点跟踪算法的研究

在光伏发电系统中,光伏电池输出特性受光照强度及环境温度影响很大,具有明显的非线性特征。为了提高光伏电池的利用效率,需要对光伏电池的最大功率点进行快速准确地跟踪控制。本研究对短路电流法与扰动观察法进行了详细的分析,最后结合两种方法的优点,提出了双模式最大功率点跟踪方法,该方法具有跟踪外部环境变化快,最大功率点无振荡现象,对光伏电池利用率高的优点。此外,日照变化剧烈情况下该方法不会失去MPPT控制的能力。