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.     

基于模型预测控制的新型电力系统光储电站调频控制策略

光伏渗透率的不断提高降低了新型电力系统的转动惯量,给系统频率稳定性带来了新的挑战,储能出力灵活且迅速,将储能与光伏结合构成光储电站参与电网调频能够提高新型电力系统频率稳定性。光储电站参与系统调频的有功出力受到光照强度、储能荷电系数等多因素的影响,然而现有的与光储电站调频控制有关的研究大多对这些多约束的处理能力较差。在此背景下,提出一种基于模型预测控制的新型电力系统光储电站调频控制策略。该控制策略以最小化系统频率偏差与频率变化率之和为目标,计及光储电站有功出力及总发电量约束,优化光储电站有功出力,并通过控制光储电站有功输出参与系统频率调节。最后,通过仿真算例说明了所提方法相比传统控制策略调频效果更优,能够在考虑多约束的情况下快速精准确定光储电站出力,提高系统频率稳定性。     

离网式光伏电解制氢系统供电单元设计技术探讨

可再生能源制氢是促进新能源电力消纳、实现零碳氢能的重要方式。与并网制氢模式相比,光伏离网制氢具有成本低、部署灵活的优点。以离网光伏电解水制氢系统为研究对象,对比了光伏并网制氢与光伏离网制氢的不同,讲述了3种制氢电解槽与可再生能源结合的适用性。重点阐述了基于碱性电解水制氢设备的离网制氢系统结构设计、电解槽的光伏直流供电单元设计、制氢辅助系统交流供电单元设计、安全供电控制单元设计,分析了系统设计和控制过程中应重点关注的问题。储能的容量配置方法和双备份的可靠供电方法为离网式光伏制氢系统提供了技术参考。     

太阳能-空气光伏/光热一体化热泵热水系统实验特性

本文研究了一种新型光伏/光热一体化(PV/T)复合热源热泵热水系统,将多孔扁盒式PV/T集热板与空气源热泵相结合,根据不同控制方式组合成双热源并联、单太阳能和单空气能三种不同运行模式。在室外环境温度28.5℃下,将200 L 30℃热水加热到55℃,研究了加热时间、热水温度、COP等性能的变化规律,结果表明双热源并联运行模式下分别比单太阳能模式和单空气能模式的加热时间缩短了42%和54%,COP分别提高了32.78%和47.64%;同时实验研究了在夏季工况下将200 L水从9∶00循环加热到17∶00过程中系统热性能,探讨了太阳辐射强度、PV/T集热板温度对光电/光热效率的影响,通过实验对比可以得出在热电模式下系统的光电效率η_(pv)比单一光电模式平均高25.8%.     

Systematically mapping innovations in electricity using startups: A comprehensive database analysis

Technology innovation is recognized as one of the main enablers of sustainable development and the transition towards a low-carbon future. Anticipating innovation trajectories could help decision-makers develop new capabilities to take advantage of incremental and disruptive innovations. This paper maps the current innovation that is taking place in the electricity sector using a bottom-up approach by analyzing the value proposition pursued by startups worldwide.We compiled a database of 320 startups whose focus is the electricity sector. We classified these startups according to their business propositions in the context of megatrends disrupting the electricity sector. From analyzing this dataset, we find the following. Photovoltaic generation (PV) is the primary technology for decarbonization. Innovation within PV focuses on developing more resistant materials, panels that are easier to install, or maintain. These innovations do not need an updated regulatory-market structure of the industry to be deployed. Digitalization is the key disruptive/breakthrough innovation. It has the potential to reshape businesses and the regulatory-market structure. Storage technologies can be very disruptive for the power sector, but the innovation we observe in storage technology is incremental. This includes batteries that last longer, that charge in shorter periods of time, or that use other less expensive raw materials, or more energy density –more kilowatt-hours per kilogram.     

并网系统逆变器锁相环的研究

在太阳能光伏发电系统中,并网逆变器是实现光伏电能馈送电网的重要环节,并网光伏逆变器的控制目标为：控制逆变电路的输出为稳定的、高品质的正弦波,且与电网电压同频、同相。鉴于此,本文提出了一种数字电流锁相的方法,并且进行了系统仿真。仿真结果表明,TMS320C2000系列DSP芯片适用于该数字锁相技术,其并网输出电流波形良好...     

Embedding HMMs-based models in a Euclidean space: the topological hidden Markov models

Current extensions of hidden Markov models such as structural, hierarchical, coupled, and others have the power to classify complex and highly organized patterns. However, one of their major limitations is the inability to cope with topology: When applied to a visible observation (VO) sequence, the traditional HMM-based techniques have difficulty predicting the n-dimensional shape formed by the symbols of the VO sequence. To fulfill this need, we propose a novel paradigm named “topological hidden Markov models” (THMMs) that classifies VO sequences by embedding the nodes of an HMM state transition graph in a Euclidean space. This is achieved by modeling the noise embedded in the shape generated by the VO sequence. We cover the first and second level topological HMMs. We describe five basic problems that are assigned to a second level topological hidden Markov model: (1) sequence probability evaluation, (2) statistical decoding, (3) structural decoding, (4) topological decoding, and (5) learning. To show the significance of this research, we have applied the concept of THMMs to: (i) predict the ASCII class assigned to a handwritten numeral, and (ii) map protein primary structures to their 3D folds. The results show that the second level THMMs outperform the SHMMs and the multi-class SVM classifiers significantly.     

基于最小概率距离和改进PV插值的图像配准

提出一种基于最小概率距离和改进部分体积(PV)插值的图像配准方法。采用Powell优化算法迭代搜索对称KL距离的最小值,获取最小概率距离,将其作为配准测度,并利用改进的PV插值算法提高图像配准的鲁棒性。实验结果表明,与基于互信息的图像配准方法相比,该方法能有效地减少耗费时间,提高配准精度。     

Quadratic optimal neural fuzzy control for synchronization of uncertain chaotic systems

This paper presents a novel quadratic optimal neural fuzzy control for synchronization of uncertain chaotic systems via H^∞ approach. In the proposed algorithm, a self-constructing neural fuzzy network (SCNFN) is developed with both structure and parameter learning phases, so that the number of fuzzy rules and network parameters can be adaptively determined. Based on the SCNFN, an uncertainty observer is first introduced to watch compound system uncertainties. Subsequently, an optimal NFN-based controller is designed to overcome the effects of unstructured uncertainty and approximation error by integrating the NFN identifier, linear optimal control and H^∞ approach as a whole. The adaptive tuning laws of network parameters are derived in the sense of quadratic stability technique and Lyapunov synthesis approach to ensure the network convergence and H^∞ synchronization performance. The merits of the proposed control scheme are not only that the conservative estimation of NFN approximation error bound is avoided but also that a suitable-sized neural structure is found to sufficiently approximate the system uncertainties. Simulation results are provided to verify the effectiveness and robustness of the proposed control method.