IoT Enabled Microgrid Framework Using a Novel Dispersal Diffusion Artificial Neural Network Controller for PV Systems and Wind Energy to Minimize Electrical Faults

A system based on a PV-Wind will ensure better efficiency and flexibility using lower energy production.Today,plenty of work is being focussed on Doubly Fed Induction Generators(DFIG)utilized in wind energy systems.DFIG is found to be the best option in the Wind Energy Conversion Systems(WECS)to mitigate the issues caused by power converters.In this work,a new Artificial Neural Network(ANN)is proposed with the Diffusion and Dispersal strategy that works on Maximum Power Point Tracking(MPPT)along with Wind Energy Conversion System(WECS)to minimize electrical faults.The controller focus was not just to increase performance but also to reduce damage owing to any phase to phase fault or Phase to phase to ground fault.To ensure optimal MPPT for the proposed WECS,ANN achieves the optimal PI controller parameters for the indirect control of active and reactive power of DFIG.The optimal allocation and size of the DGs within the distributed system and for MPPT control are obtained using a population of agents.The generated solutions are evaluated and on being successful,the agents test their hypothesis again to create a positive feedback mechanism.Simulations are carried out,and the proposed IoT framework efficiency indicates performance improvement and faster recovery against faults by 9 percent for phase to ground fault and by 7.35 percent for phase to phase fault.

Maximum power point tracking of a photovoltaic energy system using neural fuzzy techniques

In order to improve the output efficiency of a photovoltaic （PV） energy system, the real-time maximum power point （MPP） of the PV array should be tracked closely. The non-linear and time-variant characteristics of the photovoltaic array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP as in traditional control strategies. A neural fuzzy controller （NFC） in conjunction with the reasoning capability of fuzzy logical systems and the learning capability of neural networks is proposed to track the MPP in this paper. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the NFC. With a derived learning algorithm, the parameters of the NFC are updated adaptively. Experimental results show that, compared with the fuzzy logic control algorithm, the proposed control algorithm provides much better tracking performance.

基于固定电压法的太阳电池MPPT系统

针对太阳电池的输出特性随环境变化的特点,为提高电池的输出功率和系统效率,提出一种基于固定电压法的太阳电池最大功率点跟踪(maximum power point tracker,MPPT)控制芯片的设计。其特点是采用模拟电路集成实现,具有结构简单、成本低、性能稳定等特点。芯片在1.5μmBCD(Bipolar-CMOS-DMOS)工艺下设计实现,仿真验证和芯片测试结果表明,芯片性能与设计预期基本相符。采用该芯片的太阳能供电系统能够实时跟踪太阳电池的最大功率点。

一种太阳能电池MPPT控制器实现及测试方法的研究

本文根据太阳能电池的特性 ,设计了一种基于"电压扰动法"的太阳能电池最大功率点跟踪 (MPPT)控制器 ,并提出了一种简单、实用的模拟太阳能电池对控制器进行测试的方法 ,理论证明该方法是可行的 ,并得到实际验证。在此实验方法下 ,对设计的MPPT控制器进行测试 。

基于自适应神经网络控制的光伏MPPT算法改进

基于自适应神经网络控制(DANC)算法提出一种改进的最大功率追踪(MPPT)算法。针对DANC在权值更新的过程中采用的梯度下降算法因初始权值而导致局部收敛的问题,该文根据光伏(PV)阵列部分遮挡情况下的功率-电压(P-V)特征曲线,提出一种反馈负载电压遍历和DANC相结合的算法。首先通过反馈负载电压遍历方法快速达到参考电压附近,然后利用DANC在线学习算法稳定到峰值,通过比较峰值大小,最终找到全局最优解。通过Matlab仿真和硬件实验验证改进算法的可行性。

采用模糊指数趋近律的光伏MPPT滑模算法

针对现有滑模MPPT普遍采用的等速趋近律且不能兼顾MPPT的快速性与稳定性的问题,提出了一种基于模糊指数趋近律的滑模控制算法。基于光伏电池的工作特性,建立了Buck型光伏直流发电系统的周期平均状态模型,并给出了基于李亚普诺夫函数稳定性的滑模存在性证明和控制律的详细设计。用Matlab/Simulink对系统建模与仿真分析。系统仿真结果表明,设计的控制算法能很好地实现光伏系统MPPT,控制算法实用有效。

基于改进的扰动观察法在光伏发电MPPT中的应用

光伏电池是一种非线性电源,随外界环境的变化而变化,为了提高光伏阵列的利用率,光伏系统中需采用最大功率跟踪(maximum power point tracking,MPPT)。近年的研究中,提出了许多跟踪算法,其中应用最为广泛的是扰动观察法和电导增量法。在分析扰动观察法的基础上,进行优化提出了一种改进的变步长算法,它有效提高了最大功率点跟踪过程中的跟踪速率,克服了扰动方向的误判问题,消除了在最大功率点的振荡现象。仿真与实验结果证明了该方法的有效性。

基于Boost电路的光伏发电MPPT控制系统仿真研究

基于光伏电池输出特性及MPPT控制原理的分析,研究了基于Boost电路的光伏发电MPPT控制系统的模型,并分析了该模型中各子模块的工作原理,并应用Simulink软件搭建了整个系统的仿真模型。以光伏阵列STP0950S-36为例,仿真验证了所研究的MPPT控制系统能很好地实现最大功率跟踪,并能快速响应外界环境的变化,有效提高光伏发电的效率。

基于模糊PI双向线性预测的光伏MPPT控制

使用扰动观察法的光伏系统在初始化启动过程中或光强发生较大变化时,由于受扰动步长的限制,无法快速接近最大功率点,造成大量能量的浪费。针对该情况提出一种快速最大功率点预测算法。首先将光伏P-U曲线在0点附近Taylor展开,取线性部分作为功率函数P(u)的近似方程。由于P-U曲线存在两个0点,近似方程将从曲线的两个方向交于一点,然后近似认为P-U曲线在该点取得最大功率。最后P-U结合曲线恒压恒流区的特点,计算预测误差并给出剩余搜索范围。双向线性预测的思想用于近似估计最大功率点,此后通过模糊PI控制提高跟踪精度。此方法有效克服了扰动观察法速度与精度无法兼顾的矛盾,相对经验值预测法,该方法不依赖于统计规律,其精度受环境因素的影响较小。仿真及实验结果表明该方法具有快速、高效的优点。

Enhancement of Solar PV Panel Efficiency Using Double Integral Sliding Mode MPPT Control

The extraction of maximum power from the solar panels,using the sliding mode control scheme,becomes popular for partial weather atmospheric conditions due to its effective dynamic duty cycle ratio.However,the sliding mode control scheme was sophisticated with single integral and double integral sliding mode control scheme,which offer enhanced maximum power extraction and support enhanced solar panel efficiency in partial weather conditions.The operation of the sliding mode control scheme depends on the selection of a sliding surface selection based on the atmospheric weather condition,which enables the effective sliding duty cycle ratio operation for the DC/DC boost converter.The duty cycle ratio of the sliding mode control resembles the usual dynamic behavior to achieve enhanced efficiency compared to the various maximum power point tracking(MPPT)schemes.The major limitation of the sliding mode control scheme is to achieve the steady state voltage error of the solar panel in minimum settling time duration.The single integral sliding mode control scheme achieves the expected steady state voltage error limit but fails to achieve minimum settling time duration.Hence,the single integral sliding mode control is extended to a double integral sliding mode control scheme to achieve both steady state voltage error limits within the minimum settling time duration.This double integral sliding mode control scheme allows us to obtain the higher sliding surface duty cycle ratio which acts as the input signal to the boost converter.This activates the enhanced stable and reliable system operation,and nullifies the lacuna of maximum solar panel efficiency under partial weather conditions.Hence,this paper aims to present the design and performance operation of the double integral sliding mode(DISM)MPPT control scheme.To validate the performance analysis of the proposed DISM MPPT control scheme,the MATLAB/Simulink model is designed and verified.Also,the performance analysis of the proposed DISM MPPT control scheme is compared with the sliding mode controller(SMC)scheme and single integral sliding mode controller(SiSMC)scheme.The performance analysis of the proposed double integral sliding mode controller(DISMC)scheme attains 99.10%of efficiency and a very less setting time of 0.035s when compared to other existingmethods.

光伏MPPT优化控制策略研究

针对光伏发电系统最大功率点跟踪的传统扰动观察(P&O)法难以兼顾速度与精度的问题,提出了一种新的基于变步长P&O法的混合控制策略.以固定电压法启动,然后采用改进的变步长扰动观察法对最大功率点进行寻优,最后加入比例积分调节器以减小系统振荡.在Matlab/Simulink下进行了系统的建模与仿真,仿真结果表明,该混合控制策略能够快速平稳地实现光伏系统的最大功率点跟踪,有效提高了光伏发电的转换效率.

基于模糊控制的光伏发电MPPT的研究

根据光伏电池的特性分析,建立光伏发电系统。将模糊控制应用到光伏发电MPPT中,能对光伏发电系统的最大功率进行快速准确的跟踪。通过对模糊控制的光伏发电MPPT问题的研究,在Matlab/Simulink中建立基于模糊控制的光伏发电MPPT仿真模型,仿真结果表明,该模型的响应速度和跟踪精度都比较好。

Analysis and research on Maximum Power Point Tracking of Photovoltaic Array with Fuzzy Logic Control and Three-point Weight Comparison Method

The Photovoltaic Array has a best optimal operating point where the array operating can obtain the maximum power.However, the optimal operating point can be compromised by the strength of solar radiation,angle,and by the change of environment and load.Due to the constant changes in these conditions,it has become very difficult to locate the optimal operating point by following a mathematical model.Therefore,this study will focus mostly on the application of Fuzzy Logic Control theory and Three-point Weight Comparison Method in effort to locate the optimal operating point of solar panel and achieve maximum efficiency in power generation. The Three-point Weight Comparison Method is the comparison between the characteristic curves of the voltage of photovoltaic array and output power;it is a rather simple way to track the maximum power.The Fuzzy Logic Control,on the other hand,can be used to solve problems that cannot be effectively dealt with by calculation rules,such as concepts,contemplation, deductive reasoning,and identification.Therefore,this paper uses these two kinds of methods to make simulation successively. The simulation results show that,the Three-point Comparison Method is more effective under the environment with more frequent change of solar radiation;however,the Fuzzy Logic Control has better tacking efficiency under the environment with violent change of solar radiation.

A PV powered shunt active power filter for power quality improvement

This paper deals with power quality improvement using a three-phase active power filter(APF) connected to a PV power system. A direct power control(DPC) approach is proposed to eliminate harmonic current caused by any nonlinear loads and at the same time guarantees the delivery of a part of the load request from the same PV source. A boost converter is used for maximum power point(MPP) tracking purposes under various climate conditions through a fuzzy logic technique. The suggested study is tested under a MATLAB/Simulink environment. The obtained results depict the efficacy of the proposed procedures to meet the IEEE 519-1992 standard recommendation on harmonic levels.

基于改进鲸鱼优化算法的光伏发电系统MPPT控制研究

在局部阴影条件下,常规的最大功率点跟踪MPPT(maximum power point tracking)算法因含有容易陷入局部极值、跟踪精度低等弊端,使其无法及时、精确地跟踪光伏发电系统的最大功率点,因此,提出了一种基于改进型鲸鱼优化算法的光伏发电系统MPPT控制策略。首先,采用混沌映射初始化种群,增加种群的多样性。其次,通过引入非线性收敛因子使局部寻优能力和全局搜索能力达到均衡。最后,通过引入非线性时变的自适应权重使系统及时跳出局部最优解,并提高搜索的精度。经仿真验证,与粒子群优化算法、狮群优化算法、传统的鲸鱼优化算法等相比,改进的鲸鱼算法在跟踪速度、精度、稳定性等方面均有更显著的效果。

光伏并网系统MPPT控制和实现方法的研究

太阳能电池阵列输出特性具有强烈的非线性,为了提高系统的整体效率,一个重要的途径就是实时调整光伏电池的工作点,进行最大功率点跟踪(maximum power pointtracker,MPPT),使之始终工作在最大功率点附近。最大功率点跟踪方法是一个提高光伏组件效率的很有效的方法。

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 PIλDμ)control structure will be dynamically updated utilizing the value of change in PV array voltage output.During the transient,the value ofμis 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 PVarray 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.

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 wide-spread 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.

Dynamic characteristics and improved MPPT control of PV generator

This paper presents a mathematical model of photovoltaic （PV） module and gives a strategy to calculate online the maximum power point （MPP）. The variation of series and shunt resistor are taken into account in the model and are dynamically identified using the Newton-Raphson algorithm. The effectiveness of the proposed model is verified by laboratory experiments obtained by implementing the model on the dSPACE DS1104 board.

A sliding-neural network control of induction-motor-pump supplied by photovoltaic generator

Energy production from renewable sources offers an efficient alternative non-polluting and sustainable solution. Among renewable energies, solar energy represents the most important source, the most efficient and the least expensive compared to other renewable sources. Electric power generation systems from the sun’s energy typically characterized by their low efficiency. However, it is known that photovoltaic pumping systems are the most economical solution especially in rural areas. This work deals with the modeling and the vector control of a solar photovoltaic (PV) pumping system. The main objective of this study is to improve optimization techniques that maximize the overall efficiency of the pumping system. In order to optimize their energy efficiency whatever, the weather conditions, we inserted between the inverter and the photovoltaic generator (GPV) a maximum power point adapter known as Maximum Power Point Tracking (MPPT). Among the various MPPT techniques presented in the literature, we adopted the adaptive neuro-fuzzy controller (ANFIS). In addition, the performance of the sliding vector control associated with the neural network was developed and evaluated. Finally, simulation work under Matlab / Simulink was achieved to examine the performance of a photovoltaic conversion chain intended for pumping and to verify the effectiveness of the speed control under various instructions applied to the system. According to the study, we have done on the improvement of sliding mode control with neural network. Note that the sliding-neuron control provides better results compared to other techniques in terms of improved chattering phenomenon and less deviation from its reference.