基于滑模控制的光伏系统MPPT控制方案

结合光伏系统的工作原理和滑膜控制的特点,提出一种最大功率点跟踪(MPPT)控制方案。将滑模控制应用于该系统最大功率点的跟踪,包括改进变换电路,设计滑模变结构控制器。实验结果表明,该方案能快速跟踪太阳能电池的最大功率点,使系统稳定地工作在最大功率点附近,减小输出功率和电压的波动以及超调量,削弱滑模控制的稳态抖振。     

并网光伏逆变器鲁棒分数阶滑模控制设计

本文设计了一款基于扰动观测器的鲁棒分数阶滑模控制(POFO–SMC)来实现光伏逆变器的最大功率跟踪(MPPT).首先,将光伏逆变器的非线性、参数不确定性以及未建模动态聚合成一个扰动,并通过扰动观测器对其进行在线估计.随后,采用分数阶滑模控制(FOSMC)对该扰动估计进行实时完全补偿,从而实现不同工况下全局一致的控制性能.同时,POFO–SMC采用扰动实时估计而非传统滑模控制(SMC)中所使用的扰动上限值进行补偿,因此可有效解决传统SMC过于保守的缺点,使得控制成本更为合理.最后,POFO–SMC无需精确的系统模型,仅需测量光伏逆变器的q轴电流和直流侧电压,因此易于硬件实现.本文进行了两个算例的研究,即光照强度变化和电网电压跌落.仿真结果表明,与传统PI控制、反馈线性化控制(FLC)、SMC和FOSMC相比,POFO–SMC在各类工况下均具有最好的动态特性及最高的鲁棒性.基于dSpace的硬件在环实验(HIL)验证了其硬件可行性.     

采用自适应扰动观察法的光伏并网系统最大功率点跟踪

提出了一种自适应扰动观察（P＆O）算法,用于在不同天气条件下太阳能光伏（PV）并网系统的最大功率点跟踪（MPPT）控制策略。该策略对于从太阳能光伏电池板中,获取最大的功率输出是十分重要的。利用一种依赖于功率变化的可变的扰动步长,提出了改进的自适应扰动观察算法。最后将通过仿真所得到的数据与传统的扰动观察算法进行了比较,结果表明所提出MPPT算法的收敛值和速度得到了改善,稳定时间缩短25%,稳态值提高20%以上,在太阳能光伏并网系统的最大功率点跟踪时是有效而实用的。     

Climatic sensorless maximum power point tracking in PV generation systems

The problem of maximum power point tracking (MPPT) is addressed for photovoltaic (PV) arrays considered in a given panel position. The PV system includes a PV panel, a PWM boost power converter and a storing battery. Although the maximum power point (MPP) of PV generators varies with solar radiation and temperature, the MPPT is presently sought without resorting to solar radiation and temperature sensors in order to reduce the PV system cost. The proposed sensorless control solution is an adaptive nonlinear controller involving online estimation of uncertain parameters, i.e. those depending on radiation and temperature. The adaptive control problem at hand is not a standard one because parameter uncertainty affects, in addition to system dynamics, the output-reference trajectory (expressing the MPPT purpose). Therefore, the convergence of parameter estimates to their true values is necessary for MPPT achievement. It is formally shown, under mild assumptions, that the developed adaptive controller actually meets the MPPT objective.     

基于无线传感器网络的光伏系统MPPT应用研究

光伏电池输出的功率随外界环境条件的变化而变化,通常采用最大功率点跟踪技术以获得最大功率输出。结合无线传感器网络(WSNs)节点的工作方式与光伏系统的特点,提出了一种基于WSNs的光伏系统最大功率点跟踪技术。针对开路电压法的不足,利用WSNs节点的测温工作方式来进行温度补偿。当系统工作在最大功率点附近时,引入阻抗匹配算法,可有效消减光伏输出功率在最大功率点处的振荡现象,从而提高系统效率。仿真结果验证了该方法的可行性和有效性。     

双馈风力发电系统最大风能追踪滑模变结构控制

针对如何实现双馈风力发电机最大风能追踪(MPPT)问题, 本文采用滑模变结构控制原理和定子磁场定向矢量控制原理, 提出了滑模控制的最大风能追踪方案. 为此首先简要的介绍了定子磁场定向矢量控制的原理, 然后根据风机模型的非线性提出了滑模控制最大风能追踪方案. 此方法实现了双馈风力发电机的有功, 无功功率的解耦控制. 提高风力发电系统转速控制的抗干扰性, 实现了变速恒频控制和最大功率点跟踪的快速和稳定控制, 从而捕 获更多的风能. 最后仿真结果验证本文提出的控制策略的正确性和有效性.     

一种实现风力机MPPT控制的加速最优转矩法

最优转矩法因其所需测量状态较少、易于实现的特点,被广泛应用于风力机的最大功率点跟踪(Maximum power point tracking, MPPT)控制. 传统的最优转矩法只考虑系统的稳态工作点,依靠系统本身的特性进行转速调节,在一定程度上限制了转速调节速度. 本文使用滑模变结构控制的思想,在最优转矩法的基础上设计得到 一种变结构控制器,增大了转速跟踪过程中的不平衡转矩,缩短了系统的调节时间. 仿真结果表明本文提出的改进方法可以获得良好的转速跟踪效果,从而提高风力机的风能捕获效率.     

ANN-based MPPT algorithm for solar PMSM drive system fed by direct-connected PV array

In this paper, artificial neural network (ANN) based on a maximum power point tracking (MPPT) algorithm is developed for a solar permanent magnet synchronous motor (PMSM) drive system used without a boost converter and batteries. The discontinuous space vector PWM technique is used to drive two-level inverter which is directly fed by three parallel-connected Kyocera KD205GX-LP PV modules. The ANN-based MPPT algorithm estimates the voltages and currents corresponding to maximum powers produced by PV array at the maximum power point (MPP) for swiftly changing situations such as solar radiance and temperature. These maximum powers are given as input signal to vector control algorithm of PMSM. The PMSM is designed by using Infolytica/MotorSolve software so that the phase-to-phase maximum value of its operating voltage is 20 V. The use of three-phase PMSM presents more efficient solutions to the trading solar systems with dc motor or induction motor. Thus, an effective solar system is achieved. The performance of developed ANN-based MPPT algorithm, designed PMSM, vector-controlled driver and solar system is analyzed by using MATLAB/SimPowerSystems blocks under the rapidly changing environmental conditions.

     

A sliding mode control and artificial neural network based MPPT for a direct grid‐connected photovoltaic source

A robust sliding mode controller for a grid‐connected photovoltaic source is proposed in this paper. The objective of the presented control scheme is to force both the output voltage of the photovoltaic PV source and the power factor at the inverter output to follow a certain trajectory reference. The main idea is to apply the robust sliding mode controller directly to the nonlinear state model of the system composed of the PV source and the inverter with its input and output filters. In order to operate the PV system at the maximum power point and to satisfy the environmental factors, such as solar irradiance and temperature, we included a rigorous maximum power point tracker based on an artificial neural network. Simulation results are presented to illustrate the performance of the proposed control scheme. In addition, we show that the grid current satisfies the harmonic limits of the IEEE standard for interconnecting distributed energy sources with electric power systems.     

独立光伏系统中基于参数估计的双积分滑模变结构MPPT算法研究

对于独立光伏系统,当外界温度变化或光伏面板遭受遮挡时造成系统工作不稳定,系统在短时间内无法快速精确地对外输出最大功率.针对此问题,提出一种基于参数估计的双积分滑模变结构最大功率跟踪算法并设计独立光伏系统仿真模型.在仿真过程中,利用粒子群优化算法(Particle Swarm Optimization,PSO)估计太阳能...     

Output feedback control of sensorless photovoltaic systems,with maximum power point tracking

This paper presents an output feedback control of sensorless photovoltaic systems with maximum power point tracking (MPPT). The system consists of a Photovoltaic Generator (PVG) which supplies a DC centrifugal pump, via a DC/DC boost converter. This later being connected to the PVG by a long PV cable. Generally, PV systems are established near the control unit of the converter. The MPPT methods and control laws are based on the PVG voltage and current measurements. However, PV arrays must be located in a site that guarantees good solar radiation. In most cases, such a site is at great distance from the control unit. Thus, on the one hand, the PVG voltage and current measurements become difficult and, on the other hand, the PV cable parameters could significantly effect the MPPT control accuracy if only voltage and current measurements in the cable converter side are used. To overcome these issues, a state estimation for PV systems is considered in this paper. A high gain observer is designed on the basis of a PV system model that accounts for PV cable parameters. It provides estimates of PVG output voltage and current using only current and voltage measurements in the converter side of the cable. A backstepping controller is then synthesized with the view of ensuring the MPPT objective. The output feedback control convergence is formally analyzed and its performances are illustrated by simulation.     

A novel maximum power point tracking algorithms for stand-alone photovoltaic system

A novel control algorithm, namely subsection adaptive hill climbing method (SSAHC), for seeking the maximum power point (MPP) of a photovoltaic (PV) panel for any temperature and solar radiation level is proposed. The algorithm is thus a combination of the subsection and adaptive hill climbing methods. In this algorithm, the characteristic curve of power-voltage of PV panel was divided into three subsections, namely large step approximation section, adaptive hill climbing section and maximum power section. Using this method, the MPP tracker (MPPT) can tune adaptively the step to track the MPP of PV system. The main advantage of the MPPT controlled by this new algorithm, when is compared with others, is that it can draw more power at a certain weather condition, especially, in case solar radiation changes rapidly at higher radiation.     

Intelligent control of photovoltaic system using BPSO-GSA-optimized neural network and fuzzy-based PID for maximum power point tracking

The maximum power point tracking (MPPT) technique is applied in the photovoltaic (PV) systems to achieve the maximum power from a PV panel in different atmospheric conditions and to optimize the efficiency of a panel. A proportional-integral-derivative (PID) controller was used in this study for tracking the maximum power point (MPP). A fuzzy gain scheduling system with optimized rules by subtractive clustering algorithm was employed for tuning the PID controller parameters based on error and error-difference in an online mode. In addition, an Elman-type recurrent neural network (RNN) was used for inverse identification of the PV system and for estimating the solar radiation intensity to determine the MPP voltage. The optimum number of neurons in the single hidden-layer of the RNN was determined by binary particle swarm optimization algorithm. The weights of this RNN were also optimized by using a hybrid method based on the Levenberg-Marquardt algorithm and gravitational search algorithm (GSA). In the proposed fitness function for optimization, both the RNN size and its convergence accuracy were considered. Thus, the algorithm for RNN optimization attempts to minimize both the structural complexity and the mean square error. Simulation results revealed superior performance of GSA in comparison with particle swarm, cuckoo, and grey wolf optimization algorithms. The performance of the proposed MPPT method was evaluated under four different ambient conditions. Our experimental results show that the proposed MPPT method is more efficient than the three competitive methods presented in recent years.     

光伏电池输出特性与最大功率跟踪的仿真分析

该文根据光伏电池的工程数学模型,提出一种利用MATLAB软件包中的Simulation模块直接模拟光伏电池工作状况的方法,该仿真模型能准确反映光伏电池的输出特性,而且参数调节方便。文章主要对不同负载、模型内部参数变化和日照强度变化条件下光伏电池输出的特性进行了研究,得到了光伏电池输出特性变化的一般规律。数据分析结果表明,光伏电池的输出特性呈非线性。光伏电池输出功率随外部环境或内部参数的变化而发生改变,其最大值只在满足特定条件下的某一点才可能出现。基于导纳增量法,利用BOOSTDC／DC变换电路实现了光伏电池输出的最大功率跟踪,其控制算法用S函数编程实现。     

局部遮阴下光伏系统MPPT研究与优化

为了解决光伏发电系统中,光伏电池在环境中被树叶、建筑物、云层等遮挡造成局部阴影,导致光伏电池出现运行不稳定和输出功率降低的问题,提出了一种基于改进自适应动态惯性权重并引入粒子寻优目标适应度评判系数的优化粒子群算法(GPPSO).将GPPSO应用于复杂自然环境条件下的最大功率点跟踪(MPPT),结果表明:双重优化后的算法有效提高了局部精确搜索和寻优空间全局收敛能力,在目标函数最优求解过程中,精度和收敛速度都明显提高,较快地适应环境遮阴变化,能够在复杂的自然环境中准确地对光伏发电系统最大功率点进行跟踪,提高光伏系统发电效率.     

低风速风力机最大风能追踪的互补滑模控制

针对风力机系统在最大功率点跟踪(MPPT)阶段易受风速等不确定因素的影响,为了进一步提高风力机的风能捕获效率,本文在滑模控制的基础上提出了一种互补滑模控制方法.首先,建立了含有干扰项的风力机系统的线性化模型,采用广义滑模面与互补滑模面相结合的方法设计了互补滑模控制器,并在理论上证明了此控制方法能够有效保证风力机转速跟踪误差的收敛性,且能提高转速跟踪精度.其次,采用风力机专业仿真软件FAST对美国可再生能源实验室(NREL)的600 kW风力机进行了仿真实验,结果表明本文所提出的控制方法不但能提高风力机的风能捕获效率,而且能有效减小转速跟踪误差.最后,将本文所提方法与现有常见的几种控制方法相比较发现:风力机系统在互补滑模控制策略下,具有更高的风能捕获效率和更小的转速跟踪误差.     

Control of Grid Connected Photovoltaic Systems with Microinverters: New Theoretical Design and Numerical Evaluation

This paper addresses the problem of controlling grid connected photovoltaic (PV) systems that are driven with microinverters. The systems to be controlled consist of a solar panel, a boost dc–dc converter, a DC link capacitor, a single‐phase full‐bridge inverter, a filter inductor, and an isolation transformer. We seek controllers that are able to simultaneously achieve four control objectives, namely: (i) asymptotic stability of the closed loop control system; (ii) maximum power point tracking (MPPT) of the PV module; (iii) tight regulation of the DC bus voltage; and (iv) unity power factor (PF) in the grid. To achieve these objectives, a new multiloop nonlinear controller is designed using the backstepping design technique. A key feature of the control design is that it relies on an averaged nonlinear system model accounting, on the one hand, for the nonlinear dynamics of the underlying boost converter and inverter and, on the other, for the nonlinear characteristic of the PV panel. To achieve the MPPT objective, a power optimizer is designed that computes online the optimal PV panel voltage used as a reference signal by the PV voltage regulator. It is formally shown that the proposed controller meets all the objectives. This theoretical result is confirmed by numerical simulation tests.     

Development and analysis of adaptive fuzzy controllers for photovoltaic system under varying atmospheric and partial shading condition

Control of power electronics converters used in PV system is very much essential for the efficient operation of the solar system. In this paper, a modified incremental conduction maximum power point tracking (MPPT) algorithm in conjunction with an adaptive fuzzy controller is proposed to control the DC–DC boost converter in the PV system under rapidly varying atmospheric and partial shading conditions. An adaptive hysteresis current controller is proposed to control the inverter. The proposed current controller provides constant switching frequency with less harmonic content compared with fixed hysteresis current control algorithm and sinusoidal PWM controller. The modeling and simulation of PV system along with the proposed controllers are done using MATLAB/SIMSCAPE software. Simulation results show that the proposed MPPT algorithm is faster in transient state and presents smoother signal with less fluctuations in steady state. The hardware implementation of proposed MPPT algorithm and inverter current control algorithms using Xilinx spartran-3 FPGA is also presented. The experimental results show satisfactory performance of the proposed approaches.     

Sliding mode predictive control of a solar air conditioning plant

In this work, sliding mode predictive control (SMPC) was applied to an air conditioning solar plant. The process has a variable time delay with non-minimum phase behavior, whose inverse response changes with the operating point. Disturbances are produced by changes in solar irradiation and solar collectors’ inlet temperature. The algorithm combines the design technique of sliding mode control (SMC) with model-based predictive control (MPC). The SMPC showed a considerable robustness improvement with respect to MPC under experimental validation on a real plant and also showed an enhanced ability to handle set point changes and disturbance rejection.     

Hybrid Renewable Energy Source Combined Dynamic Voltage Restorer for Power Quality Improvement

In this paper, the hybrid photovoltaic-thermoelectric generator (PV-TEG) combined dynamic voltage restorer (DVR) system is proposed for the power quality disturbances compensation in a single-phase distribution system. The stable and precise level of input voltage is essential for the smooth and trouble-free operation of the electrically sensitive loads which are connected at the utility side to avoid system malfunctions. In this context, the hybrid PV-TEG energy module combined DVR system is proposed in this paper. With the support of the hybrid energy module, the DVR will perform the power quality disturbances compensation effectively with needed voltage and /or power. In the proposed system, the PV and TEG energy sources are connected electrically in series to produce adequate voltage for the DVR operation and the fractional factor-based variable incremental conduction (FFVINC) maximum power point tracking (MPPT) control algorithm is employed to extract the possible maximum power from the PV array. The intelligent fuzzy logic controller (FLC) is chosen for implementing the MPPT control algorithm. The half-bridge voltage source inverter (VSI) circuit and in-phase voltage compensation technique are used in the DVR for better power quality disturbances compensation. The performance and usefulness of the proposed DVR system are investigated by an extensive simulation study with four different modes of operation, the study results are confirmed that the proposed system promptly identifies the power quality disturbances for compensation. Moreover, the investigation proved that the combined PV and TEG energy module can provide better energy efficiency in converting solar irradiation into electricity.