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...     

独立、并网双模式户用光伏发电装置的研究与设计

针对户用光伏发电系统的特点和要求进行了系统的研究与设计。系统设计将独立逆变与并网逆变相结合,使系统既可以工作在独立逆变状态,为负载提供正弦交流电源,也可以工作在并网逆变状态,将太阳的能量或蓄电池的能量回馈到电网。本系统采用TI公司32位定点DSP芯片TMS320F2812为控制核心,利用PIC16F877A单片机构成系统的人机操作界面。本文所设计的系统具有完善的保护功能、键盘监控和液晶显示、串口通讯功能,为家庭使用提供了方便。实验结果验证了方案的可行性。     

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.     

积灰条件下光伏阵列最优倾角问题研究

在光伏发电系统设计中,安装倾角的选择对光伏发电效率具有重要影响.针对太阳能光伏阵列常见的表层积灰现象,改变传统的只考虑最大辐射量的倾角确定方法,提出了综合考虑表层积灰情况下的最优发电倾角计算方法,使倾角的确定更加合理与完善.建立了积灰辐射量统一发电模型,并基于Matlab对模型进行了仿真验证.以福建某光伏电站为例,搭建实验平台,通过相关数据的检验与预测,得到了光伏电池板的综合最优倾角.实验结果证明该模型所确定的倾角比传统模型可以得到更大的发电量,提高了光伏利用效率.     

基于NSGA-II的并网型风光互补发电系统协调控制

针对并网型风光互补发电系统中,系统最大输出功率大于给定功率时,风力发电子系统和光伏发电子系统功率如何协调的问题,提出了一种功率协调控制方法.在该方法中,根据系统并网收益最大和输出电流谐波最小构建目标函数,采用带精英策略的快速非支配排序遗传算法对风力发电子系统和光伏发电子系统的输出功率进行多目标优化,协调控制子系统的发电功率;并以甘肃华电阿克赛风光互补发电项目为例进行了仿真验证.仿真结果表明,与传统的光伏优先接入方式相比,基于NSGA-Ⅱ的并网型风光互补发电系统协调控制方法可以更加合理地利用风能和太阳能,提高新能源电能的电网友好性.     

Modeling and simulation of a grid connected PV system based on the evaluation of main PV module parameters

In this work we present a new method for the modeling and simulation study of a photovoltaic grid connected system and its experimental validation. This method has been applied in the simulation of a grid connected PV system with a rated power of 3.2 Kw_p, composed by a photovoltaic generator and a single phase grid connected inverter. First, a PV module, forming part of the whole PV array is modeled by a single diode lumped circuit and main parameters of the PV module are evaluated. Results obtained for the PV module characteristics have been validated experimentally by carrying out outdoor I-V characteristic measurements. To take into account the power conversion efficiency, the measured AC output power against DC input power is fitted to a second order efficiency model to derive its specific parameters.The simulation results have been performed through Matlab/Simulink environment. Results has shown good agreement with experimental data, whether for the I-V characteristics or for the whole operating system. The significant error indicators are reported in order to show the effectiveness of the simulation model to predict energy generation for such PV system.     

Stable reactive power balancing strategies of grid‐connected photovoltaic inverter network

In this paper, a distributed reactive power control based on balancing strategies is proposed for a grid‐connected photovoltaic (PV) inverter network. Grid‐connected PV inverters can transfer active power at the maximum power point and generate a certain amount reactive power as well. Because of the limited apparent power transfer capability of a single PV inverter, multiple PV inverters usually work together. The communication modules of PV inverters formulate a PV inverter network that allows reactive power to be cooperatively supplied by all the PV inverters. Hence, reactive power distributions emerge in the grid‐connected PV inverter network. Uniform reactive power distributions and optimal reactive power distributions are considered here. Reactive power balancing strategies are presented for both desired distributions. Invariant sets are defined to denote the desired reactive power distributions. Then, stability analysis is conducted for the invariant sets by using Lyapunov stability theory. In order to validate the proposed reactive power balancing strategies, a case study is performed on a large‐scale grid‐connected PV system considering different conditions. Copyright © 2015 John Wiley & Sons, Ltd.     

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。     

An improved power conditioning system for grid integration of solar power using ANFIS based FOPID controller

Power loss become common while integrating with common grid and in specific when power produced through Solar. This is the very lacking area which this proposal implements an Adaptive Neuro Fuzzy Inference System (ANFIS) based controller of Fractional Order Proportional Integral Derivative (FOPID) used for Tracking of Maximum PP of Grid Integrated Solar Power Conditioning System. The proposed work advances with different ambient light conditions for maximum power point traction. In this work a clear-cut Photo Voltaic (PV Cell) model has been developed and an intensive and operative training data have been extracted from the developed controller. This produced dataset have been the feeder input for the ANFIS structure in turn to locate the Tracking of Maximum PP (MPPT). Traction of MPPT is done, the FOPID controller is enforced by matching the voltage from the array of Photo Voltaic cell with attained or reference voltage produced by the ANFIS structure. In the meantime driving this PV array, DC to DC converter's duty cycle is controlled for producing maximum power from the structure. The duty cycle in FOPID controller is generated through calculating the error within the reference voltage and PV voltage. Those values are then simulated through Math Lab and the Simulation results show that this proposed work efficiency is better than the regularly employed controllers in the solar power production and conditioning system     

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.     

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.     

Power flow control based on bidirectional converter for hybrid power generation system using microcontroller

Energy flow management of photovoltaic (PV) based ON-Grid system using BDC converter is analysed and implemented in this paper. The intermittent nature of renewable energies makes the unstable operation of utility grid system. In order to ensure the stable operation of the utility grid system and to support smart grid functionalities, there is the requirement of power electronics participation is high. So that, Grid-connected PV with converters plays an essential role in power management. In this paper, a suitable bidirectional converter (BDC) with advanced optimization control strategies has proposed for power flow management. Further this converter provides a high efficiency, enhanced control flexibility and has the capability to operate in different operational modes from the input to output. The power from PV and grid satisfies the load demand and maintains the continuous power flow to load. The power balance improvement in BDC has power factor correction, harmonics elimination and voltage regulation at AC mains. This paper proposes the analysis of Genetic Algorithm tuning PID (GA-PID) control a process that effectively reduces the bidirectional converter harmonics; this aim is attained and shown in both MATLAB/Simulink and experimental results.     

Application of sliding mode control for maximum power point tracking of solar photovoltaic systems: A comprehensive review

A robust maximum power point tracking (MPPT) control is of paramount importance in the performance enhancement and the optimization of photovoltaic systems (PVSs). Solar panel exhibits nonlinear behavior under real climatic conditions and output power fluctuates with the variation in solar irradiance and temperature. Therefore, a control strategy is requisite to extract maximum power from solar panels under all operating conditions. Sliding mode control (SMC) is extensively used in non-linear control systems and has been implemented in PVSs to track maximum power point (MPP). The objective of this work is to classify, scrutinize and review the SMC techniques used to extract maximum power from PVSs in both off-grid and grid connected applications. The first order, perturb and observe, incremental conductance, linear expression based sliding mode control algorithms and their adaptive forms are discussed in detail. The advanced form of SMC, terminal sliding mode control (TSMC), super twisting theorem (STT) and artificial intelligent (AI) algorithm based are also presented with the focused application of MPPT of PVSs. A tabular comparison is provided at the end of each category to help the users to choose the most appropriate method for their PV application. It is anticipated that this work will serve as a reference and provides important insight into MPPT control of the PV systems.     

Simulation modelling and analysis of modular cascaded multilevel converter based shunt hybrid active power filter for large scale photovoltaic system interconnection

Modular multilevel inverters are promising candidates for next generation of efficient, robust and reliable inverters in large scale photovoltaic system. A modular cascaded multilevel inverter based shunt hybrid active power filter (SHAPF) for three phase grid-connected large scale PV systems is represented in this paper. The main contribution of this paper is to model and control of grid interfaced large scale photovoltaic system with embedded hybrid active power filter functions. In proposed system, the features of hybrid active power filter have been amalgamated in the control circuit of the voltage controlled voltage source inverter interfacing the photovoltaic system to the grid. As a result, the same inverter part of SHAPF is utilized to inject power generated from photovoltaic source to the grid and also to act as hybrid active power filter to compensate harmonics and reactive power demand. With using compensation ability, the grid currents are sinusoidal and in phase with grid voltages. The whole system is modeled in PSCAD/EMTDC. The simulation results are demonstrated to verify the operation and the control system of the proposed system.     

新型MPPT算法在光伏并网系统中的应用

光伏并网系统在天气急剧变化时,采用传统的最大功率跟踪算法会产生较大误判,不仅造成电池板功率的损失,而且还会导致并网电流的畸变。针对这一问题,对传统扰动观察法进行了改进,提出了一种新型MPPT算法,有效地防止了功率误判。将此新型算法配合电压电流双环控制应用于光伏并网系统。从仿真结果看到,在光照突变和电网电压扰动时,并网电流均能够快速跟踪电网电压的相位和频率。表明了新型算法的正确性和有效性。     

A fast battery charging algorithm for an intelligent PV system with capability of on-line temperature compensation

This study presents a battery fast-charging mechanism for an intelligent generic photovoltaic (PV) system and also a pulse-charging method for the on-line temperature compensation. The fuzzy logic control (FLC) is adopted for fast maximum power point tracking (MPPT) of the PV system. Along with proposed battery charging algorithms, the controller presented in this study is named a fuzzy battery-managing controller (FBMC). The fast battery charging by this controller does not only prolong battery lifetime by restoring the maximum battery state of charge (SOC) in the shortest time but also with the temperature compensation. The designed charging algorithm consists of three different stages, namely constant current (CC), pulse charging and trickle charging. In the CC mode, the current at maximum power of the PV array is used for fast charging. The pulse charging mode is next adopted to contain temperature rise while maintaining relatively fast charging speed. To prevent battery damage by charging as battery capacity is close to its full status, 100 % SOC, the float charging mode is finally activated by further decreasing charging currents. Simulations are conducted via Powersim to validate the FBMC performance and the PV system model. The FBMC is next implemented by a DSP module (TMS320F2812) in order to adjust the switching duty cycle during operations of the buck converter. Finally, experimental results were compared with a general constant current and/or voltage method. The results show favorable performance of the propose charging method.     

Intelligent-controlled doubly fed induction generator system using PFNN

An intelligent-controlled doubly fed induction generator (DFIG) system using probabilistic fuzzy neural network (PFNN) is proposed in this study. This system can be applied as a stand-alone power supply system or as the emergency power system when the electricity grid fails for all sub-synchronous, synchronous, and super-synchronous conditions. The rotor side converter is controlled using the field-oriented control to produce three-phase stator voltages with constant magnitude and frequency at different rotor speeds. Moreover, the grid side converter, which is also controlled using field-oriented control, is primarily implemented to maintain the magnitude of the DC-link voltage. Furthermore, an intelligent PFNN controller is proposed for both the rotor and grid side converters to improve the transient and steady-state responses of the DFIG system at different operating conditions. The network structure, online learning algorithm, and convergence analyses of the PFNN are introduced in detail. Finally, the feasibility of the proposed control scheme is verified using some experimental results.     

Fuzzy observer-based control for maximum power-point tracking of a photovoltaic system

This paper presents a novel fuzzy control design method for maximum power-point tracking (MPPT) via a Takagi and Sugeno (TS) fuzzy model-based approach. A knowledge-dynamic model of the PV system is first developed leading to a TS representation by a simple convex polytopic transformation. Then, based on this exact fuzzy representation, a H_∞ observer-based fuzzy controller is proposed to achieve MPPT even when we consider varying climatic conditions. A specified TS reference model is designed to generate the optimum trajectory which must be tracked to ensure maximum power operation. The controller and observer gains are obtained in a one-step procedure by solving a set of linear matrix inequalities (LMIs). The proposed method has been compared with some classical MPPT techniques taking into account convergence speed and tracking accuracy. Finally, various simulation and experimental tests have been carried out to illustrate the effectiveness of the proposed TS fuzzy MPPT strategy.     

A novel MPPT algorithm based on optimized artificial neural network by using FPSOGSA for standalone photovoltaic energy systems

Maximum power point tracking (MPPT) algorithms are used to maximize the output power of the photovoltaic (PV) panel under different temperature and irradiance conditions in photovoltaic energy sources (PVES). In this paper, a novel MPPT method based on optimized artificial neural network by using hybrid particle swarm optimization and gravitational search algorithm based on fuzzy logic (FPSOGSA) is proposed to track the operation of the PV panel in maximum power point (MPP). The performance of the proposed MPPT approach is tested by doing the simulation and experimental studies under different environmental conditions. The proposed method is compared with the conventional perturb and observation (P&O) method for standalone PVES. The results of the comparison the obtained from the simulation and experimental studies demonstrate that the proposed MPPT method provides the reduction oscillations around the MPP and the increased maximum power yield of the PV system in the steady state.

     

A two-level control strategy with fuzzy logic for large-scale photovoltaic farms to support grid frequency regulation

This study proposes a two-level coordinated control strategy with fuzzy logic for appropriately adjusting the total active power supplied to a grid by large-scale photovoltaic (PV) farms in order to regulate grid frequency. For a solar farm, the strategy includes a central coordinating controller and many local controllers at PV power assemblies, treated as agents. In detail, the central controller uses a frequency regulation module based on a new automatic-tuning fuzzy-logic controller scheme to compute the appropriate reference values according to the total power needed. Then, the individual reference value for each local controller is determined. Each local controller governs all power-electronic converters installed at the PV agent to inject power into the grid according to the individual reference value received. Additionally, each local controller uses an algorithm to manage the state-of-charge of the battery bank installed at the agent so that it remains in the safe range of 20–80% while operating and close to the desired idle value of 50% at the steady state. Besides, a special control mode is developed and integrated into the overall strategy to aid rapid recovery of the grid frequency under emergency conditions. Numerical simulations demonstrate that the suggested strategy has the good response in terms of injecting an appropriate amount of power into the grid to regulate the frequency deviation into acceptable ranges of ±0.2 (Hz) in the transient state and ±0.05 (Hz) at the steady state, even when the weather conditions (solar radiation, air temperature), AC system load, and important control parameters of the grid suddenly change. Furthermore, the effectiveness in improving the grid-frequency stabilization by using the proposed strategy is validated within a four-area power system, where four PV farms are connected and the operating parameters of the grids at the areas are fairly different.