开关磁阻风光互补发电系统最大功率输出研究

针对风能和太阳能等新能源的间歇性、可变等不稳定性特征,利用风能和太阳能的资源互补特性,将风能和太阳能通过转化、储存、控制等手段,形成稳定的电力输出。研究太阳能光伏发电系统和开关磁阻风力发电系统最大功率跟踪策略,提出一种适应天气变化较快的基于输出电压控制的最大功率点跟踪算法,结合蓄电池的智能控制策略,实现智能风光动态互补。MATLAB仿真实验验证了该控制方法的可行性和有效性。     

基于粒子群多峰值MPPT算法的光伏系统研究

局部遮荫时,光伏阵列P-V特性曲线呈现多个峰值点,传统算法难以追踪到最大功率点(MPPT),针对此问题提出了粒子群多峰值MPPT算法,该算法通过粒子群搜索最大功率点处电流,并控制光伏阵列输出电流,从而实现最大功率点跟踪;仿真结果表明粒子群多峰值MPPT算法可以用在光伏并网发电系统中,并且该算法比传统算法收敛速度更快,稳态效果更好。     

南极低温环境下小型风光互补系统仿真研究

分析了南极低温环境条件对小型风光互补发电系统的影响,模拟南极昆仑站气候条件,搭建了光伏电池和小型风机模型,进行了最大功率跟踪(MPPT)策略仿真,仿真结果证明极地环境对小型风光互补发电系统有一定影响,验证了风光互补最大功率点跟踪控制策略的正确性。针对南极昆仑站的气象数据进行了风光互补系统容量匹配计算,结果表明小型风光互补发电系统能够满足负载需求。     

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.     

基于DSP的光伏并网系统MPPT算法研究

介绍了以DSP为主控芯片的光伏并网系统,它能跟踪光伏阵列最大输出功率点,实现光伏阵列和负载优化匹配,使负载获得最大功率。在光伏阵列输出功率最大为跟踪目标的定步长MPPT一阶差分算法的基础上,采用以使负载获得最大功率为跟踪目标的变步长寻优MPPT算法,能够更好地实现最大功率点的跟踪。实验结果表明:该系统能够快速准确地跟踪太阳能电池最大功率点,并自动同步跟踪电网频率和相位,实现并网电流的正弦波形以便与电网电压同频同相馈入电网,提高了系统逆变效率和可靠性。     

智能全功率MPPT风光互补路灯控制器设计

针对一类普通风光互补路灯控制器转换能效低、稳定性差等问题,设计一种智能全功率MPPT风光互补路灯控制器.采用双MCU处理器PIC16F877A单片机为控制器核心,硬件采用模块化的设计方法,整个控制器由主控制模块、从控制模块、风力发电机智能升压MPPT模块,以及风力发电机点刹控制模块、太阳能智能升压MPPT模块、蓄电池充/放电模块、负载LED灯模块组成,在分析光伏电池和风力发电机最大功率点跟踪问题的基础上,采用风力发电机和太阳能智能全功率MPPT跟踪控制策略.最后,在实验室搭建测试平台,测试结果表明,控制器可以可靠稳定运行,跟普通控制器相比,其充电效率与能源利用能效提高22.1％,能够实现能源的最大化利用.     

模糊RBF太阳电池最大功率点跟踪算法

为了有效地利用太阳能,提高光伏发电系统的效率,通过分析太阳电池输出特性和最大功率点跟踪原理,结合模糊控制算法、神经网络与比例—积分—微分(PID)算法的优势,提出一种模糊径向基函数(RBF)神经网络的太阳电池最大功率点跟踪算法。利用模糊RBF神经网络对PID控制器的控制参数进行自适应整定,从而精确调节光伏电池变换器的功率开关占空比,达到太阳电池最大功率点跟踪控制。仿真与实测结果表明:算法能有效提高系统的响应速度、自适应能力。     

改进无差拍控制在单相并网逆变器中的应用

为解决在单相并网逆变器中应用无差拍控制时,由于采样及处理器计算的延时而导致系统不稳定的问题,提出了一种基于CMAC神经网络的无差拍控制策略。控制策略通过MPPT算法跟踪光伏电池的最大输出功率,然后将最大功率减去电路中功率损失后除以电网电压的有效值平方得到逆变器参考电流的系数k,再根据CMAC神经网络预测出下一时刻的电网电压和参考电流,最后计算出下一时刻的开关周期的占空比对逆变器进行控制。此方法实现了对采样及处理器计算的延时的补偿,可提高了系统的稳定性,使并网逆变器的输出电流快速、准确地跟踪电网电压,降低逆变器输出电流中的总谐波歧变。通过对该策略进行了仿真和试验,结果表明该控制策略有效性和可行性。     

分段变步长MPPT算法在风力发电系统中的应用

针对传统定步长爬山搜索(HCS)法在风力发电系统最大功率跟踪(MPPT)控制过程中的快速性和准确性矛盾,提出了一种基于爬山搜索法和模糊控制的分段变步长MPPT算法.该算法根据发电机P-ω特性曲线对最大功率点(MPP)跟踪过程进行分段,使系统能够根据工作点所在的区域选择合适的跟踪算法和步长完成最大功率跟踪.在Matlab/Simulink中分别对提出的模糊分段变步长算法和传统爬山搜索法进行了仿真.仿真结果表明:所提算法明显地改善了系统跟踪MPP的速度和稳态精度,在MPPT方面明显优于传统的爬山搜索法.     

光伏电池组件工作状态监测研究

由于新能源光伏发电在当今电网中愈加重要,因此通过物联网技术实时监测光伏组件的健康状况对于保障光伏发电系统的高效运行意义重大。光伏系统无法对光伏电池最大功率点的输出进行实时监测,针对现有最大功率点跟踪算法MPPT只跟踪不监测的特点,提出以太阳能电池基本电路方程为基础,构建不依赖于变换器及MPPT算法的光伏电池功率输出曲线模型,结合电压-功率输出特性曲线的特点,采用最小二乘法拟合曲线,精准得到最大功率点附近的二次曲线数学模型,进而得到实时最大功率点。通过Matlab对光伏发电系统进行建模与仿真,系统仿真结果显示,算法准确有效,可以迅速稳定地响应环境变化,适用于各种条件下MPPT的快速、精准获取。     

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.

     

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.     

三点比较法在风力发电系统中的应用

由于风的随机性,充分利用风能是风力发电系统的本质要求,在研究了各种MPPT算法的基础上,提出将三点比较法应用于风力发电系统,介绍了风力机运行特性和最大功率点跟踪(MPPT)的原理,分析三点比较控制算法在应用到风力发电系统中的缺点和不足,并在此基础上提出了改进型的三点比较法.采用Matlab软件进行仿真实验,仿真结果表明改进型三点比较法具有良好的收敛性和抗干扰性,同时证明了改进型的三点比较法的正确性和可行性.     

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.

     

Maximum wind power tracking for PMSG chaos systems – ADHDP method

Aiming at the chaotic behavior of PMSG under certain parameters, the new ADHDP method based on Cloud RBF neural network is proposed to track the point of maximum wind power, which can make the system out of chaos and track the point of maximum power stably, and the optimal control problems of the complex nonlinear system can be solved effectively. This method is realized by using the optimal power–speed curve and the vector control principle, and by adjusting the stator output voltage to control the electromagnetic torque, then the rotor speed of wind turbine can operate at the optimal speed that corresponds to the point of maximum power, meanwhile, the measure of wind speed also can be avoided. The simulation results show the effectiveness of the proposed method.     

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。     

Maximum power point tracking (MPPT) system of small wind power generator using RBFNN approach

A novel approach of combination of radial basis function neural network (RBFNN) and particle swarm optimization (PSO) is proposed to achieve the maximum power point tracking (MPPT) in this study. The measured data of the small wind generator (250 W), including wind speed, generator speed and output power of wind power generator, are applied to estimate the wind speed and output power by the proposed wind speed ANN_wind and power estimation ANN_Pe-PSO modules, respectively. Using the predicted results by the two modules of Matlab/Simulink, the MPPT point can be obtained by manipulating the generator speeds. The experimental results show that the proposed RBFNN-based approach can increase the maximum output power of the wind power generator even if the wind speed and load varies.     

新型爬山算法在大惯性风电系统中的应用

针对风力发电风速的随机性和不确定性,以及大惯性永磁风力发电系统输出功率受风机机械功率和所储的机械势能的变化率的交叉影响特点,提出一种基于爬山搜索法的新型最大风能捕捉算法。为了得到较宽的变速范围,在三相二极管整流器和PWM逆变器之间接有升压斩波器,新型爬山算法采用最佳直流电流控制,调节输入直流电流以跟从最优的参考电流,产生不同转矩控制电机的转速实现最大功率跟踪,并在爬山算法基础上添加数据存储和输出功能,避免了盲目的爬山搜索。最后通过Matlab对一个50kW永磁直驱风力发电系统进行了仿真分析,对比了传统爬山算法与新型爬山算法在最佳功率点跟踪上的差别。仿真结果验证了该算法的可靠性,表明新型爬山算法在大惯性变速恒频风力发电系统中具有更好的实用性。     

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.