基于SSA-PSO-ANFIS的短期风速预测研究

针对风速具有强非线性的特点,提出一种奇异谱分析和改进粒子群优化自适应模糊推理系统的短期风速预测模型。该方法采用奇异谱分析将原始序列分解为趋势和谐波分量,对各分量分别建立模糊神经网络模型,最后将各分量预测结果叠加得到预测风速值。为提高预测精度,改用改进粒子群算法对自适应模糊推理系统的隶属度函数进行优化。以河北某风电场实测数据进行仿真并与传统的神经网络对比分析,结果表明将风速重构后分别预测再叠加降低了原始问题的复杂度,同时提高了预测精度,在不同时间间隔的风速序列预测中该模型显著降低了多步实时预测中的误差。     

测风数据间隔对风速预测精度影响的研究

风速预测对风电场控制和电网调度具有十分重要的意义。文章以不同时间间隔的测风数据为基础,采用时间序列法和人工神经网络法对风速进行预测,通过比较风速预测绝对平均误差,说明时间间隔较短时,采用BP神经网络预测精度较高;当时间间隔增大时,采用时间序列法预测精度较高;时间间隔过大,即风速数据太少时,两种预测方法误差都较大,须谨慎使用。该研究结果对风电机组控制系统的设计以及电网调度计划的制定具有参考价值。     

基于MMAS BP算法的短期风速非线性组合预测模型

为提高风电场短期风速的预测精度,引入一种基于改进蚁群算法优化神经网络的非线性组合预测方法,按误差平方和最小原则对所建灰色GM(1,1)模型、BP网络和RBF网络三种单一预测数据进行非线性组合,并将其结果作为最终预测值。仿真结果表明,该方法的平均绝对误差及均方误差分别为17.76%和3.68%,均小于单一模型、线性组合模型及神经网络组合模型的预测结果,提高了网络的泛化能力,降低了预测风险,为风电场风速预测提供了一种新途径。     

Wind speed forecast model for wind farm based on a hybrid machine learning algorithm

This paper presents a new strategy for wind speed forecasting based on a hybrid machine learning algorithm, composed of a data filtering technique based on wavelet transform (WT) and a soft computing model based on the fuzzy ARTMAP (FA) network. The prediction capability of the proposed hybrid WT+FA model is demonstrated by an extensive comparison with some other existing wind speed forecasting methods. The results show a significant improvement in forecasting error through the application of a proposed hybrid WT+FA model. The proposed wind speed forecasting strategy is applied to real data acquired from the North Cape wind farm located in PEI, Canada.     

Hybrid boosting algorithms and artificial neural network for wind speed prediction

Energy sources are an important foundation for national economic growth. The future of energy sources depend on the energy controls. The reserves of fossil energy have declined significantly, and environmental pollution has increased dramatically due to excessive fossil fuel consumption. At this point, wind energy can be used as one of the key source of renewable energy. It has a remarkable importance among the low-carbon energy technologies. The primary aim of wind energy production is to reduce dependence on fossil fuels that affect environment adversely. Therefore, wind energy is analyzed to develop new energy resources. The main issue related to evaluation of the wind energy potential is wind speed prediction. Due to the high volatile and irregular nature of wind speed, wind speed prediction is difficult. To cope with complex data structure, this study presents the development of extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and artificial neural network (ANN) within particle swarm optimization (PSO) parameter optimization for hourly wind speed prediction. To compare the proposed hybrid methods, various performance measures, the Pearson's test, and the Taylor diagram are used. The results showed that proposed hybrid methods provide reasonable prediction results for wind speed prediction.     

A new control strategy for a stand-alone self-excited induction generator driven by a variable speed wind turbine

This paper presents a new control strategy of a stand-alone self-excited induction generator (SEIG) driven by a variable speed wind turbine. The proposed system consists of a three phase squirrel-cage induction machine connected to a wind turbine through a step-up gear box. A current controlled voltage source inverter (CC–VSI) with an electronic load controller (ELC) is connected in parallel with the main consumer load to the AC terminals of the induction machine. The proposed control strategy is based on fuzzy logic control principles which enhance the dynamic performance of the proposed system. Three fuzzy logic PI controllers and one hysteresis current controller (HCC) are used to extract the maximum available energy from the wind turbine as well as to regulate the generator terminal voltage simultaneously against wind speed and main load variations. However, in order to extract the maximum available energy from the turbine over a wide range of wind speeds, the captured energy is limited due to electrical constraints. Therefore the control strategy proposed three modes of control operation. The steady state characteristics of the proposed system are obtained and examined in order to design the required control parameters. The proposed system is modeled and simulated using Matlab/Simulink software program to examine the dynamic characteristics of the system with proposed control strategy. Dynamic simulation results demonstrate the effectiveness of the proposed control strategy.     

基于季节指数调整的循环神经网络风速时间序列预测

提出一种基于季节指数调整的神经网络风速预测方法.针对历史风速之间的非线性关系,运用神经网络非线性拟合能力并结合季节性指数调整对风速时间序列进行预测.通过时序图法和增广Dickey-Fullerd检验法判断时间序列的平稳性,结果表明该序列为非平稳序列.这种不稳定性说明时间序列中可能包含趋势、季节性、循环和不规则成分的一种...     

基于卷积和共享权系数长短时记忆网络时融合机制在风速预测上的应用研究

针对对于风能规划和应用都具有重大影响的风速存在强随机性问题,该文提出结合卷积神经网络（CNN）和共享权重长短期记忆网络（SWLSTM）的空时融合模型（CSWLSTM）,充分提取风速序列中蕴含的空域和时域信息,以提升预测精度。此外,为了获得可靠的风速概率预测结果,提出一种新的结合CNN、SWLSTM和高斯过程回归（GPR）的混合模型,称为 CSWLSTM-GPR。将CSWLSTM-GPR应用于中国内蒙古风速预测案例,从点预测精度、区间预测适用性和概率预测综合性能3个方面与相同结构的CNN和SWLSTM模型的风速预测方法进行比较。CSWLSTM-GPR的可靠性测试保证了预测结果的可靠性和说服力。实验结果表明,CSWLSTM-GPR在风速预测问题上能获得高精度的点预测、合适的预测区间和可靠的概率预测结果,也充分展现了该研究所提出CSWLSTM在风速预测方面具有较好的应用潜力。     

基于模糊逻辑的双馈型风电机组最优功率控制

在分析风电系统特性的基础上,以追踪最大风能作为有功控制目标,提出应用模糊逻辑系统设定发电机最优转速策略,实现无风速测量下最佳叶尖速比运行.以保持发电机最低损耗作为无功控制目标,研究了双馈电机损耗特性,提出应用模糊逻辑系统设定最优无功策略,优化机组效率.讨论了控制系统间的协调方案并建立了完整的风电机组模型进行仿真.结果表明所提策略有效,尤其在机组特性摄动下仍然能够保证最优功率运行.     

基于熵权时序模型的超短期风电功率预测

针对风电具有较强的随机性和波动性,传统的单一预测方法难以准确描述其规律且预测精度较低的问题,提出风速熵和功率熵的概念,在时间序列法的基础上分别采用基于风速和基于功率的预测方法,并根据风速熵和功率熵的计算结果动态设置预测点的权值,建立风电功率的熵权时序模型。算例分析结果表明,所提方法能有效提取风速及功率历史数据中的有用信息,提高超短期风电功率预测精度,预测结果的准确率和合格率均优于神经网络法、时间序列法和基于风速法。     

Comparative study of various artificial intelligence approaches applied to direct torque control of induction motor drives

In this paper, three intelligent approaches were proposed, applied to direct torque control (DTC) of induction motor drive to replace conventional hysteresis comparators and selection table, namely fuzzy logic, artificial neural network and adaptive neuro-fuzzy inference system (ANFIS). The simulated results obtained demonstrate the feasibility of the adaptive network-based fuzzy inference system based direct torque control (ANFIS-DTC). Compared with the classical direct torque control, fuzzy logic based direct torque control (FL-DTC), and neural networks based direct torque control (NN-DTC), the proposed ANFIS-based scheme optimizes the electromagnetic torque and stator flux ripples, and incurs much shorter execution times and hence the errors caused by control time delays are minimized. The validity of the proposed methods is confirmed by simulation results.     

基于多任务学习的风速实时预测方法

准确的秒级风速实时预测能够提高风电机组的运行状况和控制品质,为电网做出最优调度决策提供辅助信息。目前风速实时预测时间分辨率通常为分钟级,且在小数据集的情况下模型泛化能力弱。文章以时间分辨率为5 s的风速序列为研究对象,提出了基于多任务学习的风速实时预测方法。该方法结合了变分模态分解方法和长短期记忆神经网络。首先,通过变分模态将风速序列分解为一系列信号;然后,建立多任务学习的共享层,使用长短期记忆神经网络提取各分解信号中的共享参数,深度挖掘分享子序列预测任务间的信息;最后,建立多任务学习的特定任务层,借助多个LSTM并行预测分解后的风速子序列,并将多个预测结果叠加得到风速实时预测结果。算例结果表明:所提多任务学习模型在10步、5步预测中的均方根误差总体均值分别为0.80 m/s和0.71 m/s,与经过变分模态分解和未经过变分模态分解的单任务模型预测相比,所提模型均方根误差总体均值在10步预测中分别降低了35.5%和39.8%,在5步预测中分别降低了24.5%和45.8%。     

Wind resource assessment of an area using short term data correlated to a long term data set

A method of estimating the annual wind energy potential of a selected site using short term measurements related to one year’s recorded wind data at another reference site is presented. The proposed method utilizes the 1-year measured wind speed of one site to extrapolate the annual wind speed at a new site, using an artificial neural network (ANN). In this study, concurrent measurements from target and reference sites over periods of 1-month and 2-month were used to “train” the ANN. Topographical details or other meteorological data are not required for this approach. After derivation of the simulated wind speed time series for the target site, its mean value and its corresponding Weibull distribution parameters are calculated. The derived Weibull distribution of the simulated wind speed is used to make an assessment of the annual wind energy resource in the new area with respect to a particular wind turbine model. Three pairs of measuring stations in the southwest of Ireland were examined, where the wind potential is high and technically exploitable. Analysis of the measurements showed a reasonable cross-correlation coefficient of the wind speed between the sites. Results indicate that with this method, only a short time period of wind data acquisition in a new area might provide the information required for a satisfactory assessment of the annual wind energy resource. To evaluate the accuracy of the method, simulation results of the 1-month and 2-month training periods are compared to the corresponding actual values recorded at the sites. Also, a comparison with the results of a commercial wind energy assessment software package is presented showing similar results.     

基于风速预测的风电场虚拟惯量协调控制技术

针对现有风电场虚拟惯量协调控制在风电机组调频辅助功率协调分配方面的研究,提出一种基于风速预测的风电场虚拟惯量响应场级协同分配策略,具体是通过经验模式分解（EMD）和BP神经网络训练风速时序序列获得风速时序模型,预测短时段内的风速,根据实时转速和预测风速计算惯量分配权重因子,根据频率变化计算全场惯量响应值,通过惯量分配权重因子结合变流器容量限值给单机分配惯量响应值。该控制策略成功应用于云南某148.5 MW风电场站级调频测试,验证了算法的有效性。     

基于SVPWM的异步风力发电机直接转矩控制方法的研究

提出了一种改进的异步风力发电机直接转矩控制方法,此方法不仅简单,而且性能优于传统的滞环比较器控制方式。利用转矩模糊控制器和磁链控制器代替传统的滞环比较器,通过Matlab/Simulink仿真表明,基于空间矢量脉宽调制的直接转矩改进方法不仅改善了异步发电机稳态转矩脉动大的问题,而且减小了电机启动电流,还大大提高了整个控制系统的性能。     

Enhancement of micro-grid performance during islanding mode using storage batteries and new fuzzy logic pitch angle controller

Power system deregulation, shortage of transmission capacities and needing to reduce green house gas have led to increase interesting in distributed generations (DGs) especially renewable sources. This study developed a complete model able to analysis and simulates in details the transient dynamic performance of the Micro-Grid (MG) during and subsequent islanding process. Wind speed fluctuations cause high fluctuations in output power of wind turbine which lead to fluctuations of frequency and voltages of the MG during the islanding mode. In this paper a new fuzzy logic pitch angle controller is proposed to smooth the output power of wind turbine to reduce MG frequency and voltage fluctuations during the islanding mode. The proposed fuzzy logic pitch controller is compared with the conventional PI pitch angle controller which usually used for wind turbine power control. Results proved the effectiveness of the proposed fuzzy controller in improvement of the MG performance. Also, this paper proposed using storage batteries technique to reduce the frequency deviation and fluctuations originated from wind power solar power fluctuations. Results indicate that the storage batteries technique is superior than fuzzy logic pitch controller in reducing frequency deviation, but with more expensive than the fuzzy controller. All models and controllers are built using Matlab® Simulink® environment.     

Doubly-fed induction generator drive based WECS using fuzzy logic controller

The purpose of this paper is to improve the control performance of the variable speed, constant frequency doubly-fed induction generator in the wind turbine generation system by using fuzzy logic controllers. The control of the rotor-side converter is realized by stator flux oriented control, whereas the control of the grid-side converter is performed by a control strategy based on grid voltage orientation to maintain the DC-link voltage stability. An intelligent fuzzy inference system is proposed as an alternative of the conventional proportional and integral (PI) controller to overcome any disturbance, such as fast wind speed variation, short grid voltage fault, parameter variations and so on. Five fuzzy logic controllers are used in the rotor side converter (RSC) for maximum power point tracking (MPPT) algorithm, active and reactive power control loops, and another two fuzzy logic controllers for direct and quadratic rotor currents components control loops. The performances have been tested on 1.5 MW doubly-fed induction generator (DFIG) in a Matlab/Simulink software environment.     

A novel application of artificial neural network for wind speed estimation

Providing accurate multi-steps wind speed estimation models has increasing significance, because of the important technical and economic impacts of wind speed on power grid security and environment benefits. In this study, the combined strategies for wind speed forecasting are proposed based on an intelligent data processing system using artificial neural network (ANN). Generalized regression neural network and Elman neural network are employed to form two hybrid models. The approach employs one of ANN to model the samples achieving data denoising and assimilation and apply the other to predict wind speed using the pre-processed samples. The proposed method is demonstrated in terms of the predicting improvements of the hybrid models compared with single ANN and the typical forecasting method. To give sufficient cases for the study, four observation sites with monthly average wind speed of four given years in Western China were used to test the models. Multiple evaluation methods demonstrated that the proposed method provides a promising alternative technique in monthly average wind speed estimation.     

Bayesian adaptive combination of short-term wind speed forecasts from neural network models

Short-term wind speed forecasting is of great importance for wind farm operations and the integration of wind energy into the power grid system. Adaptive and reliable methods and techniques of wind speed forecasts are urgently needed in view of the stochastic nature of wind resource varying from time to time and from site to site. This paper presents a robust two-step methodology for accurate wind speed forecasting based on Bayesian combination algorithm, and three neural network models, namely, adaptive linear element network (ADALINE), backpropagation (BP) network, and radial basis function (RBF) network. The hourly average wind speed data from two North Dakota sites are used to demonstrate the effectiveness of the proposed approach. The results indicate that, while the performances of the neural networks are not consistent in forecasting 1-h-ahead wind speed for the two sites or under different evaluation metrics, the Bayesian combination method can always provide adaptive, reliable and comparatively accurate forecast results. The proposed methodology provides a unified approach to tackle the challenging model selection issue in wind speed forecasting.     

基于AdaBoost与BP神经网络的风速预测研究

介绍了基于AdaBoost的多神经网络集成预测方法。集成方法的预测结果优于其他方法的预测结果,这一点在理论上和经验上已经得到证明。AdaBoost是适用于时间序列预测的集成方法。基于AdaBoost算法,采用多个BP神经网络训练随机生成的风速样本,再由多个训练结果生成最终的风速预测值。用该方法预测的误差低于用单一BP神经网络进行的预测,其分析和仿真结果表明了其优越性。