Wind farm power prediction: a data‐mining approach

In this paper, models for short‐ and long‐term prediction of wind farm power are discussed. The models are built using weather forecasting data generated at different time scales and horizons. The maximum forecast length of the short‐term prediction model is 12 h, and the maximum forecast length of the long‐term prediction model is 84 h. The wind farm power prediction models are built with five different data mining algorithms. The accuracy of the generated models is analysed. The model generated by a neural network outperforms all other models for both short‐ and long‐term prediction. Two basic prediction methods are presented: the direct prediction model, whereby the power prediction is generated directly from the weather forecasting data, and the integrated prediction model, whereby the prediction of wind speed is generated with the weather data, and then the power is generated with the predicted wind speed. The direct prediction model offers better prediction performance than the integrated prediction model. The main source of the prediction error appears to be contributed by the weather forecasting data. Copyright © 2008 John Wiley & Sons, Ltd.     

Generating wind time series as a hybrid of measured and simulated data

Certain applications, such as analysing the effect of a wind farm on grid frequency regulation, require several years of wind power data measured at intervals of a few seconds. We have developed a method to generate days to years of non‐stationary wind speed time series sampled at high rates by combining measured and simulated data. Measured wind speed data, typically 10–15 min averages, capture the non‐stationary characteristics of wind speed variation: diurnal variations, the passing of weather fronts, and seasonal variations. Simulated wind speed data, generated from spectral models, add realistic turbulence between the empirical data. The wind speed time series generated with this method agree very well with measured time series, both qualitatively and quantitatively. The power output of a wind turbine simulated with wind data generated by this method demonstrates energy production, ramp rates and reserve requirements that closely match the power output of a turbine simulated turbine with measured wind data. Copyright © 2011 John Wiley & Sons, Ltd.     

Investigation on the integral output power model of a large-scale wind farm

The integral output power model of a large-scale wind farm is needed when estimating the wind farm’s output over a period of time in the future. The actual wind speed power model and calculation method of a wind farm made up of many wind turbine units are discussed. After analyzing the incoming wind flow characteristics and their energy distributions, and after considering the multi-effects among the wind turbine units and certain assumptions, the incoming wind flow model of multi-units is built. The calculation algorithms and steps of the integral output power model of a large-scale wind farm are provided. Finally, an actual power output of the wind farm is calculated and analyzed by using the practical measurement wind speed data. The characteristics of a large-scale wind farm are also discussed.     

Anticipatory Control of Wind Turbines With Data-Driven Predictive Models

The concept of anticipatory control applied to wind turbines is presented. Anticipatory control is based on the model predictive control (MPC) approach. Unlike the MPC method, noncontrollable variables (such as wind speed) are directly considered in the dynamic equations presented in the paper to predict response variables, e.g., rotor speed and turbine power output. To determine future states of the power drive with the dynamic equations, a time series model was built for wind speed. The time series model was fused with the dynamic equations to predict the response variables over a certain prediction horizon. Based on these predictions, an optimization model was solved to find the optimal control settings to improve the power output without incurring large rotor speed changes. As both the dynamic equations and time series model were built by data mining algorithms, no gradient information is available. A modified evolutionary strategy algorithm was used to solve a nonlinear constrained optimization problem. The proposed approach has been tested on the data collected from a 1.5 MW wind turbine.      

Estimation of variability and predictability of large‐scale wind energy in The Netherlands

This paper presents a data‐driven approach for estimating the degree of variability and predictability associated with large‐scale wind energy production for a planned integration in a given geographical area, with an application to The Netherlands. A new method is presented for generating realistic time series of aggregated wind power realizations and forecasts. To this end, simultaneous wind speed time series—both actual and predicted—at planned wind farm locations are needed, but not always available. A 1‐year data set of 10‐min averaged wind speeds measured at several weather stations is used. The measurements are first transformed from sensor height to hub height, then spatially interpolated using multivariate normal theory, and finally averaged over the market resolution time interval. Day‐ahead wind speed forecast time series are created from the atmospheric model HiRLAM (High Resolution Limited Area Model). Actual and forecasted wind speeds are passed through multi‐turbine power curves and summed up to create time series of actual and forecasted wind power. Two insights are derived from the developed data set: the degree of long‐term variability and the degree of predictability when Dutch wind energy production is aggregated at the national or at the market participant level. For a 7.8 GW installed wind power scenario, at the system level, the imbalance energy requirements due to wind variations across 15‐min intervals are ±14% of the total installed capacity, while the imbalance due to forecast errors vary between 53% for down‐ and 56% for up‐regulation. When aggregating at the market participant level, the balancing energy requirements are 2–3% higher. Copyright © 2008 John Wiley & Sons, Ltd.     

基于风电机组无功裕度预测的风电场无功分层控制策略

针对风电电压波动的问题,文章基于风电机组无功裕度预测,提出了一种风电场无功分层控制策略.该策略首先以并网点电压偏差和线路有功损耗最小为目标,使用二次规划算法在线实时求解最优并网电压,进而求解风电场无功参考值;其次,采用EWT-LSSVM预测算法进行风电功率预测,并提出预测功率校正方法实时修正预测功率,精确求解风电机组的...     

Predicting wind power variability events using different statistical methods driven by regional atmospheric model output

Variability in power generation from wind farms is an important issue in the energy industry. If sub‐hour variability events can be predicted, potential disruptions to the grid operations might be mitigated. Using 4 years of 5 min wind power data from the Australian Energy Market Operator for an 80 MW wind farm in south‐east Australia, we fit statistical models of variability on meteorological reanalysis data from the US National Centers for Environmental Prediction. The National Centers for Environmental Prediction fields were transformed into spatial empirical orthogonal functions, and 6 h projections onto these became explanatory covariates for generalized linear, random forest (RF), gradient boosting and support vector machine classification models. Other covariates considered were local wind speed and 6 h‐lagged empirical orthogonal function differences. Models were selected by minimizing cross‐validated misclassification rate and assessed using area under the receiver operating characteristic curve and reliability score. Considering performance and ease of tuning, RFs were preferred. Performance was poorer for larger ramps. The RFs accurately predicted their performance on the validation set. For asymmetric costs (miss‐to‐false alarm cost ratio = 10), RFs yielded competitive low‐cost models. Support vector machines produced slightly superior models but needed to be tuned manually. RF models using atmospheric model output provide a robust approach to predicting wind power variability and relatively large ramp events. We recommend the RF models as a practical and skilful method to feed into an early warning system for energy/electricity operators. Copyright © 2014 John Wiley & Sons, Ltd.     

Models for monitoring wind farm power

Different models for monitoring wind farm power output are considered. Data mining and evolutionary computation are integrated for building the models for prediction and monitoring. Different models using wind speed as input to predict the total power output of a wind farm are compared and analyzed. The k-nearest neighbor model, combined with the principal component analysis approach, outperforms other models studied in this research. However, this model performs poorly when the conditions of the wind farm are abnormal. The latter implies that the original data contains many noisy points that need to be filtered. An evolutionary computation algorithm is used to build a nonlinear parametric model to monitor the wind farm performance. This model filters the outliers according to the residual approach and control charts. The k-nearest neighbor model produces good performance for the wind farm operating in normal conditions.     

基于ADQPSO-KELM风电功率短期预测模型的研究

为了改善传统风电功率预测方法中误差较大且稳定性较差的问题,引入量子粒子群(QPSO)优化算法、自适应早熟判定准则及混合扰动算子,构建了自适应扰动量子粒子群(ADQPSO)优化算法,通过ADQPSO算法对核极限学习机(KELM)模型进行优化,建立了自适应扰动量子粒子群优化的核极限学习机(ADQPSOKELM)风电功率短期预测模型,并利用内蒙古高尔真风电场采集的风电功率时间序列数据为试验样本进行48h预测分析。结果表明,ADQPSO-KELM风电功率短期预测模型与其他基于KELM优化的风电预测模型及传统风电预测模型相比,其预测的误差更小、准确度更高,且预测稳定性显著增强。     

Power optimization of wind turbines with data mining and evolutionary computation

A data-driven approach for maximization of the power produced by wind turbines is presented. The power optimization objective is accomplished by computing optimal control settings of wind turbines using data mining and evolutionary strategy algorithms. Data mining algorithms identify a functional mapping between the power output and controllable and non-controllable variables of a wind turbine. An evolutionary strategy algorithm is applied to determine control settings maximizing the power output of a turbine based on the identified model. Computational studies have demonstrated meaningful opportunities to improve the turbine power output by optimizing blade pitch and yaw angle. It is shown that the pitch angle is an important variable in maximizing energy captured from the wind. Power output can be increased by optimization of the pitch angle. The concepts proposed in this paper are illustrated with industrial wind farm data.     

Using nacelle‐based wind speed observations to improve power curve modeling for wind power forecasting

Wind power forecasting for projection times of 0–48 h can have a particular value in facilitating the integration of wind power into power systems. Accurate observations of the wind speed received by wind turbines are important inputs for some of the most useful methods for making such forecasts. In particular, they are used to derive power curves relating wind speeds to wind power production. By using power curve modeling, this paper compares two types of wind speed observations typically available at wind farms: the wind speed and wind direction measurements at the nacelles of the wind turbines and those at one or more on‐site meteorological masts (met masts). For the three Australian wind farms studied in this project, the results favor the nacelle‐based observations despite the inherent interference from the nacelle and the blades and despite calibration corrections to the met mast observations. This trend was found to be stronger for wind farm sites with more complex terrain. In addition, a numerical weather prediction (NWP) system was used to show that, for the wind farms studied, smaller single time‐series forecast errors can be achieved with the average wind speed from the nacelle‐based observations. This suggests that the nacelle‐average observations are more representative of the wind behavior predicted by an NWP system than the met mast observations. Also, when using an NWP system to predict wind farm power production, it suggests the use of a wind farm power curve based on nacelle‐average observations instead of met mast observations. Further, it suggests that historical and real‐time nacelle‐average observations should be calculated for large wind farms and used in wind power forecasting. Copyright © 2011 John Wiley & Sons, Ltd.     

Optimal selection of time windows for preventive maintenance of offshore wind farms subject to wake losses

The maintenance of wind farms is one of the major factors affecting their profitability. During preventive maintenance, the shutdown of wind turbines causes downtime energy losses. The selection of when and which turbines to maintain can significantly impact the overall downtime energy loss. This paper leverages a wind farm power generation model to calculate downtime energy losses during preventive maintenance for an offshore wind farm. Wake effects are considered to accurately evaluate power output under specific wind conditions. In addition to wind speed and direction, the influence of wake effects is an important factor in selecting time windows for maintenance. To minimize the overall downtime energy loss of an offshore wind farm caused by preventive maintenance, a mixed-integer nonlinear optimization problem is formulated and solved by the genetic algorithm, which can select the optimal maintenance time windows of each turbine. Weather conditions are imposed as constraints to ensure the safety of maintenance personnel and transportation. Using the climatic data of Cape Cod, Massachusetts, the schedule of preventive maintenance is optimized for a simulated utility-scale offshore wind farm. The optimized schedule not only reduces the annual downtime energy loss by selecting the maintenance dates when wind speed is low but also decreases the overall influence of wake effects within the farm. The portion of downtime energy loss reduced due to consideration of wake effects each year is up to approximately 0.2% of the annual wind farm energy generation across the case studies—with other stated opportunities for further profitability improvements.     

基于CEEMDAN二次分解和LSTM的风速多步预测研究

为了提高风速预测的准确性,提出一种基于自适应噪声完备经验模态分解（CEEMDAN）二次分解和长短时记忆（LSTM）网络的风速多步预测方法。该方法首先应用变分模态分解（VMD）将原始风速序列进行一次分解,充分利用其分解后的残余分量并采用CEEMDAN方法进行二次分解;然后将分解后的所有子序列分别输入到LSTM模型中进行风速多步预测;最后将各模型输出结果进行叠加获得预测风速。以内蒙古某风电场实测数据为例进行建模和预测分析,结果表明所提出的风速多步预测模型具有较高的预测精度,具备实际应用的可行性。     

基于RTDS的风电场等值建模与验证分析

针对风电场运行条件复杂、运行工况动态变化导致风电场输出功率的分散性问题,采用数理统计方法对风电场外特性进行稳态等值,考虑到大型风电场所处地形复杂、机群分布不规则带来的风速差异性问题,以风电场内长时间尺度实测风速数据作为特征变量,采用改进动态聚类算法进行机群划分,进而基于风电场参数对等值机模型的参数进行聚合辨识。基于RTDS实时数字建模及仿真试验分析结果表明,建立的风电场等值模型能够准确地反映风电场在不同风速及电网侧短路故障下的动态特性,可用于含双馈风电机组风电场接入电力系统稳定性分析。     

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。     

A novel evolutionary algorithm for dynamic economic dispatch with energy saving and emission reduction in power system integrated wind power

An optimization algorithm is proposed in this paper to solve the economic dispatch problem that includes wind farm using the Chaotic Quantum Genetic Algorithm (CQGA). In addition to the detailed models of economic dispatch introduction and their associated constraints, the wind power effect is also included in this paper. The chaotic quantum genetic algorithm used to solve the economic dispatch process and discussed with real scenarios used for the simulation tests. After comparing the proposed algorithm with several other algorithms commonly used to solve optimization problems, the results show that the proposed algorithm is able to find the optimal solution quickly and accurately (i.e. to obtain the minimum cost for power generation in the shortest time). At the end, the impact to the total cost savings for power generation after adding (or not adding) wind power generation is also discussed. The actual implementation results prove that the proposed algorithm is economical, fast and practical. They are quite valuable for further research.     

Impacts of Wind Power on Thermal Generation Unit Commitment and Dispatch

This paper proposes a new simulation method that can fully assess the impacts of large-scale wind power on system operations from cost, reliability, and environmental perspectives. The method uses a time series of observed and predicted 15-min average wind speeds at foreseen onshore- and offshore-wind farm locations. A Unit Commitment and Economic Dispatch (UC-ED) tool is adapted to allow for frequent revisions of conventional generation unit schedules, using information on current wind energy output and forecasts for the next 36 h. This is deemed the most faithful way of simulating actual operations and short-term planning activities for a system with large wind power penetration. The problem formulation includes ramp-rate constraints for generation schedules and for reserve activation, and minimum up-time and down-time of conventional units. Results are shown for a realistic future scenario of the Dutch power system. It is shown that problems such as insufficient regulating and reserve power-which are typically associated with the variability and limited predictability of wind power-can only be assessed in conjunction with the specifics of the conventional generation system that wind power is integrated into. For the thermal system with a large share of combined heat and power (CHP) investigated here, wind power forecasting does not provide significant benefits for optimal unit commitment and dispatch. Minimum load problems do occur, which result in wasted wind in amounts increasing with the wind power installed     

酒泉地区风电场风电功率预报研究

利用NOAA天气预报模式Weather Research andForecasting Model(WRF)结合统计订正方法对酒泉地区短期风电功率预报进行了预报实验。与实际出力比较24 h短期风电功率预报精度较高。并在此基础上利用风电场附近测风塔观测数据通过时间序列发进行了0~4 h超短期预报实验,预报结果显示0~2 h预报结果有利于运行调度。