Wind Power Curve Research Articles

An abnormal wind turbine data cleaning algorithm based on color space conversion and image feature detection

Wind power curve (WPC) is established through data collected from the Supervisory Control and Data Acquisition (SCADA) system of each wind turbine, which can be used to analyze the operation status. However, numerous outliers are contained in SCADA data caused by wind turbine failures, shutdown maintenance or other extreme conditions to deform the wind power curve. This paper proposes a data cleaning algorithm for wind turbine abnormal data based on wind power curve image by color space conversion and image feature detection. Considering wind speed, wind power and data frequency, a three-dimensional (3D) WPC image is constructed. The scattered outliers are cleared by their statistical characteristics. The Canny edge detection and Hough transform are introduced to extract image features of stacked outliers and locate them accurately. The proposed algorithm is compared with three common outlier detection algorithms, including two data-based algorithms and an image-based algorithm. Extensive experiments conducted on the data of 22 wind turbines from two different wind farms in China indicate the efficiency, stability and reliability of the proposed algorithm.

Wind Power Curve Modeling With Hybrid Copula and Grey Wolf Optimization

Wind power curve modeling has important applications in many fields, such as wind turbine condition monitoring and wind power forecasting. There are various studies on wind power curve construction though, the relation between wind speed and errors is not taken into account when constructing wind power curves. To fill this gap, a hybrid copula-based wind power curve model (HCCM) is proposed in this paper. The independent distribution of wind speed is modeled by a Weibull distribution, and the independent distribution of errors is modeled by a mixture of asymmetric gaussian models. Then a hybrid copula model, including four copula functions, is designed to construct the joint distribution. Finally, a grey wolf optimization algorithm is applied to optimize the regression parameters of wind power curves by taking the log-likelihood function of the joint distribution as the fitness function. The proposed model is compared with ten benchmark models on four wind farms and two wind turbines. Experiments show that in terms of RMSE the HCCM achieves 99.3131%, 99.1197%, 99.1715% and 98.9339% on four wind farms, respectively, and around 80% improvement in RMSE on two wind turbines.

Assessing the reliability of wind power operations under a changing climate with a non-Gaussian bias correction

Facing increasing societal and economic pressure, many countries have established strategies to develop renewable energy portfolios whose penetration in the market can alleviate the dependence on fossil fuels. In the case of wind, there is a fundamental question related to the resilience and hence profitability of future wind farms to a changing climate, given that current wind turbines have lifespans of up to 30 years. In this work we develop a new non-Gaussian method to adjust assimilated observational data to simulations and to estimate future wind, predicated on a trans-Gaussian transformation and a clusterwise minimization of the Kullback–Leibler divergence. Future winds abundance will be determined for Saudi Arabia, a country with a recently established plan to develop a portfolio of up to 16 GW of wind energy. Further, we estimate the change in profits over future decades using additional high-resolution simulations, an improved method for vertical wind extrapolation and power curves from a collection of popular wind turbines. We find an overall increase in daily profit of $272,000 for the wind energy market for the optimal locations for wind farming in the country.

Collaborative scheduling of source-network-load-storage considering thermal inertia of the integrated electricity and district heating systems

ABSTRACT With the strengthening of carbon emission control, the development of clean renewable energy has become a unanimous choice for many countries. However, in the integrated electricity and district heating system (IEDHS), the tightly coupled relationship between heat and electricity will make the system lack the flexibility for wind power integration. A source-network-load-storage scheduling scheme considering thermal inertia (TI-based SNLS scheduling) is proposed to realize the coordinated operation of source, network, load, and storage in IEDHS. The simplified thermal inertia model of district heating network (DHN) and building, and the charging-discharging model of a solid regenerative electric boiler (SREB), are also introduced. Four comparative cases are conducted based on wind power data of Jilin Province for a day-ahead dispatch problem. The results indicate that the scheme achieves a 9.19% improvement on the regulation flexibility of the combined heat and power (CHP) units, and the wind curtailment rate is reduced by 16.14%. The research also finds that using thermal inertia can alleviate the contradiction between heat load and wind power curve, but the contribution from DHN is limited compared with buildings. SREB can directly absorb wind power to compensate for heating, which has considerable potential in promoting wind power utilization. Furthermore, the indicative calculation of financial payback time considering carbon trading income elucidates the economic feasibility of the case system.

Correlational broad learning for optimal scheduling of integrated energy systems considering distributed ground source heat pump heat storage systems

Renewable energies play an irreplaceable role in building an environment-friendly society; nevertheless, the curtailment phenomenon of renewable energies is serious. To improve the application of renewable energies and save economic costs, an optimal scheduling model based on distributed ground source heat pump heat storage system (DGSHPHSS) is established in this paper. The DGSHPHSS operates as an electrical load to store thermal energy during the valley load period, reducing the curtailment of wind power generation; DGSHPHSS provides thermal energy to the users during the peak load period, reducing the power cost. Besides, to more accurately and faster predict the load curve, this paper proposes a correlational broad learning (CBL) prediction model. The maximum wind power curtailment and economic costs with DGSHPHSSs under the low wind power curves are reduced by 50% and $254,500 than no DGSHPHSS, respectively; the peak values of wind power curtailment and economic costs with DGSHPHSSs under the high wind power curves are reduced by 40% and $65,300, respectively. The prediction model is inspired by sequence characteristics and external factors such as ambient temperature and time-of-use electrical prices. The prediction error obtained by the CBL prediction model can be reduced to 4.52% in simulating integrated energy systems (IESs).

Scenario forecasting for wind power using flow-based generative networks

Wind power prediction is an integral part of power system operations and planning. Due to rising penetrations of wind turbines, fluctuation and intermittence of wind powers seriously limit the accuracy of power forecasts. A popular way to mitigate this challenge is to provide a range of possible scenarios instead of deterministic point forecasting values, so operators can account for the uncertainties. This paper proposes a model-free scenario forecasting approach for wind powers using flow-based generative networks, which generate a set of high-quality scenarios to represent possible behaviors based on historical wind powers and deterministic point forecasting values. Firstly, an unsupervised deep learning framework is proposed to learn the latent patterns in historical wind power curves. Then, a large number of possible future scenarios are obtained by solving an optimization problem. Simulation results show that the proposed approach has better performance than popular baselines such as variational auto-encoder and generative adversarial networks.

Joint estimation of monotone curves via functional principal component analysis

A functional data approach is developed to jointly estimate a collection of monotone curves that are irregularly and possibly sparsely observed with noise. In this approach, the unconstrained relative curvature curves instead of the monotone-constrained functions are directly modeled. Functional principal components are used to describe the major modes of variations of curves and allow borrowing strength across curves for improved estimation. A two-step approach and an integrated approach are considered for model fitting. The simulation study shows that the integrated approach is more efficient than separate curve estimation and the two-step approach. The integrated approach also provides more interpretable principle component functions in an application of estimating weekly wind power curves of a wind turbine.

Intraday wind power forecasting employing feedback mechanism

The increasing thrust of the world to reduce the dependency on the fossil fuel-based sources has given rise to the high penetration of Renewable Energy Sources (RES). The major RES that are increasing on the large scale throughout the world are solar and wind energy. However, at the grid level, these technologies present a major drawback due to variable energy on account of intermittency and unpredictability. This study presents an intraday forecasting of wind power using the Autoregressive Integrated Moving Average model and wavelet decomposition. The forecasting interval of 15 min has been used for forecasting employing two data sets: first the actual wind power and second the error wind power, generated form NIWE forecasting model. The results were computed for 106 Wavelet-ARIMA cases to obtain the best wavelet model for the wind forecasting problem. The interval of past data blocks for forecasting has also been optimally worked alongside the discussion of the best wavelet for the forecasting problem. The study develops the concept of wavelet decomposition in-depth along with the feedback mechanism that improves the ability of the existing forecasting models with respect to following of actual wind power curve.

Evaluation Method of Wind Power Consumption Capacity Based on Multi-Fractal Theory

An analysis model of wind power consumption capacity is established with the multi-fractal theory. Firstly, the fluctuation characteristics of wind power are described through multi-fractal parameters, and the correlation between wind power fluctuation characteristics and consumption capacity are analyzed. Afterwards, the swinging door algorithm (SDA) is applied to divide the wind power curve in the evaluation period, and the fluctuation process with similar characteristics is clustered. Further, a functional analysis model to evaluate wind power consumption capacity is mentioned based on the fluctuation clustering results. Finally, the effectiveness of the method is verified by an example of a regional power grid in China, and the influence of adjustable parameters in the model on the consumption capacity is quantitatively analyzed.

Wind Power Curve Data Cleaning by Image Thresholding Based on Class Uncertainty and Shape Dissimilarity

With the rapid development of wind farm worldwide, monitoring the status of numerous wind turbines becomes the essential work. Abnormal data in wind power curve (WPC) are quite important for wind farm operations and maintenances because they usually reveal wind turbine failures or some extreme conditions. This paper proposes a new algorithm of WPC abnormal data detection and cleaning by image thresholding based on minimization of dissimilarity-and-uncertainty-based energy (MDUE). The basic idea is to transform the scattered data into a digital image and the problem of data cleaning is turned into an image segmentation problem. For all data pixels, the confidences of being classified as normal class are computed and make up a grey level feature image. Then the optimum threshold is determined by searching through the energy space based on intensity-based class uncertainty and shape dissimilarity. Finally, the normal and three types of abnormal data are marked after applying image thresholding to the feature image. The algorithm is compared with several data-based algorithms and a recently published image-based algorithm. A large number of experiments conducted on real-world WPC data collected from 37 wind turbines in two wind farms verified the superior performance of the proposed method.

Pipeline for Annual Averaged Wind Power Output Generation Prediction of Wind Turbines Based on Large Wind Speed Data Sets and Power Curve Data

In this article, an abstract framework for annual averaged wind power output generation prediction of wind turbines is presented which is heavily based on large wind speed data sets and power curve data of wind turbines due to the rising interest in wind energy as one main future renewable energy source. As combinations of arbitrary power curve modeling techniques and arbitrary wind speed distributions based on wind speed data are seldom combined, the abstract combination of these two aspects in wind power output generation prediction in one pipeline is thoroughly described here. Conclusively, one detailed example wind speed data set from a weather station situation in Bremen, Germany illustrates applicability of the presented framework.

Assessment accuracy of power system frequency security with additional frequency controls in wind turbines

Power grids all over the world are nowadays facing high penetrations of renewable power generation. The converter-based generation units play a major role in system behaviour and operation. Thereby, also the reliability of the power system is impacted in many different aspects. It has to be verified whether the frequency reliability assessment, which is often studied by simulations, is still accurate, every time a new control structure is considered. For this, the number of simulations should not be further increased if possible, in order to keep the computational efforts low. In this study, the authors verify that the assessment outcome will be accurate when the load duration curve (LDC) and wind power curve (WPC) of a system are described well enough. This is shown to be true for different frequency controls, implemented in wind power plants in the system. Besides, the effects of different LDCs and WPCs are analysed, as they can change over time. The used methodology is validated on the IEEE reliability test system (IEEE RTS).

Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days

With the continuous optimization of energy structures, wind power generation has become the dominant new energy source. The strong random fluctuation of natural wind will bring challenges to power system dispatching, so it is necessary to predict wind power. In order to improve the short-term prediction accuracy of regional wind power, this paper proposes a new combination prediction model based on convolutional neural network (CNN) and similar days analysis. Firstly, the least square fitting and batch normalization (BN) are used to preprocess the data, and then the recent historical wind power data set for CNN is established. Secondly, the Pearson correlation coefficient and cosine similarity combination method are utilized to find similar days in the long-term data set, and the prediction model based on similar days is constructed by the weighting method. Finally, based on the particle swarm optimization (PSO) method, a combined forecasting model is established. The results show that the combined model can accurately predict the future short-term wind power curve, and the prediction accuracy is improved to different extents compared to a single method.

Analysis and application of the relationship between wind power curve and power generation based on operating data

Wind power curve has a certain promotion effect on increasing the power generation. Firstly, this paper analyzes various factors that affect the power curve of a wind turbine, and establishes a mathematical model of the power curve. Secondly, the relationship between wind power generation and power curve is analyzed, and the calculation formula of annual power generation is proposed. Finally, based on the data of an actual operating wind farm, a statistical analysis of annual power generation is carried out, and the power curves of two typical units is compared to analyze the causes. Through the analysis of examples, the effectiveness of the proposed method is verified, and it can provide a reference for improving the operation level of wind turbines.

Offshore wind turbine reliability and operational simulation under uncertainties

AbstractThe fast‐growing offshore wind energy sector brings opportunities to provide a sustainable energy resource but also challenges in offshore wind turbine (OWT) operation and maintenance management. Existing operational simulation models assume deterministic input reliability and failure cost data, whereas OWT reliability and failure costs vary depending on several factors, and it is often not possible to specify them with certainty. This paper focuses on modelling reliability and failure cost uncertainties and their impacts on OWT operational and economic performance. First, we present a probabilistic method for modelling reliability data uncertainty with a quantitative parameter estimation from available reliability data resources. Then, failure cost uncertainty is modelled using fuzzy logic that relates a component's failure cost to its capital cost and downtime. A time‐sequential Monte Carlo simulation is presented to simulate operational sequences of OWT components. This operation profile is later fed into a fuzzy cost assessment and coupled with a wind power curve model to evaluate OWT availability, energy production, operational expenditures and levelised cost of energy. A case study with different sets of reliability data is presented, and the results show that impacts of uncertainty on OWT performance are magnified in databases with low components' reliability. In addition, both reliability and cost uncertainties can contribute to more than 10% of the cost of energy variation. This research can provide practitioners with methods to handle data uncertainties in reliability and operational simulation of OWTs and help them to quantify the variability and dependence of wind power performance on data uncertainties.

Turbine-specific short-term wind speed forecasting considering within-farm wind field dependencies and fluctuations

The unprecedented scale and sophistication of wind turbine technologies call for wind forecasts of high spatial resolution, i.e. turbine-tailored forecasts, to inform several operational decisions at the turbine level. Towards that, this paper is concerned with leveraging the hub-height measurements collected from a fleet of turbines on a farm to make turbine-specific short-term wind speed and power predictions. We find that the wind propagation across a dense grid of turbines induces strong spatial and temporal dependencies in the within-farm wind field, but also gives rise to high-frequency high-magnitude fluctuations which may compromise the predictive accuracy of several data-driven forecasting methods. To capture both aspects, we propose to model the total variability in the within-farm wind speed field as a combination of two independent stochastic process terms. The first term reconstructs and extrapolates the wind speed field by learning the complex spatio-temporal dependence structure using hub-height turbine-level data. The second term accounts for high-frequency high-magnitude fluctuations that are not informed by near-term spatio-temporal dependencies. The two terms are coupled to make probabilistic wind speed forecasts at each turbine, which are then translated into turbine-specific power predictions via wind power curves. Evaluation on more than 3,000,000 data points from a wind farm dataset provides a strong empirical evidence in favor of the proposed method’s forecasting accuracy. On average, our proposed method achieves 9% accuracy improvement relative to persistence forecasts, and 7–9% relative to a set of widely recognized forecasting methods such as autoregressive-based models and Gaussian Processes.

Wind Turbine Power Curve Modelling using Robust Regression Techniques

The need of accurate wind turbine power curve modelling is essential as it provides great insight on the performance of the power system and ideal estimate of generation of power by turbines. However, presence of non linear relationship between output power of turbine and its primary and secondary parameters imposes restrictions to predict exact power generated. In this work wind power curve modelling is accomplished using different robust linear techniques that reduces the effects of outliers and provides precise results in terms of power generation. Mean Absolute Error(MAE), Root Mean Square Error(RMSE), and R^2 score were used as a measure of approximation method accuracy. Since above metrics doesn't provide any information about fitted model insights like overfiiting and underfitting, so bias and variance are considered as the two important aspects that clearly assesses model complexity. By decomposing the MSE into bias and variance a clear insight about model structure is obtained with a potential way with regard to which error component is likely to contribute more degradation of model performance.

Study and Application of Wind Turbine Power Loss Assessment based on GD-BIN Algorithm

In order to accurately assess the lost production of each wind turbine, a fusion method, which is the combination of Gaussian Distribution and bins of method (GD-BIN), was established. Firstly, Complete the first cleaning of operation data according to the operation characteristics and status logs of the unit. Further, GD-BIN algorithm is employed to establish the Wind Power Curve Model (WPCM), which can reflect the real performance of each unit. Thus, the operation data of the unit is delivered to WPCM to calculate theoretical power and lost power at each statistical moment. To verify the accuracy, it is applied in a wind farm in Northeast China and is compared to the other three current methods in the industry. Result shows that the proposed algorithm could effectively estimate lost production with a high accuracy.

Research on short-term wind power combined forecasting and its Gaussian cloud uncertainty to support the integration of renewables and EVs

Under the pressure of environmental pollution and energy shortage, wind power generation and EVs with clean and pollution-free characteristics have developed rapidly. However, the randomness of EVs charging and the volatility of wind power output will bring great challenges to the reliability and economy of grid operation. Especially the accuracy and range of wind power forecasting are critical to the operation of the power system with a high proportion of renewable energy and EVs. Aiming at improving the accuracy of short-term wind power forecasting and its uncertainty, this paper puts forward a combined forecasting model, including BP, Wavelet, and RVM by information fusion strategy, Gaussian Cloud model is used to reflect the uncertainty in the forecasting process. According to the measured data of two units, the results of short-term wind power forecasting are analyzed and compared with the single forecasting method. It’s found that the combined forecasting model can improve the forecasting accuracy with more reasonable confidence interval. The power grid can guide the EVs to dynamically adjust the EVs charging time according to the forecasting wind power and EVs charging power curves, so as to maximize the absorption of wind power, achieve the economic operation and reduce pollution emissions.

Output wind power curve optimization based on design wind speed concept

The output wind power curve versus wind speed is the most important characterization parameter of wind turbines. It allows quantifying and analyzing the design performances of wind turbines, monitoring its database, and controlling the operation modes and manufacturing products. Wind power curve can be used to select the proper rotor size to estimate the potential of wind energy at candidate wind sites and to assess the control device of the operating conditions. Developing model strategies for wind farms has the basic objectives such as the optimization of wind power produced and the minimization of dynamic loads to provide the best quality of output wind power at reasonable cost. Optimal design of wind turbines requires maximum-closing to the cubical output wind power curve despite technical and economic considerations. This study aims to determine the design wind speed of a wind turbine based on modeling-optimization of the output wind power curve under certain working conditions. The procedure is applied to a unit wind turbine in Gamesa wind farm (G52/850, 10.2 MW, http://www.thewindpower.net ) connected to an electrical grid located in south-west Algeria and extrapolated for other windy sites in Algeria. From simulation results, the design wind speed to inlet wind speed ratio [Formula: see text] increased from 0.35 to 7.68 once [Formula: see text] increased from 0.001 to 2.9999. Consequently, the output wind power predicted an increase of about 17.7% and an annual specific wind energy factor of about 2.55%–4% than nominal value given by the manufacturer, reducing the unit average cost of the electricity, generated by wind farms, by about 18.75%.