Wind Power Ramp Events Research Articles

A graph attention network with spatio-temporal wind propagation graph for wind power ramp events prediction

The increasing penetration rate of wind power underscores the necessity for accurate forecasting and alerting of wind power ramp events (WPREs), given the unpredictable nature of wind. This article presents a novel approach to predicting WPREs, emphasizing the complexities of wind propagation across multiple locations, in contrast to traditional single-site analyses. By integrating a wind propagation graph into a Graph Attention Network (GAT), the prediction of ramp events is significantly enhanced. The efficacy of this approach is validated through comprehensive case studies utilizing the Spatial Dynamic Wind Power Forecasting (SDWPF) dataset from the Baidu KDD Cup 2022 and the WIND toolkit dataset from NREL, demonstrating superior results at both the wind turbine and wind farm scales.

Prediction of wind power ramp events via a self-attention based deep learning approach

With the increasing popularity of wind power generation, the importance of accurate wind power ramp events (WPRE) prediction has been further emphasized. At present, WPRE prediction model based on deep learning shows superior performance, which does not need to fully grasp the operation of wind power generation, but ignores the interdependence mechanism of prediction error along the input characteristics of the neural network. This paper develops a self-attention (SAM) and wavelet transform (WT) based hybrid 1DCNN (one-dimensional convolutional neural network) and LSTM (long short term memory networks) for WPRE forecasting. In the proposed model, WT effectively separates the middle and high frequency noise signals in wind power and SAM can redistribute the neural weights in 1DCNN-LSTM, then 1DCNN-LSTM further extracts the space-time information of effective wind power. Finally, the performance of the forecast model proposed in this paper is tested by using the wind power data from the Belgian ELIA website. The experimental results show that, compared with the other six competition models, the proposed model effectively suppresses the influence of randomness, volatility and uncertainty of wind power on the recognition of ramp events. Furthermore, through quantitative analysis, this paper explains the reason of SAM is superior to traditional AM in terms of their application object and weight control range in the proposed model.

Correlation analysis of wind power and meteorology based on the ramp detection method with adaptive sliding window

Abstract Since the penetration of wind power rises rapidly, the coupling between wind power and meteorology strengthens the dependence of the new power system on meteorology. The fluctuation of wind power and the ramp events become key challenges that affect the safe and stable operation of the power system. To balance the accuracy and efficiency of wind power ramp event detection, an adaptive sliding time window-based ramp event detection method is proposed, which includes three major steps: dataset preprocessing and compression, wind power ramp trend pattern extraction considering bump event screening, and wind power ramp detection based on adaptive sliding time window. The correlation between the ramp event of wind power and meteorological factors like temperature, wind speed and direction are analyzed, and the feasibility of the proposed method is verified. The results show that wind speed is the decisive meteorological factor influencing ramp events, and wind power is also affected by wind direction. However, the correlation between wind power and temperature is weak, which only works in a few ramp events.

The impact of future UK offshore wind farm distribution and climate change on generation performance and variability

The UK plans to significantly increase offshore wind generation capacity as part of the effort to achieve net zero targets. Current installation is densely located in a few areas, particularly off the east coast of England, and although current siting proposals include new offshore regions, significant volumes of wind generation capacity are yet to be located to meet 2050 installation targets. This paper uses a recent dataset of multi-decadal offshore wind power capacity factor timeseries to assess how UK offshore wind generation is likely to be affected by both the spatial distribution of future wind farms, and by the impacts of near-future (2020–2050) climate change. We determine that a wider geographic spread of offshore capacity results in a much-improved and less-variable UK-aggregated power generation profile, with substantial reductions in periods of low generation and extreme wind power ramping events, without negatively impacting mean or peak generation outputs. The impact of near-term climate change appears to be minor, slightly reducing overall generation and possibly resulting in an underestimation of future installation requirements, but this climate signal is outweighed by the effects of spatial distribution, and even more so by inherent hourly to inter-annual wind speed variability. This study implies that the intermittency of wind generation can be partly mitigated through increasing the spatial diversity of the existing wind farm distribution. Alongside a more in depth investigation of future climate change, and a holistic assessment of relevant geospatial factors such as Levelised Cost of Energy, infrastructure, and environmental constraints, this study could be used for optimisation of future offshore wind siting.

Analysis of bias correction of HRRR model outputs for offshore wind power ramp events

This study examines the impact of bias correction on the prediction of offshore wind power ramp events (WPREs) using NOAA's High-Resolution Rapid Refresh (HRRR) model. The analysis focuses on New England's offshore region, using Light Detection and Ranging (LiDAR) wind observations and the power curve derived from the nearby Block Island wind farm as a reference. The study characterizes bias in terms of amplitude and phase, finding a 30-min systematic lag error and a marginal underestimation of high wind speeds. Perfect bias correction treatments including both the amplitude and the phase are applied to HRRR wind speed, and their impact on WPREs is assessed using categorical performance measures. A large discrepancy in the number of cases with HRRR having much fewer cases than the reference is found, which is attributed to the model's temporal and spatial resolution. The results show that the amplitude bias correction has a marginal impact on reducing frequency bias, while the dynamic time warping (DTW) used in the phase bias correction shows promising results but limited performance. The study concludes that phase bias correction can be a feasible option to reduce bias in WPREs in offshore power systems.

Wind Speed Ramp Rate Predictions Using Wind Farm SCADA Data Assimilation and a WRF Ensemble

This study presents a novel method for improving wind power ramp events forecasts up to six hours ahead by utilizing data assimilation of SCADA measurements with an ensemble of Weather Research and Forecasting (WRF) models estimates. Leveraging data from nine wind farms in France and Belgium, the approach aims to improve WRF model predictions for wind speed and ramp event timing. The methodology employs grid and observational nudging techniques, enhancing model accuracy by incorporating real-time observational data. Key findings demonstrate that nudging significantly reduces Mean Absolute Error (MAE), decreases the Time Distortion Index (TDI), and increases the Probability of Detection (POD) of ramp events. Nudged ensemble members outperform non-nudged counterparts, exhibiting better accuracy in identifying true ramp events and reducing false alarms. MAE, TDI and POD improvements are as high as 3.7%, 8.5% and 37%, respectively. The study also explores the benefits of an ensemble approach, highlighting improved accuracy in predicting ramp rate magnitudes and providing valuable insights for grid stability management. This research contributes to wind power forecasting, showcasing the importance of integrating SCADA data into predictive models.

A wind power ramp event detection method based on eigenvalue correction and trend integration

Wind power ramp events have the characteristic of small probability and big hazard, so it is significant to improve the recognition rate and accuracy of ramp events for the safe and stable operation of the power grid. In order to improve the efficiency of ramp event detection, a wind power ramp event detection method based on eigenvalue evaluation correction and trend integration is proposed by combining the feature information of ramp events. The original wind power data is extracted with extreme value feature points and corrected with eigenvalue evaluation to achieve the trend feature extraction effect. In order to avoid being affected by small power jitter events, a trend labeling method is used to integrate the correction sequences. The actual wind power data of a wind farm in Xinjiang is used as an example for ramp event detection. The results of the case show that compared with the original swinging door algorithm, the proposed method has both better trend extraction effect and can avoid the influence of power small jitter events, and can more accurately and more completely identify ramp events.

Multi-Lever Early Warning for Wind and Photovoltaic Power Ramp Events Based on Neural Network and Fuzzy Logic

Multi-Lever Early Warning for Wind and Photovoltaic Power Ramp Events Based on Neural Network and Fuzzy Logic

Characterizing ramp events in floating offshore wind power through a fully coupled electrical-mechanical mathematical model

Floating Offshore Wind Turbines (FOWTs) exhibit a noteworthy nonlinear low-frequency dynamic response during rated and higher wind-wave scenarios, leading to a substantial exacerbation of its power characteristics, power stability, and wind energy forecast reliability. This study develops a fully coupled FOWT model to investigate the impact of wind and wave loads on wind power ramp events (WPREs), which integrates mechanical and electrical factors, including a spar buoy FOWT with generator, converter, and aero-hydro-servo-elastic (AHSE) dynamics. Results indicate that the floating structure enables periodic and significant WPREs of FOWTs, as wave load and platform natural motion causing low and ultra-low frequency response. In contrast to bottom-fixed offshore wind turbines (OWT), FOWTs exhibit a supplementary decline in rated power output ranging from 7.9 % to 40.5 % during WPREs. Moreover, ramp peak mainly depends on the aerodynamic loads, but become sensitive to wave loads characterized by wave heights over 2.52 m. FOWT power performance is highly unstable within the rated wind speed range, under WPREs within ultra short time period, resulting in failure to meet grid standards, emphasizing the external need for targeted power compensation and power signal processing. Overall, this study highlights the importance of pitch motion and wave load impact for WPRE study of FOWTs.

An Integrated Scheme for Forecasting and Controlling Ramps in Offshore Wind Farms Considering Wind Power Uncertainties during Extreme Storms

Global warming-induced extreme tropical storms disrupt the operation of offshore wind farms, causing wind power ramp events and threatening the safety of the interconnected onshore grid. In order to attenuate the impact of these ramps, this paper proposes an integrated strategy for forecasting and controlling ramps in offshore wind farms. First, the characteristics of wind power ramps during tropical storms are studied, and a general ramp control framework is established. Second, a wind power ramp prediction scheme is designed based on a minimal gated memory network (MGMN). Third, by taking into account the wind power ramp prediction results and wind power uncertainties, a chance-constraint programming-based optimal ramp control scheme is developed to simultaneously maximize wind power absorption and minimize ramp control costs. Finally, we use real-world offshore wind farm data to validate the effectiveness of the proposed strategy.

Piecewise linear approximation for identifying wind power ramp events

WPREs (wind power ramp events) are one of the most critical factors affecting the security and protection of the electrical system. Accurate ramp event detection may help power systems better manage extreme events and reduce financial damage. In this study, We present an improved piecewise linear approximation for recognizing wind ramps in Kanyakumari district. In practise, wind power ramps can be decreased by properly managing and dispatching flexible reserve and associated services. This necessitates the use of proper ramp detection techniques as well as precise ramp forecasts. The method’s plan to break down wind power signal into increasing with increasing ramps, making ramp identification easier and ensuring that all conceivable ramps of varying lengths are identified. Using observed wind power data, the ramp detection method is used to assess the performance of an energy wind farm. The results reveal that identifying wind power ramps using the segmentation method is equivalent to optical ramp identification.

A novel transformer ordinal regression network with label diversity for wind power ramp events forecasting

Large-scale integration of wind energy and large-amplitude wind power fluctuation in minutes to hours, imposes unprecedented challenges of retaining reliable and secure power systems. Proper detection and precise prediction of wind power ramp events could help power systems better optimize scheduling and mitigate the impact of extreme events. Thus, this paper proposes a novel ramp event forecasting approach named Informer Ordinal Regression Network with Label Diversity (IFORNLD), without distributional assumptions and increasing the computational complexity. The proposed framework retains the superiority of the Informer architecture which can easily handle very long sequences with efficient computation and effective gradient in training, and the strengths of the ordinal regression with label diversity (ORLD) method, which creates a multi-output model that creates diversity with similar computational complexity. The result of case studies on three wind power datasets demonstrate that the proposed framework is superior than some benchmark models, validating its effectiveness and advantages.

Analysing wind power ramp events and improving very short‐term wind power predictions by including wind speed observations

AbstractThough wind power predictions have been consistently improved in the last decade, persistent reasons for remaining uncertainties are sudden large changes in wind speed, so‐called ramps. Here, we analyse the occurrence of ramp events in a wind farm in Eastern Germany and the performance of a wind power prediction tool in forecasting these events for forecasting horizons of 15 and 30 min. Results on the seasonality of ramp events and their diurnal cycle are presented for multiple ramp definition thresholds. Ramps were found to be most frequent in March and April and least frequent in November and December. For the analysis, the wind power prediction tool is fed by different wind velocity forecast products, for example, numerical weather prediction (NWP) model and measurement data. It is shown that including observational wind speed data for very short‐term wind power forecasts improves the performance of the power prediction tool compared to the NWP reference, both in terms of ramp detection and in decreasing the mean absolute error between predicted and generated wind power. This improvement is enhanced during ramp events, highlighting the importance of wind observations for very short‐term wind power prediction.

A trend-based method for the prediction of offshore wind power ramp

Wind power ramp is a destructive event accompanied with a sharp change of wind power. Offshore wind power is expected to receive more attention for it can harvest consistent and strong winds. In this paper, a ramp detection framework based on the swinging door algorithm (SDA) and the mergence of correct ramps is proposed. Meanwhile, a trend-based prediction method (T-Method) is proposed to predict offshore wind power ramps. The ramp detection framework is utilized to identify wind power ramp events (WPREs) and label original data according to the detection results. After that, the labeled data is selected as the input of recurrent neural network to produce wind power prediction results. Finally, the prediction results are detected by the ramp detection framework to produce WPREs prediction results. Four recurrent neural network models and two traditional methods are applied to two offshore wind power datasets for corroborating the effectiveness of our method. Comparative experiments show that our proposed method performs excellent in all evaluation indicators. The proposed WPREs prediction method improves the performance of WPREs prediction under the premise of low false alarm rate and missing alarm rate. It provides early warning for power system operators to reduce the harm of WPREs.

A direct prediction method for wind power ramp events considering the class imbalanced problem

AbstractPredicting wind power ramp events directly based on the historical ramp event time series has drawn increasing attention recently. But the class imbalance problem of the ramp event time series significantly affects the prediction accuracy of ramp events. In the present study, a layer oversampling (LOS) method is proposed considering the relation characteristics of wind power amplitudes and the occurrence frequency of wind power ramp events. Meanwhile, a hybrid sampling method of error bootstrap‐LOS (EB‐LOS) is proposed by combining LOS with the EB oversampling method. After balancing the samples of the ramp and nonramp events by using different sampling methods, the backpropagation neural network (BPNN), and the long short‐term memory (LSTM) methods are employed to directly predict ramp events based on historical data collected from eight wind farms. Comparison results proved that the proposed EB‐LOS method achieves the best prediction performance with an average recall of 0.8196 when using the BPNN model to directly predict ramp events. The best prediction performance of the EB‐LOS method is also proved by using the LSTM model to directly predict ramp events.

A Visualization-Based Ramp Event Detection Model for Wind Power Generation

Wind power ramp events (WPREs) are a common phenomenon in wind power generation. This unavoidable phenomenon poses a great harm to the balance of active power and the stability of frequency in the power supply system, which seriously threatens the safety, stability, and economic operation of the power grid. In order to deal with the impact of ramp events, accurate and rapid detection of ramp events is of great significance for the formulation of response measures. However, some attribute information is ignored in previous studies, and the laws and characteristics of ramp events are difficult to present intuitively. In this paper, we propose a visualization-based ramp event detection model for wind power generation. Firstly, a ramp event detection model is designed considering the multidimensional attributes of ramp events. Then, an uncertainty analysis scheme of ramp events based on the confidence is proposed, enabling users to analyze and judge the detection results of ramp events from different dimensions. In addition, an interactive optimization model is designed, supporting users to update samples interactively, to realize iterative optimization of the detection model. Finally, a set of visual designs and user-friendly interactions are implemented, enabling users to explore WPREs, judge the identification results, and interactively optimize the model. Case studies and expert interviews based on real-world datasets further demonstrate the effectiveness of our system in the WPREs identification, the exploration of the accuracy of identification results, and interactive optimization.

An algorithm for forecasting day-ahead wind power via novel long short-term memory and wind power ramp events

Reliable wind power and ramp event prediction is essential for the safe and stable operation of electric power systems. Previous prediction methods struggled to forecast large fluctuations in wind power caused by extreme weather conditions, severely limiting the development of wind power prediction techniques. Based on this problem, an improved hybrid model is presented in this study, that utilises long short-term memory (LSTM) by considering wind power ramp events (WPREs). First, the LSTM network was driven by numerical weather prediction (NWP) to forecast day-ahead wind power. Second, a novel improved dynamic swinging door algorithm (ImDSDA) and a fuzzy C-means (FCM) model were utilised for WPRE detection and classification respectively. Third, a similarity-matching mechanism was proposed to correct the predicted WPREs. Finally, the predicted wind power was reconstructed using the optimised WPREs.The model, which was validated in three mountainous wind farms in central China, can capture the temporal dynamics of wind power using deep learning and WPRE prediction. The proposed model's results outperformed a few existing methods and can provide scientific guidance for the safe dispatching and economic operation of power systems.

On wavelet transform based convolutional neural network and twin support vector regression for wind power ramp event prediction

Power produced from renewable energy sources carbon negative and promises an increased reliability for grid integration. Wind energy sector globally has an installed capacity of over 650 GW and promises to grow substantially. In this work, wind power ramp events that arise from sudden change in wind power are studied. Forecasting wind power ramp events is an important problem statement in the current wind power industry. Wind power integration to utility grid is impacted by the forecast accuracy. To improve the reliability and security, wind speed and power forecasts are extensively studied. Ramp events are predicted in this manuscript using hybrid techniques employing wavelet decomposition transform in tandem with convolutional neural network and twin support vector machines. Wind speed datasets from Spain, Germany and Argentina are considered and error metrics are computed. It is observed that CNN based method is 28.76% and 26.43% superior from wavelet based random forest and TSVR method.

A mixed simulation methodology for long-term wind power with 4D fluctuation features clustering method and asymmetric fluctuations

Long-term wind power time series (WPTS) simulation plays an important role in long-term grid planning. The study focuses on building a mixed simulation methodology for the long-term WPTS based on asymmetric fluctuations. First, we combine fixed threshold wavelet denoising with wavelet transform to get less noise and more detailed information. Second, a 4D fluctuation features clustering method (FFCM) is proposed to optimize the clustering results. In addition, based on multiple asymmetric function fitting models (AFFMs), an improved Markov Chain Monte Carlo (MCMC) method is proposed to prevent wind power ramp events. Numerical experimental results show that the average simulation error of simulated WPTS based on AFFMs is 3.02% lower than that of the symmetric function fitting model (SFFM). Therefore, the mixed simulation methodology can decrease the simulation error and improve the model accuracy in simulating long-term WPTS and provides a mathematical model for policy guidance on efficient use of wind resources.

Offshore wind power ramp prediction based on optimal combination model

ABSTRACT Wind power ramp event is a small probability and large hazard event, for effectively avoiding its impact on the grid, an offshore wind power ramp prediction method based on optimal combination model is proposed. First, on the basis of the output power data of existing offshore wind turbines, the data preprocessing of the wind power series is carried out by using the Variational Modal Decomposition method optimized by the novel Seagull Optimization Algorithm, which is decomposed into several Intrinsic Mode Function components and one residual component to extract the salient features of the power signal and reduce the data fluctuation. Second, the prediction models of Extreme Learning Machine and Bayesian optimized Long Short Term Memory network are built, respectively, while the optimal combined prediction method is adopted to combine the two single models for obtaining the optimal combined model. Individual components are treated as the inputs of the model, then the final offshore wind power is obtained by superimposing and integrating the prediction results of each component. Eventually, adopting the definition method to predict and characterize the offshore wind power ramp. The research shows that, compared with BP, RNN, LSTM and single model, the wind power prediction error of the combined model proposed in this paper is smaller, with RMSE of 71.10 kW, MAE of 50.26 kW and SMAPE of 0.01%, and it can completely predict 7 ramp events with a higher accuracy. Meanwhile, the applicability of the proposed method is verified by varying the time resolution. The final results show that the proposed method in this paper has a high accuracy in offshore wind power ramp prediction.