Wind Speed Forecasting for Wind Power Production Based on Frequency-Enhanced Transformer

For large wind farms with grid-connected power generation systems, power prediction is very important. A sparrow search algorithm is proposed for optimizing the wind power prediction in BP neural network to improve the accuracy and stability of wind power prediction. Tent chaotic mapping gets used to increase the species diversity of the sparrow search algorithm and improve the ability of the algorithm to step out of local optimization and global search. The improved SSA-BP algorithm and traditional BP neural network method are used to predict the wind power. The prediction results and errors of the two methods are compared. The comparative analysis of the actual measured data and model prediction data shows that the SSA-BP method can better track the trend of the actual wind speed data, reduce the error and improve the prediction accuracy.

Wind power prediction method based on regime of switching kernel functions

This study introduces a data augmentation technique based on generative adversarial networks (GANs) to improve the accuracy of day-ahead wind power predictions. To address the peculiarities of abrupt weather data, we propose a novel method for detecting mutation rates (MR) and local mutation rates (LMR). By analyzing historical data, we curated datasets that met specific mutation rate criteria. These transformed wind speed datasets were used as training instances, and using GAN-based methodologies, we generated a series of augmented training sets. The enriched dataset was then used to train the wind power prediction model, and the resulting prediction results were meticulously evaluated. Our empirical findings clearly demonstrate a significant improvement in the accuracy of day-ahead wind power prediction due to the proposed data augmentation approach. A comparative analysis with traditional methods showed an approximate 5% increase in monthly average prediction accuracy. This highlights the potential of leveraging mutated wind speed data and GAN-based techniques for data augmentation, leading to improved accuracy and reliability in wind power predictions. In conclusion, this paper presents a robust data augmentation method for wind power prediction, contributing to the potential enhancement of day-ahead prediction accuracy. Future research could explore additional mutation rate detection methods and strategies to further enhance GAN models, thereby amplifying the effectiveness of wind power prediction.

A short-term wind power prediction method is proposed in this paper with experimental results obtained from a wind farm located in Northeast China. In order to improve the accuracy of the prediction method using a traditional back-propagation (BP) neural network algorithm, the improved grey wolf optimization (IGWO) algorithm has been adopted to optimize its parameters. The performance of the proposed method has been evaluated by experiments. First, the features of the wind farm are described to show the fundamental information of the experiments. A single turbine with rated power of 1500 kW and power generation coefficient of 2.74 in the wind farm was introduced to show the technical details of the turbines. Original wind power data of the whole farm were preprocessed by using the quartile method to remove the abnormal data points. Then, the retained wind power data were predicted and analysed by using the proposed IGWO–BP algorithm. Analysis of the results proves the practicability and efficiency of the prediction model. Results show that the average accuracy of prediction is ~11% greater than the traditional BP method. In this way, the proposed wind power prediction method can be adopted to improve the accuracy of prediction and to ensure the effective utilization of wind energy. A short-term wind power prediction method is designed and tested with experimental results obtained from a wind farm located in Northeast China. In order to improve the accuracy of the prediction method, the improved grey wolf optimization algorithm has been adopted to optimize its parameters.

Wind power predication is significant for the safety and stability of power system operation. Due to the power forecasting single model or combination model without considering the information fusion and the influence of forecasting error on power dispatching, forecasting results are not accurate enough. In the paper, a novel combination model for wind power forecasting with error evaluation parameter based on cross entropy theory is proposed. Wind power prediction is seen as information fusion, and the weights of different wind power prediction methods is set according to their cross levels. Besides, an error evaluation parameter is also proposed to estimate the different influences of positive and negative error on power scheduling. Application results in an actual wind farm show that the cross levels of different wind power prediction methods can be identified effectively and prediction precision is improved by the proposed method.

This paper presents a method to improve the short-term wind power prediction at a given turbine using information from numerical weather prediction and from multiple observation points, which correspond to the locations of nearby turbines at a particular wind farm site. The prediction of wind power is achieved in two stages; in the first stage wind speed is predicted using our proposed method. In the second stage, the wind speed to output power conversion is accomplished using power curve model. The proposed wind power prediction method is tested using real measurements and NWP data from one of the wind farm sites in Australia. The performance is compared with the persistence and Grey predictor model in terms of Mean Absolute Error and Root Mean Square Error.

A generalized data preprocessing method is proposed in this paper to reduce the amount of outliers among historical data and further improve the power prediction accuracy. Historical data of wind farms are fit with an S-shape curve via Linear Regression Model. Based on this statistical curve, outliers can be identified considering different fitting error. Furthermore, the expansion of wind farm is identified through the number of outliers. Then a selection method for the allowed maximum fitting errors is recommended. The presented method has been integrated into the prediction system in Inner Mongolia of China with 36 farms. The actual application shows that the wind farm power prediction accuracy has been improved by at least 28% with this model. It is noteworthy that the proposed preprocessing method is just based on statistical analysis of historical data and thus compatible with various wind power prediction methods.

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This paper introduces a method of short-term wind power prediction for a wind power plant by training neural networks based on historical data of wind speed and wind direction. The model proposed is shown to achieve a high accuracy with respect to the measured data.

The applicability of ultra-short-term wind power prediction(USTWPP)models is reviewed.The USTWPP method proposed extracts featrues from historical data of wind power time series(WPTS),and classifies every short WPTS into one of several different subsets well defined by stationary patterns.All the WPTS that cannot match any one of the stationary patterns are sorted into the subset of nonstationary pattern.Every above WPTS subset needs a USTWPP model specially optimized for it offline.For on-line application,the pattern of the last short WPTS is recognized,then the corresponding prediction model is called for USTWPP.The validity of the proposed method is verified by simulations.

Soft sensors have been extensively utilized to approximate real-time power prediction in wind power generation, which is challenging to measure instantaneously. The short-term forecast of wind power aims at providing a reference for the dispatch of the intraday power grid. This study proposes a soft sensor model based on the Long Short-Term Memory (LSTM) network by combining data preprocessing with Variational Modal Decomposition (VMD) to improve wind power prediction accuracy. It does so by adopting the isolation forest algorithm for anomaly detection of the original wind power series and processing the missing data by multiple imputation. Based on the process data samples, VMD technology is used to achieve power data decomposition and noise reduction. The LSTM network is introduced to predict each modal component separately, and further sum reconstructs the prediction results of each component to complete the wind power prediction. From the experimental results, it can be seen that the LSTM network which uses an Adam optimizing algorithm has better convergence accuracy. The VMD method exhibited superior decomposition outcomes due to its inherent Wiener filter capabilities, which effectively mitigate noise and forestall modal aliasing. The Mean Absolute Percentage Error (MAPE) was reduced by 9.3508%, which indicates that the LSTM network combined with the VMD method has better prediction accuracy.

With the advancement of energy transformation, more and more electric power departments are required to absorb new energy such as wind power in full. The output voltage will fluctuate considerably due to the non-linear and non-stationary characteristics of wind speed itself, which will cause danger to the power system. A wind power prediction method is presented based on EEMDSE-GGRU in this paper to reduce the disadvantages of wind power. The model combines Ensemble Empirical Mode Decomposition to decompose the fluctuating time series signals, then recombine them with sample entropy. Finally, the optimal parameters of the model are obtained by unsupervised learning of the GRU model using the improved whale algorithm. During prediction, each component data is predicted in turn and the final predicted value is accumulated. The proposed method is of good prediction accuracy and stability by simulation analysis.

With the advancement of new energy technologies, renewable energy generation is predicted to dominate the power system's black-start recovery process as a black-start power source. This study investigates the operating principle of BSWPS, factors influencing its output, effective output capacity, and its potential to engage in primary and secondary frequency regulation during power system black start. A BSWPS simulation model is developed, the control strategy of BSWPS as a system black start power source is investigated, and the possibility of BSWPS participating in frequency regulation during the black start phase is investigated. In order to allocate power reasonably, the paper suggests a strategy that combines a black start optimization model and a battery storage-based wind power system scheduling system model. The wind power prediction method is used to assess the wind power output, and Gurobi is used to solve the scheduling system model. The black start of the power system is effectively accomplished, according to test results in a system that has been retrofitted with a real grid, and the frequency stability of the system is greatly improved.

Regional wind power prediction (WPP) is conducive to achieving scientific dispatch of the power system as well as improving its reliability. Data mining based on a large number of samples is an effective way to improve the accuracy of WPP. A new regional power forecasting method in the context of data mining is proposed in the article. First, a previously described regional WPP method, single feature similarity matching (SFSM) method, is analyzed and a new multifeature similarity matching (MFSM) method is proposed. Second, the optimization and analysis processes of four key parameters in the MFSM method are proposed and the impact of each key parameter on prediction error and the applicability of the method under different regional scales is analyzed. Finally, the new method and the optimization and analysis processes are verified by wind farm clusters, composed of 52 wind farms in northeast China, and the new method is compared with the conventional method, such as additive method, statistical up-scaling method, and the SFSM method. The results show that the improved MFSM method is superior to the conventional prediction method, whereby the root mean square error (RMSE) of 0 to 2 h is reduced by 3.65% and the RMSE of 0 to 4 h is reduced by 1.56%. The new method has been proved to be an effective WPP method for wind farm clusters and has potential industrial application prospects.

A new short-term wind power prediction methodology based on linear and nonlinear hybrid models