A novel clustering algorithm based on mathematical morphology for wind power generation prediction

Wind power prediction accuracy is closely related to the quality of wind power data. However, most of traditional prediction methods do not consider the cybersecurity of raw data. So, it would be important to study the prediction accuracy under false data injection attack (one type of cybersecurity attacks) and its influence on the economy and security of power system. In this paper, very short-term forecasting approach of wind power generation is studied for choosing the optimal prediction model. Different scenarios of false data injection attack are then simulated upon wind power data. Wind power prediction accuracy after being affected by false data injection is observed. The robustness of wind power prediction model is evaluated. Finally, wind power prediction information is applied to day-ahead unit commitment to evaluate the impact of cybersecurity of wind power forecasting on power system economy and security.

The accuracy of short-term wind power forecast is important for the power system operation. Based on the real-time wind power data, a wind power prediction model using wavelet neural network (WNN) is proposed. In order to overcome such disadvantages of WNN as easily falling into local minimum, this paper put forward using Particle Swarm Optimization (PSO) algorithm to optimize the weight and threshold of WNN. It’s advisable to use Support Vector Machine (SVM) to comparatively do prediction and put two outcomes as input vector for Generalized Regression Neural Network (GRNN) to do nonlinear combination forecasting. Simulation shows that combination prediction model can improve the accuracy of the short-term wind power prediction.

The continuous increase in energy consumption has made the potential of wind-power generation tremendous. However, the obvious intermittency and randomness of wind speed results in the fluctuation of the output power in a wind farm, seriously affecting the power quality. Therefore, the accurate prediction of wind power in advance can improve the ability of wind-power integration and enhance the reliability of the power system. In this paper, a model of wavelet decomposition (WD) and weighted random forest (WRF) optimized by the niche immune lion algorithm (NILA-WRF) is presented for ultra-short-term wind power prediction. Firstly, the original serials of wind speed and power are decomposed into several sub-serials by WD because the original serials have no obvious day characteristics. Then, the model parameters are set and the model trained with the sub-serials of wind speed and wind power decomposed. Finally, the WD-NILA-WRF model is used to predict the wind power of the relative sub-serials and the result is reconstructed to obtain the final prediction result. The WD-NILA-WRF model combines the advantage of each single model, which uses WD for signal de-noising, and uses the niche immune lion algorithm (NILA) to improve the model’s optimization efficiency. In this paper, two empirical analyses are carried out to prove the accuracy of the model, and the experimental results verify the proposed model’s validity and superiority compared with the back propagation neural network (BP neural network), support vector machine (SVM), RF and NILA-RF, indicating that the proposed method is superior in cases influenced by noise and unstable factors, and possesses an excellent generalization ability and robustness.

The volatility and intermittency of wind energy result in highly unpredictable wind power output, which poses challenges to the stability of the intact power system when integrating large-scale wind power. The accuracy of wind power prediction is critical for maximizing the utilization of wind energy, improving the quality of power supply, and maintaining the stable operation of the power grid. To address this challenge, this paper proposes a novel hybrid forecasting model, referred to as Hybrid WT–PSO–NARMAX, which combines wavelet transform, randomness operator-based particle swarm optimization (ROPSO), and non-linear autoregressive moving average with external inputs (NARMAX). The model is specifically designed for power generation forecasting in wind energy systems, and it incorporates the interactions between the wind system’s supervisory control and data acquisition’s (SCADA) actual power record and numerical weather prediction (NWP) meteorological data for one year. In the proposed model, wavelet transform is utilized to significantly improve the quality of the chaotic meteorological and SCADA data. The NARMAX techniques are used to map the non-linear relationship between the NWP meteorological variables and SCADA wind power. ROPSO is then employed to optimize the parameters of NARMAX to achieve higher forecasting accuracy. The performance of the proposed model is compared with other forecasting strategies, and it outperforms in terms of forecasting accuracy improvement. Additionally, the proposed Prediction Error-Based Power Forecasting (PEBF) approach is introduced, which retrains the model to update the results whenever the difference between forecasted and actual wind powers exceeds a certain limit. The efficiency of the developed scheme is evaluated through a real case study involving a 180 MW grid-connected wind energy system located in Shenyang, China. The proposed model’s forecasting accuracy is evaluated using various assessment metrics, including mean absolute error (MAE) and root mean square error (RMSE), with the average values of MAE and RMSE being 0.27% and 0.30%, respectively. The simulation and numerical results demonstrated that the proposed model accurately predicts wind output power.

To satisfy the grid operation scheduling requirements for wind power forecasting model accuracy, the measured wind speed near the height of the wind turbine hub is added to the wind power combined forecasting model. First, the relationship between the numerical weather prediction wind speed and the measured wind speed at different heights are analysed, and the correlation between each wind speed and the wind power is compared. Second, the random forest algorithm combined with the cumulative contribution rate is used to select several meteorological types of numerical weather prediction data as the input of the long short‐term memory network to predict wind speed. Third, while inputting the meteorological data provided by numerical weather prediction, which is highly related to wind power, the wind power prediction network also uses the predicted wind speed of the upper network as input to predict wind power. Finally, the entropy method is used to dynamically determine the combined weights of each forecasting model and improve the adaptability of the model. Research and analysis using measured data from two wind farms located in northeast China have verified the effectiveness of the method.

Accurate wind power prediction can reduce the negative impact of wind power on power grid and reduce the operation cost of power system. Wind power forecasting is of great significance in spot trading, but the instability of wind power series brings difficulties to forecasting. With the rapid development of deep learning technology, prediction algorithm based on deep learning has been widely used in wind power prediction. The long short-term memory (LSTM) network performs well in a variety of prediction scenarios, but there is still room for improvement. In order to improve the prediction accuracy of LSTM in wind power prediction, this paper adopts a new gating mechanism in LSTM network, which can improve the context information modeling ability of LSTM network. The experimental results based on real data show that the proposed improved LSTM prediction model can improve the prediction accuracy of LSTM prediction model in ultra-short term wind power prediction scenarios.

This paper introduces Generalized Regression Neural Network (GRNN) for long term wind speed prediction of major wind power potential states in India. The performance of proposed GRNN model is evaluated using the publicly available online dataset of National Aeronautics and Space Administration (NASA). Data samples of 26 cities are used for training the generalized regression neural network and remaining 5 cities data samples are used for testing purpose. Air temperature, earth temperature, relative humidity, daily solar radiation, elevation, latitude, heating degree days, cooling degree days, frost days, longitude and atmospheric pressure are used as input variables. Mean square error between measured and forecasted wind speed using training data samples and testing data samples are found to be 0.000042279 and 0.1543. Here it is important to impart that the proposed GRNN model is trained and tested with data samples of different geographical locations in order to make it feasible for wind speed prediction of any other location. Wind power of prominent wind power potential states in India are predicted by a variable pitch and speed control wind turbine G80-2MW.

Introduction: Wind energy is a kind of clean energy, and wind power generation is an important means of utilizing wind energy. However, wind power generation is highly volatile and prone to causing grid operation accidents, so it is necessary to predict wind power generation. The annual power generation of wind turbines is determined by the Turbine Power Curve (TPC), which, however, is easily affected by different meteorological conditions. The successful deployment of wind turbines requires accurate predictions of wind farm power before construction and near-real-time power predictions after construction to facilitate grid uptake. However, the existing research methods have not considered the quantitative influence of climate characteristics on the prediction accuracy of wind power, limiting the accuracy of wind power generation predictions. In order to solve this problem, this investigation applies a machine learning model that is based on decision trees to decrease the ambiguity associated with wind resource evaluations and to enhance the precision of wind energy forecasts. Method: The machine learning model, based on decision trees, was trained on four distinct classifications of vertical wind profiles to depict wind velocities necessitating complex computations across different rotor layer altitudes. Results: The findings indicated that the model after integration of rotor-equivalent wind speed and temperature lapse rate achieved a 21.48% enhancement in predictive accuracy for the dataset in question, surpassing traditional power curve techniques. Discussion: The model scrutinized the utility of incorporating parameters such as the wind speed at hub height, the wind speed equivalent to the rotor, and the rate of direct temperature decrease as variables for predicting power output. Climate feature data were also utilized to train the regression tree model, enabling the correlation of wind power with wind profile and climate features for predicting wind power based on physical relationships. Conclusion: This methodology has emerged as the optimal strategy for power forecasting across all categorized vertical wind profile types. Notably, the model incorporating the temperature lapse rate in the prediction exhibited higher accuracy than the other one, highlighting the significance of considering climatic characteristics in wind power prediction.

In a recent paper (Escribano et al. in Discrete Geometry for Computer Imagery 2008. Lecture Notes in Computer Science, vol. 4992, pp. 81---92, 2008) we have introduced a notion of continuity in digital spaces which extends the usual notion of digital continuity. Our approach, which uses multivalued functions, provides a better framework to define topological notions, like retractions, in a far more realistic way than by using just single-valued digitally continuous functions. In this work we develop properties of this family of continuous functions, now concentrating on morphological operations and thinning algorithms. We show that our notion of continuity provides a suitable framework for the basic operations in mathematical morphology: erosion, dilation, closing, and opening. On the other hand, concerning thinning algorithms, we give conditions under which the existence of a retraction F:X?X?D guarantees that D is deletable. The converse is not true, in general, although it is in certain particular important cases which are at the basis of many thinning algorithms.

Wind power has been valued by countries for its renewability and cleanness and has become most of the focus of energy development in all countries. However, due to the uncertainty and volatility of wind power generation, making the grid-connected wind power system presents some serious challenges. Improving the accuracy of wind power prediction has become the focus of current research. Therefore, this paper proposes a combined short-term wind power prediction model based on T-LSTNet_markov to improve prediction accuracy. First, perform data cleaning and data preprocessing operations on the original data. Second, forecast using T-LSTNet model in original wind power data. Finally, calculate the error between the forecast value and the actual value. The k-means++ method and Weighted Markov process are used to correct errors and to get the result of the final prediction. The data that are collected from a wind farm in Inner Mongolia Autonomous Region, China, are selected as a case study to demonstrate the effectiveness of the proposed combined models. The empirical results show that the prediction accuracy is further improved after correcting errors.

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

A novel approach to ultra-short-term multi-step wind power predictions based on encoder–decoder architecture in natural language processing

With the gradual warming of the global climate, wind energy, as one of the representatives of clean energy, has been rapidly developed in countries worldwide. However, wind power generation has strong intermittency and variability, constantly impacting the stability of the power grid. In order to improve the accuracy of wind power prediction, this paper proposes a BP neural network model optimized by genetic algorithm (GA) for wind power prediction. This model uses GA to optimize the initial weights and thresholds of the BP neural network to improve the computational accuracy and convergence speed of the model, effectively avoiding the problem of a single BP neural network model easily falling into local minima and the slow training speed of GA algorithm. By preprocessing and normalizing meteorological data such as weather, temperature, and wind direction, a wind power prediction model is constructed, and the prediction results show that the model effectively improves the computational accuracy and convergence speed compared to the traditional BP neural network model. This result proves the feasibility of using GA to optimize BP neural network for wind power prediction in this paper.

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

Day-ahead wind power forecasting based on the clustering of equivalent power curves