Regional Wind Power Forecasting Based on Cloud Model and Wind Rate Vector

2013 ◽  
Vol 860-863 ◽  
pp. 405-408
Author(s):  
Dun Nan Liu ◽  
Yu Hu ◽  
Qun Li ◽  
Guang Hui Shao ◽  
Hai Ming Zhou ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Turning Point ◽  
Cloud Model ◽  
Historical Change ◽  
Power Prediction ◽  
Wind Power Forecast ◽  
Wind Rate ◽  
Rate Vector

The accuracy of wind power forecast is important to the power system operation. A new prediction model is proposed based on cloud reasoning and wind rate vector , combining with the current and the historical change rule of wind speed, using the change rule of wind speed in a period of time to forecast the power gradient in a point-in-time, The wind turbine power prediction is discussed based on power gradient and power eigenvalue. Simulation results on the case study of historical wind speed and generated power data in some area in China demonstrate that the proposed methodology can improve the accuracy of wind speed forecast and has practical value, especially for the wind turning point.

scholarly journals Enhancing Wind Turbine Power Forecast via Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 261
Author(s):  
Tianyang Liu ◽  
Zunkai Huang ◽  
Li Tian ◽  
Yongxin Zhu ◽  
Hui Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Rapid Development ◽  
Superior Performance ◽  
Learning Platform ◽  
Novel Approach ◽  
Wind Power Forecast ◽  
Transformation Methods

The rapid development in wind power comes with new technical challenges. Reliable and accurate wind power forecast is of considerable significance to the electricity system’s daily dispatching and production. Traditional forecast methods usually utilize wind speed and turbine parameters as the model inputs. However, they are not sufficient to account for complex weather variability and the various wind turbine features in the real world. Inspired by the excellent performance of convolutional neural networks (CNN) in computer vision, we propose a novel approach to predicting short-term wind power by converting time series into images and exploit a CNN to analyze them. In our approach, we first propose two transformation methods to map wind speed and precipitation data time series into image matrices. After integrating multi-dimensional information and extracting features, we design a novel CNN framework to forecast 24-h wind turbine power. Our method is implemented on the Keras deep learning platform and tested on 10 sets of 3-year wind turbine data from Hangzhou, China. The superior performance of the proposed method is demonstrated through comparisons using state-of-the-art techniques in wind turbine power forecasting.

Simulation Research of Wind Power Rotate Speed Based on Cloud Model of Fuzzy Reasoning PID Control

2013 ◽  
Vol 816-817 ◽  
pp. 857-861
Author(s):  
You Jun Yue ◽  
Yue Xu ◽  
Hui Zhao ◽  
Hong Jun Wang
Keyword(s):  
Wind Speed ◽  
Fuzzy Control ◽  
Wind Power ◽  
Pid Control ◽  
Fuzzy Controller ◽  
Cloud Model ◽  
Fuzzy Reasoning ◽  
Fuzzy Pid Control ◽  
Simulation Research ◽  
Speed Change

When wind turbine works under rated wind speed, we often use fuzzy controller to control rotate speed and keep the best sharp blade speed to achieve the aim of capture the largest wind power. Due to the nonlinearity of wind power, the uncertainty of timely change and other factors, though fuzzy PID control is the combination of fuzzy control and PID control, which can solve the problem of nonlinear very well, it focuses only on the fuzziness, and fails to consider the random error brought by wind speed change. Therefore this paper designed fuzzy reasoning PID controller based on cloud model on the base of analyzing parameters of wind power and advantage as well as shortage of both PID control and fuzzy control. Then start the RT-LAB simulation platform. The simulation result proved that this method can effectively depress the overshoot. And its stability and dynamic speed response is better than PID control and fuzzy control. It achieved ideal result.

Study on the Forecast of the Economic Loss in Storm Surge Disaster Based on Fourier Series - A Case Study of China

2013 ◽  
Vol 734-737 ◽  
pp. 3280-3285
Author(s):  
Ling Di Zhao ◽  
Ya Ru Hao
Keyword(s):  
Fourier Series ◽  
Wind Speed ◽  
Wind Power ◽  
Storm Surge ◽  
Guangdong Province ◽  
Economic Loss ◽  
Forecasting Model ◽  
Storm Surge Disaster

The economic loss forecasting model is built up on the basis of the Fourier series to simulate economic loss and grades in storm surge disaster of Zhejiang, Fujian and Guangdong Provinces. The wind speed can be used to forecast the economic loss of Guangdong Province, and the accuracy of trend and grade forecasting is good (80%). The wind power data can be used in Zhejiang and Fujian Provinces, and the accuracy results are both inferior (60%). Therefore, in the economic warning of storm surge disaster, the Fourier series model can be applied to forecast economic loss and grades.

scholarly journals A PSO-LSTM Model of Offshore Wind Power Forecast considering the Variation of Wind Speed in Second-Level Time Scale

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chao Yuan ◽  
Yiming Tang ◽  
Rui Mei ◽  
Fei Mo ◽  
Hong Wang
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Time Scale ◽  
Forecast Model ◽  
Offshore Wind ◽  
Offshore Wind Power ◽  
Wind Speed Prediction ◽  
Wind Power Forecast ◽  
Speed Prediction ◽  
Power Sequence

To enable power generation companies to make full use of effective wind energy resources and grid companies to correctly schedule wind power, this paper proposes a model of offshore wind power forecast considering the variation of wind speed in second-level time scale. First, data preprocessing is utilized to process the abnormal data and complete the normalization of offshore wind speed and wind power. Then, a wind speed prediction model is established in the second time scale through the differential smoothing power sequence. Finally, a rolling PSO-LSTM memory network is authorized to realize the prediction of second-level time scale wind speed and power. An offshore wind power case is utilized to illustrate and characterize the performance of the wind power forecast model.

scholarly journals Development of Wind Power Prediction Models for Pawan Danavi Wind Farm in Sri Lanka

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Piyal Ekanayake ◽  
Amila T. Peiris ◽  
J. M. Jeevani W. Jayasinghe ◽  
Upaka Rathnayake
Keyword(s):  
Wind Speed ◽  
Sri Lanka ◽  
Ambient Temperature ◽  
Wind Power ◽  
Correlation Coefficient ◽  
Wind Farm ◽  
Prediction Models ◽  
Power Prediction ◽  
Independent Variables ◽  
Wind Power Prediction

This paper presents the development of wind power prediction models for a wind farm in Sri Lanka using an artificial neural network (ANN), multiple linear regression (MLR), and power regression (PR) techniques. Power generation data over five years since 2015 were used as the dependent variable in modeling, while the corresponding wind speed and ambient temperature values were used as independent variables. Variation of these three variables over time was analyzed to identify monthly, seasonal, and annual patterns. The monthly patterns are coherent with the seasonal monsoon winds exhibiting little annual variation, in the absence of extreme meteorological changes during the period of 2015–2020. The correlation within each pair of variables was also examined by applying statistical techniques, which are presented in terms of Pearson’s and Spearman’s correlation coefficients. The impact of unit increase (or decrease) in the wind speed and ambient temperature around their mean values on the output power was also quantified. Finally, the accuracy of each model was evaluated by means of the correlation coefficient, root mean squared error (RMSE), bias, and the Nash number. All the models demonstrated acceptable accuracy with correlation coefficient and Nash number closer to 1, very low RMSE, and bias closer to 0. Although the ANN-based model is the most accurate due to advanced features in machine learning, it does not express the generated power output in terms of the independent variables. In contrast, the regression-based statistical models of MLR and PR are advantageous, providing an insight into modeling the power generated by the other wind farms in the same region, which are influenced by similar climate conditions.

scholarly journals Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks

2019 ◽  
Vol 11 (3) ◽  
pp. 650 ◽  
Author(s):  
Jianguo Zhou ◽  
Xiaolei Xu ◽  
Xuejing Huo ◽  
Yushuo Li
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Meteorological Data ◽  
Frequency Component ◽  
High Frequency Component ◽  
Power Prediction ◽  
Short Term ◽  
Combined Model ◽  
Wind Power Prediction

The randomness and volatility of wind power poses a serious threat to the stability, continuity, and adjustability of the power system when it is connected to the grid. Accurate short-term wind power prediction methods have important practical value for achieving high-precision prediction of wind farm power generation and safety and economic dispatch. Therefore, this paper proposes a novel combined model to improve the accuracy of short-term wind power prediction, which involves grey correlation degree analysis, ESMD (extreme-point symmetric mode decomposition), sample entropy (SampEn) theory, and a hybrid prediction model based on three prediction algorithms. The meteorological data at different times and altitudes is firstly selected as the influencing factors of wind power. Then, the wind power sub-series obtained by the ESMD method is reconstructed into three wind power characteristic components, namely PHC (high frequency component of wind power), PMC (medium frequency component of wind power), and PLC (low frequency component of wind power). Similarly, the wind speed sub-series obtained by the ESMD method is reconstructed into three wind speed characteristic components, called SHC (high frequency component of wind speed), SMC (medium frequency component of wind speed), and SLC (low frequency component of wind speed). Subsequently, the Bat-BP model, Adaboost-ENN model, and ENN (Elman neural network), which have high forecasting accuracy, are selected to predict PHC, PMC, and PLC, respectively. Finally, the prediction results of three characteristic components are aggregated into the final prediction values of the original wind power series. To evaluate the prediction performance of the proposed combined model, 15-min wind power and meteorological data from the wind farm in China are adopted as case studies. The prediction results show that the combined model shows better performance in short-term wind power prediction compared with other models.

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