scholarly journals A new wake‐merging method for wind‐farm power prediction in the presence of heterogeneous background velocity fields

Wind Energy ◽  
2021 ◽  
Author(s):  
Luca Lanzilao ◽  
Johan Meyers
Keyword(s):  
Wind Farm ◽  
Velocity Fields ◽  
Power Prediction ◽  
Merging Method

Long-Term Wind Power Prediction Based on Rough Set

2013 ◽  
Vol 329 ◽  
pp. 411-415 ◽  
Author(s):  
Shuang Gao ◽  
Lei Dong ◽  
Xiao Zhong Liao ◽  
Yang Gao
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Rough Set ◽  
Wind Farm ◽  
Rough Set Theory ◽  
Prediction Method ◽  
Key Factors ◽  
Power Prediction ◽  
Wind Power Prediction

In long-term wind power prediction, dealing with the relevant factors correctly is the key point to improve the prediction accuracy. This paper presents a prediction method with rough set analysis. The key factors that affect the wind power prediction are identified by rough set theory. The chaotic characteristics of wind speed time series are analyzed. The rough set neural network prediction model is built by adding the key factors as the additional inputs to the chaotic neural network model. Data of Fujin wind farm are used for this paper to verify the new method of long-term wind power prediction. The results show that rough set method is a useful tool in long-term prediction of wind power.

Health Assessment Methods for Wind Turbines Based on Power Prediction and Mahalanobis Distance

2018 ◽  
Vol 33 (02) ◽  
pp. 1951001 ◽  
Author(s):  
Jun Zhan ◽  
Ronglin Wang ◽  
Lingzhi Yi ◽  
Yaguo Wang ◽  
Zhengjuan Xie
Keyword(s):  
Health Status ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Mahalanobis Distance ◽  
Wind Farm ◽  
Health Status Assessment ◽  
Power Prediction ◽  
Residual Set ◽  
Scada System ◽  
Status Assessment

The output power of wind turbine has great relation with its health state, and the health status assessment for wind turbines influences operational maintenance and economic benefit of wind farm. Aiming at the current problem that the health status for the whole machine in wind farm is hard to get accurately, in this paper, we propose a health status assessment method in order to assess and predict the health status of the whole wind turbine, which is based on the power prediction and Mahalanobis distance (MD). Firstly, on the basis of Bates theory, the scientific analysis for historical data from SCADA system in wind farm explains the relation between wind power and running states of wind turbines. Secondly, the active power prediction model is utilized to obtain the power forecasting value under the health status of wind turbines. And the difference between the forecasting value and actual value constructs the standard residual set which is seen as the benchmark of health status assessment for wind turbines. In the process of assessment, the test set residual is gained by network model. The MD is calculated by the test residual set and normal residual set and then normalized as the health status assessment value of wind turbines. This method innovatively constructs evaluation index which can reflect the electricity generating performance of wind turbines rapidly and precisely. So it effectively avoids the defect that the existing methods are generally and easily influenced by subjective consciousness. Finally, SCADA system data in one wind farm of Fujian province has been used to verify this method. The results indicate that this new method can make effective assessment for the health status variation trend of wind turbines and provide new means for fault warning of wind turbines.

Forecast of Short-Term Wind Power Based on GA Optimized Elman Neural Network

2014 ◽  
Vol 536-537 ◽  
pp. 470-475
Author(s):  
Ye Chen
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Wind Power ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Prediction ◽  
Elman Neural Network ◽  
Wind Power Prediction ◽  
Forecasting Method ◽  
Safety And Stability

Due to the features of being fluctuant, intermittent, and stochastic of wind power, interconnection of large capacity wind farms with the power grid will bring about impact on the safety and stability of power systems. Based on the real-time wind power data, wind power prediction model using Elman neural network is proposed. At the same time in order to overcome the disadvantages of the Elman neural network for easily fall into local minimum and slow convergence speed, this paper put forward using the GA algorithm to optimize the weight and threshold of Elman neural network. Through the analysis of the measured data of one wind farm, shows that the forecasting method can improve the accuracy of the wind power prediction, so it has great practical value.

A comparative study of optimal hybrid methods for wind power prediction in wind farm of Alberta, Canada

2013 ◽  
Vol 27 ◽  
pp. 20-29 ◽  
Author(s):  
Nooshin Bigdeli ◽  
Karim Afshar ◽  
Amin Shokri Gazafroudi ◽  
Mostafa Yousefi Ramandi
Keyword(s):  
Comparative Study ◽  
Wind Power ◽  
Wind Farm ◽  
Hybrid Methods ◽  
Power Prediction ◽  
Wind Power Prediction

Wind farm power prediction based on wavelet decomposition and chaotic time series

2011 ◽  
Vol 38 (9) ◽  
pp. 11280-11285 ◽  
Author(s):  
Xueli An ◽  
Dongxiang Jiang ◽  
Chao Liu ◽  
Minghao Zhao
Keyword(s):  
Time Series ◽  
Wavelet Decomposition ◽  
Wind Farm ◽  
Chaotic Time Series ◽  
Power Prediction

Wind Power Prediction Based on Grey Theory and Simulation Study

2013 ◽  
Vol 448-453 ◽  
pp. 1835-1839
Author(s):  
Zhong Hua Cai ◽  
Ting Ting An ◽  
Hong Tu Zhang
Keyword(s):  
Wind Power ◽  
Simulation Study ◽  
Wind Farm ◽  
Grey Theory ◽  
Grey Prediction ◽  
Power Prediction ◽  
Wind Power Integration ◽  
Proposed Model ◽  
Wind Power Prediction ◽  
New Research

Due to the significant instability, anti-peak-regulation and intermittency of wind power, wind power integration needs an accurate prediction technique to be a basis. At present, the difficulty of wind power integration has resulted in a large number of wind curtailment phenomena and wasted a lot of renewable energy. Grey prediction model has many advantages such as requiring little historical data and the simple model, with high prediction accuracy and convenient calculation, and without regard to regularities of distribution, etc. This paper puts forward the method for short-term wind power prediction using gray model GM (1, 1) and carries out simulation study and empirical analysis using the data from a wind farm of Jilin province, which shows the science and operability of the proposed model. It provides a new research method for the wind power prediction.

scholarly journals A Prediction Model for Ultra-Short-Term Output Power of Wind Farms Based on Deep Learning

2020 ◽  
Vol 15 (4) ◽  
Author(s):  
Yongsheng Wang ◽  
Jing Gao ◽  
Zhiwei Xu ◽  
Jidong Luo ◽  
Leixiao Li
Keyword(s):  
Wind Energy ◽  
Prediction Model ◽  
Wind Power ◽  
Output Power ◽  
Prediction Accuracy ◽  
Wind Farm ◽  
Short Term Memory ◽  
Evaluation Metrics ◽  
Power Prediction ◽  
Short Term

The output power prediction of wind farm is the key to effective utilization of wind energy and reduction of wind curtailment. However, the prediction of output power has long been a difficulty faced by both academia and the wind power industry, due to the high stochasticity of wind energy. This paper attempts to improve the ultra-short-term prediction accuracy of output power in wind farm. For this purpose, an output power prediction model was constructed for wind farm based on the time sliding window (TSW) and long short-term memory (LSTM) network. Firstly, the wind power data from multiple sources were fused, and cleaned through operations like dimension reduction and standardization. Then, the cyclic features of the actual output powers were extracted, and used to construct the input dataset by the TSW algorithm. On this basis, the TSW-LSTM prediction model was established to predict the output power of wind farm in ultra-short-term. Next, two regression evaluation metrics were designed to evaluate the prediction accuracy. Finally, the proposed TSW-LSTM model was compared with four other models through experiments on the dataset from an actual wind farm. Our model achieved a super-high prediction accuracy 92.7% as measured by d_MAE, an evidence of its effectiveness. To sum up, this research simplifies the complex prediction features, unifies the evaluation metrics, and provides an accurate prediction model for output power of wind farm with strong generalization ability.

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