Wavelet Neural Network Based Wind Speed Forecasting and Wind Power Incorporated Economic Dispatch with Losses

2015 ◽  
Vol 39 (3) ◽  
pp. 237-251
Author(s):  
M. Ulagammai ◽  
R. P. Kumudini Devi
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Economic Dispatch ◽  
Wavelet Neural Network ◽  
Wind Speed Forecasting

Wavelet neural network–based wind speed forecasting and application of shuffled frog leap algorithm for economic dispatch with prohibited zones incorporating wind power

2017 ◽  
Vol 42 (1) ◽  
pp. 3-15 ◽  
Author(s):  
Ulagammai Meyyappan
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Wind Speed ◽  
Power Systems ◽  
Wind Power ◽  
Economic Dispatch ◽  
Wavelet Neural Network ◽  
Wind Power Generation ◽  
Smooth Functions ◽  
Wind Speed Forecasting

Wind speed and wind power generation are characterized by their inherent variability and uncertainty. To overcome this drawback, an accurate prediction of wind speed is essential. The purpose of this article is to develop a hybrid wavelet neural network model for wind speed forecasting and thus, in turn, for wind power generation. The combined optimal economic scheduling of the wind generators and conventional generators has also been investigated in this article. This article proposes shuffled frog leap algorithm for solving economic dispatch problem in power systems. The non-linear characteristics of the generator such as prohibited operating zone and non-smooth functions are considered. The feasibility of the proposed algorithm is demonstrated for 5 units, 6 units and 15 units systems and it is compared with the existing solution techniques. The results show that the proposed algorithm is indeed capable of handling economic dispatch problems.

Wind Speed Forecasting Based on FNN in Wind Farm

2014 ◽  
Vol 651-653 ◽  
pp. 1117-1122
Author(s):  
Zheng Ning Fu ◽  
Hong Wen Xie
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Power Systems ◽  
Wind Power ◽  
Power Plants ◽  
Fuzzy Neural Network ◽  
Negative Impact ◽  
Wind Speed Forecasting ◽  
Wind Power Plants ◽  
Fuzzy Neural

Wind speed forecasting plays a significant role to the operation of wind power plants and power systems. An accurate forecasting on wind power can effectively relieve or avoid the negative impact of wind power plants on power systems and enhance the competition of wind power plants in electric power market. Based on a fuzzy neural network (FNN), a method of wind speed forecasting is presented in this paper. By mining historical data as the learning stylebook, the fuzzy neural network (FNN) forecasts the wind speed. The simulation results show that this method can improve the accuracy of wind speed forecasting effectively.

scholarly journals A Comparative Study of Activation Functions of NAR and NARX Neural Network for Long-Term Wind Speed Forecasting in Malaysia

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Rasel Sarkar ◽  
Sabariah Julai ◽  
Sazzad Hossain ◽  
Wen Tong Chong ◽  
Mahmudur Rahman
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Wind Power Generation ◽  
Activation Functions ◽  
Wind Speed Forecasting ◽  
Nonlinear Autoregressive ◽  
Narx Neural Network

Since wind power is directly influenced by wind speed, long-term wind speed forecasting (WSF) plays an important role for wind farm installation. WSF is essential for controlling, energy management and scheduled wind power generation in wind farm. The proposed investigation in this paper provides 30-days-ahead WSF. Nonlinear Autoregressive (NAR) and Nonlinear Autoregressive Exogenous (NARX) Neural Network (NN) with different network settings have been used to facilitate the wind power generation. The essence of this study is that it compares the effect of activation functions (namely, tansig and logsig) in the performance of time series forecasting since activation function is the core element of any artificial neural network model. A set of wind speed data was collected from different meteorological stations in Malaysia, situated in Kuala Lumpur, Kuantan, and Melaka. The proposed activation functions tansig of NARNN and NARXNN resulted in promising outcomes in terms of very small error between actual and predicted wind speed as well as the comparison for the logsig transfer function results.

scholarly journals Adaptive Wavelet Neural Network Based Wind Speed Forecasting Studies

2014 ◽  
Vol 9 (6) ◽  
pp. 1812-1821 ◽  
Author(s):  
D. Rakesh Chandra ◽  
Matam Sailaja Kumari ◽  
Maheswarapu Sydulu ◽  
F. Grimaccia ◽  
M. Mussetta
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wavelet Neural Network ◽  
Adaptive Wavelet ◽  
Wind Speed Forecasting

scholarly journals Multistep Wind Speed Forecasting Based on a Hybrid Model of VMD and Nonlinear Autoregressive Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yuqiao Zheng ◽  
Bo Dong ◽  
Yuhan Liu ◽  
Xiaolei Tong ◽  
Lei Wang
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Maintenance Strategies ◽  
Wind Speed Forecasting ◽  
Operation And Maintenance ◽  
Comparison Results ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

Reducing the costs of wind power requires reasonable wind farm operation and maintenance strategies, and then to develop these strategies, the 24-hour ahead forecasting of wind speed is necessary. However, existing prediction work is mostly limited to 5 hours. This work developed a diurnal forecasting methodology for the regional wind farm according to real-life data of the supervisory control and data acquisition (SCADA) system of a wind farm from Jiangxi Province. The methodology used the variational mode decomposition (VMD) to extract wind characteristics, and then, the characteristics were put in the nonlinear autoregressive neural network (Narnet) and long short-term memory network (LSTM) for prediction; the forecast results of VMD-Narnet and VMD-LSTM are compared with the actual wind speed. The comparison results indicate that compared with the LSTM, the Narnet improves the accuracy by 61.90% in 24 hours on wind speed forecasting, and the predicted time horizon was improved by 6.8 hours. This work strongly supports the development of wind farm operation and maintenance strategies and provides a foundation for the reduction of wind power costs.

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