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.

Short-Term Power Fluctuations of Large Wind Power Plants*

2002 ◽  
Vol 124 (4) ◽  
pp. 427-431 ◽  
Author(s):  
Yih-huei Wan ◽  
Demy Bucaneg,
Keyword(s):  
Wind Speed ◽  
Power Systems ◽  
Wind Power ◽  
Power Plants ◽  
Power Level ◽  
Short Term ◽  
Wind Power Plants ◽  
Weather Events ◽  
Power Fluctuations ◽  
Large Wind

To evaluate short-term wind power fluctuations and their impact on electric power systems, the National Renewable Energy Laboratory, in cooperation with Enron Wind, has started a project to record output power from several large commercial wind power plants at the 1-Hertz rate. This paper presents statistical properties of the data collected so far and discusses the results of data analysis. From the available data, we can already conclude that despite the stochastic nature of wind power fluctuations, the magnitudes and rates of wind power changes caused by wind speed variations are seldom extreme, nor are they totally random. Their values are bounded in narrow ranges. Power output data also show significant spatial variations within a large wind power plant. The data also offer encouraging evidence that accurate wind power forecasting is feasible. To the utility system, large wind power plants are not really random burdens. The narrow range of power level step changes provides a lot of information with which system operators can make short-term predictions of wind power. Large swings of wind power do occur, but those infrequent large changes (caused by wind speed changes) are always related to well-defined weather events, most of which can be accurately predicted in advance.

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.

scholarly journals Control the System and Environment of Post-Production Wind Turbine Blade Waste Using Life Cycle Models. Part 1. Environmental Transformation Models

Polymers ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1828 ◽  
Author(s):  
Izabela Piasecka ◽  
Patrycja Bałdowska-Witos ◽  
Józef Flizikowski ◽  
Katarzyna Piotrowska ◽  
Andrzej Tomporowski
Keyword(s):  
Life Cycle ◽  
Power Plant ◽  
Wind Power ◽  
Power Plants ◽  
Negative Impact ◽  
Polymer Materials ◽  
Wind Power Plant ◽  
Polymer Waste ◽  
Wind Power Plants ◽  
The Impact

Controlling the system—the environment of power plants is called such a transformation—their material, energy and information inputs in time, which will ensure that the purpose of the operation of this system or the state of the environment, is achieved. The transformations of systems and environmental inputs and their goals describe the different models, e.g., LCA model groups and methods. When converting wind kinetic energy into electricity, wind power plants emit literally no harmful substances into the environment. However, the production and postuse management stages of their components require large amounts of energy and materials. The biggest controlling problem during postuse management is wind power plant blades, followed by waste generated during their production. Therefore, this publication is aimed at carrying out an ecological, technical and energetical transformation analysis of selected postproduction waste of wind power plant blades based on the LCA models and methods. The research object of control was eight different types of postproduction waste (fiberglass mat, roving fabric, resin discs, distribution hoses, spiral hoses with resin, vacuum bag film, infusion materials residues, surplus mater), mainly made of polymer materials, making it difficult for postuse management and dangerous for the environment. Three groups of models and methods were used: Eco-indicator 99, IPCC and CED. The impact of analysis objects on human health, ecosystem quality and resources was controlled and assessed. Of all the tested waste, the life cycle of resin discs made of epoxy resin was characterized by the highest level of harmful technology impact on the environment and the highest energy consumption. Postuse control and management in the form of recycling would reduce the negative impact on the environment of the tested waste (in the perspective of their entire life cycle). Based on the results obtained, guidelines and models for the proecological postuse control of postproduction polymer waste of wind power plants blades were proposed.

Sign in / Sign up

Export Citation Format

Share Document