Applications Advantages
Dublin Institute of Technology (DIT) is seeking companies to license software that estimates the power generated by wind and wave energy conversion technologies based on measurements of the wind velocity and associated variables using new statistical modelling methods. It is well known that the power output from an ideal wind turbine is proportional to the rotor area and the wind speed cubed (Betz law) and that the power output from an ideal wave energy converter depends on the wave amplitude and frequency range which is largely determined by the wind force or wind speed gradient. Thus, in both cases (i.e. wind and wave power) a statistical analysis of the wind speed is critical in determining the ideal location for constructing a wind or wave farm, monitoring the power output of the farm and providing estimates of future power quality. Current methods of statistical analysis on wind speed data are predicated on the use of a normally distributed model for the velocity gradient or wind force. However, it is well known, that normal or ‘Gaussian’ distributed models are inaccurate. Thus, any power quality estimate and/or prediction that is based on Gaussian statistics is prone to inaccuracy. This has been shown to be the case in many instances relating to the location and construction of wind farms when the output power has been significantly less than predicted. Researchers at DIT have developed a new approach for monitoring and predicting power output that is based on a non-Gaussian statistical analysis. Using Levy distributions to model the statistical characteristics of the wind force it has been shown that the average power output is inversely related to the Levy index computed from the wind velocity. Coupled with a toolbox of associated statistical modelling methods, this fundamental result can be used to accurately monitor and predict the ‘quality of power’ generated by wind and wave farms and has already provided differential estimates between the wind power generated in urban and rural regions with an accuracy in excess of 90%.
Applications • Construction – evaluating optimum geographical locations for the construction of wind and wave farms. • Power quality monitoring – estimating the likely output power of wind and wave farms based on wind velocity data and associated parameters. • Predictive analysis – forecasting the likely power output from wind and wave farms.
Advantages • Improved wind/wave farm location – location optimisation through environmental monitoring. • Improved wind/wave farm performance – delivers cost savings, enhanced power generation, improved efficiency and better selection of wind and wave farm size. • Improved power output forecasting – enables better contingency planning, synchronisation with the grid and access to the grid. • Statistical accuracy – power generation forecast accuracy of 90% has been achieved using statistical evaluation of wind velocity data measured over different scales. • Control engineering – ‘quality of power’ output monitoring of wind and wave farms for controlling and maintaining the performance of the grid.
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CLEAN TECHNOLOGY
DIT Hothouse DIT Aungier Street Dublin 2 Tel: +353 1 402 7144 E: [email protected] W: www.dit.ie/hothouse
DIT Hothouse Docklands Innovation Park 128-130 East Wall Road Dublin 3 Tel: +353 1 240 1300 W: www.dit.ie/hothouse
The quality of power (i.e. the sustainable power output as a function of time) of any wind dependent energy converter (including wind turbines and wave energy converters) is determined by many design and environmental factors but time dependent variations in the wind speed are critically important and the most difficult to monitor and predict. A non-Gaussian model for statistically evaluating wind velocity data has been developed. In particular, Levy-type distributions for the statistical characteristics of the wind velocity gradient are used, leading to new statistical models for the dynamics of the wind velocity whose solutions are characterized by the Levy index. This model facilitates the derivation of a relationship between the power output from a wind turbine and the Levy index, with a similar relationship for a wave energy converter. In both cases, the average power output as a function of time is (inversely) related to the Levy index for the wind velocity. This is the basis for a toolbox of statistical analysis software that brings a number of advantages based on a fundamental paradigm shift in the way that time dependent environmental variables such as the wind velocity are analyzed. These advantages include consistent performance indicators for the ‘quality of power’ produced by wind and wave farms and improved power output forecasting allowing optimum choice of wind and wave farm scale and location.
Stage of Development Dublin Institute of Technology is looking for companies to license this software for application with wind power and wave energy conversion technologies.
Professor Jonathan Blackledge Professor Blackledge is the Stokes Professor of digital signal processing and information and communications technology in the School of Electrical Engineering Systems at DIT. Professor Blackledge holds a Ph.D. in theoretical physics from London University and a Ph.D. in mathematical information technology from Jyvaskyla University. He has published over 100 scientific and engineering research papers, developed six industrial software systems, has filed 15 patents, authored 12 books and supervised over fifty research (Ph.D.) graduates. His current research interests include fractal geometry, digital signal and image processing, computer vision, nonlinear dynamical systems modelling and computer network security, working in both an academic and commercial context.
Professor Jonathan Blackledge
Information and Communications Research Group The Information and Communications Security Research Group is based in the School of Electrical Engineering Systems at DIT. The research group is under the technical direction of Professor J. M. Blackledge. The group has a range of proprietary technologies available for license. Research interests include: data encryption using chaos, information hiding and steganography, e-document authentication, printed document authentication using texture coding, forensically inert software engineering, covert encryption methods, management of encrypted information and software solutions for professional and consumer security applications. DIT Hothouse is the award winning Innovation and Technology Transfer Centre at Dublin Institute of Technology. DIT Hothouse draws in entrepreneurial and academic talent, ignites creativity and provides a dynamic environment to fast-track businesses and technologies to commercial success.
CLEAN TECHNOLOGY
DIT Hothouse DIT Aungier Street Dublin 2 Tel: +353 1 402 7144 E: [email protected] W: www.dit.ie/hothouse
DIT Hothouse Docklands Innovation Park 128-130 East Wall Road Dublin 3 Tel: +353 1 240 1300 W: www.dit.ie/hothouse
Applications • Construction – evaluating optimum geographical locations for the construction of wind and wave farms. • Power quality monitoring – estimating the likely output power of wind and wave farms based on wind velocity data and associated parameters. • Predictive analysis – forecasting the likely power output from wind and wave farms.
Advantages • Improved wind/wave farm location – location optimisation through environmental monitoring. • Improved wind/wave farm performance – delivers cost savings, enhanced power generation, improved efficiency and better selection of wind and wave farm size. • Improved power output forecasting – enables better contingency planning, synchronisation with the grid and access to the grid. • Statistical accuracy – power generation forecast accuracy of 90% has been achieved using statistical evaluation of wind velocity data measured over different scales. • Control engineering – ‘quality of power’ output monitoring of wind and wave farms for controlling and maintaining the performance of the grid.
____________________________________________________________________________________
CLEAN TECHNOLOGY
DIT Hothouse DIT Aungier Street Dublin 2 Tel: +353 1 402 7144 E: [email protected] W: www.dit.ie/hothouse
DIT Hothouse Docklands Innovation Park 128-130 East Wall Road Dublin 3 Tel: +353 1 240 1300 W: www.dit.ie/hothouse
The quality of power (i.e. the sustainable power output as a function of time) of any wind dependent energy converter (including wind turbines and wave energy converters) is determined by many design and environmental factors but time dependent variations in the wind speed are critically important and the most difficult to monitor and predict. A non-Gaussian model for statistically evaluating wind velocity data has been developed. In particular, Levy-type distributions for the statistical characteristics of the wind velocity gradient are used, leading to new statistical models for the dynamics of the wind velocity whose solutions are characterized by the Levy index. This model facilitates the derivation of a relationship between the power output from a wind turbine and the Levy index, with a similar relationship for a wave energy converter. In both cases, the average power output as a function of time is (inversely) related to the Levy index for the wind velocity. This is the basis for a toolbox of statistical analysis software that brings a number of advantages based on a fundamental paradigm shift in the way that time dependent environmental variables such as the wind velocity are analyzed. These advantages include consistent performance indicators for the ‘quality of power’ produced by wind and wave farms and improved power output forecasting allowing optimum choice of wind and wave farm scale and location.
Stage of Development Dublin Institute of Technology is looking for companies to license this software for application with wind power and wave energy conversion technologies.
Professor Jonathan Blackledge Professor Blackledge is the Stokes Professor of digital signal processing and information and communications technology in the School of Electrical Engineering Systems at DIT. Professor Blackledge holds a Ph.D. in theoretical physics from London University and a Ph.D. in mathematical information technology from Jyvaskyla University. He has published over 100 scientific and engineering research papers, developed six industrial software systems, has filed 15 patents, authored 12 books and supervised over fifty research (Ph.D.) graduates. His current research interests include fractal geometry, digital signal and image processing, computer vision, nonlinear dynamical systems modelling and computer network security, working in both an academic and commercial context.
Professor Jonathan Blackledge
Information and Communications Research Group The Information and Communications Security Research Group is based in the School of Electrical Engineering Systems at DIT. The research group is under the technical direction of Professor J. M. Blackledge. The group has a range of proprietary technologies available for license. Research interests include: data encryption using chaos, information hiding and steganography, e-document authentication, printed document authentication using texture coding, forensically inert software engineering, covert encryption methods, management of encrypted information and software solutions for professional and consumer security applications. DIT Hothouse is the award winning Innovation and Technology Transfer Centre at Dublin Institute of Technology. DIT Hothouse draws in entrepreneurial and academic talent, ignites creativity and provides a dynamic environment to fast-track businesses and technologies to commercial success.
CLEAN TECHNOLOGY
DIT Hothouse DIT Aungier Street Dublin 2 Tel: +353 1 402 7144 E: [email protected] W: www.dit.ie/hothouse
DIT Hothouse Docklands Innovation Park 128-130 East Wall Road Dublin 3 Tel: +353 1 240 1300 W: www.dit.ie/hothouse
