0000000000352779
AUTHOR
Sathyajith Mathew
Power production forecast for distributed wind energy systems using support vector regression
Due to the inherent intermittency in wind power production, reliable short-term wind power production forecasting has become essential for the efficient grid and market integration of wind energy. The current wind power production forecasting schemes are predominantly developed for wind farms. With the rapid growth in the microgrid sector and the increasing number of wind turbines integrated with these local grids, power production forecasting schemes are becoming essential for distributed wind energy systems as well. This paper proposes a power production forecasting scheme developed explicitly for distributed wind energy projects. The proposed system integrates two submodels based on supp…
Machine-learned models for the performance of six different solar PV technologies under the tropical environment
Due to the recent environmental concerns and long-term challenges in energy security, the Global energy scenarios are shifting more towards sustainable and renewable energy resources. Brunei has planned to increase the use of cleaner energy technologies by contributing 10 percent or 954 GWh of renewable energy in its power generation mix by 2035. Out of the available renewable options, solar is the most promising one for Brunei, for example, the daily average solar installation is around 5kWh per day [1]. Though solar energy is an abundant resource, for optimally designing and successfully managing solar power projects, its availability in different time scales are to be analyzed and unders…
Downscaling and improving the wind forecasts from NWP for wind energy applications using support vector regression
Abstract The stochastic nature of wind poses challenges in the large scale integration of wind energy with the grid. Wind characteristics at a site may significantly vary with time. which will be reflected on the wind power production. Understanding and managing such variations could be challenging for wind farm owners. energy traders and grid operators. In this work. we propose models based on support vector regression (SVR) to downscale the speed and direction of wind at a specific site using both historical observed measurements and numerical weather predictions (NWP). Several meteorological variables. considered to have potential influence on the wind. were used in the feature selection…
Intelligent estimation of wind farm performance with direct and indirect ‘point’ forecasting approaches integrating several NWP models
Reliable wind power forecasting is essential for profitably trading wind energy in the electricity market and efficiently integrating wind-generated electricity into the power grids. In this paper, we propose short- and medium-term wind power forecasting systems targeted to the Nordic energy market, which integrate inputs on the wind flow conditions from three numerical weather prediction sources. A point forecasting scheme is adopted, which forecasts the power at the individual turbine level. Both direct and indirect forecasting approaches are considered and compared. An automated machine-learning pipeline, built and optimized using genetic programming, is implemented for developing the pr…
Econo- Environmental Dispatch Solutions for Power Systems Integrated with Renewable Energy Resources
Due to the global initiatives for sustainable energy supply, the electric grids are increasingly integrated with environment-friendly and renewable energy resources. Hence, the power dispatch strategies are to be timely modified by incorporating the environmental aspects of generation along with the economic considerations. In this paper, we propose such an Econo- Environmental dispatch (EED) system for a power grids, which are integrated with renewable energy sources. The EED problem is formulated with two objective functions which aims at minimizing the unit cost of generation as well as minimizing the emissions caused during the power production. For attaining these objectives, cost and …
Parametric Models for Predicting the Performance of Wind Turbines
Abstract Performances of eight parametric power curve models for wind turbines, which can be used for the planning and management of wind energy projects, are compared in this study. Initially, the manufacturer’s power curves of four wind turbines are compared with their field performances. Then, the parametric models are developed for these turbines which are tested with their site performances. Out of the models, WERA showed the best performance in case of all the turbines. Finally, a method for using WERA in extrapolating the performance of turbines with limited test data is demonstrated with the case of a 1 kW turbine.