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Author: | Saeed, Umar |
Title: | Resource-Efficient Widely-Linear Collaborative Wind Forecasting for Renewable Energy Generation |
Publication type: | Master's thesis |
Publication year: | 2012 |
Pages: | [8] + 52 Language: eng |
Department/School: | SignaalinkÀsittelyn ja akustiikan laitos |
Main subject: | SignaalinkÀsittelytekniikka (S-88) |
Supervisor: | Werner, Stefan |
Instructor: | Riihonen, Taneli |
Electronic version URL: | http://urn.fi/URN:NBN:fi:aalto-201209213140 |
OEVS: | Electronic archive copy is available via Aalto Thesis Database.
Instructions Reading digital theses in the closed network of the Aalto University Harald Herlin Learning CentreIn the closed network of Learning Centre you can read digital and digitized theses not available in the open network. The Learning Centre contact details and opening hours: https://learningcentre.aalto.fi/en/harald-herlin-learning-centre/ You can read theses on the Learning Centre customer computers, which are available on all floors.
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Location: | P1 Ark Aalto 1368 | Archive |
Keywords: | forecasting complex wind signal non-circular widely-linear |
Abstract (eng): | Wind is an important source of renewable energy with abundant availability at many places of the world. The integration of wind energy into the electricity grid brings new challenges on the system reliability due to its intermittent nature. A robust tool with good accuracy for wind power forecasting is an essential part of wind based power system. Wind power is directly related to the cube of wind speed and hence a small improvement in the forecasting of wind speed provides larger improvement in wind power forecasts. The accuracy of a wind speed forecasting model depends upon the characteristics of wind signal incorporated in the model. Wind direction has significant effect on wind speed forecasts and the complex representation of wind signal provides a convenient tool to include the effect of direction. The complex wind signal is non-circular, and widely-linear modeling of the complex wind signal provides optimal results. Wind signal has strong temporal and spatial correlations as well. Most of the wind forecasting models at present do not incorporate all of these characteristics of the wind signal. In this thesis work, we will develop a widely-linear collaborative wind forecasting model which takes into account the non-circular nature of the complex wind signal and its temporal and spatial correlations as well. The parameters of the model are updated using recursive least squares approach. The forecasting model is further optimized for efficient resource utilization by using a set-membership formulation. A data model for the complex wind signal is developed and the algorithm is tested on the simulated wind data. Simulation results show better forecasting performance with the developed approach. Real world wind data is also used to verify the results. |
ED: | 2012-04-25 |
INSSI record number: 44304
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