Improved Artificial Intelligence-Based Methods for Power System Forecasting Applications
| Autor: | LU, HENG-JIU, 盧恆究 |
|---|---|
| Rok vydání: | 2017 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 105 Increasing renewable energy penetration has been a global trend in the past decades. Wind and solar power are expected to contribute significantly to the renewable energy targets owing to advancements in renewable energy technologies, abundance of free resource and commercial viability. Therefore, the predictability of wind and solar power in managing load and generation balance is crucial to system operations. This thesis proposes improved models for wind and solar power output at different interval forecast. Also, due to the operation of the large electric devices (e.g. wind power generator, electric arc furnace, etc.) which would make the power system produce serious voltage flicker. If the flicker levels are predictable, corrective solution such as static var compensation may be developed for both electric utilities and the customer. This thesis adopts electric arc furnace at steel industrial company as investigated target for three indices of flicker severity (ΔV10, Pst, and Plt) forecast. This thesis proposes improved artificial intelligence methods to solve above-mentioned problems which include the improved radial basis function neural network (IRBFNN), radial basis function neural network with an error feedback (RBFNN-EF), improved radial basis function neural network with an error feedback (IRBFNN-EF), Gaussian mixture model neural network (GMMNN), radial basis function neural network integrated with grey theory (Grey-RBFNN), improved radial basis function neural network integrated with grey theory (Grey-IRBFNN), deep learning neural networks integrated with grey theory (Grey-DLNN), and Grey theory with look-up table (Grey-LUT). Performance comparisons between the proposed and traditional methods are reported for wind speed at 10-minute interval, wind power at 10-minute interval, wind power output at one-minute interval, solar power generation at one-minute interval, and three kinds of flicker severity level forecast. Simulated results (i.e. wind speed and power forecast, solar power forecast, and flicker severity forecast) can provide more accurate and effective forecast than other compared methods to the actual power system forecasting problems. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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