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標題: | 基於深度學習之風電系統對電力品質的衝擊預測與分析 Prediction and Analysis of Impact on Power Quality for a Wind Power System Based on Deep Learning |
作者: | Yu-Dian Syu 許鈺鈿 |
指導教授: | 江昭皚(Joe-Air Jiang) |
關鍵字: | 深度學習,併網型風電系統,即時數位模擬器, Deep Learning,Grid-Connected Wind Power System,RTDS, |
出版年 : | 2020 |
學位: | 碩士 |
摘要: | 本研究將對於臺灣中部地區興建併網型風電系統,對於周遭電力系統進行衝擊預測。基於深度學習技術,提出風速的預測方法,利用歷史數據建立機器學習預測模型,在本研究中,透過實際風電系統長期收集風速風向,與相關環境資料作為訓練資料,使用兩種機器學習方法中常用的遞迴神經網路-長短期記憶網路(Long short-term memory, LSTM)與其變形版本(Gated recurrent unit, GRU),分別進行短期(未來10分鐘)的風速預測。此外,本研究亦使用即時數位模擬系統(Real Time Digital Simulator, RTDS)建立併網型風電系統模型,以長期風電系統資料與預測結果輸入至模型中產生系統衝擊資料。由結果顯示,在風速預測階段,其中LSTM模型的十分鐘短期預測誤差(Mean absolute percentage error MAPE)為5.6%,三十分鐘短期預測MAPE為8.2%,而GRU模型的十分鐘短期預測MAPE為5.8%,三十分鐘短期預測則為8.5%。基於預測結果的一定準度上,藉由預測風速透過電力電子即時數位模擬器進行更為精確的電力系統衝擊分析,此結果有助於提供臺灣電力公司人員調整調度電力決策,提高電網穩健性。 This study establishes a grid-connected wind power system with a real-time digital simulator (RTDS) model based on a real-world power grid in the central Taiwan to simulate and analyze the impact of the wind power system on the whole power system. Based on deep learning technology, a wind speed prediction method is proposed, and the historical data are used to establish the deep learning prediction model. Long-term wind speed and related environmental information are collected by the wind power system, and additional cloud features are extracted from satellite images to improve the prediction accuracy. With the two common recursive neural network models, the long short-term memory (LSTM) and its modified version (GRU), the wind speed is predicted in a short-term manner (the next 10 minutes) and a medium-term manner (the next 30 minutes). The 10-minute short-term prediction error (MAPE) for the LSTM model is 5.6% and the 30-minute short-term prediction MAPE is 8.2%. In addition, the 10-minute short-term prediction MAPE for the GRU model is 5.8% and the 30-minute short-term prediction MAPE is 8.5%. Based on these prediction accuracy results, a real-time digital simulator is used to examine the impact of the wind speed system on the whole power system. The simulation results help Taiwan Power Company to make proper power dispatch decisions, so that the grid stability can be largely improved. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/69060 |
DOI: | 10.6342/NTU202003637 |
全文授權: | 有償授權 |
顯示於系所單位: | 生物機電工程學系 |
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U0001-1608202023420000.pdf 目前未授權公開取用 | 5.63 MB | Adobe PDF |
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