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研究生: 蔡佳翰
Jia-Han Cai
論文名稱: 應用倒傳遞類神經網路於具太陽能發電之配電饋線電壓控制
Application of Back-propagation Neural Network to Voltage Control of Distribution Feeders with Photovoltaic Generation
指導教授: 吳啟瑞
Chi-Jui Wu
口試委員: 楊念哲
Nien-Che Yang
陳坤隆
Kun-Long Chen
學位類別: 碩士
Master
系所名稱: 電資學院 - 電機工程系
Department of Electrical Engineering
論文出版年: 2021
畢業學年度: 109
語文別: 中文
論文頁數: 151
中文關鍵詞: 太陽能發電配電系統虛功率調控智慧變流器電壓控制牛頓法倒傳遞類神經網路歸一化
外文關鍵詞: photovoltaic generation, distribution systems, reactive power control, smart inverter, voltage control, Newton-Raphson Method, back-propagation neural network, normalization
相關次數: 點閱:1173下載:3
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  • 本論文研究應用倒傳遞類神經網路於具太陽能發電之配電饋線電壓控制,提出利用不同輸入變數,推估太陽能智慧變流器之虛功率吸收變化量,達到電壓控制。本研究先使用MATLAB/Simulink所建立之兩種配電系統模型與牛頓法程式,結合六種逐時負載量與五種逐時太陽能發電量,執行經智慧變流器電壓-虛功率控制法調控前後之電力潮流,分析並比較其電壓分布及虛功率吸收量。取得負載量、發電量、電壓變化量及虛功率吸收變化量數據後,設定類神經網路輸入及輸出變數,以歸一化和實際值數據之不同組合進行數據訓練。再利用類神經網路推估各併網點匯流排應有之虛功率吸收情形,並提出相關建議,期望能提供實務運轉上之助益。


    The purpose of this thesis is to investigate the application of back-propagation neural network to voltage control of distribution feeders with photovoltaic generation. The method that the reactive power absorbed by a smart inverter of photovoltaic generation can be evaluated with different input variables is proposed in this thesis. And its objective is to achieve voltage control. In this study, we firstly established 2 types of distribution system. The respective programs are the Newton-Raphson Method program and Simulink model in the MATLAB. Secondly, we analyze and compare the voltage profile and reactive power absorbing amount before and after volt-var control of smart inverters. The power flows includes the combination of 6 kinds of 24-hr load and 5 kinds of 24-hr photovoltaic generation. Afterwards, we access the data of loads, photovoltaic generation, voltage variation and variation of reactive power absorbing amount. Then, we setup the input and output variables of the neural network and perform data learning with different combinations of normalized and actual data. Finally, we can evaluate reactive power absorbing amount of buses connected to photovoltaic generation with the neural network. We also propose some related suggestion with the novel method. It is expected to provide advantages for practical operation with using the novel approach.

    摘要 i Abstract ii 致謝 iii 目錄 iv 圖目錄 viii 表目錄 xviii 第一章 緒論 1 1.1 研究背景 1 1.2 文獻探討 4 1.3 研究目標與步驟 8 1.4 論文架構概述 10 第二章 配電系統與再生能源發展 13 2.1 簡介 13 2.2 配電系統架構 14 2.3 負載模型 17 2.4 再生能源發展 19 2.5 再生能源併網規範 23 第三章 配電饋線電壓控制方法 27 3.1 簡介 27 3.2 電壓及虛功率之控制原理 27 3.3 電壓控制設備與動作原理 29 3.3.1 並聯電容器 29 3.3.2 有載分接頭切換器 32 3.3.3 饋線調壓器 35 3.3.4 智慧變流器 39 3.4 分接頭切換設備電壓調整方法 43 3.5 台電現行之電壓控制模式 46 3.6 台電電力系統電壓控制目標 52 第四章 智慧變流器虛功率調控模式之應用與倒傳遞類神經控制法簡介 55 4.1 前言 55 4.2 鎖相環 55 4.3 虛功率調控模式之應用 57 4.3.1 固定功率因數調控模式 57 4.3.2 電壓-虛功率調控模式 58 4.4 倒傳遞類神經控制法 59 4.4.1 類神經網路簡介 59 4.4.2 倒傳遞類神經網路學習及回想過程 59 第五章 含太陽能發電與智慧變流器之配電饋線模擬及電力潮流計算 65 5.1 模擬系統架構及參數設定 65 5.2 模擬軟體介紹 67 5.2.1 模擬方法 67 5.3 牛頓法電力潮流計算及程式撰寫步驟 69 5.4 情境設定 72 5.5 電壓-虛功率控制法情境模擬比較 75 5.6 本章小結 85 第六章 應用倒傳遞類神經控制法於電壓控制 87 6.1 前言 87 6.2 倒傳遞類神經控制法設定 87 6.3 含1具太陽能併網之系統電壓控制 89 6.4 含8具太陽能併網之系統電壓控制 92 6.5 本章小結 110 第七章 結論及未來研究方向 111 7.1 結論 111 7.2 未來研究方向 112 參考文獻 113 附錄 121

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