以TSK機率模糊類神經網路控制之磷酸鋰鐵電池儲能系統之研製

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姓名

官啟玄(Chi-Hsuan Kuan) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

以TSK機率模糊類神經網路控制之磷酸鋰鐵電池儲能系統之研製
(Development of TSK-Type Probabilistic Fuzzy Neural Network Control for LiFePO4 Battery Storage System)

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摘要(中)

本論文提出一以數位訊號處理器為基礎之TSK 機率模糊類神經網路智慧型控制器以控制磷酸鋰鐵電池儲能系統，此電池儲能系統具有電池管理系統與雙向功率流動之三相交流-直流轉換器，本電池儲
能系統並可對電網以實虛功控制策略進行併網及充電。為了要改善功率在命令變動時之暫態響應，本文因此採用TSK 機率模糊類神經網路控制器以取代傳統的比例積分控制器。本文將詳細介紹TSK 機率模糊類神經網路的架構以及線上學習法則，而所提出之智慧型電池儲能系統皆實現於以32 位元定點運算之數位訊號處理器
TMS320F28035 上。另一方面，為了增強數位訊號處理器之運算效
率，本論文將以組合語言撰寫所推導之控制法則。最後，將由實驗結果驗證所提出之TSK 機率模糊類神經網路控制器實現在此電池儲能系統上之控制性能。

摘要(英)

A digital signal processor (DSP)-based TSK-Type probabilistic fuzzy neural network (TSKPFNN) is proposed in this thesis to control a 4 LiFePO battery
storage system. The storage system includes 4 LiFePO battery module with battery management system (BMS) and bidirectional power flow three-phase AC-DC converter. Moreover, the designed storage system adopts active and
reactive power control for grid connection. Furthermore, to improve the transient of command variation, a TSKPFNN controller is proposed to replace the traditional proportional-integral (PI) controller. The network structure and the online learning algorithms of the TSKPFNN controller are introduced in detail. In addition, all the control algorithms for the proposed battery storage
system are realized in a 32-bit fixed point DSP, TMS320F28035, using assembly language for enhancing the calculate efficiency of the DSP. Finally, the control
performances of the proposed TSKPFNN control system are evaluated by some experimental results.

關鍵字(中)

★ 三相交流-直流轉換器
★ TSK 機率模糊類神經網路
★ 磷酸鋰鐵電池組
★ 數位訊號處理器

關鍵字(英)

★ TSK-Type probabilistic fuzzy neural network (TSK
★ three-phase AC-DC converter
★ digital signal processor (DSP)
★ LiFePO4 battery

論文目次

中文摘要 I
英文摘要 II
目錄 III
圖目錄 VII
表目錄 XI
第一章緒論 1
1.1 研究動機與目的 1
1.2 文獻回顧 4
1.3 論文大綱 6
第二章鋰離子電池與電池管理系統 8
2.1 簡介 8
2.2 電池相關名詞定義 8
2.3 鋰離子電池原理 11
2.3.1 鋰離子電池之電化學原理 11
2.3.2 鋰離子電池之特性與規格 14
2.4 二次電池充電法簡介 16
2.4.1 定電壓充電法 16
2.4.2 定電流充電法 16
2.4.3 混合式充電法 17
2.4.4 脈衝式充電法 17
2.5 鋰離子電池模型與其模擬 18
2.5.1 鋰離子電池模型 18
2.5.2 模擬鋰離子電池模型 20
2.6 電池管理系統 21
2.6.1 保護裝置 22
2.6.2 電池平衡裝置 22
2.6.3 本論文之電池管理系統 24
第三章以數位訊號處理器為基礎之磷酸鋰鐵電池儲能系統控制晶片 26
3.1 數位訊號處理器TMS320F28035 26
3.1.1 數位訊號處理器TMS320F28035之功能簡介 26
3.1.2 記憶體規劃 28
3.2 TMS320F28035週邊功能介紹 29
3.2.1 增強型脈波寬度調變模組 29
3.2.2 中斷處理之流程 31
3.2.3 類比/數位轉換器 32
3.2.4 串列週邊介面模組 33
第四章磷酸鋰鐵電池儲能系統架構與控制策略之研製 36
4.1 簡介 36
4.2 三相座標軸轉換 37
4.2.1 靜止座標軸轉換 40
4.4.2 靜止座標軸與同步旋轉座標之轉換關係 41
4.3 三相交流-直流轉換器電路模型 43
4.4 三相電壓相位同步法 46
4.4.1 三相線電壓軸轉換方程式 46
4.4.2 電壓濾波法 46
4.4.3 零交越偵測法 47
4.4.4 鎖相迴路法 48
4.5 實虛功控制與電流控制 50
4.6 控制器設計 52
4.7 磷酸鋰鐵電池儲能系統之模擬 57
第五章 TSK機率模糊類神經網路控制器與模擬 64
5.1 簡介 64
5.2 TSK機率模糊類神經網路 65
5.2.1 TSK機率模糊類神經網路之描述 65
5.2.2 線上學習法則 67
5.2.3 TSK機率模糊類神經網路之收斂性分析 70
5.3 TSK機率模糊類神經網路控制器之模擬 73
第六章硬體系統說明與實驗結果 76
6.1 簡介 76
6.2 數位訊號處理器之週邊硬體電路 76
6.2.1 電壓感測電路 77
6.2.2 電流感測電路 77
6.2.3 數位訊號處理器之類比數位轉換器保護電路 78
6.2.4 功率級保護電路 79
6.2.5 人機介面電路 80
6.2.6 類比訊號輸出模組 81
6.3 系統軟體流程圖 83
6.4 實驗結果與說明 84
6.4.1 鎖相迴路實驗結果與說明 86
6.4.2 穩態時之實驗結果 87
6.4.3 以比例積分控制器控制功率外迴路之實驗結果 88
6.4.4 以TSK機率模糊類神經網路控制器控制功率外迴路之實驗結果 91
第七章結論與未來展望 95
7.1 結論 95
7.2 未來展望 95
參考文獻 96
作者簡歷 101

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指導教授

林法正(Faa-Jeng Lin)

審核日期

2012-8-22

推文