臺灣博碩士論文加值系統

本論文提出一個在不增加主動式電力濾波器的情況下補償三相不平衡電流之智慧型雙級太陽光電發電系統。太陽光電發電系統由一個交錯式直流至直流轉換器與一個三階層中性點箝位變流器所組成。此外，太陽光電發電系統具有智慧型變流器功能，其中太陽光電變流器的輸出實、虛功是根據電力法規所要求的功率因數所預先設定的。在所提出的方法中，透過低通濾波器獲得 軸補償電流以補償饋入電網的三相不平衡電流。此外，為了提高太陽光電發電系統在不平衡負載變動情況下的直流鏈電壓的控制性能，本文提出了一種線上學習的非對稱補償模糊類神經網路，用來取代傳統的比例積分控制器用於直流鏈電壓控制。在所提出的非對稱補償模糊類神經網路中，將模糊系統的悲觀和樂觀操作互相結合產生的補償參數加入到補償模糊類神經網路中，並且採用非對稱型高斯函數為非對稱歸屬函數以增加訓練維度。另外，所提出的太陽光電發電系統的控制器，透過使用數位訊號處理器的兩個控制平台來實現。最後，從模擬與實驗結果可以看出在三相不平衡負載下，補償饋入電網之三相不平衡電流的優良性能。

A improved method is proposed to compensate the three-phase unbalanced currents of power grid under three-phase unbalanced load for a two-stage photovoltaic (PV) power system without the augmentation of active power filter (APF). The PV power system is composed of an interleaved DC/DC converter and a three-level neutral-point clamped (NPC) inverter. Moreover, the PV power system possesses the smart inverter function, in which the output active and reactive powers of the PV inverter are predetermined by a power factor according to grid codes of the utilities. In the proposed method, -axis compensation currents are obtained through low pass filters (LPFs) to compensate the three-phase unbalanced currents of power grid. Furthermore, in order to improve the control performance of the DC bus voltage of the PV power system under unbalanced load variation condition, an online trained compensatory neural fuzzy network with an asymmetric membership function (CFNN-AMF) is proposed to replace the traditional proportional-integral (PI) controller for the DC bus voltage control. In the proposed CFNN-AMF, the compensatory parameter to integrate pessimistic and optimistic operations of fuzzy systems is embedded in the CFNN. In addition, the dimensions of the Gaussian membership functions are directly extended to AMFs. Additionally, the proposed controllers of the PV power system are implemented by two control platforms using floating-point digital signal processor (DSP). Finally, excellent compensation performance for the three-phase currents of power grid under three-phase unbalanced load can be achieved from the experimental results.

中文摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VII
表目錄 XV
第一章 緒論 1
1.1 研究背景與動機 1
1.2 文獻回顧 3
1.3 本文貢獻 5
1.4 論文大綱 6
第二章 智慧型太陽光電發電系統介紹 7
2.1 簡介 7
2.2 太陽能電池特性 7
2.3 太陽能電池最大功率點追蹤 11
2.3.1 擾動觀察法(Perturb and Observe Method) 11
2.3.2 增量電導法(Incremental Conductance Method) 12
2.4 兩級式電路架構 14
2.4.1 交錯式直流至直流轉換器 16
2.4.2 三階層中性點箝位變流器 18
2.4.3 三階層中性點箝位變流器控制原理 25
2.4.4 鎖相迴路設計 27
2.5 變流器之實虛功控制與電流控制 28
2.6 不平衡負載電流補償之架構與控制策略 29
2.6.1 直流鏈電壓控制 29
2.6.2 補償電流計算 30
第三章 智慧型太陽光電發電系統之雙模式控制策略 31
3.1 前言 31
3.2 Mode I：最大功率點追蹤模式 33
3.3 Mode II：智慧型變流器模式 37
第四章 非對稱歸屬函數之補償模糊類神經網路 39
4.1 簡介 39
4.2 非對稱歸屬函數之補償模糊類神經網路架構 39
4.3 非對稱歸屬函數之補償模糊類神經網路線上學習法則 42
4.4 非對稱歸屬函數之補償模糊類神經網路收斂性分析 45
第五章 硬體設備與規劃 49
5.1 硬體規劃 49
5.2 數位訊號處理器與周邊電路 50
5.2.1 TMS320F28335控制電路板 50
5.2.2 市電電壓偵測電路 50
5.2.3 直流電壓偵測電路 51
5.2.4 電流感測電路 52
5.3 硬體設備 53
5.3.1 可程控直流電源供應器(具太陽能電池陣列模擬功能) 53
5.3.2 三相交流電源供應器 54
5.3.3 可變動之三相不平衡負載 56
5.3.4 三相變壓器 57
第六章 模擬與實驗結果 58
6.1 前言 58
6.2 模擬結果 58
6.3 實驗結果 86
第七章 結論與未來研究方向 116
7.1 結論 116
7.2 未來研究方向 117
參考文獻 118
作者簡歷 124

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