| DC 欄位 |
值 |
語言 |
| DC.contributor | 電機工程學系 | zh_TW |
| DC.creator | 郭哲男 | zh_TW |
| DC.creator | Che-Nan Kuo | en_US |
| dc.date.accessioned | 2020-8-13T07:39:07Z | |
| dc.date.available | 2020-8-13T07:39:07Z | |
| dc.date.issued | 2020 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=107521063 | |
| dc.contributor.department | 電機工程學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 太陽光電系統受太陽光照度及環境溫度等因素影響，輸出功率具有高間歇性。由於功率瞬間變化量大，若大量併入電力系統，將影響市電的電力品質。因此，本論文提出以遞迴式機率小波模糊類神經網路為架構之智慧型控制，應用於微電網電能管理系統，目的為減緩太陽光電輸出至電網的功率波動，其原理為利用電池儲能系統，來彌補太陽光電實際輸出功率與平滑化後輸出功率之間的差值。本論文之電能管理系統使用聯齊科技的數據閘道器Atto做為硬體，並整合中興電工的微電網系統，Atto每分鐘收集微電網電力資訊儲存並上傳雲端，併網時，於Atto內執行太陽光電功率平滑化演算法，控制電池儲能系統的充放電來平滑化市電功率波動，市電發生異常切換至孤島時，太陽光電系統與電池儲能系統將提供穩定的交流電壓，使微電網維持正常運作。本論文將詳細介紹遞迴式機率小波模糊類神經網路之架構與線上學習法則，並證明其收斂性。除此之外，於功率平滑化控制中，電池會頻繁地充放電，本論文提出基於模糊邏輯之減少電池淺充淺放次數控制法，降低電池的充放電次數使電池壽命延長，並以庫倫積分法估測電池電量狀態。根據再生能源導入電網實功率波動之規範，本研究所提之遞迴式機率小波模糊類神經網路不僅能符合規範，並且與其他平滑化控制法相比所需電池容量最小。最後，利用模擬與實驗結果驗證所提出之功率平滑化控制，應用於微電網電能管理系統且在不同照度變化情況下之成效。 | zh_TW |
| dc.description.abstract | Due to illumination and temperature, intermittent characteristics of a PV power system cause negative impacts on power systems. This thesis presents an intelligent controller based on the recurrent probabilistic wavelet fuzzy neural network (RPWFNN) algorithm for the energy management system to mitigate the fluctuation of PV output power directly fed to the grid. The energy management system uses NextDrive′s data collection gateway Atto and integrates CHEM′s microgrid system. Atto collects microgrid power information every minute and uploads it to the cloud. In grid-connected mode, Atto performs PV power smoothing algorithm. It controls the charging or discharging of the battery energy storage system to smooth the power fluctuations of the grid. When the grid fails, the PV power system and battery energy storage system will provide a stable AC voltage, so that the microgrid can work normally in island mode. The network structure of the RPWFNN and its online learning algorithms are described in detail. In addition, battery life might be decreased by frequent charge-discharge cycling in PV power smoothing control. The method based on fuzzy logic is proposed in this thesis to reduce shallow cycles, and extends the battery life. Moreover, state of charge estimation using Coulomb counting method is adopted in the energy management of battery. According to the grid active power fluctuation limit set in this study, compared with other smooth control methods, RPWFNN not only meets the specifications, but also has the minimum battery energy capacity. Finally, simulated and experimental results of various PV variation scenarios verify the effectiveness of the proposed intelligent PV power smoothing control. | en_US |
| DC.subject | 電能管理系統 | zh_TW |
| DC.subject | 太陽光電系統 | zh_TW |
| DC.subject | 電池儲能系統 | zh_TW |
| DC.subject | 功率平滑化 | zh_TW |
| DC.subject | 遞迴式機率小波模糊類神經網路 | zh_TW |
| DC.subject | 電池狀態 | zh_TW |
| DC.subject | energy management system | en_US |
| DC.subject | PV power system | en_US |
| DC.subject | battery energy storage system | en_US |
| DC.subject | power smoothing | en_US |
| DC.subject | recurrent probabilistic wavelet fuzzy neural network | en_US |
| DC.subject | battery status | en_US |
| DC.title | 具智慧型太陽光電功率平滑化控制之微電網電能管理系統 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Intelligent PV Power Smoothing Control for Microgrid Energy Management System | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |