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研究生:賴益祺
研究生(外文):Yi-ChiLai
論文名稱:於平行多電器辨識系統實現輕量化波形識別方法
論文名稱(外文):A Lightweight Waveform Identification Method for Parallel Multi-Appliance Recognition System
指導教授:黃悅民黃悅民引用關係
指導教授(外文):Yueh-Min Huang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:工程科學系碩博士班
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:61
中文關鍵詞:平行式多電器辨識智慧電錶波形辨識演算法
外文關鍵詞:parallel multiple appliance recognitionsmart meterwaveform identification method
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近年來由於能源議題逐漸受到重視,政府與研究單位已積極合作進行智慧電錶之建置,同時隨著智慧聯網與家庭能源管理系統議題之興起,智慧電錶為了提供智慧電網的配電需求,進而轉型成智慧家庭息息相關的控制與用電決策服務。當前智慧電錶存在著裝設不易的問題,且大多僅提供功率消耗、電費、基本電能、諧波偵測等資訊,然而相較於電費資訊,有效偵測使用之電器與耗電量,更能提供使用者較佳的用電參考進而改變其用電習慣。
此外,當前電器辨識研究多以單一電器辨識為主,然而針對一般家庭用戶之用電習慣,同時開啟與關閉多台電器的可能性是存在地,因此本研究探討於平行狀態進行多電器辨識,亦即提供多台電器同時開啟與關閉之電器辨識。然而實現平行式多電器辨識系統需要大量之辨識特徵資料庫,如何建立辨識用樣本並減少運算量,使其適用於輕量化之嵌入式系統之運行,便成為本研究所面臨之問題。
針對上述之兩大問題,前者,本研究提出一非侵入式智慧電錶系統,為考量不熟悉電器的使用者之用電習慣,此設計僅需要將智慧電錶插入用電迴路上即可使用。後者,本研究透過建立資料庫機制、電器辨識分類法與波形辨識方法,解決當前電器辨識系統大資料量的問題。且相對於其他電器辨識系統,本研究採用低階嵌入式系統晶片提供低功耗的優勢,同時具備高度擴充性與易用性。在實驗過程有別於其他電器辨識系統之研究環境,考量到平行式多電器辨識與普遍使用者之用電習慣,本研究在實驗於一般日常用電習慣下,總系統辨識率可達86.14%,而單一電器總辨識率可達96.14%,顯示本研究具有高度可行性。
Recently there have been heavy discussions on power consumption, governments and research facilities had worked diligently in implementing smart power meters. Along with the uprising of the topics Internet of Things and Home Energy Management Systems, smart meters provide the basic provision for smart power grids, and they are vital for planning and controlling energy service plans in smart homes. However smart power meters are hard to install and many only provide the power cost, electric billing, basic energy data, and wave detection. Comparative to power billing information, how to effectively use the appliances and energy costs can give the user constructive advice on improving appliance usage behaviors.
Also, most current power appliance recognition focuses on single appliance recognition, which is unrealistic for home scenarios where multiple appliances can be powered on or off simultaneously, hence this research conducts multiple power appliance recognition to able to recognize multiple power appliances during simultaneous shut down or power up. To implement appliance recognition requires vast amounts of recognition feature databases. How to achieve recognition samples while decreasing the computing load in order to lower the costs to embedded implementation becomes the main obstacle within this research.
Two issues are mentioned above, first this research proposes a non-intrusive smart power meter system, considering with unfamiliarity with power appliance usage behaviors of the user, this design only requires to plug in the power meter in the circuit for usage. The latter is by using a database building mechanism, power appliance recognition and wave recognition methods within this research to solve the data amount problem in the power recognition system. Comparative to other power appliance recognition, this research uses low cost embedded system for low power cost advantage, while having both expandability and ease of usage advantages. This research differs also in research environment, considering Parallel multiple appliance recognition and user power appliance usage behavior, this research is performed under normal power appliance usage. The overall recognition rate can reach 86.14% while single power appliance recognition can reach 96.14%, displaying the fact that this research is highly plausible.
摘要 I
Abstract II
誌謝 III
圖目錄 VI
表目錄 VIII
第一章 緒論 1
1.1研究動機 1
1.2 研究目的 2
1.3 章節提要 3
第二章 背景介紹與文獻回顧 4
2.1 Smart Grid 4
2.2 Smart Meter 5
2.3 HEMS 7
2.4 Appliance Recognition 8
2.4.1辨識特徵值 9
2.5 資料探勘 12
2.5.1 知識挖掘流程 12
2.5.2 資料探勘模型 14
第三章 硬體平台介紹與設計 15
3.1 電能感測元件介紹 15
3.1.1 ACS714電流感測器晶片 15
3.1.2 變壓元件 18
3.2 微處理器開發版STM3223 ENYS 19
3.3 智慧電錶設計 21
3.3.1 電壓感測系統設計 21
3.3.2 電流感測系統設計 23
第四章 系統原理與設計架構 25
4.1 系統整體架構 25
4.2系統整體工作流程 26
4.3 電能擷取方法與流程 27
4.3.1 電能波形擷取 27
4.3.2 電能波形擷取流程 27
4.4 電能資料處理方法與流程 28
4.4.1 狀態偵測 28
4.4.2 雜訊抑制 31
4.5平行式多電器辨識方法與流程 34
4.5.1 資料庫建立 34
4.5.2 子樹分類 37
4.6 波形辨識演算法 40
4.6.1 Euclidean Distance 40
4.6.2 Dynamic Time Warping 40
第五章 系統實作與結果分析 45
5.1 實驗環境 45
5.2 雜訊抑制 45
5.3 平行式多電器辨識 50
第六章 結論與未來展望 57
6.1 結論 57
6.2 未來展望 57
6.2.1 演算法效能增進 57
6.2.2 動態分析 58
6.2.3 分散式電器辨識 58
參考資料 59
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