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研究生: 何任軒
Ren-Xuan He
論文名稱: 在智慧電網中使用波型取樣之電壓品質變異快速偵測
Quickest Detection for Smart Grids Voltage Quality Events Using Waveform Observations
指導教授: 林士駿
Shih-Chun Lin
口試委員: 林士駿
Shih-Chun Lin
張縱輝
Tsung-Hui Chang 
黃昱智
Yu-Chih Huang
楊緒文
Hsu-Wen Young
學位類別: 碩士
Master
系所名稱: 電資學院 - 電子工程系
Department of Electronic and Computer Engineering
論文出版年: 2018
畢業學年度: 106
語文別: 英文
論文頁數: 43
中文關鍵詞: 智慧電網電壓品質變異快速偵測波型取樣
外文關鍵詞: Quickest Detection, Smart Grids, Voltage Quality Events, Waveform Observations
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  • 智慧電網是一種電力網絡,包括各種運營和能源措施,其中包括智能電錶、智能電器、可再生能源和節能資源。電力調節和電力生產和分配的控制是智慧電網的重要方面,而智慧電網最重要的目的是為下一代開發更可靠的安全性和環保性。不幸的是,由於智能電網中不穩定的可再生能源,使電力品質(PQ)事件更容易發生。因此,我們將關注於快速變化檢測問題,該問題旨在最大限度地減少檢測延遲和假警報的錯誤率。 在本文中,智慧電網中的智能電錶用於得到觀測值並將其通過演算法計算的本地決策傳送到聚合中心做出最終決定。


    A smart grid is an electrical grid which includes a variety of operational and energy measures including smart meters, smart appliances, renewable energy resources, and energy efficient resources. Electronic power conditioning and control of the production and distribution of electricity are important aspects of smart grids. The most important purpose of smart grids is to develop a more reliable security and environmentally friendly for the next generation. Unfortunately, power quality (PQ) events would be much easier to happen because of the unstable renewable energy sources in smart grid. Thus, we focus on the quickest change detection (QCD) problem, which aims to minimize the detection delay and error probabilities of false alarm. In this paper, a smart meter in smart grids is used for obtaining the observations and transmitting its local decision calculated by algorithms to the fusion center for making final decision.

    Contents 1 INTRODUCTION 2 2 SYSTEM MODEL 5 3 PROPOSED THREE LOCAL DECISION ALGORITHMS FOR COMPARISON 8 3.1 Problem Model . . . . . . . . . . . . . . . . . . . 8 3.2 The CuSum Algorithm . . . . . . . . . . . . . . . 9 3.3 The Dynamic CuSum (D-CuSum) Algorithm . . . 13 3.4 The Binned Generalized CuSum (BG-CuSum) Algorithm . . . . . . . . . . . . . . . . . . . . . . 18 4 SIMULATION 21 4.0.1 Simulation Environment . . . . . . . . . . 21 4.0.2 State Observation . . . . . . . . . . . . . . 24 4.0.3 Simulation Results . . . . . . . . . . . . . 26 5 CONCLUSION 29 6 APPENDIX 30

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