臺灣博碩士論文加值系統

目前全球能源日漸短缺及環保意識提升，各國均面臨節能減碳之課題，我國核四廠停建、深澳燃煤發電機廠擴充及台中火力發電廠之爭議，顯示台灣電力之缺乏有逐漸升高之趨勢。因此，開源與節流為政府當務之急，而軌道運輸包括：臺鐵、高鐵及各地捷運之廣泛使用，雖是大眾運輸最佳選項，然而交通運具亦是消耗電力能源之大宗，電氣化鐵路使用大量電力，如何將列車煞車時轉為再生電能回收送到電車線或第三軌再利用的計算方式，使得營運機構清楚電力消耗，以便能尋求正確運轉方法而降低能源消耗，為本論文主要目的。
城際鐵路之快速及大量運輸是我國大眾交通的主要選項，臺鐵自1978年開始鐵路電氣化，電力的使用、列車班次日益增加，相對能源消耗亦增加，本論文係利用蜘蛛覓食最佳路徑法，建立列車運行時的能源使用公式，針對列車總體能源使用與阻力，以及列車運行之參數，完成整體能源消耗公式與阻力參數，模擬列車整體模型運轉狀態，將列車性能與路線狀態之輸入，進行能源消耗分析，由於智慧型列車能源消耗與舊有列車有明顯差別。本論文利用列車運轉條件及能源消耗態樣，將其區分為三種狀態分析，分別為交錯加速、滑行、減速，針對全程的耗能進行分析，因為列車運行模式的不同，以及坡道參數的變化，能源消耗的變動會受到很多的因素影響，將列車的能源消耗以最佳化演算法，再以快速的最佳化運算結果，能減少人為運轉所產生的較多能源消耗與誤差，又能夠在表定時間抵達目的地，藉由能源公式、列車參數，能計算能源的使用，亦能使得列車在準確的時間內到達。
本論文主要經由蜘蛛網內獵物傳遞之振動強度，進而統計掌握最豐富食物位置，即等效列車加速的最佳解。在演算模型中，加入隨機移動之行為模式，本演算法可兼具對於路段的搜尋機制，增加最佳解之機率。本論文所提方法之可行性方面，利用臺鐵進行模擬測試，再與實際的量測數值進行比較。經由實際的量測結果可知，本論文所提方法確實具有列車節能運行規劃之應用潛力，應可以提供司機員運轉之參考，本論文亦與TLBO(Teaching-Learning-Based Optimize, TLBO)演算法進行分析比較，證明本方法的優越性，求解最佳列車省能運行的電力能源使用，亦能確保列車的運行時間的成本，以期邁向高運轉效能之列車運轉。

In view of the global energy crisis and the increase of environmental protection, each country all have the issue of energy conservation and carbon reduction. Taiwan is no exception due to the suspension of the construction of the No. 4 nuclear power plant, the Shen-Ao and Tai-Chung coal-fired power plants, and electric power shortage will be gradually rising. Finding source and reduction have become important policies of the government. Rail transportation undoubtedly is great option for mass transportation. However, Rolling Stock is the huge energy-consuming. The electrical railways include high-speed rail, convention Railway, and metro. Reduction or regenerative brake is the main direction of this research.
The rapid and mass transportation of intercity has always been an important transportation for domestic transportation. Taiwan Railways Administration (TRA) first electrified in 1978. Nowadays TRA increased number of trains operation and huge energy consumption as well. This study uses Social Spider Algorithm establishes the energy use formula for the train operation of Taiwan Railway, and calculates the overall energy consumption and resistance parameters for the energy use and resistance of the train State, different energy usage analysis for different train performance and route status input, because the energy consumption of the new generation of smart trains is significantly different from that of the old type electric Rolling Stock. Therefore, train operating conditions and energy consumption patterns can be used to distinguish them into three states for analysis, which are acceleration, costs and deceleration, and analyze the energy of the whole process because of different train operation modes and railway ramp parameters. Changes, energy consumption will be affected by many factors, in order to reduce energy consumption, the optimize train energy consumption formula algorithm, and then optimize the results with fast calculations, and then such results put into the train propulsion soft, can reduce the excess energy consumption and error caused by human operation, and can reach the destination on time. By using the energy consumption formula and train parameters, not only can the reduced energy, but also the train can arrive in time. .
This study mainly uses the "Application of Social Spider Algorithm (SSA) to Optimize Energy of Train” to simulate the predatory behavior of social spider, that is, the vibration intensity transmitted through the prey in the spider web, and then statistically grasp the most abundant food position etc. The optimize solution suitable for efficient train acceleration. At the same time, in the calculus model, the behavior pattern of random movement is added, so that the novel algorithm can have an approach for the track segment and increase the possibility of the optimal solution. In support of the feasibility of the proposed method in this study, the simulated using the TRA train and compared with the actual measured values. According to the actual measurement results, the method proposed in this study does have the application potential of the train energy-saving operation plan, and should provide a reference for the driver's operation. At the same time, this study is also compared with the teaching-learning-based optimization, Teaching-learning-based optimize (TLBO) algorithm to prove the superiority of this method, to solve the best train energy-saving operation of energy use, and also to ensure the running time of the train in order to move towards high-performance trains.

第1章 緒論 1
1.1 背景與研究動機 1
1.1.1. 背景 1
1.1.2. 研究動機 3
1.2 研究目標 6
1.3 論文結構 8
第2章 鐵路牽引系統簡介 10
2.1 簡介 10
2.2 鐵路牽引系統 10
2.2.1. 電力牽引系統 10
2.2.2. 柴油-電力牽引系統 12
2.2.3. 混合動力牽引系統 13
2.2.4. 直流電機驅動 14
2.2.5. 交流電機驅動 16
第3章 運行規劃的最佳化演算法簡介 21
3.1 最佳化演算法簡介 21
3.1.1. 規劃列車最佳運行的簡介 21
3.2 文獻回顧 23
3.3 TLBO演算法簡介 29
3.4 蜘蛛社群演算法簡介 32
3.4.1. 蜘蛛社群行為之簡介 32
3.4.2. 蜘蛛社群演算法之模型建立 33
3.4.3. 蜘蛛社群移動行為 34
第4章 模擬列車運動與牽引力 36
4.1 簡介 36
4.2 列車運動物理學 36
4.2.1. 一般介紹 36
4.2.2. 粘附力 38
4.2.3. 阻力 39
4.2.4. 有效質量 41
4.2.5. 一般列車運動方程 42
4.3 模型建立與模擬 43
4.3.1. 列車狀態開關 43
4.3.2. 運轉控制的輸入 46
4.3.3. 能源消耗模型建立 52
第5章 臺鐵TEMU1000型太魯閣號傾斜式電聯車之運行量測與分析 54
5.1 簡介 54
5.2 特性分析 55
5.3 量測資料 57
5.3.1. 冬山-新馬路段 57
5.3.2. 貢寮到福隆 59
5.4 多目標省能規劃 61
5.4.1. 牽引力Te(v) 61
5.4.2. 行駛阻力Re(v) 62
5.4.3. 彎道阻力(R

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