臺灣博碩士論文加值系統

一旦壓水式反應器發生暫態事件，如果運轉員可以辨識其類型，將有助於使系統回到安全運轉狀態。監測反應器之熱狀態，反應器冷卻系統(Reactor Cooling System，RCS)之溫度與壓力，可以幫助運轉員掌握飽和餘裕及事件的發展。在本研究中，先後發展了決定性與隨機性方法做為異常事件辨識之用。決定性方法除上述溫度與壓力外，另以反應器冷卻系統冷端與熱端溫差做為第3個系統關鍵參數；隨機性方法則增加蒸汽產生器壓力為第4個系統關鍵參數。
事件數據使用RELAP5最佳估算模式進行模擬，由其中可觀察到每個事件之暫態反應系統關鍵參數都有其特性，決定性方法利用在特有之辨識時間內之變動範圍及熱狀態變化趨勢做為事件辨識。隨機性方法以每個事件其數據的機率累加分配，每0.05為增量，從0.05到1.00，做為事件之基本資料，用作辨識區分之用。考慮量測存在雜訊，模擬的數據以亂數方式加以處理，而得到95%的信賴區間，做為異常事件資料庫之數據。辨識一個事件必須符合80個數值之比較，這些數據分別屬於4個系統關鍵參數。
由於辨識過程簡單，在非常短的時間即可完成。驗證結果皆能有效地辨識暫態事件，此兩種方法將有助於運轉員更有效地執行緊急操作程序。

Once an event in a pressurized water reactor (PWR) occurs and the operator has identified it, the system may then be returned to safe operational status. Usually, monitoring the thermal state of the reactor, e.g., the temperature and pressure of the reactor cooling system (RCS), the operator can realize the margin to the safety limit and the progress of the event. A deterministic approach and a stochastic approach are developed in this study. In deterministic approach, the temperature difference between hot and cold legs is additionally adopted as the third parameters used for identification. And, the steam generator pressure is adopted as the forth parameter in stochastic approach.
The event data are generated using the best estimated model RELAP5. In deterministic approach, since the variation ranges of system key parameters at a specific time duration represent the specific character of each initiating event, the identification procedure can easily determine the case by comparing the variation range of on-line data and the event data in data pool. In the stochastic approach, which accounts for measurement noise, the cumulative distribution function (CDF) is constructed and system parameters variables at the cumulative probability from 0.05 to 1.00, with 0.05 increments, are chosen for identification. The random value is added to the simulated data, and then a 95% confidence interval is obtained. To identify an event, eighty data points, i.e., twenty data points for each parameter and four parameters for each event, should match the stored data.
Since the identification procedures are simple, the computation is very fast and the results show that the event can be properly identified. These two methods will be beneficial in the context of executing an emergency operating procedure more effectively.

摘 要 iii
Abstract iv
誌 謝 v
目 錄 vi
表 目 錄 ix
圖 目 錄 1
第一章 緒論 4
1.1 研究緣起 4
1.2 研究方法及目的 4
1.3 論文大綱 7
第二章 研究原理 8
2.1 次冷態熱力性質 8
2.2 系統關鍵參數之選擇 9
2.3 系統關鍵參數暫態 11
2.4 異常事件暫態模擬 15
2.4.1 事件A：飼水系統失效造成飼水溫度降低 17
2.4.2 事件B：飼水系統失效造成飼水流量增加 18
2.4.3 事件C：過度增加二次側蒸汽流量率 19
2.4.4 事件D：蒸汽產生器動力釋壓閥或安全閥誤開啟 20
2.4.5 事件E：蒸汽系統管路斷裂 22
2.4.6 事件F：蒸汽壓力調節器失效造成蒸汽流量減低 23
2.4.7 事件G：喪失外在負載 24
2.4.8 事件H：汽機跳脫 25
2.4.9 事件I： 主蒸汽隔離閥誤關閉 26
2.4.10 事件J：冷凝器喪失真空或其它原因造成汽機跳脫 27
2.4.11 事件K：電廠輔助系統喪失非緊急交流電源 29
2.4.12 事件L：喪失正常飼水流 30
2.4.13 事件M：飼水系統管路斷裂 31
2.4.14 事件N：功率運轉下誤啟動緊急爐心冷卻系統 32
2.4.15 事件O：調壓槽安全閥或動力釋壓閥誤開 33
2.4.16 事件P：蒸汽產生器內管破裂 34
2.4.17 事件Q：汽機跳脫預期暫態未急停 35
2.4.18 事件R：飼水跳脫預期暫態未急停 37
2.4.19 事件S：反應器冷卻系統熱端管路破口 38
2.4.20 事件T：反應器冷卻系統冷端管路破口 39
第三章 確定性方法 41
3.1 決定性方法 41
3.2 異常資料庫之準備 42
3.3 辨識程序 43
第四章 隨機性方法 48
4.1 隨機性方法 48
4.2 系統量測雜訊模擬 50
4.3 異常事件資料庫準備 52
4.4 辨識程序 55
第五章 辨識程序驗證與結果 58
5.1 決定性辨識程序驗證 58
5.2 隨機性辨識程序驗證 64
5.2.1 辨識程序閾值 64
5.2.2 辨識程序驗證 65
第六章 結論與建議 68
參考文獻 71
 

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