為了能有效地預防及減緩核電廠的嚴重事故 (severe accident)，在意外發生的早期，識別電廠的狀態是非常重要的，而此研究的主要目的是運用人工智能的方法來檢查警訊及電廠狀態和辨識出可能發生的肇始事件。
龍門核電廠 (Lungmen Nuclear Power Station, LNPS) ─台灣第四座核能電廠且為台灣第一座進步型沸水式反應器，被選為本研究的目標電廠，用其全域模擬器 (3keymaster)來產生測試資料，以利幫助研究方法之建立。本研究考慮了許多肇始事件，像是大中小破口的喪失冷卻水事故(Loss of Coolant Accidents)、喪失全飼水事故(Loss of all Feedwater)、全部的主蒸汽隔離閥關閉(Closure of all Main Steam Isolation Valves)、全再循環泵跳脫(Trip of all Recirculation Pump)、喪失冷凝器真空(Loss of Condenser Vacuum)等，建立了一個時間依賴性的查找表 (Look-up-table)，並發展圖形辨識理論來快速地追蹤電廠狀態，所建立的系統將作為一個預警工具，幫助運轉員並以結合圍阻體事件樹(Containment Event Tree, CET) 的方式來減緩嚴重事故的發生。

To prevent and mitigate an accident in nuclear power plant (NPP), it is important to identify the plant condition during the early stage of an accident. This research uses artificial intelligence techniques to check out the alarm status and to identify possible events.
The Taiwan's Lungmen nuclear power station (LNPS), which is an advanced boiling water reactor (ABWR), is chosen as the target plant for the current study. A full scope engineering simulator is used to generate the testing data for method development. The following initiating events are considered in this study: small and large loss of coolant accidents (LOCAs), loss of feedwater events, closure of all main steam isolation valves, trip of all recirculation pumps, and loss of condenser vacuum. A time dependent look-up-table is established for each possible event. A pattern recognition algorithm is developed for fast tracking of the plant status. The developed algorithm is to serve as an early warning tool and to assist operator in cooperate with Containment Event Tree (CET) for mitigation of accidents.

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