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- W4385587084 abstract "Even though new generation nuclear power plants are equipped with reliable technology based operating systems, those are susceptible to rare anticipated operational occurrences. Those postulated failures often known as Internal Initiating Events are crucial for health of nuclear power plants and calls for immediate precise measures to arrest event progression. Nuclear establishments being connected with large cordial international communities like IAEA, WANO receives timely alert from any unprecedented events in nuclear power plants worldwide, thus are well informed about all industrial risks as well as good practices. Nuclear power plants keep inclusive procedures to handle all anticipated events and beyond and those procedures are event-based or symptom-based. Event identification is an important juncture in successful handling, as of now event identification in Indian standard PHWR is solely dependent on operating crew judgement and skill of trade. Events once correctly diagnosed are easier to manage using Emergency Operating Procedures (EOP) but events at times it could be difficult to identify under stressful transient operation of large, complex interactive systems. Events if wrongly identified can divert the operating crew into off target and real scenario may remain unattended for critical initial period of action. Due to this risk of uncertainty, nuclear fraternity has gone conservative and preferring more generic approach of symptoms-based Emergency response than optimized event-based EOP. This paper proposes a precise yet fast approach to classify events in real time scenario with help of data driven machine learning technique using Python for industry use. The model proposed has 100% accuracy in event identification with runtime less than 5 s. This development of Operator Support System could strengthen the confidence within the nuclear fraternity to move towards more optimised event-based approach under dynamic transient plant condition." @default.
- W4385587084 created "2023-08-05" @default.
- W4385587084 creator A5042918687 @default.
- W4385587084 date "2023-01-01" @default.
- W4385587084 modified "2023-09-24" @default.
- W4385587084 title "Event Identification in Indian Standard PHWR NPP Using Machine Learning Technique" @default.
- W4385587084 cites W2797844224 @default.
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- W4385587084 doi "https://doi.org/10.1007/978-981-99-5049-2_1" @default.
- W4385587084 hasPublicationYear "2023" @default.
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