پیاده سازی صنعتی تکنیک های سیستم هوشمند برای نظارت بر وضعیت نیروگاه هسته ای
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|5581||2012||9 صفحه PDF||سفارش دهید|
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله تقریباً شامل 6186 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 39, Issue 8, 15 June 2012, Pages 7432–7440
As the nuclear power plants within the UK age, there is an increased requirement for condition monitoring to ensure that the plants are still be able to operate safely. This paper describes the novel application of Intelligent Systems (IS) techniques to provide decision support to the condition monitoring of Nuclear Power Plant (NPP) reactor cores within the UK. The resulting system, BETA (British Energy Trace Analysis) is deployed within the UK’s nuclear operator and provides automated decision support for the analysis of refuelling data, a lead indicator of the health of AGR (Advanced Gas-cooled Reactor) nuclear power plant cores. The key contribution of this work is the improvement of existing manual, labour-intensive analysis through the application of IS techniques to provide decision support to NPP reactor core condition monitoring. This enables an existing source of condition monitoring data to be analysed in a rapid and repeatable manner, providing additional information relating to core health on a more regular basis than routine inspection data allows. The application of IS techniques addresses two issues with the existing manual interpretation of the data, namely the limited availability of expertise and the variability of assessment between different experts. Decision support is provided by four applications of intelligent systems techniques. Two instances of a rule-based expert system are deployed, the first to automatically identify key features within the refuelling data and the second to classify specific types of anomaly. Clustering techniques are applied to support the definition of benchmark behaviour, which is used to detect the presence of anomalies within the refuelling data. Finally data mining techniques are used to track the evolution of the normal benchmark behaviour over time. This results in a system that not only provides support for analysing new refuelling events but also provides the platform to allow future events to be analysed. The BETA system has been deployed within the nuclear operator in the UK and is used at both the engineering offices and on station to support the analysis of refuelling events from two AGR stations, with a view to expanding it to the rest of the fleet in the near future.
As the AGR stations in the UK age, there is an increasing need to understand the condition of the reactor core, the major life-limiting component in an AGR station. Inspections undertaken during routine outages, every two-three years, provide high fidelity information on a limited number of channels. Additional information about core condition can be gained from monitoring data obtained during refuelling operations. These refuelling events occur much more frequently than outage inspections, but the raw data requires significant interpretation effort to provide meaningful results. The application of intelligent systems can aid this process, providing a repeatable and reliable method of automatically assessing refuelling data. In addition, intelligent system techniques can be applied to large volumes of this data to uncover trends, which relate to the age and degradation of the graphite core. These trends can be used to supplement existing understanding of the ageing process of nuclear graphite and supports the case for continued and extended operation of the AGR NPPs. This paper is split into the following sections. Firstly a brief introduction to Nuclear Power Generation in the UK is given along with more technical detail concerning the refuelling process and the associated monitoring data. The ageing process of nuclear graphite, from which the major components of the reactor core are constructed, is also described. The second section deals with the use of intelligent analysis techniques to support various aspects of analysing refuelling data, and how the application of these techniques can also provide valuable understanding into long-term trends in the data. These trends can then support continued operation and lifetime extension of the Advanced Gas-cooled Reactor (AGR) Nuclear Power Plant (NPP) fleet in the UK. The final section describes the industrial implementation of these techniques in a decision support system that aids the analysis of refuelling event data for two NPPs in the UK.
نتیجه گیری انگلیسی
This paper has described the application of intelligent systems techniques to refuelling data gathered from Nuclear Power Plants in the UK to provide automated decision support. The analysis of FGLT data is important as it augments the existing programme of in-core inspections by providing more frequent information relating to reactor core condition. Intelligent Systems techniques provide support to existing manual analysis by providing an automated, repeatable and auditable means of assessing the data and thus reducing the reliance on individual experts by making their expertise available within the developed system. Intelligent Systems techniques have been shown to provide automated analysis of new FGLT events, but also support the definition of benchmarks of normal behaviour through the use of clustering algorithms. This allows relevant benchmarks to be defined and maintained, ensuring that the system can adapt to the degradation process of the core. Furthermore, the techniques have also been shown to support the identification of long-term trends in the data. The resulting industrial implementation of these intelligent systems techniques, BETA, is used on a regular basis by graphite core engineers to support the analysis of all refuelling data for two AGR plants in the UK.