数字孪生:核电厂机组最优控制方案的预测与形成

Elena Jharko, K. Chernyshov
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引用次数: 0

摘要

各行业,特别是能源行业,正在利用数字孪生来提高工作效率和优化运营模式。数字孪生基于精确描述物体特征的几何、物理属性、行为和规则的模型。本文提出了一个数字孪生模型功能去中心化的例子,将一个以VVER-1000反应堆为控制对象的核电站动力装置(PU)分解为一组功能组的技术子系统。提出了核电厂动力装置数字孪生的创建方法,以及基于动力装置动态模型的数字孪生在核电厂动力装置先进控制系统中应用的主要方向。在智能操作员支持系统的框架下,提出了一种基于数字孪生的核电厂机组状态预测的“三阶段”方法,并给出了机组最优控制的形成原则。提出的创建数字孪生的方法用于为运营商创建智能支持系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Digital Twins: Forecasting and Formation of Optimal Control Programs for NPP Power Units
Industries, especially in the energy sector, are using digital twins to improve work efficiency and optimize operating modes. Digital twins have based on models that accurately describe the geometry, physical properties, behavior, and rules that characterize an object. The article presents an example of functional decentralization of digital twin models as a decomposition of an NPP power unit (PU) with a VVER-1000 reactor as a control object into a set of technological subsystems of functional groups. Approaches to the digital twins’ creation of NPP PU and the main directions for using digital twins based on dynamic models of a PU in advanced control systems for NPP power units are presented. Within the framework of the intelligent operator support system, a "three-stage" approach to the problem of forecasting the state of the NPP PU, based on digital twins, and the principles of forming the PU optimal control are proposed. The proposed approaches to creating digital twins are used to create intelligent support systems for operators.
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