Application of Digital Twin Technology in Information and Measurement Systems

Introduction. The article addresses the problem of creating an automated system for data collection from heat metering units, as well as a digital twin of such a system. Digital twins are widely used in the energy sector to optimize the operation of thermal power plants, including their timely maintenance, forecasting various emergency scenarios, or planning thermal energy production. Practical examples of such systems are presented. The relevance of this work lies in the possibility of predicting the size of pipeline defects based on both measurement and digital twin data. Aim. Development of a distributed information and measurement system for heat supply monitoring with the introduction of a digital twin. Materials and methods. The parameters of the heat-carrying agent, such as temperature, pressure, and flow, were simulated according to the normal distribution law and the thermal schedule of power plants. This information was further used to develop a mathematical and algorithmic support for predicting the state of technological equipment. The depth of a cavity defect which may occur in the pipeline was predicted. The strength condition was used as a criterion for the failure limit state. To determine the ultimate strength of the pipe wall, OST 153-39.4-010–2002 and the Barlow formula were used. Results. The obtained results include a digital twin of the heat supply control system, the structure of a distributed information and measurement system for the unit of heat supply monitoring, algorithmic and software systems for the operation of the distributed information and measurement system and for predicting the failure state of the pipeline. The software operability was verified in normal operation and in the absence of access to the server. Conclusion. The use of digital twin technology in heat supply monitoring makes it possible to optimize the thermal graph of the object by simulating the optimal values of the heat-carrying agent based on environmental parameters with an error in modeling the water temperature in the supply pipeline of Δ t = ±5°C at ambient temperatures from –8 to +3 °C.