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| Project publications: | |
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| Project title: | Industrial Internet methods for electrical energy conversion systems monitoring and diagnostics |
| Project short title: | EMONDI |
| Number of project implementation agreement: | LT08-2-LMT-K-01-040 |
| Registration number: | 4441 |
| Department: | Department of Industrial Electronics and Electrical Technologies |
| Administrating department: | Project Implementation and Coordination Unit |
| Finance fund: | Eiropas Ekonomikas zonas un Norvēģijas finanšu instrumenti |
| Project RTU role: | project partner |
| Status: | Ended |
| Project start date: | 01.01.2021. |
| Project end date: | 31.12.2023. |
| Title of grant issuer: | EEA Finansial Mechanism |
| General manager: | Jānis Zaķis |
| Administrative manager: | Ilona Bērza-Šulte |
| Total finance: | |
| 993 750.00 | |
| Summary: | |
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Modern energy systems, such as wind turbines, motor drives in industry, and electric vehicles are prone to failures, resulting in loss of production, unavailability of services, or environmental disasters in a worst case. Electrical, mechanical, and thermal stresses are directly or indirectly responsible for these failures. To prevent these issues, energy systems must be regularly checked through routines and schedule specified by the manufactures. This schedule-based condition monitoring approach provides very little information on the remaining lifetime of the devices and does not allow for their prognostic and full exploitation. Furthermore, it is costly and presents problems related to the fact that devices might fail in between the routine check, which causes environmental risks and unsustainable use of resources. In this proposal, we present solutions for these drawbacks by combining Virtual Sensors (VS) with powerful Artificial Intelligence (AI) tools.We will develop models of the underlying devices that can run in real time and thus serve as Virtual Sensor fed by real operation data from the actual devices. The VS will monitor thermal, mechanical, and electrical stresses. The data from the VS will be used in failure models to predict the remaining lifetime of the devices allowing for fault-tolerant and overload usage of the said devices, as well as condition-based maintenance. This is possible if the models are used in combination with AI or machine learning engines running in the clouds. The data for training the AI-engines will be generated from physical models of the devices, such as the finite element models of electrical machines, or in some cases from reduced models of these devices, to speed up the learning process. We expect the methodology to detect localized failure potentials in critical components, such as bearings, gearboxes, motors and generators. The possibility to apply the methodology to power electronic devices will be investigated. |
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| Activities: | |
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1. State of the art and project management 2. Development of physical models of different energy conversion systems components and the related reduced models of these components, which will serve to construct the digital twin of the system. 3. Development and implementation of the concept of Virtual Sensors based on the developed Digital Twin concept. 4. Development of an Artificial Intelligence-based system that allows for the usage of the Virtual Sensors in the diagnosis and prognosis of the said electrical energy conversion systems. 5. Development of a small-scale demonstrator of the above concepts and show its ability to fulfil the main goal above at least in one example energy system. 6. Development of a small-scale demonstrator model of power electronic converter with sensor-fault-tolerant control algorithm. 7. Development of the methodology for self-monitoring of power electronic converter main components nominals and self-detection of parasitic components. 8. Reporting and dissemination |
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| Project published on RTU website: | 05.01.2021. |