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Description
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.
Summary of project results
The Baltic region, like the rest of the world, is rapidly witnessing the implementation of modern electric power conversion systems, such as electric power converters for wind and photovoltaic power plants, inverters for improving the electric power quality, and frequency converters for electric motor drives in industry and electric vehicles. These devices use power electronics equipment whose semiconductor elements operate at high voltages and conduct large currents. Additionally, the failure rate is increasing due to thermal and mechanical stresses that often affect power conversion systems. Therefore, their failures are more common compared to low-power electronic devices. Faults in power conversion systems cause economic losses or even lead to disasters.
To prevent these issues, electric power conversion systems must undergo regular checks according to routines and schedules specified by manufacturers. However, this schedule-based condition monitoring approach provides minimal information on the remaining lifetime of the devices and does not allow for prognostics and full exploitation. Furthermore, it is costly and presents problems related to the fact that devices might fail between routine checks, causing environmental risks and unsustainable use of resources.
In the project, solutions to these problems were presented by a consortium of research institutions through the combination of Virtual Sensors with powerful Artificial Intelligence (AI) tools. Models of the underlying devices were developed that could run in real time and serve as VS fed by real operational data from the actual devices. The Virtual Sensors are monitoring thermal, mechanical, and electrical stresses. The data from the Virtual Sensors is used in failure models to predict the remaining lifetime of the devices, allowing for fault-tolerant and overload usage of the 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 cloud.
A prototype of the cloud based monitoring system with integrated developed models was implemented. The data for training the AI engines is generated from physical models of the devices, such as finite element models of electrical machines, or in some cases, from reduced models of these devices to speed up the learning process. The methodology detects localised failure potentials in critical components, such as bearings, gearboxes, motors, and generators. The possibility of applying the methodology to power electronic devices was also investigated.
The project gave us the opportunity to develop and supplement our competencies in ML based solution development for energy sector, establish practical collaboration with Public and private companies in Lithuania, Research institutions in Norway, Denmark, Latvia, Estonia. Take a part in developing new innovative solutions for this sector, that focuses on more efficient energy production forecasting, balancing and consumption.
Summary of bilateral results
In this project, we anticipated a higher level of international collaboration, including researchers and PhD student exchanges, shared data and software, and joint publications. We also look forward to collaborating further with partners, establishing a robust research network between partners, and further connecting with industry organisations in the respective countries. The project enhanced the mobility of researchers and PhD students between partner countries, facilitating the transfer of technology and knowledge.