Industrial Internet methods for electrical energy conversion systems monitoring and diagnostics

About this project

<p>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.&nbsp;</p> <p>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.</p>

Bilateral cooperation results

In this project, the project partners anticipated to strengthen their collaboration by researchers and PhD student exchanges, shared data and software. They 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. It should be noted that each partner organisation and country had its own specific field of research. Nevertheless, there was sufficient overlap to facilitate cooperation and add value to their respective research outcomes. Several industry collaborators from each participating country agreed to cooperate within the project by providing and obtaining information. These organisations confirmed their willingness through submitted letters. The active participation and collaboration of these organisations enhanced their relations and consolidated cooperation with the partner organisations. The project consortium gained valuable inputs from the industrial partners to focus on research aspects necessary for the industry. Despite the epidemiologic situation preventing planned visits and physical meetings, the synergies between the partners were not jeopardised. Each partner specialised in a specific research field necessary for achieving the project goals, creating synergy within the entire project team. The primary description of this synergy is evident through the number of joint publications. Project Committee meetings occurred every two months, discussing project management, scientific outputs, platform implementation challenges, and communication possibilities.

Project partners

• University of Adger Donor project partner· Norway
• Riga Technical University Project partner· Latvia
• Tallin University of Technology Project partner· Estonia