A Real-Time Digital Twin and Neural Net Cluster-Based Framework for Faults Identification in Power Converters of Microgrids, Self Organized Map Neural Network

Abstract

In developing distribution networks, the deployment of alternative generation sources is heavily motivated by the growing energy demand, as by environmental and political motives. Consequently, microgrids are implemented to coordinate the operation of these energy generation assets. Microgrids are systems that rely on power conversion technologies based on high-frequency switching devices to generate a stable distribution network. However, disrupting scenarios can occur in deployed systems, causing faults at the sub-component and the system level of microgrids where its identification is an economical and technological challenge. This paradigm can be addressed by having a digital twin of the low-level components to monitor and analyze their response and identify faults to take preventive or corrective actions. Nonetheless, accurate execution of digital twins of low-level components in traditional simulation systems is a difficult task to achieve due to the fast dynamics of the power converter devices, leading to inaccurate results and false identification of system faults. Therefore, this work proposes a fault identification framework for low-level components that includes the combination of Real-Time systems with the Digital Twin concept to guarantee the dynamic consistency of the low-level components. The proposed framework includes an offline trained Self Organized Map Neural Network in a hexagonal topology to identify such faults within a Real-Time system. As a case study, the proposed framework is applied to a three-phase two-level inverter connected to its digital model in a Real-Time simulator for open circuit faults identification.

A New Digital Twins-Based Overcurrent Protection Scheme for Distributed Energy Resources Integrated Distribution Networks

“…Reference [19] presents a hierarchical energy management strategy for isolated microgrids (MG), using the hardware-in-the-loop (HIL) test to validate the hierarchical control. Reference [20] presents a fault identification framework for low-level components that combines real-time systems with the digital twin concept to guarantee the dynamic consistency of the low-level components. In addition, the authors of [21] have presented a digital twin for monitoring the power flow of a remote microgrid, where validation is performed for different types of loads.…”

A Low-Cost Microcontroller-Based Normal and Abnormal Conditions Classification Model for Induction Motors Using Self-Organizing Feature Maps (SOFM)

“…On the other hand, in the emerging landscape of industrial applications, digital twins have gained prominence, with SOM being deployed in real-time simulators. However, such sophisticated simulators can present significant cost implications [24]. Moreover, machine learning coupled with digital twins has been applied to fault detection, diagnosis, and lifetime prediction in electric machines.…”