A review of the fault diagnostic techniques based on machine is presented in this paper. As the world is moving towards industry 4.0 standards, the problems of limited computational power and available memory are decreasing day by day. A significant amount of data with a variety of faulty conditions of electrical machines working under different environments can be handled remotely using cloud computation. Moreover, the mathematical models of electrical machines can be utilized for the training of AI algorithms. This is true because the collection of big data is a challenging task for the industry and laboratory because of related limited resources. In this paper, some promising machine learning-based diagnostic techniques are presented in the perspective of their attributes.
The uptake of electric vehicles (EV) is increasing every year and will eventually replace the traditional transport system in the near future. This imminent increase is urging stakeholders to plan up-gradation in the electric power system infrastructure. However, for efficient planning to support an additional load, an accurate assessment of the electric vehicle load and power quality indices is required. Although several EV models to estimate the charging profile and additional electrical load are available, but they are not capable of providing a high-resolution evaluation of charging current, especially at a higher frequency. This paper presents a probabilistic approach capable of estimating the time-dependent charging and harmonic currents for the future EV load. The model is based on the detailed travel activities of the existing car owners reported in the travel survey. The probability distribution functions of departure time, distance, arrival time, and time span are calculated. The charging profiles are based on the measurements of several EVs.
Reference implementation of Digital Twins for electrical energy conversion systems is an important and open question in the industrial domain. Digital Twins can predict the future performance, behaviour, and maintenance needs of a complex system. Today the concept of Digital Twins is not only an emulation or simulation of the physical object along with its development history but also contains much information from the respective manufacturers and services. This paper presents the current stateoftheart of Digital Twins in relation to some interesting novel applications from different fields of electrical engineering. The objective of the paper is to give an overview of the successful application of Digital Twins in electrical energy conversion systems, such as industrial robotics and wind turbines; to discuss trends in applications like electric vehicles; and to suggest new applications, such as telescopes. Special attention is paid to the possible application of Digital Twins in faults diagnostics and prognostics of electrical energy conversion systems. Successful implementation of Digital Twins in any electrical energy conversion system diagnostics and prognostics allows for lowcost maintenance, higher utilization of the individual devices and systems, as well as lower usage of material and human resources. A SWOT analysis was performed for Digital Twin applications in electrical energy conversion systems. The latter is a useful analysis technique that explores possibilities for new achievements or solutions to existing problems and makes decisions about the best path.
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