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Guest Editorial: Special Section on Applications of Artificial Intelligence in Industrial Power Electronics and Systems

THE GRID infrastructures and modernization, as well as the integration of renewable energies and using smart meters, can generate a large amount of data. These can lead to high complexity in the power system/electronics operation and control. Moreover, grid contingencies due to the natural disasters and cyber/physical attacks are highly unpredictable and costly preventable, which require fast and reliable data processing to preserve grid reliability and resiliency. Furthermore, the reliability of the power electronics devices and interfaces are very important, and can be improved by using the large data of measurements during long term operation. To this end, artificial intelligence (AI) techniques can potentially make it possible to provide new solutions to power electronics and power system operations and analysis. This special issue aims to investigate applications of AI in power system operation, analysis, planning, cybersecurity, as well as power electronics control, modulation techniques, reliability of the power electronics switches, and efficiency improvement in power electronics applications. In response to the call for papers for this Special Section, more than 60 full papers were received and thoroughly reviewed, out of which nine high-quality manuscripts were accepted and included in this Special Section. A summary of the accepted articles is provided as follows. The first article investigates the distribution grids outage data to find dominant factors in occurring vegetation, animal, and equipment-related outages. To this end, the visualization techniques have been used to demonstrate the impact of features on the outage occurrence. Also, the rule mining technique has been utilized to find factors correlating with each outage type. The results of this article show the effectiveness and merits of the proposed framework to identify factors related to outage occurrences, which potentially can be used for distribution grids planning and the operation scheduling. The second article developed a secured framework to detect and stop data integrity attacks in wireless sensors networks, more specifically on the microgrids networks. In this article, an intelligent anomaly detection technique according to the prediction intervals has been utilized to distinguish malicious attacks of different severities. Also, to provide optimal feasible prediction intervals over the smart meter, the developed anomaly detection