Future's smart grid consists of an increasing number of dispersed energy generation and consumption devices. Due to its complexity, the energy sector yields development of decentralized monitoring and control framework, where each node in the grid will be a potential location for power quality device. In order to continually improve the stability of the energy system, those devices need a tool for accurate and real-time detection and classification of power quality disturbances. However, despite the continuous progress in the field, the development of that kind of tool is still a challenge. This manuscript presents a real-time implementation of optimized power quality events classifier for detection and classification of 21 classes of single and combined disturbances. The focal point of the presented classifier is the real-time implementation of: optimal feature extraction, optimized classification, accurate zero-crossing detection and efficient handling of different noise levels present in the voltage signal. The implementation is performed on myRIO-1900 using LabVIEW interface. Testing and validation results show that this implementation exhibits high classification accuracy, even in cases when the classification relies on classes obtained as combination of four power quality disturbances.

With the development of technology allowing for a rapid expansion of data science and machine learning in our everyday lives, a significant gap is forming in the global job market where the demand for qualified workers in these fields cannot be properly satisfied. This worrying trend calls for an immediate action in education, where these skills must be taught to students at all levels in an efficient and up-to-date manner. This paper gives an overview of the current state of data science and machine learning education globally and both at the high school and university levels, while outlining some illustrative and positive examples. Special focus is given to vocational education and training (VET), where the teaching of these skills is at its very beginning. Also presented and analysed are survey results concerning VET students in Slovenia, Serbia, and North Macedonia, and their knowledge, interests, and prerequisites regarding data science and machine learning. These results confirm the need for development of efficient and accessible curricula and courses on these subjects in vocational schools.