IoT-based SCADA System for Smart Grid Stability Monitoring using Machine Learning Algorithms

This paper proposes a different approach for a Smart Grid Stability Monitoring system using an IoT -based SCADA and Machine Learning algorithms. Different models based on Machine Learning are trained and evaluated using a real dataset to select the best algorithm to predict the electrical grid stability. In addition, this work emphasizes the use of the MQTT protocol to develop an IoT -based platform that connects the company's SCADA interface to the cloud and improves the interaction between the users and the electricity company. The model with the best prediction is Extra Trees algorithm with the best R2 index performance. Compared to previous works, this study used an IoT-based SCADA system and Machine Learning algorithms, in order to improve the interaction between the users and the electricity service provider as it is desired for a Smart Grid.