A comparison of Two Embedded Systems to Detect Electrical Disturbances using Decision Tree Algorithm

The Electrical Power Quality (EPQ) is a relevant subject in the academic area because of its importance on real-world problems. The anomalies on an electrical network can cause strong losses in equipment and data. In this context, much effort has been made by many types of research approaches to get solutions for this kind of problem, seeking for better accuracy on the classification of the anomalies, or building a system to detect them. This paper, therefore, aims to compare two systems built to classify electrical disturbances even in noised environments. For this purpose, it was used a microprocessor system (Raspberry Pi3) and a micro-controller system (NodeMCU Amica), analyzing their time to classify the input signal. The microprocessor achieves better results (45.50ms against 267.10ms), with an accuracy of 97.96% in an ideal environment and 76.79% in a noisy environment (20dB of SNR) for both systems.