Adaptive energy monitoring and management system for electrical networks using real-time sensors and Raspberry Pi
1
Salesian Polytechnic University
Submission date: 2025-10-06
Final revision date: 2026-03-03
Acceptance date: 2026-04-17
Online publication date: 2026-04-22
Publication date: 2026-04-22
KEYWORDS
TOPICS
ABSTRACT
This work proposes a system that performs continuous, real-time monitoring using on sensor networks, enabling the early detection of faults in the electrical system. The main objective is to prevent short circuits, breakdowns, and other issues associated with inadequate installations by analysing electrical variables such as voltage and current. This is achieved through the implementation of Raspberry Pi-type equipment, programmed with Python, which enables data collection and real-time processing. The data comes from distribution boards and electrical equipment that are in a critical phase, to obtain favourable measurements that can be processed in real time. This results in an innovative, high-impact solution that identifies anomalies proactively, effectively, accurately, and quickly, optimizing the maintenance process and significantly reducing downtime. The most conclusive results of the work are based on the measures taken, which allowed the load to be redistributed more evenly between the phases, considerably reducing negative sequence currents (35%) and voltage drop from 1.8% to 1.2%, which in turn translates into a significant decrease compared to other works with the same structure. In which a difference of 0.6% can be seen between the generated model and its results, compared to another research.
FUNDING
This research received no external funding.
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