A Low-Cost IoT Sensors Network for Monitoring Three-Phase Induction Motor Mechanical Power Adopting an Indirect Measuring Method
Fabrizio Ciancetta,
Edoardo Fiorucci,
Antonio Ometto,
Andrea Fioravanti,
Simone Mari,
Maria-Anna Segreto
Affiliations
Fabrizio Ciancetta
Department of Industrial and Information Engineering and Economics (DIIIE), University of L’Aquila, Piazzale Ernesto Pontieri 1, Monteluco di Roio, 67100 L’Aquila, Italy
Edoardo Fiorucci
Department of Industrial and Information Engineering and Economics (DIIIE), University of L’Aquila, Piazzale Ernesto Pontieri 1, Monteluco di Roio, 67100 L’Aquila, Italy
Antonio Ometto
Department of Industrial and Information Engineering and Economics (DIIIE), University of L’Aquila, Piazzale Ernesto Pontieri 1, Monteluco di Roio, 67100 L’Aquila, Italy
Andrea Fioravanti
Department of Industrial and Information Engineering and Economics (DIIIE), University of L’Aquila, Piazzale Ernesto Pontieri 1, Monteluco di Roio, 67100 L’Aquila, Italy
Simone Mari
Department of Industrial and Information Engineering and Economics (DIIIE), University of L’Aquila, Piazzale Ernesto Pontieri 1, Monteluco di Roio, 67100 L’Aquila, Italy
Maria-Anna Segreto
LAERTE Laboratory (Italy), ENEA (Italian National Agency for New Technologies Energy and Sustainable Economic Development), Via Martiri di Monte Sole 4, 40129 Bologna, Italy
Three-phase induction motors are widely diffused in the industrial environment. Many times, the rated power of three-phase induction motors is not properly chosen causing incorrect operating conditions from an energetic point of view. Monitoring the mechanical dimension of a new motor is helpful, should an existing motor need to be replaced. This paper presents an IoT sensors network for monitoring the mechanical power produced by three-phase induction motors, adopting an indirect measuring method. The proposed technique can be easily adopted to monitor the mechanical power using only one line of current transducer, reducing the cost of the monitoring system. The proposed indirect measurement technique has been implemented on a low-cost IoT system, based on a Photon Particle SoC. The results show that the proposed IoT system can estimate the mechanical power with a relative error of within 8%.
