Sviluppo di una Metodologia per la Selezione e il Controllo Qualità di Ventilatori per Cappe Aspiranti in Linea di Produzione Mediante Analisi Vibrazionale

Fin dai primi anni del secolo scorso i ricercatori hanno condotto ricerche e sviluppato soluzioni per diagnosticare l’insorgere di difettosità nei motori ad induzione per aumentarne l’affidabilità e la qualità. La letteratura è ricca di esempi nei quali vengono utilizzate le più conosciute tecniche di elaborazione del segnale e negli ultimi anni l’utilizzo di algoritmi di intelligenza artificiale ha portato ad ulteriori miglioramenti nella prevenzione dei guasti e delle loro conseguenze. In questo lavoro viene presentato un approccio differente per diagnosticare la presenza di difettosità nei motori ad induzione, una metodologia originale per il tipo di applicazione basata sul calcolo delle divergenze statistiche tra distribuzioni di probabilità e sul calcolo delle entropie e della cross-entropia. Vengono proposti e confrontati cinque diversi metodi per ottenere le distribuzioni di probabilità dai segnali misurati, due differenti formulazioni per il calcolo delle divergenze e quattro per il calcolo dell’entropia. L’efficacia e la maggiore robustezza degli indicatori calcolati con il metodo proposto rispetto ai tradizionali indicatori statistici sono dimostrate tramite le analisi condotte sulle misure accelerometriche acquisite durante lo sviluppo della procedura per il controllo qualità dei ventilatori per cappe aspiranti uscenti dalla linea di produzione di SIT S.p.A. Ne viene presentata inoltre una versione modificata utilizzando la trasmissibilità del banco di collaudo come filtro inverso, soluzione che la rende efficace anche quando applicata alle misure acquisite dal sensore accelerometrico di linea. La procedura proposta ha dimostrato capacità di classificazione con un accuratezza superiore al 95%. Infine, sfruttando le potenzialità del machine learning, viene proposta una soluzione che, utilizzando un Autoencoder, è in grado di migliorare i risultati ottenuti in precedenza, raggiungendo valori analoghi come accuratezza ma migliori in termini di falsi negativi.Since the early years of the last century, researchers have conducted research and developed solutions to diagnose the onset of defects in induction motors to increase their reliability and quality. The literature is full of examples in which the well-known signal processing techniques are used and in recent years the use of artificial intelligence algorithms has led to further improvements in the prevention of faults and their consequences. In this work a different approach is presented to diagnose the presence of defects in induction motors, an original methodology for the type of application based on the calculation of statistical divergences between probability distributions and on the calculation of entropies and cross-entropy. Five different methods for obtaining probability distributions from measured signals are proposed and compared, two different formulations for calculating divergences and four for calculating entropy. The effectiveness and greater robustness of the indicators calculated with the proposed method compared to traditional statistical indicators are demonstrated through the analyses conducted on the accelerometric measurements acquired during the development of the procedure for the quality control of the fans for extractor hoods leaving the production line of SIT S.p.A. A modified version is also presented using the transmissibility of the production bench as an inverse filter, a solution that makes it effective even when applied to the measurements acquired by the accelerometric sensor positioned on the production station. The proposed procedure has demonstrated classification capabilities with an accuracy greater than 95%. Finally, exploiting the potential of machine learning, a solution is proposed which, using an Autoencoder, is able to improve the results previously obtained, reaching similar values in terms of accuracy but better in terms of false negatives

Peer-to-peer energy trading in electrical distribution networks

In response to the challenges posed by the increasing penetration of distributed generation from renewable energy sources and the increasing electricity retail prices with decreasing Feed-In Tariff rates, a new energy trading arrangement, “peer-to-peer (P2P) energy trading” has been proposed. It refers to the direct energy trading among consumers and prosumers in distribution networks, which is developed based on the “P2P economy” concept (also known as sharing economy). A hierarchical system architecture model has been proposed in order to identify and categorise the key elements and technologies involved in P2P energy trading. A P2P energy trading platform called “Elecbay” is designed. The P2P bidding is simulated using game theory. Test results in a grid-connected LV Microgrid with distributed generators and flexible demands show that P2P energy trading is able to improve the local balance of energy generation and consumption, and the enhanced variety of peers is able to further facilitate the balance. Two necessary control systems are proposed for the Microgrid with “Elecbay”. A voltage control system which combines droop control and on-load-tap-changer (OLTC) control is designed and simulated. Simulation results show that the proposed voltage control system is sufficient for supporting the P2P energy trading in the Microgrid. The total number of operation times of the OLTC is reduced with P2P energy trading compared to the reference scenario. The information and communication technology (ICT) infrastructures for the P2P bidding platform and the voltage control system are investigated. The information exchange among peers and other parties (Elecbay, distribution system operators, etc.) is designed based on TCP/IP protocol. Existing and private communication networks with different communication medium, bandwidths, etc., are modelled. Simulation results show that the existing ICT infrastructures are sufficient for supporting both the P2P energy trading platform and the voltage control system. Therefore, no large amount of additional investments are required