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Ortombina, Ludovico (2018) Innovative solutions for converters and motor drives oriented to smart cities and communities. [Ph.D. thesis]

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Abstract (italian or english)

Alcune aree definite dall'Unione Europea nel contesto delle smart cities and communities si fondono pienamente con i motori elettrici come, per esempio, l'efficienza energetica, le tecnologie a basse emissioni di carbonio e la mobilità. I motori elettrici sono utilizzati in molteplici applicazioni industriali e non, consumando tra il 43% e il 46% dell'energia elettrica prodotta su scala mondiale.Nonostante alcune applicazioni siano contraddistinte da dinamiche elevate, come manipolatori o macchine utensili, la maggior parte di esse sono caratterizzate da basse dinamiche in quanto facenti parte di processi industriali, per esempio pompe, compressori, ventilatori o nastri trasportatori. Si è stimato che il costo dell'intero ciclo di vita di un motore elettrico è ascrivibile per il 92% - 95% all'energia consumata, il che indurrebbe un tempo di ritorno dall'investimento per installazione di un azionamento elettrico minore di due anni. Nonostante il notevole risparmio economico e ambientale ottenibile, è piuttosto sorprendente apprendere che solo il 10% - 15% di tutti i motori industriali siano controllati da azionamenti elettrici.

Per quanto riguarda le diverse tecnologie di motori elettrici, i motori sincroni a riluttanza stanno ricevendo una notevole attenzione sia da ricercatori industriali che accademici. Il crescente interesse è principalmente motivato dalle loro intrinseche caratteristiche quali l'alta efficienza, il basso costo e il basso impatto ambientale dovuto alla mancanza di magneti permanenti. Per di più, le loro caratteristiche soddisfano appieno i requisiti imposti dalle smart cities and communities e sono adatti per tutte le applicazione, caratterizzate da una bassa dinamica, viste sopra. Per questi motivi, questa tecnologia di motori può essere posta al centro dei processi di rinnovamento di quelle applicazioni. Vi è ampio consenso sul potenziale incremento delle vendite sia di azionamenti elettrici che di motori sincroni a riluttanza.

I motori sincroni a riluttanza sono soggetti a una marcata saturazione magnetica, rendendo i classici modelli a parametri concentrati poco adatti. La prima parte di questa tesi riguarda lo sviluppo di un innovativo modello magnetico per motori anisotropi. Si basa su una rete neurale non tradizionale, chiamata Radial Basis Function. La sua proprietà locale rende questo tipo di rete neurale particolarmente adatta ad un addestramento durante il normale funzionamento del motore. Si propone una completa procedura di design e addestramento della stessa. In particolare vengono fatte alcune considerazione le quali permettono di definire a priori alcuni parametri della rete neurale rendendo il problema di addestramento lineare. Si descrivono due algoritmi di addestramento, il primo veloce ma computazionalmente dispendioso perciò adatto per un'implementazione offline mentre il secondo idoneo ad un addestramento online. Infine, per concludere l'identificazione parametrica del motore, si propone uno schema basato sull'iniezione di una corrente continua il quale permette di stimare la resistenza di statore indipendentemente da tutti gli altri parametri della macchina. L'indipendenza parametrica permette un notevolmente miglioramento nell'accuratezza di stima del modello magnetico ottenuto con la rete neurale.

La seconda parte di questa tesi, invece, tratta il controllo del motore e come sia possibile migliorarne le performance utilizzando il modello identificato. Innanzitutto, per incrementarne l'efficienza si presenta un innovativo metodo per trovare la curva a massima coppia per corrente. La tecnica proposta lavora in stretta simbiosi con l'identificazione del modello magnetico in quanto è in grado di capire dove si trova la curva cercata rispetto all'attuale punto di lavoro sfruttando la stima locale dei flussi magnetici. Identificata la direzione di movimento, l'azionamento continuamente muove il punto di lavoro coerentemente. Infine, si propongono tre diversi controlli di corrente pensati per gestire un motore fortemente non lineare, tutti basati sul modello stimato. Il primo è un controllore proporzionale-integrale nel quale i parametri vengono modificati al variare del punto di lavoro con lo scopo di mantenere la dinamica della corrente di motore costante. Il secondo è anch'esso basato su un controllore proporzionale-integrale ma a guadagni costanti accoppiato ad un'azione di feed--forward la quale compensa tutte le non linearità presenti nella mappa magnetica. Infine, il terzo è un controllo predittivo il quale determina direttamente la posizione degli switch tali per cui la funzione di costo è minimizzata. All'interno del controllo, è inserito un vincolo sulla corrente massima e si utilizza un particolare algoritmo per ottenere un lungo orizzonte di predizione.

Tutti i metodi presentati nella tesi sono stata verificati attraverso dettagliate simulazioni e prove sperimentali, eccezione fatta per il controllo predittivo il quale è stato testato attraverso simulazioni.

Abstract (a different language)

Smart cities and communities are conjugated by European Union in different areas, including energy efficiency, low carbon technologies and mobility which are deeply merged with electric motors. Electric machines are ubiquitous in industry for a wide range of applications, consuming between 43% and 46% of all electricity that is generated in the world. Although some machines are used for high-performance applications, such as robots and machine tools, the majority are used in industrial processes for pumps, compressors, fans, conveyors, and other slower-dynamic applications. It is estimated that 92% - 95% of the life cycle costs of electric motors are associated with the energy they consume, leading to typical payback periods of < 2 years for the installation of an adjustable-speed drive. It is rather surprising to learn that, despite overwhelming evidence of the attainable savings, only 10% - 15% of all industrial motors presently use electronic adjustable speed drives. On the motor side, Synchronous Reluctance (SynR) motors are gaining lots of attention from industrial researchers and academics, due to their inherent characteristics like the high efficiency, the low cost and the low environmental footprint. Their characteristics fully meet the requirements imposed by smart cities and communities and the aforementioned low-dynamics applications, so they could be the heart of the revamping of those plants. There is wide agreement that the potential for future growth in the sales of industrial drives and SynR motors is still very substantial.

SynR motors are prone to magnetic saturation, making the classic model with lumped parameters unsuitable. The main part of this thesis concerns the development of a new magnetic model for anisotropic motors, especially for SynR motors. It is based on a special kind of neural network (NN), called Radial Bases Function (RBF) NN, which is particularly advisable for an online updating due to its local property. A complete training procedure is proposed in which some considerations are done to define several NN parameters and to convert the nonlinear training problem into a linear one. Two different training algorithms are presented, the former one is fast but computationally cumbersome then suitable for an offline training while the latter one is lighter then proper for an online training. In order to complete the online parameters identification, a scheme based on a DC current injection is developed to estimate the stator resistance. An exhaustive analysis is carried out to disclose that the proposed method is independent from other motor parameters which is a strength asset in a saturable motor. An accurate stator resistance value improves in turn of the magnetic model.

The second part of this dissertation deals with how to exploit an accurate magnetic model to enhance the motor control. In order to improve the efficiency of the motor, exploiting the RBF NN model and the online training algorithm, the Maximum Torque per Ampere (MTPA) curve is found. Starting from a blank NN, it is continuously online trained and a proper algorithm understands where the MTPA curve is respect to the current working point. Afterwards, the drive moves itself towards the actual MTPA. Finally, three different current control schemes tailored for anisotropic motors are presented, all based on the available NN-based magnetic model. The first one is a gain-scheduling PI control where the control gains are accordingly tuned to the working point to keep constant the control bandwidth. The second one is based on a classical PI regulator with a FF action to compensate for all the nonlinearity of magnetic maps. The third one is a constrained direct Model Predictive Control (MPC) where a long prediction horizon is achieved. In order to accomplish a long prediction horizon, the Sphere Decoding Algorithm is properly modified to make it suitable for a nonlinear system.

The whole thesis was fully validated through an intensive simulation and experimental stage, except the long--horizon MPC which was tested only by simulation.

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EPrint type:Ph.D. thesis
Tutor:Zigliotto, Mauro
Ph.D. course:Ciclo 31 > Corsi 31 > INGEGNERIA MECCATRONICA E DELL'INNOVAZIONE MECCANICA DEL PRODOTTO
Data di deposito della tesi:03 April 2019
Anno di Pubblicazione:31 November 2018
Key Words:Synchronous Reluctance motor, Neural Network, Model Predictive Control, Maximum Torque per Ampere, Adaptive Control
Settori scientifico-disciplinari MIUR:Area 09 - Ingegneria industriale e dell'informazione > ING-IND/32 Convertitori, macchine e azionamenti elettrici
Struttura di riferimento:Dipartimenti > Dipartimento di Tecnica e Gestione dei Sistemi Industriali
Codice ID:11871
Depositato il:08 Nov 2019 10:33
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