ZIGLIOTTO MAURO

Application of neural networks to electric drives. This line of research pursues the new "More Intelligent Drives" paradigm of applying artificial intelligence and other sophisticated methods to make electric drives as smart and easy to use as any other consumer device, such as a smartphone. The general trend towards more-intelligent energy-aware ac drives is driving the development of new motor topologies and advanced model-based control techniques. In particular, the research focuses on pure reluctance and anisotropic permanent magnet motors for their reduced environmental footprint with respect to permanent magnet motors. The availability of accurate mathematical models that describe these motors is essential to the design of any model-based advanced control. As a first line, the research aims at describing the nonlinear relations between currents and flux linkages through innovative radial-basis function neural networks. These special drive-oriented neural networks take as inputs the motor voltages and currents, returning as output the motor flux linkages, inclusive of any nonlinearity and cross-coupling effect. The simple structure of the neural network fits for implementation on standard drives. The second research line is about the comprehensive approach to convolutional neural networks based condition monitoring of synchronous motor drives. The increasing complexity of modern industrial systems calls for automatic and innovative predictive maintenance techniques. As suggested by the Industry 4.0 process, this demand translates in the need of more-intelligent drives. The research goal is the use of convolutional neural networks, successfully implemented in image recognition, to interpret the data from motor currents for diagnostic purposes. The early detection of possible faults in the electrical motor allows programmed maintenance and reduces the risk of unplanned shutdowns.

Maximum efficiency combined control in solar water pumping systems. Water provision in remote or isolated areas lacking of water and energy distribution systems has always been a challenge. In those regions, typically desert or mountainous, that are not easily accessible by conventional transportation means, water pumping represents the only viable method to ensure a minimum water supply even during severe droughts. Hand pumps, possibly operated with the assistance of animals, are only suitable for low consumption rates and pumping heads. On the other hand, mechanically operated pumps can sustain all those activities, besides of conventional domestic uses, that are typical of a small rural community, such as irrigation and livestock. Most of them are powered by diesel engines. However, especially for small communities in developing countries, their use could be impracticable because of the limited availability and high cost of fuel. The advent of cheaper photovoltaic panels and mass produced pumps has made solar powered water pumping a viable and competitive solution, especially in those areas interested by the presence of a large solar radiation. Solar pumping systems offer many advantages over the more traditional diesel pumps, including improved reliability and reduced operational and maintenance costs. The availability of pumping power in these systems is well matched to the water demand, which is in fact largest at daytime. However, it is also affected by weather and environmental conditions, which are by their nature variable. In particular, intermittent power shortages could easily occur, especially in a cloudy day. In order to maximise the energy utilisation and thus reduce the breakeven point with respect to other solutions, the PV source should be always operated at its point of maximum efficiency. This line of research is devoted to the study and development of integrated optimization of the solar panel and motor pump, taking advantage of the latest findings in advanced electric drive control.

Sensorless control of electrical drives. The reduction of the human environmental footprint also comes through the use of more efficient motors, such as synchronous reluctance motors. Since these motors are intended to compete directly with induction motors, they are usually sold without position sensors. The availability of sensorless control techniques is then of paramount importance. Due to their robustness and adaptability, for decades one of the research lines of the Electric Drives Laboratory was the development of position estimators based on the extended Kalman filter in permanent magnet synchronous motors. The time is ripe to move towards the application to reluctance motors as well. This recently inaugurated line of research, in collaboration with Leuphana University in Luenenburg (D), aims to eliminate the elements that hinder the transition. All passes through the availability of an accurate and analytical magnetic model, which is obtained by Artificial Intelligence tools.