ANTECEDENTES DE DERECHO

The most recognized electromechanical energy-conversion device in our modern civilization is the alternating current electric machinery. It has played essential roles in electric power generation, transportation, industrial processes, and residential applications. In many applications, they are subjected to environmental stresses, such as high ambient temperature, high moisture or dampness, corrosive environments, dusty medium, oil surrounding and water intrusion. These environmental stresses, combined with machines’ internal stresses (electrical, mechanical, magnetic and thermal), could seed faults in electric machinery. These faults can evolve into disastrous machine failures if they are left

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undetected. This results in not only costly repairs, such as reconstructions or part replacement, but also significant financial losses because of unscheduled downtime in the production of industrial plants (idle time or off-line period) or safety hazards in transportation applications (electric vehicles).

The history of condition monitoring (CM), fault detection (FD), fault identification and diagnostics of electric machines is almost as old as electric machineries themselves. Originally, the electric machines were protected only against a misuse such as over current or over voltage [21]. The condition monitoring was periodic, with long intervals of time between inspections (months or semesters), and off-line (requiring machine disconnection and possibly dismantling). The maintenance and health monitoring of the machinery was executed only when it presents obvious malfunction problems. With the advent of semiconductor technology, many areas have been developed including sensors or transducers, digital-to-analog conversion, analog-to-digital conversion, data acquisition electronics, microprocessors, digital signal processors, field-programmable gate arrays, computation capability, etc. The advancements of semiconductor technology and signal processing techniques bring the possibility to study fault detection and identification of machinery in a more effective manner: the health monitoring can be performed while the electric machine is still in-service (on-line) and the time interval between inspections can be reduced to a point where it can be defined as continuous. The CM practitioners have made an effort to prove that the on-line and continuous diagnostics with real-time updates of the machine condition increases business profit through informed maintenance and service decisions. These capabilities enable the FD at their very inception, well before material machine failure evolution occurs [22]–[24]. The detection of incipient faults of

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electric machinery allows time for preventive maintenance to be scheduled, which improves the system reliability, availability, and maintainability.

To monitor the health of electric machines and thus detect and isolate a specific incipient faults, different types of sensors are added to them. These include search coils for stator or rotor faults [25], infrared sensors for corrupted electrical connection [26], thermal transducers, accelerometers for vibrations caused by bearing faults [27], magnetic velocity pickup [28], piezoelectric acoustic sensors, laser detectors, micro electro-mechanical sensors [28], etc. However, sensors and its associated installations and wirings are expensive, intrusive and difficult to implement for many applications. Therefore, only large high power and costly electric machines are equipped with some of these sensor-based CM schemes.

Recently, the research on the so-called sensorless CM of electric machines has gained a lot of interest. Instead of installing expensive special-purpose sensors for FD, existing hardware and transducers can provide health information of the machine condition that has traditionally not been exploited. For instance, in industrial plants, existing motor voltage and current measurements can be used for condition monitoring purposes. In motor drives, the existing current sensors and dc bus measurement can provide useful information about the wellbeing of the electric machines. Moreover, industrial plants and motor drives resources count with intelligent control devices (microprocessors or digital signal processors) that can offer processing capabilities for the fault detection computational burden. The actual advances on computational capabilities (high processor speed and large size of RAM) make these intelligent devices suitable and able of handling both control and diagnostic.

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Due to historic reasons, the majority of the FD methodologies are available for machines connected to directly to the mains or the so-called directly-on-line (DOL) electric machines. The emergent emphasis on energy efficiency and the inexpensive availability of power semiconductor devices cause more electric machines being now interconnected with various types of power electronic converters, some of which even have sophisticated control strategies. Whereas the malleable energy forms produced by these power electronic converters significantly enhance the performance of electric machines in an expanded operating region, they also have a substantial impact on fault behaviors of electric machines and introduce a greater complexity in terms of fault detection, fault identification, monitoring and protection. Typically, conventional FD methods already investigated for DOL electric machines cannot be applied to power-converter-fed machines anymore. This requires detailed analyses of the power converter effects on various faults of electric machines so that simple and reliable FD schemes can be created.

The plethora of diverse power electronic converters and electric machines available makes it impractical to study every possible combination. Additionally, the large number of fault types in an electric machinery make this situation even worse. One of the main goals of this PhD research focused on the condition monitoring of power-converter-fed three-phase squirrel-cage induction machine driven through a closed-loop controller. Induction motors can be connected directly to the grid (grid-connected or DOL), or connected to power converters such as open-loop inverter drives or closed-loop inverter drives. This study mainly focuses on the condition monitoring of closed-loop inverter-fed induction machines. Closed-loop inverters are power electronic converters with current, torque, flux and/or speed feedback control capabilities. Closed-loop induction motor drives

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find many applications in traction system with high-performance motion control such as cranes, hoists, elevators, winding process, material handling, electric vehicles (EV), hybrid EV, recent railway system, diesel-electric ships, and rolling steel mills for mention some of them. In this applications zero-speed torque capability and highly dynamic torque response are normally required. Compared to the DOL or open-loop inverter-fed electric machines, the machines fed by closed-loop inverters are generally connected to the most critical types of loads. Equipped with sensors (current, voltage and/or speed) and digital processors, the closed-loop inverter drives are also ideal platforms for implementing the sensorless health monitoring and FD schemes. In actual fact, however, closed-loop controlled machines are the least studied category in terms of FD, for two reasons: first because the closed-loop control dramatically changes the fault behaviors of the electric machines and complicates the fault analysis [29] and second due to historic reasons, closed- loop controllers are newer than DOL and open-loop ones.

One of the weakest components in induction machines is the stator winding insulation which accounts for about 30~40% of induction machine failures in industrial applications [21], [30]–[32]. Stator windings and their insulation system constitute the second potential source of failures since the various stresses that act on the motor cause gradual deterioration and aging of stator insulation. Short-circuit and open-circuit of stator windings, and magnetic core faults are the failure modes associated with the stator. Coil-to-coil, phase- to-phase and phase-to-ground short-circuit variants are consequences of insulation failure, often due to a combination of temperature rise, mechanical and electric stress. In closed- loop induction motor drives, the large / caused by the square voltage wave of the inverter will result in even higher electric stress on the induction machine, particularly on

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the first few turns in the terminal end of the stator winding [33]. Therefore, the condition monitoring of the stator insulation of inverter-fed induction machine is the primary focus of this work. The thesis will propose methods that not only detect solid stator insulation faults but understand the phenomena of the inter-turn short-circuit and its influence on the machine drive.

1.2.2 Condition Monitoring and Performance Enhancement of the Power