Partial discharge (PD) is a prime cause of premature failure of inverter-fed motor winding insulation. With the emergence of fast-switching wide-bandgap silicon-carbide (SiC) power devices, random-wound motors are more vulnerable to highly repetitive PDs due to the high-frequency steep-fronted switching transitions that result in overvoltage oscillations at the motor terminals and non-uniform voltage distribution within the motor winding turns. This paper investigates the impact of the applied PWM voltage waveforms on the PD behavior in SiC-based inverter-fed motor drives, including two-level and three-level PWM waveforms. The electric field distribution inside insulation defects (air cavities) is analysed for the PWM voltage waveforms to theoretically predict the number and phase of probable PD events within the fundamental cycle. An experimental PD measurement setup is used to validate the theoretical analysis by applying the PWM voltage waveforms, that are generated by SiC-based power converters, on a typical turn-to-turn motor winding insulation system created by a twisted pair of enamelled magnet wire. Phase-resolved PD patterns are generated to assess the PD behavior against the PWM characteristics of each voltage waveform and the associated overvoltage pattern when power cables are used to emulate the voltage reflections in cable-fed motor drives.

Electrical networks across the globe are gearing towards environmentally friendly operation with many renewable sources being introduced. Among them, the popularity of electrical generation by wind has gained the most ground. Wind, as one of the meteorological conditions, also has high influence on line ratings and such manner of operating the line is known as dynamic thermal rating (DTR) system. Seeing this relationship, it is intuitively important to investigate the reliability of a electrical network incorporating DTR and wind farm. Hence, the objective of this paper is to propose a methodology that can perform this task. In the methodology, sequential Monte Carlo (SMC) simulation was performed due to time dependencies of line ratings and wind power. As a result, modeling of these time-series data was performed using the auto-regressive and moving-average (ARMA) model. Moreover, correlations between line rating and wind power were also considered to provide a practical approach to the modeling. Results have shown that DTR system is able to increase network reliability and allows for higher wind energy penetration.

This paper presents a nanomanipulation system for operation inside scanning electron microscopes (SEM). The system is small in size, capable of being mounted onto and demounted from an SEM through the specimen exchange chamber without breaking the high vacuum of the SEM. This advance eliminates frequent opening of the high-vacuum chamber, thus, incurs less contamination to the SEM, avoids lengthy pumping, and significantly eases the exchange of end-effectors (e.g., nano probes and grippers). The system consists of two independent 3-DOF Cartesian nanomanipulators based on piezo motors and piezo actuators. High-resolution optical encoders are integrated into the nanomanipulators to provide position feedback for closed-loop control. A look-then-move control system and a contact detection algorithm are implemented for horizontal and vertical nanopositioning. The system design, system characterization details, and system performance are described.