This paper reviews the current state of the art of condition monitoring and fault diagnosis techniques for permanent magnet (PM) machines. It also takes into account the past research on this subject as appropriate. The discussion in the paper includes issues related to common abnormalities and specific faults in permanent magnet machines and drives, such as magnet damage and demagnetization, rotor eccentricity, unbalanced magnetic pull, open and short-circuit windings, and switch faults. In this paper, a detailed review is present on the sources of these faults, their analytical model and fault detection tools. Finally, an integrated methodology for condition monitoring of PM machines is discussed.

Insulated gate bipolar transistor (IGBT) has been widely used in diverse power electronics systems. As IGBT is one of the most vulnerable components in power electronics converter, remaining useful life (RUL) estimation of this switch has become highlight of the research in recent years. This RUL estimation helps in schedule maintenance based on health status of IGBT to avoid the unexpected failure of converters. However, there has been commonly a large variance in RUL estimation due to presence of random noise, which leads to erroneous result in IGBT prognosis. To reduce this variance in RUL estimation, particle filter methods including sequential importance sampling and sequential importance resampling have been employed recently. Still, these methods lead to nonnegligible estimation variance due to degeneracy and impoverishment of samples. In this paper, RUL estimation approach based on auxiliary particle filter (APF) is proposed. The proposed method will sufficiently reduce estimation variance by increasing dimensionality of samples as well as by sustaining diversity in samples. In addition, a simple slope-based method is proposed to identify the region when the degradation is evident in IGBT. An APF method is applied when the IGBTs under test enter this region. This step is able to reduce variation in RUL estimation and computation cost. The performance of the proposed RUL estimation method is theoretically and experimentally verified.

A new heuristic demand response technique for consumption scheduling of appliances in order to decrease peak to average ratio (PAR) of power demand is introduced. The proposed technique uses a hopping scheme to schedule the appliances with flexible schedule without the need to obtain individual consumption of appliances thereby providing a high level of consumers’ confidentiality. The proposed demand response is built on simple mathematical equations that significantly simplify advanced metering infrastructure (AMI) as well as communication requirements. In the stochastic programming, energy consumption scheduler (ECS) embedded in AMI defines appliances’ consumption vector based on the information vector that is provided in real time by network’s control center. To show the effectiveness of the proposed scheme, its performance in reducing the peak to average and energy retail price is evaluated numerically and compared to idealistic as well as practical benchmarks.

In this paper, a design and optimization method of ferrite core is proposed for wireless charging system of plug-in hybrid electric vehicle to improve its misalignment tolerance and minimize the core loss. While optimizing for higher misalignment tolerance, the primary focus is given to maintain uniform flux density in the core to minimize the core loss. An excessive use of core increases the weight and loss of the system, which need to be minimized through optimal design. Moreover, the flux density changes with misalignment, and the misalignment probability for car parking could be different in different direction. Hence the directional probability of misalignment needs to be considered during core design. In this paper, a core structure is proposed for a circular and bar-shaped core to achieve uniform flux density and an optimization method is proposed based on sequential nonlinear programming algorithm in FEA. Compared to typical uniform thickness core structure, in an optimized core, the core loss per unit volume is reduced by 30% for circular core and by 39% for bar-shaped core and the core volume per unit core loss is reduced by 17.5% in optimized circular shaped core.

The wide variety of arc faults induced by different load types renders residential series arc fault detection complicated and challenging. Series dc arc faults could cause fire accidents and adversely affect power systems if not promptly detected. However, in practical power systems, they are difficult to detect because of a low arc current, absence of a zero-crossing period, and various abnormal behavior based on different types of power loads and controllers. In particular, conventional protection fuses may not be activated when they occur. Undetected arc faults could cause false operation of power systems and potentially lead to damage to property and human casualties. Therefore, it is imperative to develop a detection system for series arc faults in DC systems for the reliable and efficient operation of such systems. In this study, several typical loads, especially nonlinear and complex loads such as power electronic loads, were chosen and analyzed, and five time-domain parameters of the current—average value, median value, variance value, RMS value, and distance of the maximum and minimum values—were chosen for arc fault detection. Various machine learning algorithms were used for arc fault detection and their detection accuracies were compared.