Many standards and specifications related to medium-voltage (MV) motor and feeder circuits continue to center around the improper generic use of IEEE Device 52 as a circuit identifier for the switching device used within various points of power systems. IEEE Device 52 is defined as a device that is used to close and interrupt an ac power circuit under normal conditions or to interrupt this circuit under fault or emergency conditions. While many of the present standards include detailed protection and coordination recommendations that are centered only on the protective and control settings for circuit breakers (CBs), the protection schemes for CBs and fused contactor-controlled motors are documented in IEEE Standard 3004.8. This article clarifies some key differences between MV contactors and MV CB ratings. It also compares the appropriate protection variations and requirements between the two devices, highlighting the dangers to personnel and equipment if misapplied. Recommended protective device functions for motor protection for fused contactor-controlled or breaker-controlled motors are presented with companion drawings.

Medium-Voltage (MV) motor control centers designed around the requirements of UL 347 [1] have been successfully utilized in petrochemical industries for many decades. Since they continue to provide consistent control of large MV assets, they have also become key components within the intelligent control requirements of today’s complex petrochemical facilities. Through the incorporation of proven technologies employed in other safety-based products used globally, these control products can be moved beyond their traditional safety requirements specified by the current product design and testing standards. This article covers the implementation and utilization of these proven technologies that can provide additional levels of personnel safety within these traditional designs.

This paper proposes a new active-front-end (AFE) control method for regenerative Cascaded H-bridge (CHB) motor drives. Conventional CHB converters have dominated the market for the medium-voltage industrial motor drives. However, conventional CHB converters cannot provide regenerative capability. To enable regeneration; diode-front-ends (DFEs) in power cells are replaced with IGBT-based PWM AFEs. Despite the appealing dynamic performance of PWM AFEs, their integration to the CHB converters is not optimal. First, they introduce more semiconductor losses due to the high frequency switching. Second, they introduce switching harmonics that are not cancelled by phase-shifting transformers. Therefore, they require additional harmonic filtering solutions to comply with grid harmonic standards. To resolve these challenges, this paper proposes a new AFE control for regenerative CHB converters based on fundamental frequency switching (FFE) to reduce switching frequency and thus power losses. The operation of FFEs at nominal and sag voltage conditions is presented, in addition to the capability of filter-less interfacing to the transformer. Finally, the performance of the proposed control is validated experimentally on a seven-level regenerative CHB drive.

Paper production around the niagara river region began in the mid-1800s. Mills were situated on various local canals whose waterpower (hydraulic) was needed to grind and process the rags into paper. The regions around Niagara Falls were prime locations to set up these mills.

This paper proposes a new scheme to use active flux on q-axis for sensorless control of synchronous reluctance machines (SynRM). Conventionally, “Active Flux” on d-axis is adopted to convert a salient pole machine into a fictitious non-salient pole machine. However, the injected d-axis flux can deteriorate high frequency injection (HFI) sensorless control performance or even run the system into unstable region at low speed. This paper demonstrates active flux on q-axis can support back-EMF sensorless control at high speed and improve low speed HFI performance substantially. A seamless transition from HFI sensorless method to back-EMF voltage method is attained after adopting the proposed active q flux. Experiment results are used to validate the proposed method.

This paper proposes a new prognostics analysis approach for power electronics by combining physics-based and data-driven techniques. Starting with Weibull degradation model, machine learning (ML) techniques are applied to degradation data progressively for continuous reliability monitoring and predictive maintenance decision-making. No prior knowledge of components or mission profiles is required for model training and prediction. Extracted features from analysis can be used to cluster the iteration-based predictions effectively. Another advantage is abrupt change of operation condition can be captured through machine learning for potential lifetime improvement through predictive maintenance. Proposed method can be generalized to other hardware components beyond power electronics.

Authors submitted over 2,500 original and revised manuscripts for possible publication in these TRANSACTIONS and IEEE Industry Applications Magazine in 2022. This Special Feature is offered to recognize the people who volunteered their time and expertise to evaluate those manuscrips and to make the recommendations that determined which papers would be published. Their efforts are greatly appreciated. The names, affiliations, and countries listed below are taken from data records and user profiles stored in ScholarOne Manuscripts. Some of the entries have been edited for consistency in spelling, length and/or capitalization. We apologize for any errors or omissions.

ABSTRACT The emergence of the Internet of Things (IoT), cloud computing, cyber-physical systems, system integration, big data, and data analytics for Industry 4.0 have transformed the world of traditional manufacturing into an era of smart manufacturing (SM). Smart manufacturing’s central focus is to process real-time IoT data and leverage advanced analytical approaches to detect abnormal behaviors. Social smart manufacturing applies analytics tools to empower decision makers and minimize duplication by executing the repetitive data processing work more consistently and precisely than can be done by a human operator. In smart manufacturing, the majority of industrial data is imbalanced. However, most traditional machine learning algorithms tend to be biased toward the majority class and under-represent the minority class. This research proposes a model selection architecture to automate the procedure of preprocessing input data and selecting the best combination of algorithms for anomaly detection. This design will play an essential role in producing high-quality products and improving quality control and business processes in diverse applications including predictive maintenance and fault detection. The framework is transferrable to any smart manufacturing task in the supervised learning domain.