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Due to the successive switching actions in a power electronic system, considerable conducted electromagnetic interference (EMI) is generated. This has been a major concern in designing power converters, which requires additional EMI filtering efforts. Passive EMI filters are the most common solutions to EMI reduction but usually consume a large portion of the volume as well as weight in power converters. Active EMI filters (AEFs) or hybrid EMI filters (HEFs) are considered best candidates over traditional passive EMI filters to achieve higher power density in power converters. The utilization of current transformer to sense or to compensate the EMI noise can increase the volume of an AEF. In this article, a transformer-less AEF, sprung from the concept of virtual impedance enhancement, is proposed. The proposed design features feedforward control with current-sensing-voltage-compensating. The operation principle of the proposed AEF is thoroughly introduced, and its performance is evaluated through simulations and experiments. Experimental results with an ac–dc power converter and a dc–ac inverter show that the proposed AEF can be generally applied to reduce conducted EMI on either ac or dc power lines. [ABSTRACT FROM AUTHOR]

This paper presents generalized logic-based methods for intelligent fault diagnosis in power electronic converters based on correlation between faults and basic measurements. Fault recovery is then applied based on this correlation by using necessary signals and quantities from existing measurements. The main purpose of the proposed fault diagnosis methods is for power electronic systems to survive from fault conditions that could occur in various components, to cope with the notion of a smart grid and extend their lifetime. The proposed methods are online, i.e., real-time or near-real-time, and can be applied to any power electronic system. Existing intelligent control of power electronic systems along with various short- and open-circuit faults in major power electronic components are reviewed. Two methods are established to diagnose faults and engage redundancy for fault recovery with one method using combinational logic and another using fuzzy logic. In both methods, two quantities are observed for each of the measured signals: 1) the signal's average value and 2) the signal's RMS value. Total harmonic distortion is also used in some simulations but is not required in experimental implementation. A systematic methodology to reduce the number of measured quantities while maintaining effective diagnosis is introduced. A solar PV microinverter in standalone mode is used as an example testing platform for the proposed methods. A simulation model is experimentally validated and the effect of each fault on different voltage and current measurements are observed, then both methods are tested in simulation and hardware. Results show the ability of both methods to diagnose several faults in the inverter's power stage along with their ability to engage redundancy for fault recovery. [ABSTRACT FROM AUTHOR]

This paper links the theory of ripple correlation control (RCC) and extremum seeking control (ESC) with emphasis on application in a solar photovoltaic (PV) system. ESC has been well-established in the automatic control literature to find the extremum of an objective function. The RCC theory and applications have been developed in the power electronics literature for real-time optimization. Both ESC and RCC are reviewed and discussed. RCC is formulated in a similar approach to ESC, but distinct based on the source of perturbations—mainly external perturbations with ESC and inherent ripple with RCC. While some recent ESC implementations utilize inherent perturbations, RCC uses high-frequency perturbations in power electronics systems not currently utilized by ESC. The formulation and equivalencies presented here are intended for future research in both methods which can benefit from existing research for further development in theory and applications. Shared aspects that include stability and convergence characteristics are discussed. RCC formulation from an ESC perspective is then applied for maximum power point tracking of a solar PV panel, using high-frequency inherent ripple in power electronics. This RCC formulation is confirmed to have high tracking effectiveness and fast convergence. [ABSTRACT FROM AUTHOR]

A smooth fault-tolerant control (FTC) of induction motor drives with sensor failures is presented in this paper. The drive uses concurrently active direct torque control (DTC), indirect field-oriented control (IFOC), and volts-per-hertz (V/f) controllers as replacements of each other in the case of sensor failure or recovery. Speed encoder, current, and voltage sensors are considered. The DTC and IFOC controllers are properly modified and integrated with the V/f controller in synchronous frame, which allows switching between dc-type voltage commands of different controllers. Therefore, difficult synchronization of different controllers is avoided, and a simple rate limiter is able to achieve smooth handoff transition of different controllers. Details of the DTC, IFOC, and V/f controllers, as well as their integration are presented. Simulation and experimental verification of the proposed FTC are given, where normal abc-frame switching and the proposed synchronous-frame switching of controllers are compared with different switching schemes and at different machine operation conditions. Simulation and experimental results show significantly smoother handoff transients. [ABSTRACT FROM AUTHOR]

This paper highlights key contributions in extremum seeking (ES) control and ripple correlation control (RCC). Maximum power point tracking (MPPT) utilizing the 120-Hz inherent inverter ripple is shown to be developed in the early 1980s. Even though that specific MPPT development did not explicitly mention ES control, the analysis presented there shows otherwise. RCC stability and operating properties are also addressed where the fundamental difference between RCC and popular ES control methods is the perturbation source. Since RCC utilizes inherent switching ripple in power electronics, the high ripple frequency leads to fast RCC convergence. [ABSTRACT FROM AUTHOR]

Power electronics (PE) and high-frequency switching circuits are key to superior performance of electric vehicles. It is vital to monitor the condition of the PE components in real-time for safety and reliability. In this article, we propose two anomaly detection methods based on a combination of data preprocessing to suppress noise and outliers, multivariate regression models to predict signals of interest under nominal operation, and sequential analysis of residuals. In particular, the methods utilize median filtering to extract on-state medians in each switching cycle in nonlinear autoregressive exogenous neural network models or filtered on-state data in partial least squares-based models to represent the nominal circuit behavior. Optimal and approximate dynamic programming-based feature selection methods are developed to select the most informative signals or their transformations. Predictions from the learned models are used to generate the residuals for anomaly detection by Page’s cumulative sum test. The proposed models and anomaly detection methods are validated on three accelerated aging experimental datasets, comprised of 60 power mosfet devices with low-frequency and high-frequency switching under disparate operating conditions. Due to the simplicity and efficiency of the data-driven anomaly detection schemes, the proposed methods can potentially be embedded in real-time digital platforms. [ABSTRACT FROM AUTHOR]

This article proposes an unsupervised learning approach for fault prognosis of silicon carbide (SiC) mosfets. The proposed approach utilizes the changing trend of a device's voltage, current, temperature, and other device characteristics with its degradation. The failure modes of semiconductors are reviewed along with existing methods for fault prognosis. The proposed approach is the first to address prognostics of SiC devices, and it can avoid the effects from system noise and data errors. It is not limited to offline analysis and is targeted at online implementation. It is easy to implement on standard digital platforms, and has fast computational speed. Offline data analysis is performed to verify the effectiveness of the proposed method, and a processor-in-the-loop system is used to verify its ability to perform online fault prognosis. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal efficiency control scheme for a wind system with doubly fed induction generator (DFIG). The suggested control scheme combines loss minimization (LM) in the DFIG and maximum power point tracking (MPPT) in the wind turbine and therefore maximum electrical energy generation, by the same wind energy potential, is achieved. Moreover, since the cut-in wind speed is reduced, extension of the exploitable wind speed range toward the lower speed region is attained. The LM is achieved by properly controlling the flux-linkage of the DFIG with respect to the stator current and the MPPT is accomplished by regulating the rotor speed through the rotor current. The parameters of the LM and MPPT controllers can be determined experimentally, and thus, the knowledge of the wind energy conversion system (WECS) model is not required. For the implementation of the proposed control strategy, a new structure of the WECS has been adopted. However, the hardware requirements of the WECS and considerably the cost have not been considerably affected compared to the conventional configuration. Selective simulation and experimental results are presented to validate the effectiveness of the proposed control strategy and demonstrate the operational improvements. [ABSTRACT FROM AUTHOR]