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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]

This paper presents an efficient, low-cost, and versatile LED-based solar simulator intended to produce a well-characterized spectrum for tests of solar cells and other photosensitive devices. Three major design aspects are addressed: LED spectra, power converters for LED drive, and control. The visible light of a standard solar spectrum is simulated using six LED colors. The number of LEDs and their placement for uniformity are addressed. Boost converters under current-mode control are used to achieve reproducible LED brightness through adjustable currents, or equivalent radiant-flux commands. The independent control of the six colors can simulate a range of different light sources and solar spectra. Uniformity tests verify that the system achieves standard spectral uniformity requirements over an area of 100 mm \times 100 mm in simulations and 100 mm \times 50 mm in experiments. LEDs in the proposed simulator consume less power and reduce the simulator size compared to the available state of the art. The user-friendly interface also allows active control of the simulated spectrum. [ABSTRACT FROM AUTHOR]

This paper presents a time-efficient modeling and simulation strategy for aggregated microinverters in large-scale photovoltaic systems. As photovoltaic microinverter systems are typically comprised of multiple power electronic converters, a suitable modeling and simulation strategy that can be used for rapid prototyping is required. Dynamic models incorporating switching action may induce significant computational burdens and long simulation durations. This paper introduces a single-matrix-form approach using the average model of a basic microinverter with two power stages consisting of a dc-dc and dc-ac converter. The proposed methodology using a common or intermediate source between two average models of cascaded converters to find the overall average model is introduced and is applicable to many other converter topologies and combinations. It provides better flexibility and simplicity when investigating various power topologies in system-level studies of microinverter and other power electronic systems. A 200 W prototype microinverter is tested to verify the proposed average and dynamic models. Furthermore, MATLAB/Simulink (2010a, Mathworks, Natick, MA, USA) is used to show the improved simulation speed and maintained accuracy of the multiple microinverter configurations when the derived average model is compared to a dynamic switching simulation model. [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]

One of the major issues with pulsewidth modulation (PWM) is that considerable conducted emissions will be generated from power electronics converters. Random modulations are considered one of the viable solutions to reduce conducted electromagnetic interference (EMI) without adding bulky passive filtering components. Literature that investigates the application of such techniques in active front end (AFE) drives is limited. In this article, a novel random switching frequency PWM (RSF-PWM) is proposed for a grid-tied AFE drive to suppress conducted emissions. The statistical behavior of switching frequency is based on uniform distribution. Simulation and experiments are carried out on a 10-hp AFE drive. Results demonstrate that the proposed RSF-PWM can effectively reduce conducted EMI by approximately 20 dB within low-frequency range, compared with conventional constant switching frequency PWM. [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]