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In this article, an isolated zero-voltage switching three-port dc–dc converter (TPC) using a planer transformer is proposed and operated for the frequency of 1 MHz. The proposed converter has a bidirectional operation. In the proposed converter, the main transformer is used to isolate low dc voltage ports from the high dc voltage port and to increase the voltage conversion ratio of the secondary side port, which supplies the load. The proposed converter is partially isolated TPC and has the advantage of galvanically isolation between one of the ports which are interfaced to the main load and the other two ports (input and storage-side ports) are nonisolated and common-grounded. The proposed converter is compared with other recent converters of the same type, and it is concluded that the proposed converter has achieved the capabilities of having an isolated structure with lower volume, interfacing three different dc voltages in the three dc ports, having the bidirectional operation, high-voltage gain, zero-voltage switching operation of all four switches, simple switching pattern, achieving simple controlling way for all ranges of duty cycles both step-up and step-down operations, utilizing a nonlinear digital controlling system, smaller size, higher operating frequency, higher efficiency, and high power density.

We propose a novel unsupervised anomaly detection and diagnosis algorithm in power electronic networks. Since most anomaly detection and diagnosis algorithms in the literature are based on supervised methods that can hardly be generalized to broader scenarios, we propose unsupervised algorithms. Our algorithm extracts the Time-Frequency Domain (TFD) features from the three-phase currents and three-phase voltages of the point of coupling (PCC) nodes to detect anomalies and distinguish between different types of anomalies, such as cyber-attacks and physical faults. To detect anomalies through TFD features, we propose a novel Informative Leveraging for Anomaly Detection (ILAD) algorithm. The proposed unsupervised ILAD algorithm automatically extracts noise-reduced anomalous signals, resulting in more accurate anomaly detection results than other score-based methods. To assign anomaly types for anomaly diagnosis, we apply a novel Multivariate Functional Principal Component Analysis (MFPCA) clustering method. Unlike the deep learning methods, the MFPCA clustering method does not require labels for training and provides more accurate results than other deep embedding-based clustering approaches. Furthermore, it is even comparable to supervised algorithms in both offline and online experiments. To the best of our knowledge, the proposed unsupervised framework accomplishing anomaly detection and anomaly diagnosis tasks is the first of its kind in power electronic networks.

As power densification demands are placing electronic packages under greater reliability risk, the consequence of complementary or interacting stresses in producing failure are becoming increasingly significant. As such, it is important that reliability methods and package designs consider how multiple-stress interactions may impact product life. Here, the coordination between a novel accelerated testing method and electronic design automation efforts has demonstrated a successful optimization approach for a wire-bonded 2D module layout combining failure mechanisms of electromigration and mechanical stressing. Utilizing custom, physics-of-failure approaches in accelerated testing, interactions can be observed in failure acceleration, which then can be incorporated into design for reliability (DfR) optimization tools. The PowerSynth 2 platform has been utilized as a design for reliability tool to perform a rapid reliability evaluation incorporating multi-stress scenarios. This work demonstrates the value added to reliability evaluation techniques when accounting for interacting failure mechanisms and suggests that next generation power devices consider these effects in lifetime estimation. [ABSTRACT FROM AUTHOR]