Applications of Switch-Mode Rectifiers on Micro-grid Incorporating with EV and BESS

A switch-mode rectifier (SMR) can provide adjustable and well-regulated DC output voltage from the available AC source with good line drawn power quality. Depending on the input/output voltage transfer characteristics, the schematics, the operation quadrant, and control, SMRs possess many classifications and application. Typical potential application examples include grid powered motor drives, battery chargers, various power electronic facilities, micro-grids, and grid-connected battery energy storage system (BESS), etc. In micro-grids, the SMR can be employed as the AC generator-followed converter to yield better generating efficiency. The SMR operation of its grid-connected inverter let the grid-to-microgrid (G2M) operation be conductable in addition to the microgrid-to-grid (M2G) operation. As for the electric vehicle (EV), the bidirectional inverter can be arranged to perform G2V/V2G operations in idle case, wherein the SMR operation is made in G2V battery charging

Investigation of inverterless control of interior permanent-magnet alternators

Copyright © 2006 IEEEThis paper investigates the performance and control of a low-cost 6-kW concept demonstrator of an "inverterless" automotive alternator. This is based on a switched-mode rectifier (SMR) combined with a high-flux interior permanent-magnet (PM) machine. Duty cycle control of the SMR is described and the theoretical predictions are compared with open-loop experimental results. The efficiency of the concept demonstrator is examined as a function of speed and load. Control issues regarding automotive operation are discussed.Chong-Zhi Liaw, David M. Whaley, Wen L. Soong and Nesimi Ertugru

Nindestructive Evaluation of Metal Matrix Composite Products with Implanted Defects

The Westinghouse Science and Technology Center has undertaken a program to develop nondestructive evaluation (NDE) techniques for characterizing the internal structure of SiC particle-reinforced aluminum matrix composites at critical stages during fabrication [1–5]. Because of the large number of processing variables in the manufacture of metal matrix composites (MMC), the likelihood of having detrimental discontinuities is high. The detection of potential defects early in the processing cycle would increase the overall system yield, lower costs, and enhance final product quality [4]. The aim of this investigation was to develop and conduct NDE at various stages of MMC fabrication, correlate the results with microstructural characterization, and establish qualified product quality assurance processes. A large-scale billet was fabricated specially using powder metallurgy techniques to facilitate this objective. The billet contained implanted silicon-carbide particle and aluminum powder clusters as inspection targets. The billet was subsequently extruded into a primary cylindrical extrusion, and finally into a flat plate. The NDE objectives included evaluating the detectability and mapping the implanted defects through each of the processing steps. Comprehensive evaluation of MMC structures requires the use of multiple NDE techniques, including ultrasonic, eddy current, and radiographic testing. This paper concentrates on the results of the ultrasonic investigations. Our experimental approach was: (1) fabricate a MMC billet with intentionally placed inhomogeneities; (2) develop and implement NDE techniques to characterize the MMC internal structure; (3) extend the NDE techniques to intermediate processing and final product forms; and (4) correlate the NDE data with microstructural characterization and mechanical testing results

Peierls barrier characteristic and anomalous strain hardening provoked by dynamic-strain-aging strengthening in a body-centered-cubic high-entropy alloy

The temperature effect on the mechanical behavior of the HfNbTaTiZr high entropy alloy (HEA) was investigated at 77–673 K. The decrease of the yield strength with increasing the temperature was mechanistically analyzed by considering contributions from various strengthening mechanisms. An anomalous dependence of strain hardening on temperature was observed and was justified to be caused by dynamic strain aging (DSA) as an extra strengthening mechanism at elevated temperatures. A model was constructed to split the overall strain hardening into forest hardening and DSA hardening, both of which were theoretically quantified at all temperatures considered. The work quantifies the height of Peierls barriers in the bcc HfNbTaTiZr HEA, and reveals dynamic strain aging as the strengthening mechanism causing the anomalous strain hardening at elevated temperatures

Using machine-learning approach to distinguish patients with methamphetamine dependence from healthy subjects in a virtual reality environment

Background: The aim of this study was to evaluate whether machine learning (ML) can be used to distinguish patients with methamphetamine dependence from healthy controls by using their surface electroencephalography (EEG) and galvanic skin response (GSR) in a drug-simulated virtual reality (VR) environment. Methods: A total of 333 participants with methamphetamine (METH) dependence and 332 healthy control subjects were recruited between January 2018 and January 2019. EEG (five electrodes) and GSR signals were collected under four VR environments: one neutral scenario and three METH-simulated scenarios. Three ML classification techniques were evaluated: random forest (RF), support vector machine (SVM), and logistic regression (LR). Results: The MANOVA showed no interaction effects among the two subject groups and the 4 VR scenarios. Taking patient groups as the main effect, the METH user group had significantly lower GSR, lower EEG power in delta (p < .001), and alpha bands (p < .001) than healthy subjects. The EEG power of beta band (p < .001) and gamma band (p < .001) was significantly higher in METH group than the control group. Taking the VR scenarios (Neutral versus METH‐VR) as the main effects, the GSR, EEG power in delta, theta, and alpha bands in neutral scenario were significantly higher than in the METH‐VR scenario (p < .001). The LR algorithm showed the highest specificity and sensitivity in distinguishing methamphetamine‐dependent patients from healthy controls. Conclusion: The study shows the potential of using machine learning to distinguish methamphetamine-dependent patients from healthy subjects by using EEG and GSR data. The LR algorithm shows the best performance comparing with SVM and RF algorithm

Morphological identification of Bighead Carp, Silver Carp, and Grass Carp eggs using random forests machine learning classification

Visual identification of fish eggs is difficult and unreliable due to a lack of information on the morphological egg characteristics of many species. We used random forests machine learning to predict the identity of genetically identified Bighead Carp Hypophthalmichthys nobilis, Grass Carp Ctenopharyngodon idella, and Silver Carp H. molitrix eggs based on egg morphometric and environmental characteristics. Family, genus, and species taxonomic-level random forests models were explored to assess the performance and accuracy of the predictor variables. The egg characteristics of Bighead Carp, Grass Carp, and Silver Carp were similar, and they were difficult to distinguish from one another. When combined into a single invasive carp class, the random forests models were ≥ 97% accurate at identifying invasive carp eggs, with a ≤5% false positive rate. Egg membrane diameter was the most important predictive variable, but the addition of ten other variables resulted in a 98% success rate for identifying invasive carp eggs from 26 other upper Mississippi River basin species. Our results revealed that a combination of morphometric and environmental measurements can be used to identify invasive carp eggs. Similar machine learning approaches could be used to identify the eggs of other fishes. These results will help managers more easily and quickly assess invasive carp reproduction