This work analytically establishes a multi-variable energy function for a three-phase grid-following inverter leveraging a unified equivalent-circuit model for its physical- and control-layer subsystems. This is a sig...
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This work analytically establishes a multi-variable energy function for a three-phase grid-following inverter leveraging a unified equivalent-circuit model for its physical- and control-layer subsystems. This is a significant contribution to the prior art in which analytical approaches to large-signal stability for inverters have largely been attempted with simplified models. Central to our effort is to cast physical- and control-layer dynamics of each dynamical subsystem as an equivalent circuit consisting of familiar circuit elements adopting a positive-sequence modeling framework. An energy function for the inverter is then constructively synthesized by summing the energy functions across the various subsystems that are readily derived from circuit-theoretic principles. Numerical simulations are presented to validate the equivalent-circuit model of the inverter as well as the efficacy of the synthesized energy function in characterizing large-signal stability following a disturbance. IEEE
Steady-state linear Kalman filter-based frequency locked loop control algorithm is introduced to operate distribution static compensator in distribution system which utilizes Kalman filter theory which employs the dyn...
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This paper addresses two issues that concern the electricity market participants under the European day-ahead market(DAM)framework,namely the feasibility of the attained schedules and the non-confiscation of cleared *...
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This paper addresses two issues that concern the electricity market participants under the European day-ahead market(DAM)framework,namely the feasibility of the attained schedules and the non-confiscation of cleared *** address the first issue,new resource-specific orders,i.e.,thermal orders for thermal generating units,demand response orders for load responsive resources,and energy limited orders for storage resources,are proposed and incorporated in the existing European DAM clearing *** address the second issue,two approaches which lead to a non-confiscatory market are analyzed:①discriminatory pricing with side-payments(***);and②non-discriminatory pricing excluding out-ofmoney orders(European paradigm).A comparison is performed between the two approaches to investigate the most appropriate pricing rule in terms of social welfare,derived revenues for the sellers,and efficiency of the attained *** proposed model with new resource-specific products is evaluated in a European test system,achieving robust *** feasibility of the attained schedules is demonstrated when using resource-specific orders compared with block ***,the results indicate the supremacy of discriminatory pricing with side-payments compared with the current European pricing rule.
As wafer circuit widths shrink less than 10 nm,stringent quality control is imposed on the wafer fabrication processes. Therefore, wafer residency time constraints and chamber cleaning operations are widely required i...
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As wafer circuit widths shrink less than 10 nm,stringent quality control is imposed on the wafer fabrication processes. Therefore, wafer residency time constraints and chamber cleaning operations are widely required in chemical vapor deposition, coating processes, etc. They increase scheduling complexity in cluster tools. In this paper, we focus on scheduling single-arm multi-cluster tools with chamber cleaning operations subject to wafer residency time constraints. When a chamber is being cleaned, it can be viewed as processing a virtual wafer. In this way, chamber cleaning operations can be performed while wafer residency time constraints for real wafers are not violated. Based on such a method, we present the necessary and sufficient conditions to analytically check whether a single-arm multi-cluster tool can be scheduled with a chamber cleaning operation and wafer residency time constraints. An algorithm is proposed to adjust the cycle time for a cleaning operation that lasts a long cleaning ***, algorithms for a feasible schedule are also *** an algorithm is presented for operating a multi-cluster tool back to a steady state after the cleaning. Illustrative examples are given to show the application and effectiveness of the proposed method.
Supervisory control and data acquisition (SCADA) systems are vital in monitoring and controlling industrial processes through the web. However, while such systems result in lower costs, greater utilisation efficiency,...
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Metal flat surface in-line surface defect detection is notoriously difficult due to obstacles such as high surface reflectivity,pseudo-defect interference,and random elastic *** study evaluates the approach for detect...
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Metal flat surface in-line surface defect detection is notoriously difficult due to obstacles such as high surface reflectivity,pseudo-defect interference,and random elastic *** study evaluates the approach for detecting scratches on a metal surface in order to address a problem in the detection *** paper proposes an improved Gauss-Laplace(LoG)operator combined with a deep learning technique for metal surface scratch identification in order to solve the difficulties that it is challenging to reduce noise and that the edges are unclear when utilizing existing edge detection *** the process of scratch identification,it is challenging to differentiate between the scratch edge and the interference ***,local texture screening is utilized by deep learning techniques that evaluate and identify scratch edges and interference edges based on the local texture characteristics of *** have proven that by combining the improved LoG operator with a deep learning strategy,it is able to effectively detect image edges,distinguish between scratch edges and interference edges,and identify clear scratch *** based on the six categories of meta scratches indicate that the proposedmethod has achieved rolled-in crazing(100%),inclusion(94.4%),patches(100%),pitted(100%),rolled(100%),and scratches(100%),respectively.
Electric vehicles (EVs) are rapidly replacing conventional fuel vehicles, offering powerful, emission-free performance. This paper introduces an innovative three-phase bidirectional charger for grid-to-vehicle (G2V) a...
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Cooperative communication through energy harvested relays in Cognitive Internet of Things(CIoT)has been envisioned as a promising solution to support massive connectivity of Cognitive Radio(CR)based IoT devices and to...
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Cooperative communication through energy harvested relays in Cognitive Internet of Things(CIoT)has been envisioned as a promising solution to support massive connectivity of Cognitive Radio(CR)based IoT devices and to achieve maximal energy and spectral efficiency in upcoming wireless *** this work,a cooperative CIoT system is contemplated,in which a source acts as a satellite,communicating with multiple CIoT devices over numerous *** Aerial Vehicles(UAVs)are used as relays,which are equipped with onboard Energy Harvesting(EH)*** adopted a Power Splitting(PS)method for EH at relays,which are harvested from the Radio frequency(RF)*** conjunction with this,the Decode and Forward(DF)relaying strategy is used at UAV relays to transmit the messages from the satellite source to the CIoT *** developed a Multi-Objective Optimization(MOO)framework for joint optimization of source power allocation,CIoT device selection,UAV relay assignment,and PS ratio *** formulated three objectives:maximizing the sum rate and the number of admitted CIoT in the network and minimizing the carbon dioxide *** MOO formulation is a Mixed-Integer Non-Linear Programming(MINLP)problem,which is challenging to *** address the joint optimization problem for an epsilon optimal solution,an Outer Approximation Algorithm(OAA)is proposed with reduced *** simulation results show that the proposed OAA is superior in terms of CIoT device selection and network utility maximization when compared to those obtained using the Nonlinear Optimization with Mesh Adaptive Direct-search(NOMAD)algorithm.
Purpose: Ultrasound (US) elastography is a technique for non-invasive quantification of material properties, such as stiffness, from ultrasound images of deforming tissue. The material properties are calculated by sol...
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Purpose: Ultrasound (US) elastography is a technique for non-invasive quantification of material properties, such as stiffness, from ultrasound images of deforming tissue. The material properties are calculated by solving the inverse problem on the measured displacement field from the ultrasound images. The limitations of traditional inverse problem techniques in US elastography are either slow and computationally intensive (iterative techniques) or sensitive to measurement noise and dependent on full displacement field data (direct techniques). Thus, we develop and validate a deep learning approach for solving the inverse problem in US elastography. This involves recovering the spatial modulus distribution of the elastic modulus from one component of the US-measured displacement field. Approach: We present a U-Net-based deep learning neural network to address the inverse problem in ultrasound elastography. This approach diverges from traditional methods by focusing on a data-driven model. The neural network is trained using data generated from a forward finite element model. This simulation incorporates variations in the displacement fields that correspond to the elastic modulus distribution, allowing the network to learn without the need for extensive real-world measurement data. The inverse problem of predicting the modulus spatial distribution from ultrasound-measured displacement fields is addressed using a trained neural network. The neural network is evaluated with mean squared error (MSE) and mean absolute percentage error (MAPE) metrics. To extend our model to practical purposes, we conduct phantom experiments and also apply our model to clinical data. Results: Our simulated results indicate that our deep learning (DL) model effectively reconstructs modulus distributions, as evidenced by low MSE and MAPE evaluation metrics. We obtain a mean MAPE of 0.32% for a hard inclusion and 0.39% for a soft inclusion. Similarly, in our phantom studies, the predicted mo
Recently renewable energy such as a photovoltaic (PV) system has been utilized more and more since it is pollution-free and permanent. To maintain the PV system functioning at, or near, the peak power point of the PV ...
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