Interleaved Half-Bridge Submodules with Sensorless Leg Current Balancing in Modular Multilevel Converters

Authorea Preprints

Published On 2023/11/8

A new state observer-based current balancing method for Modular Multilevel Converters with Interleaved half-bridge Sub-Modules (ISM-MMC) is presented in this paper. The developed observer allows estimating currents through interleaved half-bridge legs in each submodule of ISM-MMC basing only on arm current and submodule’s capacitor voltage measurements. Then, the interleaved current balancing control uses the estimated currents to reduce the interleaved currents imbalance caused by upstream control actions. This technique minimizes the number of required current sensors in ISM-MMC, thereby reducing the converter’s cost, weight, and volume. Capabilities of the proposed interleaved currents sensorless balancing control has been tested against standard parameter tolerances of the composing passive elements. The feasibility of the proposed method is verified by extensive simulation and experimental tests.

Journal

Authorea Preprints

Published On

2023/11/8

Authors

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Position

Professor at

H-Index(all)

104

H-Index(since 2020)

72

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Power Electronics

Smart Batteries

AI

University Profile Page

Tamás Kerekes

Tamás Kerekes

Aalborg Universitet

Position

H-Index(all)

42

H-Index(since 2020)

31

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

power electronics

grid connection

renewable energy

University Profile Page

Gabriele Grandi

Gabriele Grandi

Università degli Studi di Bologna

Position

Full Professor (IT) Senior Member IEEE

H-Index(all)

40

H-Index(since 2020)

24

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Power electronics

Electric circuits

Photovoltaics

University Profile Page

Mattia Ricco

Mattia Ricco

Università degli Studi di Bologna

Position

Assistant Professor -

H-Index(all)

17

H-Index(since 2020)

15

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Power Electronics

EV Chargers

Modular Multilevel Converters

Battery Management Systems

Renewable Energy

University Profile Page

Riccardo Mandrioli, PhD

Riccardo Mandrioli, PhD

Università degli Studi di Bologna

Position

PhD Student and Teaching Assistant

H-Index(all)

11

H-Index(since 2020)

11

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Power Electronics

Power Converters

EV Charger

Transportation Electrification

Modular Multilevel

University Profile Page

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Tamás Kerekes

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Aalborg Universitet

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Remus Teodorescu

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Authorea Preprints

Disrupting direct inputs from the dorsal subiculum to the granular retrosplenial cortex impairs flexible spatial memory in the rat

The dorsal subiculum is the primary source of hippocampal projections to the rat retrosplenial cortex. Although, both regions are implicated in spatial memory and navigation, the significance of their direct interconnections remains poorly understood. The present study selectively disrupted dorsal subiculum projections to retrosplenial cortex with inhibitory designer-receptors exclusively activated by designer drugs (iDREADDs), activated locally by clozapine. iDREADDs were injected in the dorsal subiculum in adult male rats (N=14), where they were transported anterogradely to granular retrosplenial cortex. In a separate control group, GFP expressing adeno-associated virus was injected into the dorsal subiculum (N=8). Both groups received behavioural sessions preceded either by intracerebral infusions of clozapine or saline within retrosplenial cortex. Behavioural testing involved reinforced T-maze alternation, with five test variations that differentially taxed intra-maze, extra-maze, and egocentric strategies. Disruption of the subiculum to retrosplenial projections impaired spatial working memory whenever the test variant created a conflict between cue-types, associated with a switch between different strategies. These findings suggest that the direct projections from the dorsal subiculum to the granular retrosplenial cortex help to maintain the flexible integration of different spatial cue-types.

Silvia Mercurio

Silvia Mercurio

Università degli Studi di Milano

Authorea Preprints

Enigmatic cysts discovered in a population of European salamanders

New pathologies are causing dramatic declines and extinctions of multiple amphibian species. In 2013, we found fire salamanders with undescribed cysts at the throat level in one population in Northern Italy, which existence is not reported in amphibians yet. With the aim of describing this novel phenomenon, we performed repeated surveys to assess the frequency of affected salamanders from 2014 to 2020, and integrated morphological, histological and molecular analyses. Cysts affected up to 22 % of salamanders of the study population and started spreading to nearby populations. Cysts are formed by mucus surrounding cells about 10 μm long, characterized by numerous undulipodia. Morphological and genetic analyses did not yield a clear match with any described organism or salamander cell. The occurrence of these cysts calls for more studies on the origin and impact on wild populations.

Abdulmalik Humayed

Abdulmalik Humayed

Jazan University

Authorea Preprints

Testing a rich sample of cybercrimes dataset by using powerful classifiers’ competences

Key goal for this study was to conduct a real network traffic sample dataset and did a deep mining to survey for secure the Saudi community by report how the Saudi cyberspace’s pattern is. A kind of a heterogenous simultaneous optical multiprocessor exchange bus architecture used as a backbone network for collecting the network traffic. First, crucial cleaning processes were performed to clean the very noisy and dirty dataset. A total of 1048575 datapoints and 22 features were considered for the model/data evaluation processes. Second, Lazy predict mechanism was recruited to nominate the top-ranking learning models candidates. Third, a powerful supervised computation algorithms used to shape and picture the terra-Byte payload traffic across the Saudi cyber domain. Finally, for choosing the best Saudi cybercrime classification model, an intense digging processes were experimented and analyzed. Performance metrics used are accuracy (Acc), balanced accuracy (BAcc), F1-score, learning time taken, and confusion matrix. Evaluating the performance of different models based on “Destination” as target decision tree classifier (DTC) was the first model (ie, highest BAcc with low time taken) and Saudi Arabia was the 9th country as a generated source target.

Tom Gedeon

Tom Gedeon

Australian National University

Authorea Preprints

A Critical Analysis of Unsupervised Learning in the Context of Raven's Progressive Matrices

This paper undertakes a critical examination of unsupervised learning within the context of Raven's Progressive Matrices (RPMs). We trace the historical trajectory of computational models for RPMs, from early rule-based approaches to modern neural networks, and we focus on the innovative work of Zhuo et al. in introducing semi-supervised learning to RPMs. Our discussion highlights the nuances of unsupervised learning, emphasising the role of noisy labels as a form of guidance, albeit with a trade-off in precision compared to traditional supervised learning. In this paper, we recognise the challenge in formalising the distinction between supervised and unsupervised learning, but we underscore the importance of precision in communication and nomenclature, especially in regards to facilitating knowledge transfer and directing future research. We hope that this contribution enhances the discourse on unsupervised learning and offers valuable insights towards the challenges and opportunities in attaining human-level reasoning capabilities in machine learning and artificial intelligence.

Bastian Stanislaw Generowicz

Bastian Stanislaw Generowicz

Erasmus Universiteit Rotterdam

Authorea Preprints

Evoked Component Analysis (ECA): Decomposing the Functional Ultrasound Signal with GLM-Regularization

We propose a novel technique for identifying evoked components by using prior information of the stimulus time course as a guiding factor, allowing for modeling of trial variability, in a regularized optimization framework.

Manoj K. Yadav

Manoj K. Yadav

Ceské vysoké ucení technické v Praze

Authorea Preprints

Modeling Control, Lockdown & Exit Strategies for COVID-19 Pandemic

COVID-19--a viral infectious disease--has quickly emerged as a global pandemic infecting millions of people with a significant number of deaths across the globe. The symptoms of this disease vary widely. Depending on the symptoms an infected person is broadly classified into two categories namely, asymptomatic and symptomatic. Asymptomatic individuals display mild or no symptoms but continue to transmit the infection to otherwise healthy individuals. This particular aspect of asymptomatic infection poses a major obstacle in managing and controlling the transmission of the infectious disease. In this paper, we attempt to mathematically model the spread of COVID-19 under various intervention strategies. The impact of various factors, such as the presence of asymptotic individuals, lockdown strategies, social distancing practices, quarantine, and hospitalization, on the disease transmission is extensively studied. We consider SEIR type epidemiological models, incorporated with social contact matrix representing contact structures among different age groups of the population. Numerical simulation of the model shows the dependence of the second wave on the lockdown and its exit policies during the first wave.

Siobhan O'Mahony

Siobhan O'Mahony

Boston University

Authorea Preprints

ScholarOne-BEYOND THE BUZZ: SCHOLARLY APPROACHES TO THE STUDY OF WORK

The place of work in organization studies and management has waxed and waned. Yet, today, social and technological developments have raised again interest in the study of work and this curated discussion brings together experts in key approaches to this topic. Seven contributions have been selected to provide a panorama of what we know about work while pointing to some uncharted territories worthy of future exploration. The contributions outline the principles behind and value of systemic, contextualized, or holistic view of work and report insights on how changes in some work components reverberate in its broader ecology. We hope this curated discussion will make us more aware of the collective journey scholars have charted so far while posing new questions and opening or re-directing new avenues of inquiry.

Marcello Campos

Marcello Campos

Universidade Federal do Rio de Janeiro

Authorea Preprints

Consistent Volumetric Search for Accurate Passive Coherent Location

This paper addresses multiple-transmitter/receiver passive coherent location (PCL) system observing a single target with possible bistatic range mismeasurements (outliers) caused by non-line-of-sight effects. It proposes a unified mathematical framework for target location algorithms including spherical interpolation (SI), spherical intersection (SX), and nonlinearly constrained least squares (NLCLS),  generalizing them for scenarios with multiple transmitters and receivers.  While algorithms SI and SX employ closed-form expressions without considering all nonlinear relationships among the optimization variables, the NLCLS takes these nonlinearities into account via constraints; its simplified and faster version,  with promising results and reduced computational burden, is also proposed. The Cramer-Rao lower bound is derived for this application. To handle outliers in a 3D PCL system, the paper proposes a …

Boris Bonev

Boris Bonev

École Polytechnique Fédérale de Lausanne

Authorea Preprints

Application of the AI2 Climate Emulator to E3SMv2's global atmosphere model, with a focus on precipitation fidelity

Can the current successes of global machine learning-based weather simulators be generalized beyond two-week forecasts to stable and accurate multiyear runs? The recently developed AI2 Climate Emulator (ACE) suggests this is feasible, based upon 10-year simulations trained on a realistic global atmosphere model using a grid spacing of approximately 110~km and forced by a repeating annual cycle of sea-surface temperature. Here we show that ACE, without modification, can be trained to emulate another major atmospheric model, EAMv2, run at a comparable grid spacing for at least ten years with similarly small climate biases. ACE accurately reproduces EAMv2’s frequency distribution of daily-mean precipitation, its time-mean spatial pattern of precipitation, and its space-time structure of tropical precipitation, including the Madden-Julian Oscillation. Moreover, ACE’s climate biases with respect to EAMv2 …

Manuel Roda

Manuel Roda

Università degli Studi di Milano

Authorea Preprints

Thermo-mechanical effects of microcontinent collision on ocean-continent subduction system

Microcontinents are globally recognized as continental regions partially or entirely surrounded by oceanic lithosphere.

Jan-Willem van Wingerden

Jan-Willem van Wingerden

Technische Universiteit Delft

Authorea Preprints

Analysis and optimal calibration of model-based wind turbine controllers

The combined wind speed estimator and tip-speed ratio (WSE-TSR) tracking wind turbine control scheme has seen recent and increased traction from the wind industry. The modern control scheme provides a flexible trade-off between power and load objectives. In academia, the Kω2 controller is often used based on its simplicity and steady-state optimality and is taken as a baseline here. This paper demonstrates the steady-state equivalence and dynamic differences between these controllers and presents a systematic procedure for their optimal calibration. For calibration of the control schemes, a multi-objective optimisation problem is formulated with the conflicting objectives of power maximisation and torque fluctuations minimisation. The optimisation problem is solved by approximating the Pareto front based on the set of optimal solutions found by an explorative search. The Pareto fronts obtained for calibration of the baseline and for increasing fidelities of the WSE-TSR tracking controller show that no optimal solution exists, translating into increased power capture with respect to the baseline Kω2 controller. The frequency-domain analysis, however, shows increased control bandwidth for tip-speed ratio reference tracking for the solution leading to power maximisation. If the objective is to reduce the torque variance, the controller bandwidth decreases with a mild penalty on the energy yield. High-fidelity simulations confirm this trend, proving that, if properly calibrated, the WSE-TSR tracking controller obtains approximately the same generated power of the baseline while reducing torque actuation effort.