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Respiratory ailments are a very serious health issue and can be life-threatening, especially for patients with COVID. Respiration rate (RR) is a very important vital health indicator for patients. Any abnormality in this metric indicates a deterioration in health. Hence, continuous monitoring of RR can act as an early indicator. Despite that, RR monitoring equipment is generally provided only to intensive care unit (ICU) patients. Recent studies have established the feasibility of using photoplethysmogram (PPG) signals to estimate RR. This paper proposes a deep-learning-based end-to-end solution for estimating RR directly from the PPG signal. The system was evaluated on two popular public datasets: VORTAL and BIDMC. A lightweight model, ConvMixer, outperformed all of the other deep neural networks. The model provided a root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R) of 1.75 breaths per minute (bpm), 1.27 bpm, and 0.92, respectively, for VORTAL, while these metrics were 1.20 bpm, 0.77 bpm, and 0.92, respectively, for BIDMC. The authors also showed how fine-tuning a small subset could increase the performance of the model in the case of an out-of-distribution dataset. In the fine-tuning experiments, the models produced an average R of 0.81. Hence, this lightweight model can be deployed to mobile devices for real-time monitoring of patients. 2022 by the authors. This work was made possible by NPRP12S-0227-190164 from the Qatar National Research Fund, a member of Qatar Foundation, a member of Qatar Foundation, Doha, Qatar and Grant numbers DIP-2020-004 and GUP-2021-019 from Universiti Kebangsaan Malaysia. The statements made herein are solely the responsibility of the authors. Scopus

Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help to reduce the adverse effects of diseases and also overcome the shortcomings of continuous human monitoring. In this work, we propose the use of a deep learning architecture based on a recent convolutional neural network called EfficientNet on 18,161 plain and segmented tomato leaf images to classify tomato diseases. The performance of two segmentation models i.e., U-net and Modified U-net, for the segmentation of leaves is reported. The comparative performance of the models for binary classification (healthy and unhealthy leaves), six-class classification (healthy and various groups of diseased leaves), and ten-class classification (healthy and various types of unhealthy leaves) are also reported. The modified U-net segmentation model showed accuracy, IoU, and Dice score of 98.66%, 98.5%, and 98.73%, respectively, for the segmentation of leaf images. EfficientNet-B7 showed superior performance for the binary classification and six-class classification using segmented images with an accuracy of 99.95% and 99.12%, respectively. Finally, EfficientNet-B4 achieved an accuracy of 99.89% for ten-class classification using segmented images. It can be concluded that all the architectures performed better in classifying the diseases when trained with deeper networks on segmented images. The performance of each of the experimental studies reported in this work outperforms the existing literature. 2021 by the authors. Licensee MDPI, Basel, Switzerland. Funding: The open-access publication of this article was funded by the Qatar National Library and this work was made possible by HSREP02-1230-190019 from the Qatar National Research Fund, a member of Qatar Foundation, Doha, Qatar. The statements made herein are solely the responsibility of the authors. Scopus

In this article, a new interleaved multilevel boost converter (interleaved-MBC) is suggested with minimal voltage multiplier (VM) cells for high-voltage step-up applications. The interleaved-MBC is derived in such a way that the maximum utilization of the VM circuit operation can be achieved by the interleaved structure. Furthermore, compared to existing multilevel interleaved converters, the reduced number of capacitors and diode with equal voltage rating makes it more attractive. Similar to the existing multilevel converter, the feature of the interleaved-MBC provides the extension of the number of levels to achieve the necessary voltages just by adding similar capacitor–diode stages (single capacitor and single diode are required to increase the stage by one). The features like continuous input current, low-input ripples, high voltage conversion ratio, and reduced stress on devices make the proposed converter more suitable for the voltage step-up applications, such as dc link, hybrid distribution systems, hybrid photovoltaic systems, etc. The detailed analysis of the converter is carried out by considering the nonidealities in the power circuit. The operation of the interleaved-MBC is presented for continuous and discontinuous conduction modes with boundary conditions. The components selection criterion and the comparison of converters are presented with suitable discussions. The converter is experimentally tested, and the obtained results validate its performance and functionality.

Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help to reduce the adverse effects of diseases and also helps to overcome the shortcomings of continuous human monitoring. In this study, we have extensively studied the performance of the different state-of-the-art convolutional neural networks (CNNs) classification network architectures i.e. ResNet18, MobileNet, DenseNet201, and InceptionV3 on 18,162 plain tomato leaf images to classify tomato diseases. The comparative performance of the models for the binary classification (healthy and unhealthy leaves), six-class classification (healthy and various groups of diseased leaves), and ten-class classification (healthy and various types of unhealthy leaves) are also reported. InceptionV3 showed superior performance for the binary classification using plain leaf images with an accuracy of 99.2%. DenseNet201 also outperform for six-class classification with an accuracy of 97.99%. Finally, DenseNet201 achieved an accuracy of 98.05% for ten-class classification. It can be concluded that deep architectures performed better at classifying the diseases for the three experiments. The performance of each of the experimental studies reported in this work outperforms the existing literature.

Compared to conventional multilevel topologies, the Packed U-Cell multilevel topology utilizes the minimum number of components and provides both maximum voltage levels and high reliability. When used in a battery energy storage system, its dc-link consists of a top branch battery storage and bottom branch flying capacitors, whose state of charge and voltages must be balanced.This paper proposes a novel control scheme with two cascaded controllers: the inner loop regulates the output filter current and dc-link voltages using a novel finite-set model predictive control, the outer one is fully integrated with the model predictive controller and regulates the battery state of charge by injecting a zero-sequence voltage. The proposed control scheme was successfully implemented and tested through simulation and real-time testing. The obtained results highlight good dynamic performance and robustness of the proposed scheme during active power injection/absorption and low voltage ride-through capability. Qatar University Internal Grant No. QUCP-CENG-EE-15 16–4

Background: COVID-19 is a pandemic that had already infected more than forty-six million people and caused more than a million deaths by 1st of November 2020. The virus pandemic appears to have had a catastrophic effect on the global population's safety. Therefore, efficient detection of infected patients is a key phase in the battle against COVID-19. One of the main screening methods is radiological testing. The goal of this study is using chest X-ray images to detect COVID-19 pneumonia patients while optimizing detection efficiency. Methods: As shown in Figure 1, we combined three methods to detect COVID-19 namely: convolutional neural network, transfer learning, and the focal loss1 function which are used for unbalanced classes, to build three binary classifiers which are COVID-19 versus normal, COVID-19 versus pneumonia, and COVID-19 versus normal pneumonia (normal and pneumonia). The database used2 includes a mixture of 400 COVID-19, 1,340 viral pneumonia, 2,560 bacterial pneumonia, and 1,340 normal chest X-ray images for training, validation, and testing of four pre-trained deep convolutional neural networks. Then, the pre-trained model that gives the best results was chosen to improve its performances by two enhancement techniques which are image augmentation, allowing us to reach approximately 2,500 images per class, and the adjustment of focal loss hyperparameters. Results: A comparative study was conducted of our proposed classifiers with well-known classifiers and obtained much better results in terms of accuracy, specificity, sensitivity and precision, as illustrated in Table 1. Conclusion: The high performance of this computer-aided diagnostic technique may greatly increase the screening speed and reliability of COVID-19 diagnostic cases. Particularly, at the crowded emergency services, it will be particularly helpful in this pandemic when the risk of infection and the necessity for prevention initiatives run contrary to the available resources.

Recently, switched inductor (SI) and switched capacitor techniques in dc-dc converter are recommended to achieve high voltage by using the principle of parallel charging and series discharging of reactive elements. It is noteworthy that four diodes, one high-voltage rating switch, and two inductors are required to design classical SI boost converter (SIBC). Moreover, in classical SIBC, the switch voltage stress is equal to the output voltage. In this article, modified SIBC (mSIBC) is proposed with reduced voltage stress across active switches. The proposed mSIBC configuration in this article is transformerless and simply derived by replacing the one diode of the classical SI structure with an active switch. As a result, mSIBC required low-voltage rating active switches, since the total output voltage is shared into two active switches. Moreover, the proposed mSIBC is low in cost, provides higher efficiency, and requires the same number of components compared with the classical SIBC. The continuous conduction mode and discontinuous conduction mode analysis, the effect of nonidealities on voltage gain, design methodology, and comparison are presented in detail. The operation and performance of the designed 500-W mSIBC are experimentally validated under different perturbations. Manuscript received July 14, 2019; revised October 15, 2019 and December 13, 2019; accepted January 3, 2020. Date of publication February 5, 2020; date of current version October 30, 2020. This work was supported by Qatar University High Impact under Grant QUHI-CENG-19/20-2, from the Qatar University. The publication charges are funded by the Qatar National Library. The statements made herein are solely the responsibility of the authors. (Corresponding author: Atif Iqbal.) S. Sadaf, M. Meraj, A. Iqbal, and N. Al-Emadi are with the Department of Electrical Engineering, Qatar University, Doha 2713, Qatar (e-mail: s.sadaf@qu.edu.qa; meraj@qu.edu.qa; atif.iqbal@qu.edu.qa; alemadin@qu.edu.qa). Scopus

Cloud computing is attracting a lot of attention in the past few years. Although, even with its wide acceptance, cloud security is still one of the most essential concerns of cloud computing. Many systems have been proposed to protect the cloud from attacks using attack signatures. Most of them may seem effective and efficient; however, there are many drawbacks such as the attack detection performance and the system maintenance. Recently, learning-based methods for security applications have been proposed for cloud anomaly detection especially with the advents of machine learning techniques. However, most researchers do not consider the attack classification which is an important parameter for proposing an appropriate countermeasure for each attack type. In this paper, we propose a new firewall model called Secure Packet Classifier (SPC) for cloud anomalies detection and classification. The proposed model is constructed based on collaborative filtering using two machine learning algorithms to gain the advantages of both learning schemes. This strategy increases the learning performance and the system's accuracy. To generate our results, a publicly available dataset is used for training and testing the performance of the proposed SPC. Our results show that the accuracy of the SPC model increases the detection accuracy by 20% compared to the existing machine learning algorithms while keeping a high attack detection rate. 2021 IEEE ACKNOWLEDGMENT This work was supported by Qatar University Internal Grant IRCC-2020-001. The statements made herein are solely the responsibility of the author[s]. Scopus

COVID-19 is a virus that has infected more than one hundred and fifty million people and caused more than three million deaths by 13th of Mai 2021 and is having a catastrophic effect on the world population's safety. Therefore, early detection of infected people is essential to fight this pandemic and one of the main screening methods is radiological testing. The goal of this study is the usage of chest x-ray images (CXRs) to effectively identify patients with COVID-19 pneumonia. To achieve an efficient model, we combined three methods named: Convolution Neural Network (CNN), transfer learning, and the focal loss function which is used for imbalanced classes to build 3 binary classifiers, namely COVID-19 vs Normal, COVID-19 vs pneumonia and COVID-19 vs Normal Pneumonia (Normal and Pneumonia). A comparative study has been made between our proposed classifiers with well-known classifiers and provided enhanced results in terms of accuracy, specificity, sensitivity and precision. The high performance of this computer-Aided diagnostic technique may greatly increase the screening speed and reliability of COVID-19 detection. 2021 IEEE. This work was supported by Qatar University Internal Grant IRCC-2020-001. The statements made herein are solely the responsibility of the authors. Scopus

Conventional phase-shifted pulsewidth modulation (PS-PWM) is a usual switching technique for Z-source/quasi-Z-Source (qZS)-based cascaded multilevel inverters (CMIs) (qZS-CMIs). The PS-PWM scheme causes higher switching losses and creates an electromagnetic interference (EMI) problem for higher number of cascaded modules. To address these issues, a novel, modified level-shifted PWM (LS-PWM) technique is proposed with the aim of obtaining equal power from cascaded modules under abnormal conditions. The direct use of the alternate phase-opposed disposed PWM (APOD-PWM) results in unequal power sharing between the qZSI modules, under all operating conditions. An effective carrier rotation is incorporated in the conventional APOD-PWM to make equal power sharing between the qZSI modules. The proposed scheme is an excellent solution for photovoltaic (PV) systems to address the problem of partial or complete shading, temperature variation, PV module failure, and dust accumulation on the PV panels. Furthermore, the relationship between PS-PWM and APOD-PWM is geometrically obtained, which indicates that the proposed modulation scheme gives higher voltage gain over LS-PWM and PS-PWM techniques. In addition, detailed switching loss analysis for the proposed PWM methods is added to validate low-switching losses, and thus high efficiency. The MATLAB Simulink simulations are presented to verify the proposed modulation. An experimental prototype is developed, and the experimental outcomes validate the improved performance of the multilevel qZSI with proposed modulation. ACKNOWLEDGMENT This work was supported by the Qatar National Research Fund (a member of Qatar Foundation) under Grant NPRP Scopus

The residential and commercial buildings are going green to reduce the carbon footprint. To improve the energy efficiency of the buildings, the Internet of Things can be used in conjunction with power electronic converters to improve the performance of energy efficient buildings. The distributed generation for the buildings mainly consists of battery energy storage, wind generator, solar PV panels along with the national utility grid. So, the power/energy from renewable sources can be efficiently managed by the power electronics converter. Mainly the power electronic converters are two categories one for the energy utilization side and the second is the generation, transmission, and distribution of the electrical energy. The power electronic converter has been developed to address the multiple source’s communications at the central converter, which reduces the utility bill payment to the energy (grid) suppliers and depends fully on the renewable energy sources along with the battery energy storage. To achieve this a novel power electronic converter has been proposed. This directly connects the PV panels with the semiconductor devices, passive components like inductors and capacitors along with the battery energy storage. The resultant output is directly feeding o the utility grid and simultaneously commercial home. A detailed mathematical analysis has been carried out along with the novel switching algorithm has been proposed. The simulation is performed in the MATLAB Simulink to validate the operation/working of the proposed converter. An experimental prototype 500 watt has been developed to test the mathematical validity and a suitable control algorithm has been developed for the proper operation.

Growing plants in the gulf region can be challenging as it is mostly desert, and the climate is dry. A few species of plants have the capability to grow in such a climate. However, those plants are not suitable as a food source. The aim of this work is to design and construct an indoor automatic vertical hydroponic system that does not depend on the outside climate. The designed system is capable to grow common type of crops that can be used as a food source inside homes without the need of large space. The design of the system was made after studying different types of vertical hydroponic systems in terms of price, power consumption and suitability to be built as an indoor automated system. A microcontroller was working as a brain of the system, which communicates with different types of sensors to control all the system parameters and to minimize the human intervention. An open internet of things (IoT) platform was used to store and display the system parameters and graphical interface for remote access. The designed system is capable of maintaining healthy growing parameters for the plants with minimal input from the user. The functionality of the overall system was confirmed by evaluating the response from individual system components and monitoring them in the IoT platform. The system was consuming 120.59 and 230.59 kWh respectively without and with air conditioning control during peak summer, which is equivalent to the system running cost of 13.26 and 25.36 Qatari Riyal (QAR) respectively. This system was circulating around 104 k gallons of nutrient solution monthly however, only 8&ndash, 10 L water was consumed by the system. This system offers real-time notifications to alert the hydroponic system user when the conditions are not favorable. So, the user can monitor several parameters without using laboratory instruments, which will allow to control the entire system remotely. Moreover, the system also provides a wide range of information, which could be essential for plant researchers and provides a greater understanding of how the key parameters of hydroponic system correlate with plant growth. The proposed platform can be used both for quantitatively optimizing the setup of the indoor farming and for automating some of the most labor-intensive maintenance activities. Moreover, such a monitoring system can also potentially be used for high-level decision making, once enough data will be collected. This work presents significant opportunities for the people who live in the gulf region to produce food as per their requirements.

In this study, common mode voltage (CMV) reduction using space vector pulse-width modulation (SVPWM) technique is proposed for a three-phase induction motor drive fed by a three-to-three phase indirect matrix converter (MC). The proposed SVPWM effectively reduces the peak of the CMV without affecting the output voltage gain. By adopting the proposed SVPWM there is no change in the dv/dt of CMV at the terminal of the machine when compared to the existing method. The proposed control is possible by proper placement of zero vectors in the rectifier stage and discarding zero voltage vectors in the inverter stage of the indirect MC. The control technique is implemented in MATLAB/Simulink environment. Hardware setup is developed and control algorithm is implemented using dSPACE working in conjunction with the FPGA interface board. The results of the proposed SVPWM are compared with the existing method of CMV elimination and the improvement is established. The simulation results obtained are verified with the experimental results. The obtained results confirm a reduction of CMV by 48% (peak) and validate the viability of the proposed scheme in a three-phase induction motor drive system. The Institution of Engineering and Technology. Scopus

Several potential benefits to the quality and reliability of delivered power can be attained with the installation of distributed generation units. To take full advantage of these benefits, it is essential to place optimally sized distributed generation units at appropriate locations. Otherwise, their installation could provoke negative effects to power quality and system operation. Over the years, various powerful optimization tools were developed for optimal integration of distributed generation. Therefore, optimization techniques are continuously evolving and have been recently the focus of many new studies. This paper reviews recent optimization methods applied to solve the problem of placement and sizing of distributed generation units from renewable energy sources based on a classification of the most recent and highly cited papers. In addition, this paper analyses the environmental, economic, technological, technical, and regulatory drivers that have led to the growing interest on distributed generation integration in combination with an overview about the challenges to overcome. Finally, it examines all significant methods applying optimization techniques of the integration of distributed generation from renewable energy sources. A summary of common heuristic optimization algorithms with Pro-Con lists are discussed in order to raise new potential tracks of hybrid methods that haven't been explored yet.

With the advent of SiC devices, the packed U-Cell converter has become a promising topology among the multilevel converters for medium voltage DSTATCOM application. It utilizes fewer components while achieving the maximum voltage levels with higher reliability. However, as a reactive power compensator, its dc-link consists of flying capacitors, whose voltages must be balanced which presents a challenging problem for a seven-level packed U-Cell inverter, as there are no-redundant switching states. This paper proposes a novel control scheme to keep these flying capacitors balanced under normal/abnormal grid conditions. It consists of a standard cascaded: inner and outer controllers. The inner current controller regulates both the active and reactive currents and the outer controller regulates the upper flying capacitor voltage. In parallel with the cascaded controllers, a novel model predictive voltage balancing controller is proposed to maintain the lower flying capacitor voltage at a given setpoint. To cope with the unbalance grid conditions, an additional zero-sequence injection regulator is also integrated into the designed control scheme. The proposed control scheme was simulated and real-time tested on the Hardware-In-the-Loop system. The results show that the proposed controller effectively balances the two flying capacitor voltages while controlling the reactive power. Qatar University Internal Grant no. QUCP-CENG-EE-15 16-4

Wi-Fi as a wireless networking technology has become a widely acceptable commonplace. Over the course of time, the applications landscape of Wi-Fi networks is growing tremendously. The proliferation of new services is driving the industry to adopt novel and agile approaches to ensure the quality of experience delivered to the end user. To enhance end user experience, transmission throughput is an important metric that has a strong impact on the end-user quality of experience. The accurate real-time prediction of throughput can bring several new possibilities to enhance user experience in future self-organizing cognitive networks. However the real-time prediction of transmission throughput is challenging due to the dependency on several parameters. Previous studies on throughput prediction are primarily focused on non real-time prediction in less-dynamic networks. The studies also do not provide high accuracy as required in cognitive networks for efficient decision making. The purpose of this study is to use data-driven machine learning (ML) techniques and evaluating their accuracy and efficiency to predict the transmission throughput in Wi-Fi networks. Four algorithms are used namely multi-layer perceptrons (MLP), support vector regressors (SVR), decision trees (DT) and random forests (RF). It is widely understood that the accuracy and efficiency of machine learning (ML) algorithms hugely depend upon the datasets being used to train the model. Hence, this study proposes two distinct data models for creating ML-ready datasets using feature engineering. The accuracy of each ML algorithm over these datasets is evaluated. The evaluation results show a maximum prediction accuracy of 96.2% using MLP algorithm, followed by DT (94.5%), RF (93.3%) and SVR (91.0%) respectively. Furthermore, the complexity analysis is also presented to support the adaptation of such schemes in real-time applications.

This paper utilizes the electric vehicles (EVs) connected to both the building and grid at the same time. In the building, the vehicle-to-home (V2H) operation is modeled. In the gird, the vehicle-to-grid (V2G) operation is studied. The EV s are modeled in the building connected to the electrical grid. The EV s are able to send energy to both grid and building. The building is also equipped with solar panels and electrical loads. Uncertainty of solar energy and loads are incorporated by stochastic programming. The optimization is implemented in GAMS to find the minimum daily cost. The optimal charging-discharging regime is denoted for EV s. The results illustrate that V2H - V2G operation can efficiently minimize the energy cost under solar-load volatility. 2019 IEEE. Scopus 2-s2.0-85082309035

This paper presents a new high gain 2LC-Y Multilevel-Boost-Converter or 2LC-Y MBC topologies for renewable energy conversion. Four new topologies 2LC-L MBC, 2LC-2L MBC, 2LC-2LC MBC, and 2LC-2LCm MBC are developed by combination of the voltage multiplier characteristics and the traditional 2LC-Y topologies of XY family (2LC-L, 2LC-2L, 2LC-2LC, and 2LC-2LCm) to achieve high voltage gain. These topologies are beneficial when used in applications where low to high voltage conversion is required, which makes it suitable for renewable energy applications like photovoltaic systems, fuel-cell system, hybrid vehicle, HVDC, and DC drives etc. Additionally, a stack of multiple capacitors present at the output side makes the proposed topologies highly suitable for utility grid using multilevel inverter (MLI). The operating principle of proposed 2LC-2LCm MBC topology is explained in detail and voltage gain analysis of the proposed converters is presented. Moreover, all the proposed topologies are compared with each others in terms of voltage gain. The simulation results validated the operation of the proposed converter and perfectly matching with the theoretical analysis. - 2019 IEEE. ACKNOWLEDGMENT This publication was made possible by High Impact grant # [QUHI-CENG-19/20-2] from the Qatar University. The statements made herein are solely the responsibility of the authors. Scopus

Scaled down version of the Micro grid is nanogrid, where each residential home, commercial building etc. are optimized and power is shared between the Battery Energy Storage (BES) and the utility grid. The recently proposed impedance source converters (ISC) are attractive solution for the nanogrid applications. Among them Conventional Simple Boost Converter (SBC) is a good choice. Here, a novel quasi Switched Boost converter (qSBC) is proposed. With the proposed structure, high gain from the DC-AC for multiple input (Photovoltaic (PV) and BES) and multiple output (BES and AC gird) is obtained. This paper proposes new Pulse Width Modulation (PWM) control strategy to increase the gain and the modulation index. The benefits of the proposed circuit include: 1) High modulation index during DC - AC conversion, 2) Significantly reduced voltage stresses across the components 3) Continuous input current from PV panel 4) Reduced inductor current ripple 5) Low shoot-through current and 6) high efficiency. All the operating modes and circuit analysis for the proposed converter are investigated in detail. Mathematical modeling of the proposed circuit is carried out in MATLAB/Simulink® and obtained results are discussed. Experimental prototype for 500W, 240V/50Hz output voltage is developed to validate the proposed converter with the developed PWM control algorithm. Hardware results obtained validates the performance and advantages of the proposed circuit and associated modulation method. ACKNOWLEDGMENT This article was made possible by NPRP X-033-2-007 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

Automated early detection and identification of switch faults are essential in high-voltage applications. Modular multilevel converter (MMC) is a new and promising topology for such applications. MMC is composed of many identical controlled voltage sources called modules or cells. Each cell may have one or more switches and a switch failure may occur in anyone of these cells. The steady-state normal and fault behavior of a cell voltage will also significantly vary according to the changes in the load current and the fault timing. This makes it a challenging problem to detect and identify such faults as soon as they occur. In this paper, we propose a real-time and highly accurate MMC circuit monitoring system for early fault detection and identification using adaptive one-dimensional convolutional neural networks. The proposed approach is directly applicable to the raw voltage and current data and thus eliminates the need for any feature extraction algorithm, resulting in a highly efficient and reliable system. Simulation results obtained using a four-cell, eight-switch MMC topology demonstrate that the proposed system has a high reliability to avoid any false alarm and achieves a detection probability of 0.989, and average identification probability of 0.997 in less than 100 ms. - 2018 IEEE. Manuscript received October 23, 2017; revised February 11, 2018 and March 27, 2018; accepted April 16, 2018. Date of publication May 3, 2018; date of current version June 28, 2019. This work was supported by the National Priorities Research Program award (NPRP10-1203-160008) from the Qatar National Research Fund, a member of the Qatar Foundation. (Corresponding author: Serkan Kiranyaz.) S. Kiranyaz, A. Gastli, L. Ben-Brahim, and N. Al-Emadi are with the Department of Electrical Engineering, Qatar University, Doha 2713, Qatar (e-mail: mkiranyaz@qu.edu.qa; adel.gastli@qu.edu.qa; brahim@qu.edu.qa; alemadin@qu.edu.qa). Scopus

The pulse-width modulation (PWM)-controlled modern electric drives causes some serious problems such as the generation of common-mode voltage (CMV) and high dv/dt. The CMV might cause high-frequency leakage currents that flow through the possible path that is bearing of the motor. Thus, the flow of current through the motor bearing leads to its premature failure. One of the main challenges in developing PWM methods for control of motor drives is the problem of CMV and its reduction/elimination. In this study, a modulation technique based on space-vector approach is developed to reduce the CMV in a three-to-seven-phase direct matrix converter. CMV reduction becomes possible by selecting suitable zero-voltage vectors for a specific duration and the use of a selected set of active vectors for the output voltage synthesis. The proposed space-vector PWM (SVPWM) for the reduction of CMV is performed with a seven-phase R-L load to show the viability of the proposed technique. The proposed SVPWM technique is implemented experimentally in dSPACE working in conjunction with field-programmable gate array processor board using a system generator. Hardware results that validate the proposed control algorithm are discussed. - The Institution of Engineering and Technology 2019 This publication was made possible by the Qatar University internal Grant QUCP-CENG-17\18-2. The statements made herein are solely the responsibility of the authors. Scopus