Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced Hierarchical Control of Hybrid Energy Storage System in Microgrids

The energy management system (EMS) of microgrid deals with lots of complex issues, both from generation and demand side. The intermittent nature of renewable energy sources and constantly fluctuating load demands require robust energy management. This paper introduces the structure of hierarchical control strategies and common control approaches in the microgrid. In this paper, a novel enhanced EMS in islanding mode is presented for the hybrid energy storage system (HESS), comprising of two batteries and supercapacitors. A coordination control scheme is introduced at the primary level for the HESS. This will reduce the power stress on batteries, while improving the power quality.
Moving personal-cell network: Characteristics and performance evaluation

Recent years have witnessed a huge demand for ubiquitous communications services from continuously moving users. In order to provide seamless network services to high-mobility users, one of the promising solution proposed by 3GPP is the deployment of moving-relays. In this article, we introduce the concept of Moving-Personal-Cell (mPC), which is a type of moving-relays. mPC is a user-centric network, which aims to provide reliable network services to moving users. A mPC receives data-traffic from eNB and its neighboring mPCs via wireless backhaul and sidehual links respectively and forwards the received data to its serving users. In addition to this, mPC can also increase the network capacity by caching and distributing the popular contents to its serving users. Besides these pros, the mPC also has some limitations, as its performance is highly affected by cross-tier and co-tier interferences. In this article, we analyze the effect of these interferences on mPCs performance. Our results show that the performance of mPC network is equally affected by the capacity of wireless backhaul, sidehaul, and access links. Moreover, since mPCs accommodate data traffic from wireless backhaul, sidehaul links, and content cache, their performance is also affected by the ratio of data-traffic delivered via these links.
Modular EV Fast Charging Station Architectures based on Multiphase-Medium-Frequency Transformer

The aim of reducing charging duration and the expected growth of the number of electric cars leads to a variable and high power demand on the current and near future fast charging stations (FCS). Currently, the voltage adaption from the MV level to the FCS is provided by a bulky 50/60 Hz transformer, leading to limited possibilities in the scalability in terms of charging power of the FCS. Therefore, in this paper three different modular possibilities to realize these systems by directly connecting a power electronic based solution to the medium voltage (MV) grid are investigated. The necessary isolation is realized in the DC-DC stage by the medium frequency transformer of the quadruple active bridge, which, as it will be shown, can be connected in different configurations. The prospective best solution, the cascaded H-bridge + interphase quadruple active bridge, is analyzed in more detail. The benefit of this architecture is the direct MV connection possibility and simultaneously natural power balancing among the three phases. Furthermore, the architecture provides modularity and scalability with respect to the desired power level of the charging station.
ASTM: An Attentional Segmentation Based Topic Model for Short Texts

To address the data sparsity problem in short text understanding, various alternative topic models leveraging word embeddings as background knowledge have been developed recently. However, existing models combine auxiliary information and topic modeling in a straightforward way without considering human reading habits. In contrast, extensive studies have proven that it is full of potential in textual analysis by taking into account human attention. Therefore, we propose a novel model, Attentional Segmentation based Topic Model (ASTM), to integrate both word embeddings as supplementary information and an attention mechanism that segments short text documents into fragments of adjacent words receiving similar attention. Each segment is assigned to a topic and each document can have multiple topics. We evaluate the performance of our model on three real-world short text datasets. The experimental results demonstrate that our model outperforms the state-of-the-art in terms of both topic coherence and text classification.
Two Classes Classification Using Different Optimizers in Convolutional Neural Network

Convolutional Neural Network (CNN) is the most powerful architecture of pattern recognition problems in machine learning. CNN have ability to automatically detect relevant data from training examples. In CNN, different optimizers are used for the classification purposes. Each optimizer performs well in its predefined problem and parameters. In this paper, we have taken the dataset of cats and dogs for classification purposes and evaluated the performance of multiple optimizers against our designed CNN model with different learning rate and epochs. We have tested it on CPU and computed optimizers performance in term of accuracy and error rate. The results shows that after 5 epochs, momentum optimizer performs well as compared to others for the classification problem of two classes.
Virtual Synchronous Machine Control of VSC HVDC for Power System Oscillation Damping

Two different methods for implementing inertial damping on a Virtual Synchronous Machine (VSM) and their potential for attenuating power system oscillations when utilized in a VSC HVDC terminal are investigated in this paper. As a reference case, the VSM is considered with only a frequency droop providing damping in the virtual swing equation. Then, the effect of damping based on high-pass filtering of the virtual speed is compared to damping based on high-pass filtering of the measured grid frequency. A simplified model of a power system with two equivalent generators and a VSC HVDC terminal is introduced as a case study. Analysis of the small-signal dynamics indicates that damping based on the VSM speed has limited influence on the power system oscillations, while improved attenuation can be obtained by introducing damping based on the locally measured grid frequency. The presented analysis and the operation of the proposed VSM-based damping strategy is validated by numerical simulation of a 150 MVA VSM controlled VSC HVDC terminal connected to a dynamic model of the assumed grid configuration.
Unified Power Flow Algorithm for Standalone AC/DC Hybrid Microgrids

Recent studies on power flow analysis of islanded microgrids have become increasingly important and different solutions have been proposed. However, they are often limited to only ac or only dc microgrids and hence, the power flow must be solved separately. This paper proposes a straightforward and efficient method to solve power flows of hybrid ac/dc microgrids simultaneously, based on the well-established Newton–Raphson approach. It considers the lack of slack bus during islanded operation, the coupling of ac frequency and dc bus voltage, as well as the droop control of distributed generators. To achieve power sharing between the ac and dc subgrids, this method incorporates the interlinking converter droop constants in the equation. The algorithm was tested on modified hybrid microgrids involving multiple ac–dc subgrids. The results are compared against results from time-domain simulations to validate the algorithm’s accuracy. The proposed technique can be easily implemented to aid the design and planning process of hybrid microgrids.
sEMG Signal Classification Using DWT and Bagging for Basic Hand Movements

The hands play a significant role for grasping and manipulating different objects. The loss of even a single hand affect the human activity and the prosthetic hand is a solution to equip the armless subject. Recognition of the surface electromyogram (sEMG) patterns is vital in the design of prosthesis hand. For this reason, there is a need for a proper analysis of muscle behavior. In this research, sEMG signals are used to control a robotic hand by using discrete wavelet transform (DWT) and bagging. Different types of muscle contraction can cause EMG signals to vary, affecting classification performance. In this study, MSPCA is used for denoising and DWT is used for feature extraction to evaluate their efficiency at classifying sEMG signals, which were recorded during the grasping movements with various objects. Furthermore, the performance of different classifiers with bagging were studied. An effective combination of DWT, and Bagging achieves good performance, using k-fold cross validation regarding to the total classification accuracy. The proposed methods in this study have potential applications in the prosthetic hand control.
Capacity Limit Allocation for Active Congestion Management of Distribution Grids Using Flexible User's Profiles in Microgrids

Nowadays, statistics show that more than 90% of renewable energy systems are connected to the European distribution networks inducing technical issues and costs increase for network reinforcement. However, network reinforcement may not be the most efficient solution from both energy and economic point of views. Therefore, new strategies need to be implemented in order to efficiently manage both grid infrastructure and renewable resources. These strategies should use flexible passive and active users, such as demand-response and storage systems. Hence, in this paper, a contract-based framework for capacity allocation of microgrid's flexibilities is discussed to implement an active management of distribution grids. Moreover, an optimization model for day-ahead congestion management scheduling based on generation and flexible load flexibilities in microgrids is proposed. At the end, the effectiveness of the proposed model for both distribution system operators and microgrids is tested with a case study and simulation results are reported.
Consolidating Virtual Data Centers with Negative Load Correlations to Alleviate Performance Degradation Due to Load Burstiness (Invited)

The load burstiness of a virtual machine (VM) or a virtual link (VL) in a Virtual Data Center (VDC) may affect other VDCs that share a common host and degrade the performance of these co-hosted VDCs. To tackle this, we propose embedding VDCs whose loads are negatively correlated, onto common physical servers and links. An Integer Linear Programming (ILP) model and a load-correlation-based heuristic algorithm are developed to simulate and study the effectiveness of this approach.
Designing a Transverse Flux PMSG with Analytical Methods for Applications in Wind Turbines

This paper proposes and describes the main steps for developing a flexible methodology for the analytical design of Transverse-Flux Generators. The aim of this methodology is to have a validated tool for implementing new topologies of TF-PMSG's for their use in wind turbines located in regions with higher wind potential. The higher specific torque and power densities is the main advantage of TF-PMSG technology against other topologies of PMSG machines and conventional machines. The methodology is well described in the form of an algorithmic solution. Finally, the proposed methodology is validated with JMAG-Designer 12.0.
Investigation of Different Balancing Methods for Modular 3-level T-type Voltage Source Converters with Distributed DC-link Capacitors

With the increasing relevance of regenerative power generation and more power electronic converters connected to the grid, the requirements on grid current quality increase. Since a 3-level power converter has a higher quality output waveform compared to a 2-level converter, it is a popular power converter topology for wind power and photovoltaic systems. However, the 3-level power converter has a split dc-link. Regarding the control, the main challenge is to balance the two capacitor voltages. In this paper different methods to solve the balancing problem of the neutral-point potential of the three-level T-type neutral-point-clamped (TNPC) voltage source converters (VSC) are investigated. The converter is used as an active front end (AFE) converter, which is composed of three single phase cells with distributed dc-link capacitors. Based on a simulation model of a TNPC, the different balancing methods are analyzed and compared. Furthermore, a new balancing strategy is proposed. In order to verify the investigated balancing methods, a three phase converter with newly designed intelligent single phase printed circuit board (PCB) cells has been developed, built and assembled.
A Novel Routing Algorithm for Inter-Group Load Balancing in Wireless Mesh Networks

Wireless networks throughout the year have experienced colossal innovative progressions. Because of reconciliation of each of the three kinds of administrations viz. voice, information, video, amid change in remote advances, Bandwidth has turned into the most pivotal system asset that should be upgraded for these bandwidth hungry administrations. In multi-hop situations (in WMNs), execution relies upon the routing protocols to appropriately pick courses, given the present system conditions. To determine this issue, this paper proposes a load balancing routing algorithm, called LBRA, in WMNs to disperse the activity stack among different gateways. Through simulation, we demonstrate that the proposed algorithm can disperse the traffic load consistently among various gateways and stifle the expansion in the routing path length at most 20% contrasted with the shortest path routing.
Performance Analysis of an Indoor Localization and Mapping System Using 2D Laser Range Finder Sensor

There is several implementations of techniques for solving the SLAM problem that is a process to expand autonomous mobile robots field. In this context, we will be comparing swarm algorithms for solving the simultaneous localization and mapping problem such as Firefly Algorithm, Particle Swarm Optimization and Glowworm Swarm Optimization, we directed a group of experiments using a wireless system composed of 2D laser range finder UTM-30LX, iRobot Roomba 600 and Raspberry Pi 3 as an experimental environment.
A Reinforcement Learning for Criminal’s Escape Path Prediction

A real-time decision support system with the capability to provide information related to possible criminal’s escape path can be very useful for a law enforcement to pursue a perpetrator after a crime has been committed. Typically, the exact escape path is unknown, and pursuers must relied on a predicted path based on available information about the environment. In static environment, a perpetrator may escape through an optimal path that is predicted using any existing optimal path finding algorithms. However, the path can be dynamic when environment is changed. The perpetrator may decide to change path when there is information about foremost changes in environment. This paper models the perpetrator’s path selection as a Markov Decision Process (MDP) and apply Q-learning to solve for a perpetrator’s escape path. The experiment results shows that our algorithm can find most probable escape path in the dynamic environment, which can be significant reference in a real-time decision support system for law enforcement applications.
Contextualizing Inferred Programming Difficulties

Communicating automatically inferred programming difficulty to appropriate observers can promote help if they have enough context to determine whether they should and can offer help. Buffered workspace awareness keeps a segment of the developer’s actions around an inferred difficulty as context, and allows the recording to be played a single or multiple times. Semi-structured interviews with mentor-intern pairs in a large company showed that they liked the general idea of buffered workspace awareness. We performed a two-phase user study where observers used both single- and multi-pass awareness to determine the problems developers’ had and the solution to those problems. Almost all solutions required a one-line fix. We could find no statistical difference in the correctness of their solutions in the two conditions, though the observers overwhelmingly preferred and were more confident using the multi-pass mechanism, and made use of its rewind and pause commands. Both kinds of mechanisms allowed the observers to solve the majority of problems by looking only at 5 minutes of the workers’ interaction before the difficulty. The time spent by them processing the context was a small fraction of the time spent by the developers on the difficulties. The time wasted on abandoned difficulties was a small fraction of the time spent on difficulties.
Topic Model Based Knowledge Graph for Entity Similarity Measuring

Entity similarity measuring is the basic work of academic search and recommendation. To analyze and measure the similarity among entities, existing methods are mainly based on either textual content or relationship unilaterally. Thus they can not measure similarity between paper and scholar or are limited in a small range of field. To address this, we propose a topic model, utilizing both textual content of papers and relationship between entities, and then introduce an algorithm for computing similarity between graph entities based on knowledge graph enhanced by topic model. Through the experiments we show that our model outperforms traditional topic model in topic coherence and we are able to list similar papers, scholars and conferences simultaneously and more accurately.
End User Development in the IoT: A Semantic Approach

The Internet of Things (IoT) is, nowadays, a well recognized paradigm. In this field, End User Development (EUD) is a promising approach that allows users to program their devices and services. The representation models adopted by contemporary EUD interfaces, however, are often highly technology-dependent, and the interaction between users and the IoT ecosystem is put to a hard test. The goal of my research is to explore new approaches and tools for helping end-users to program their technological devices and services. For this purpose, I proposed EUPont, an ontological model able to represent abstract and technology independent trigger-action rules, that can be adapted to different contextual situations. EUPont has been evaluated in terms of understandability, completeness, and usefulness. Currently, I am using the semantic features of the model in different research projects, e.g., to optimize the layout of EUD interfaces, and to design a recommender system of trigger-action rules. Preliminary results are promising, and confirm the benefit of using the semantic information of EUPont for helping end-users to better deal with the forthcoming IoT world.
Design Optimization of Axial Flux Permanent Magnet Brushless DC Micromotor Using Response Surface Methodology and Bat Algorithm

This paper presents design optimization technique of Axial Flux Permanent Magnet (AFPM)Brushless DC (BLDC)micromotor. The objective of the optimization process is to minimize the motor volume and improve joules efficiency with the constraints of minimum required torque and maximum back EMF using response surface modeling and Bat Algorithm (BA). Finite element computations have been used for numerical experiments on geometrical design variables in order to evaluate the coefficients of a second-order empirical model for the response surface representation. BA is used as an optimization technique to minimize volume and improve joules efficiency in separate optimization problem in terms of design variables. The optimization results were compared with other metaheuristic algorithms, including Genetic Algorithms (GA), and Particle Swarm Optimization (PSO). The bat algorithm shows a potential competitive against GA and PSO.
Load Shedding Control Strategy for Power System Based on the System Frequency and Voltage Stability(Apr 2018)

The traditional load shedding schemes––––under frequency load shedding (UFLS) and under voltage load shedding (UVLS), are designed independently and they constitute the last line of defense against frequency and voltage instability. However, the traditional load shedding schemes are not capable of dealing with combined instabilities. Moreover, a new Adaptive load shedding under load shedding method was proposed based on wide area measurement information just because the traditional load shedding schemes was not optimization algorithm. The main purpose of the method goal is to set up an emergency load shedding strategy, considering the recovery system of frequency stability at the same time, improved the voltage stability. This process has two steps, first of all, when considering the system disturbance amplitude and classifying the degree of interference according to system disturbance, Furthermore, the voltage sensitivity analysis was considered to determine the shedding location and corresponding shedding value. Finally, this algorithm was applied in IEEE 10-generator 39-bus test system. The implementation emergency of traditional and new shedding load two methods Under the system by different levels of power disturbances, comparing and analysand the feasibility and effectiveness of the new project. The results showed that the new method avoiding shedding over, more shedding part of the reactive power load, and then improving the system voltage, also avoiding damage to the voltage stability in the process of the system restore.
Synchronous Rectifier for High-Power Wireless Transfer Applications

The appliance of wireless power transfer based on inductive coupling, nowadays, is a state-of-the-art approach and widely used in the industry. Different kinds of standards have been established in the past years, e.g. Qi or WPC (Wireless Power Consortium)to name but a few. Regardless of the development, the rectification of the alternative current (AC)itself is, in general, not part of the standard. Up to now, low-cost devices such as smartphone chargers use standard half- or full-wave passive rectifiers. This might give acceptable results for low-power and/or low-cost devices but the achievable efficiency is limited by this approach. The dissipated heat itself is for low-power devices, in most cases, neglectable as the charger itself is mostly connected to the power grid. In this paper, a straightforward solution is proposed which has been successfully implemented for < 600 W wireless-power application. The solution is not limited to sinusoidal waveforms and prohibits reverse-current flow automatically. The approach automatically switches to passive rectification, when running idle or if a low-load is present, to maximize the efficiency without the requirement to know the waveform for adjustment in advance.
A Direct PI Controller Without the Feedforward Terms for a VSC-based Permanent Magnet Synchronous Generator for a Wind Turbine

Renewable energy (RE) sources - wind and solar, have received attention in South Africa and across the world from investors. The development of the Voltage Source Converter (VSC) has enabled large amount of RE to be integrated into the grid. Direct-drive permanent magnet synchronous generators (PMSGs) are now widely available. A PMSG wind turbine employs a full back-to-back VSC arrangement which is controlled via a pulse width modulated system of insulated gate bipolar transistors (IGBTs). Conventional vector control has been widely used to control VSC based PMSGs. The effects of adding decoupled terms to the controller configuration has not been documented hence this work addresses this issue. This paper investigates conventional vector control and the proposed control which excludes the decoupled terms in the configurations. A 690 V, 2 MW direct-drive PMSG wind turbine is modelled in PowerSim (PSIM) to investigate both the conventional and proposed vector control configuration performances under varying wind speeds. The proposed control structure has superior performance.