Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of BESS Virtual Inertia Controller Effect on Frequency Stability Using DIgSILENT PowerFactory Modelling

Renewable energy sources are experiencing significant development and growth as their immense potential in combating climate change and transitioning towards a sustainable future. However, the characteristics of renewable energy sources are intermittent and inertia-less, causing system frequency instability in power systems. In traditional power systems, synchronous generators provide inertia, which helps stabilize the grid against disturbance. Therefore, the system of many renewable energy sources requires additional inertia which can dampen oscillations and reduce frequency deviation. Virtual inertia controller (VIC) is a potential method that could mimic the inertia characteristics of synchronous generator. VIC ensures that the grid remains stable and can respond effectively to sudden power demand or supply changes. This paper provides the analysis of VIC implementation in IEEE 9 bus system considering generator control equipment using DIgSILENT PowerFactory 2018. Moreover, the research aims to find the effect of adding VIC based on battery energy storage systems (BESS) on frequency stability. The simulation results show that VIC’s active power compensation based on BESS can reduce the frequency deviation by 20 percent and the rate of change of frequency (RoCoF) by 70 percent when integrated with the photovoltaic and additional load on the system. In addition, the active power response of the generator decreased by 12.6 percent and the rotor angle of each generator decreased by 1.9 percent. Meanwhile, the response of the bus voltage did not change significantly, by only 0.15 percent. These improvements can avoid system instability, cascading outages, and power blackouts.
High Efficiency and High-Power Quality Modulation Strategy for Single-Stage Electrolytic Capacitor-less On-board EV Charger

This article presents a modulation scheme for achieving high-efficiency and high-power quality based on the secondary duty cycle and the phase shift for a single-stage electrolytic capacitor-less bidirectional onboard charger. Zero voltage switching is achieved for all switches for wide voltage range operation based on an optimized duty cycle which increased the efficiency of the converter. Meanwhile, high power quality is guaranteed by a varying phase shift which is directly generated by a controller. To validate the proposed modulation scheme, experimental results from the 3.7kW onboard charger are provided at 1kW power showing an improvement of 0.4% over the conventional method. Furthermore, the efficiency of the converter is improved by 3% at low battery voltage where zero voltage switching is severe while using the conventional method.
Towards the 5G-Enabled Factories of the Future

5G is a flexible key technology for Industry 4.0. Combined with edge-computing, Industrial Internet-of-Things (IIoT) systems, and other secondary wireless technologies such as Wi-Fi and MuLTEfire, it will transform the Factories of the Future into more advanced, safer, and more flexible smart production environments. In this paper, we present our research visions, and our reference implementation of a 5G-enabled factory where industrial applications and solutions can be developed and tested in operational conditions. The paper introduces the main technological developments, including a selection of observations and performance evaluations useful for engineers in both the communications and manufacturing domains. Future research visions such as the integration of a digital twin environment are also briefly discussed.
EV Charging Scheduling with Genetic Algorithm as Intermittent PV Mitigation in Centralized Residential Charging Stations

Residential charging stations are a necessity by increasing electric vehicles (EVs) that promise to be environmentally friendly. Residential charging stations systems that use renewable energy such as PV have become an important concern in an effort to build green energy. Unfortunately, intermittent PV is an unavoidable problem. The EV charging demand characteristics of residential charging stations can be utilized to work together with the generator side to achieve a power balance which suitable for the smart grid concept. Our contribution is to propose a novel charging scheduling based on intermittent PV. The charging schedule is used to reduce peak loads during intermittent PV events. The charging scheduling scenario is run using the genetic algorithm with Google Colab to find out that the charging schedule has succeeded in maintaining power balance in the residential charging station systems. The innovative formula of fitness calculation used in the genetic algorithm is a way to get optimal charging scheduling according to PV capabilities. The simulation results confirm that the peak load can be decreased by 30% according to the intermittent PV value in all case modes.
Intelligent Sensing Terminal Distributed Computing Architecture of IoT for EMS

In view of the serious fragmentation of the functions, forms, basic software and hardware of the Internet of Things intelligent perception terminal, a software architecture based on the Internet of Things intelligent perception terminal is proposed, and the software architecture is designed from the upper application aspect. At the same time, in order to achieve distributed computing in the EMS Internet of Things, a new decision algorithm has been proposed to solve the allocation problem of computing power nodes (ECNs). In addition, in order to construct a multi-terminal distributed autonomous collaborative control architecture with hierarchical structural features, three terminal autonomous collaborative methods were proposed.
Digitization of the Distribution Grid to Support e-Mobility Charging Infrastructure

The article addresses a subproject of the Energy ECS project [1], in which the development and realization of suitable components for condition monitoring of electrical distribution grids is carried out. By means of suitable sensor systems, communication and IT infrastructure components, relevant information on network infrastructure elements at neuralgic points is collected, aggregated and communicated. This information can be used directly for a highly dynamic grid-serving control of grid components to ensure a robust and efficient use of the distribution grid. The project makes a direct contribution to the digitization of distribution grids and thus contributes to the successful integration of decentralized generators and electromobility. The main objective of the subproject is the development and realization of energy-autonomous sensor systems for recording the status of relevant components of a low-voltage distribution network. The work to be carried out includes sensor principles as well as the field of energy harvesting or the field of sensor signal processing.
A 2-D Multi-Dielectric Capacitance Solver Based on Floating Random Walk Method

We present a 2-D capacitance solver based on floating random walk (FRW) method for handling realistic interconnect structures in ICs. The solver is able to accurately simulate the structures with multiple and conformal dielectrics, and circular conductors in the cross-section of 3-D IC. An approach of using transition circles and space management is proposed to accelerate the computation for structure with circular conductors. A visualization interface is presented to demonstrate the FRW algorithm as well. Experiments with industrial cases show that the presented solver runs much faster than the existing solvers based on finite difference method (FDM), while preserving reliable accuracy with error within 3%.
Vision-based Runway Detection and Landing for Unmanned Aerial Vehicle Enhanced Autonomy

Introducing autonomy is a task of paramount importance and is currently investigated in many areas, especially for autonomous cars and Unmanned Aerial Vehicles (UAVs). Most UAVs are still remotely human-controlled. A necessity is to implement on-board solutions, able to work in all weather conditions and at any time. Hence, on this topic, we give an overview of recent advances for vision-based landing of UAVs. A thorough classification of the main recently developed methods is introduced with a discussion of their advantages and disadvantages. The paper presents a new solution for autonomous UAV vision-based landing, focusing on runway detection using a hybrid approach combining multi-image matching, SIFT and object tracking. The results are evaluated and validated using simulated images sampled with the X-Plane 11 flight simulator and real-world videos collected during automated flights performed by the ULTRA vehicle, one of the biggest UAVs in the UK [1]. The statistical analysis from the validation of the proposed approach shows a high level of accuracy around 94.89% in clear weather conditions and real-time computational performance.
Carrier-Based Minimum-Loss Discontinuous PWM for Three-Level Inverters

This paper analyzes the possible zero clamping region of a three-level inverter to reduce switching loss and how to select positive, negative, and zero clamping in discontinuous PWM (DPWM). Based on these analyses, carrier-based minimum-loss DPWM (MLDPWM) is proposed for the three-level inverters within all ranges of power factor angle and high modulation index. Under consideration of not only positive and negative clamping but also zero clamping, the proposed method could increase the clamping region furthermore in the vicinity of a peak current at a lower power factor and higher modulation index. Thus, the switching loss can be minimized by reducing the number of switching near the peak current even under low power factor operation. The performance of the proposed method is compared with that of the conventional DPWM method in the aspect of switching loss function (SLF). Lastly, the effectiveness of the proposed method is verified by simulation and experimental results.
Small-signal Impedance Model of an Inductive Power Transfer System Using LCC-LCC Compensation

Unregulated inductive power transfer systems are widely adopted for wireless charging scenarios. Such a system is usually cascaded with a dc/dc converter for input or output regulation. The overall system stability needs an in-depth study of the terminal impedance from the small signal perspective. This paper focuses on the impedance model of an LCC-LCC compensated system. An equivalent circuit model is built and used to explicitly derive the input and output impedance. The experiment results show that the model is simple and accurate up to 1/5 of the switching frequency.
Comparative Study of Seq2Seq and Transformer Model for Chat Bot

Today, every company relies on technology for the effective delivery of services and the optimal use of technical resources. With the increasing adoption of AI based technologies in company operations worldwide, including India, the global Chatbot market will grow rapidly in the coming years. Chatbot technology has gained widespread acceptance in recent years, especially in the food delivery, finance, and e-commerce industries. Chatbots are computer programs that can answer questions posed by humans, and they are accessible anytime, providing quick and efficient communication with thousands of people simultaneously. Chatbots are becoming increasingly popular in human-machine interactions due to their ability to provide information without requiring time-consuming searches. In this paper, we will compare the methodologies, underlying algorithms, accuracy, and constraints of the various models for chat bots. Models compared in this study are Seq2seq Model with Attention Mechanism and Transformer. The result shows that the transformer model requires less number of iterations (epochs) to train than seq2seq model. Compared to LSTM networks, training transformer networks is easier due to the smaller number of parameters involved and Transformer network is faster than RNN based models.
Generic Memory Modeling with Recurrent Neural Network

In this work, a methodology for developing a memory compact model is described using recurrent neural network (RNN). Compared to traditional modeling approaches, it is flexible and able to develop an accurate model based on physical data before the material physics is fully understood. A simple ReRAM-type memory model is demonstrated using Nonlinear Autoregressive with External Input (NARX) machine learning approach. To enable the neural network to capture the resistive switching characteristics of ReRAM, the output at a range of cycling voltages was captured and used as training data. The trained model is used for DC prediction under different voltage amplitudes, and the prediction results are consistent with the physical data, with MSE values below $10^{-9}$. The accuracy of the RNN-assisted ReRAM model during the read, write and erase operations is also evaluated to show the validity of the approach. The calibration between the prediction results of the model and experimental data further proves the feasibility of the approach.
Scaling Collaborative Space Networks with Deep Multi-Agent Reinforcement Learning

Future space communication architectures deployed across heterogeneous space systems will require novel methods of coordinating inter-system communication and command distribution. As network complexity increases in time and distance, the ability to facilitate command and control across a large number of systems is a significant constraint on mission performance. This study presents the application of multi-agent reinforcement learning (MARL) to demonstrate a collaborative mesh network of inter-satellite links that self-configure and self-optimize in response to varying mission data needs.This paper explores methods of scaling distributed reinforcement learning-based approaches where satellites modeled as RL agents can observe their local wireless environment, share knowledge with other satellites, and cooperatively achieve network-wide mission objectives. It also implements a transfer learning approach for increasing the network size of a distributed, multi-agent system without modifying action and observation spaces.
Comparative Study of Online Loss Estimators Based on Electrical and Thermal Models for AC Permanent Magnet Machines

Loss estimation in electric machines is important for performance evaluation, condition monitoring, and optimal control. The parameters determining losses can vary with operating conditions; therefore, accurate estimation of losses is challenging. Online loss estimation is one solution, where parameters are identified and updated as they vary with operating conditions over time. For applications requiring high accuracy, estimators which are robust to model uncertainty are needed. This paper presents a comparative robustness analysis for online loss estimators of surface mount permanent magnet (SMPM) machines based on both electrical and thermal machine models. It is found that estimators based on thermal models are more robust than those based on electrical models over a wide range of operating conditions. The conditions under which the thermal model can achieve better robustness is obtained through analysis and validated through simulation.
Classification of EEG signals based on CNN-Transformer model

Brain-computer interfaces (BCI) based on EEG have attracted extensive research and attention worldwide, while motor imagery (MI), mental arithmetic (MA), and P300 event-related potentials are a few of the more commonly used paradigms.Vision Transformer(ViT) is a new Transformer model that has superior global processing power compared to Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). In this study, we propose a hybrid CNN-Transformer based model that uses CNN to convolve EEG signals in time and space, followed by ViT for global processing, and finally optimizes the model using 10-run $\times 10$-fold cross-validation and validates it on a publicly available dataset of 29 subjects. Final accuracies of 87.23% and 90.79% were achieved on the MI and MA tasks, respectively. Compared to other literature, the model achieved higher classification accuracies.
Long-Range Space Data Communication Autonomous Distributed Scheduling

To realize NASA’s ambition of a solar system wide Internet (starting with the Earth-Moon system’s LunaNet) will require optimized communications through limited data bandwidth communication. Our project, DREAMS, focuses on two aspects of optimal communications: routing and link optimization. Routing is the ability, given that links are already optimized and known (to the extent possible), to optimally schedule storage and transmission of data to maximize throughput, i.e., routing chains bundle transmissions together separated by periods of bundle storage. Link optimization is the ability to optimize a link by tuning modulation scheme, transmit power, symbol rate, etc., where optimality is determined by a weighted sum of metrics including Bit Error Rate (BER), throughput, and power consumption. The DREAMS system consists of three primary components: 1) a ML-based RF link optimizer which optimizes each RF link and apprises distributed schedulers of what data bandwidth each link can support; 2) a packet predictor which predicts and apprises distributed schedulers of the volume of future packets expected from specific applications from the daily activity schedule and the expected volume of packets not tied to the schedule; and 3) distributed schedulers, which exchange network status and schedule information with one another to continuously update and re-optimize the transmission and storage schedule.
Analysis of the impact of modifications on the performance of graphene nanoribbon field effect transistors

The graphene-based transistors are considered as the potential candidates for low-power design for future nanoelectronics application. In this paper, graphene nanoribbons (GNRs) and graphene nanoribbon field effect transistors (GNRFET) are studied by simulation. The band structure and electronic properties of graphene nanoribbons under different chirality are simulated using tight binding theory. Then, based on the theoretical framework of non-equilibrium Green’s function, the electronic and transport characteristics of double-gate (DG) armchair GNRFET are studied with the change of device structural parameters such as AGNR chirality, gate oxide thickness and dielectric constant. Moreover, the device performance metrics such as subthreshold slope and drain induced barrier lowering (DIBL) effect are analyzed emphatically. This work presents an advance in the understanding of the GNRFET structure with important implications toward the device design.
Analysis of Machine Learning Algorithms for Cyber Attack Detection in SCADA Power Systems

Cybersecurity is a rapidly growing concern in many technological areas worldwide. Supervisory Control and Data Acquisition (SCADA) systems are especially vulnerable to cyber attacks due to increased inter-connectivity. SCADA systems need to be equipped with the proper tools and techniques to detect cyber attacks, distinguish them accurately from normal traffic, overcome cyber attacks when present, and prevent future cyber attacks from disrupting these systems. In this paper, we first analyze 10 well-known traditional machine learning algorithms in terms of how effective they are when detecting cyber attacks. Then, we construct a stacking ensemble learner using these methods via different meta learners. Our experiments show that ensemble methods perform better than individual methods, demonstrating the need for a more comprehensive solution when defending against cyber attacks in SCADA systems.
Exploiting Spatial Correlations: Group Key Generation for Secure IoT Device Networks

With the exponential growth in IoT devices, especially in healthcare systems, where multiple devices are attributed to one person, the need for a secure and effective group secret key (GSK) generation is intrinsic. GSKs facilitate simplified and energy-efficient key management. Moreover, GSKs are suitable for encrypting broadcast control and alert messages. Existing group key generation schemes show many pitfalls, with the majority of them being either computationally inefficient or impractical. In this paper, we propose a novel approach to generating GSKs by exploiting the channel state information (CSI) of spatially correlated IoT nodes. We implement a feedforward neural network (FNN) for identical channel feature generation for the nodes. We then employ a group key generation scheme based on the generated channel features. Results verify the performance of the proposed scheme and demonstrate outstanding performance in the presence of a passive adversary.
Experimental Electromagnetic Compatibility Test on a Fault Line Indicator Prototype in MV Distribution Networks

Medium voltage (MV) distribution networks are becoming widespread with the increase in smart grid applications in global power systems and the need for more sensitive fault detection is increasing. This study first of all describes the fault line indicator prototype in which different communication protocols can be used successfully. The capacity of equipment or systems to operate properly in their electromagnetic environment without contributing excessive electromagnetic disturbance to anything in that environment is measured by electromagnetic compatibility (EMC) testing. This paper also contains various compatibility and immunity test methods and their results of a device prototype that finds faults in high voltage lines using the Rogowski coil technique alongside with electromagnetic field immunity tests. In this study, four different electromagnetic compatibility tests are applied to the device designed as a fault line indicator with the parameters specified in the standards and successfully results are obtained.
PWM Carrier Effects on the Harmonic Distortion of Output Voltage in Single-Delta Bridge Cell MMC

A modular multilevel converter (MMC) based on single-delta bridge-cells is a promising multilevel converter for various high-voltage application. When the cluster voltage synthesis method based on the conventional triangular carrier wave of phase-shifted pulse width modulation (PSPWM) is applied, the quality of the cluster output voltage differs depending on the parity (which is the property of an integer of whether it is even or odd) of the number of cells per cluster. These differences of the output voltage quality are caused by the different effective level of cluster output voltage under the conventional triangular carrier based PSPWM. In other words, the total harmonic distortion (THD) of the cluster output voltage is indirectly related to the number of levels of the cluster output voltage. In this paper, the THD of the cluster output voltage is analyzed according to the relationship between the number of cells per cluster and the type of the carrier. In addition, the recommended carrier type for PSPWM is determined by whether the number of cells per cluster is odd or even. To demonstrate the analysis and comparison in this paper, a full-scale real-time simulation has been conducted.
Roaming DTN: Integrating Unscheduled Nodes into Contact Plan Based DTN Networks

Current space DTN networks rely on the assumption that all node movements in relation to each other are able to be summarized ahead of time. These movements are stored in the form of static contact plans, which each node independently uses to inform its own routing decisions. However this method of routing falls short in the face of nodes with inherently unpredictable movements. In this paper, we explore preliminary work on how space DTN networks can be augmented to handle both scheduled nodes and autonomously moving unscheduled nodes. In particular, we introduce Roaming DTN (RDTN), a system that enables unpredictable nodes to communicate over a reliable network backbone. In addition, we tackle the issue of autonomous nodes navigating to remote areas with no connectivity, and how radio localization techniques may be used to alleviate this issue. We showcase experimental results examining this system’s feasibility and compare it to alternative routing methods.