Miracle-dz/UniFAR-FTUs huggingface dataset:
A toolbox for comparing congestion management solutions for distribution networks

To address the emerging challenges in electricity distribution networks, various solutions have been proposed such as alternative tariff design, local flexibility markets (LFMs), bilateral contracts, and local energy markets (LEMs). However, choosing a suitable solution is not straightforward due to multi-dimensional complexity of the challenges which may vary under different circumstances. This paper proposes a toolbox for qualitative and quantitative comparison of the different solutions. The toolbox includes a multi-dimensional analytical framework and a flexible modeling and demonstration platform for conducting quantitative comparison studies. Four solutions i.e. LFM, LEM, cost-reflective tariffs, and bilateral contracts are compared qualitatively using the framework and a real demonstration example of an LFM design is presented utilizing the modeling platform. The toolbox can facilitate research on the local grid challenges and contribute to finding a suitable solution from a multi-dimensional perspective.
Evaluation of Power System Identification Methods and Their Applicability for Online PSS Tuning

This paper compares four methods and their accuracy in the identification of a simplified power system model. The identified model is to be used for power system stabilizer (PSS) tuning. Therefore, the focus is on identifying a low-order linear model that is able to accurately capture the dynamics of the system in the frequency bandwidth in which the PSS has typically to act in order to damp low frequency oscillations. We evaluate polynomial transfer function models and state space model structures under different estimation methods are considered. The two-area four-generator power system model is used as test case, and the results of the four methods are compared to the analytical results in terms of both low-frequency oscillation modes and frequency response in the 0.1 - 2Hz interval. The combination of the Predictor Based Subspace IDentification (PBSID) method and the Prediction-Error Minimization (PEM) method showed the better performance.
Three-dimensional Electromagnetic Thermal Fluid Simulation of Cathode Jet Contributing to Motion of Cathode Spots in Vacuum Arc

Vacuum arcs have been applied to circuit breakers, metal processing and other applications. However, its physical phenomena have not been elucidated today, especially the physics of the cathode spots. The mechanism of the motion of the cathode spot under external magnetic field has not been clarified quantitatively. The ions generated from the cathode spots are accelerated by the pressure gradient to generate a cathode jet with high speed. This behavior of the ions is considered to be responsible for the motion of the cathode spots, especially the retrograde motion. In this research, the three-dimensional electromagnetic thermal fluid simulation was developed to clarify the physics and conditions of cathode spot motion under external magnetic field. As a result, it found that the moving direction of cathode spots plasma was determined by electromagnetic force formed by the cathode jet containing ions from cathode spots and external transverse magnetic field.
Dictionary Learning (DL)-based Sparse Cascaded Channel Estimation in IRS-assisted Terahertz MU-SIMO Systems With Hardware Impairments

This work conceives a sparse channel estimation (CE) scheme for multi-user (MU) intelligent reflecting surface (IRS)-aided Terahertz (THz) systems. The proposed framework also incorporates hardware impairments that arise due to manufacturing errors in practical THz systems, such as mutual coupling, irregular antenna spacing, and antenna gain/phase errors. A dictionary learning (DL) algorithm is proposed to learn the best sparsifying dictionary for an IRS-aided THz system in the presence of hardware impairments. The dictionary thus obtained is subsequently employed to leverage the sparsity inherent in the IRS-aided cascaded THz system toward channel estimation (CE). Simulation results corroborate our analytical findings and demonstrate the improved performance with respect to an agnostic scheme that ignores the non-idealities.
A Reduced Switching Two-Vector MMPC Scheme for MMCs in Medium Voltage Applications

A two-vector Modulated Model Predictive Control (MMPC) scheme is proposed in this paper that achieves reduced switching in the output pole voltage of a Modular Multilevel Converter (MMC). This switching state change reduction is significant when compared to the conventional three-vector pulse width modulation (PWM) scheme. The proposed two-vector scheme is generalized for any n-level pole voltage operation and is suitable for digital implementation. The three main control parameters in MMC i.e. output current, inner circulating current and submodule capacitor voltages are satisfactorily controlled while a reduced switching two-vector scheme is applied. The simulation results considering 10 submodules in each arm of 3-⊘MMC are showcased while feeding power to the medium voltage grid at unity power factor. Hardware results along with transient performance are also incorporated.
The Illuminator: An Open Source Energy System Integration Development Kit

This paper introduces a flexible and extendable easy-to-use energy system integration development kit: the Illuminator. The Illuminator illustrates challenges arising from the energy transition. Hence, it is suitable in education and for demonstration. It also acts as a sandbox for testing new research concepts, and particularly, distributed energy coordination algorithms in real and non-real time. The Illuminator technology is primarely a modular software platform developed to run on a Raspberry Pi (RasPi) cluster. It is open-source, available at GitHub and developed in Python. The Illuminator comprises models of common energy technologies, such as photovoltaic (PV) panels, wind turbines, batteries, and hydrogen systems. The uniqueness of the Illuminator is in its modularity and flexibility to reconfigure scenarios and cases on the fly, even by non-experts in a plug-and-play fashion. This paper introduces the Illuminator and shows its performance in a simple case study.
Real-Time Implementation of Short-Term Voltage Stability Prediction Scheme

A pre-identified voltage template based Short-term Voltage Stability (SVS) assessment scheme is implemented on a real-time platform to verify the operational accuracy by mitigating practical issues such as Phasor Measurement Unit (PMU) data losses. This system is implemented on a supervisory level computer with the aid of synchrophasor application platform called PhasorSmart®. The real-time voltage magnitudes required to mimic power system operational conditions are provided through a Real-time Digital Simulator (RTDS®). The IEEE Nordic 32 bus system is simulated, and the real-time voltage magnitudes are published through synchrophasor protocol. The data received at the supervisory-level computer is then fed to the PhasorSmart® platform where proposed scheme is programmed using C++. Finally, performance of the proposed SVS prediction schemes under practical operational scenarios is analyzed and validated.
Ripening Assessment Classification using Artificial Intelligence Algorithms with Electrochemical Impedance Spectroscopy Data

The increasingly growth of the world’s population requires more efficient efforts to manage people’s needs. One important problem lies on food distribution, quality and sustainability throughout the chain, from the producer to the consumer’s hands. It’s more and more important to achieve intelligent non-destructive methods to assess food quality, at the crop, storage and shelves at the supermarket. This paper approaches an application of machine learning (ML) on impedance data from bananas to assess whether the fruit is good for consumption or not. To fulfill the proposed objective, impedance data of 10 bananas during 32 days was acquired through electrochemical impedance spectroscopy (EIS), using an Analog Discovery 2. A database was produced with impedance, humidity and temperature values from each measurement. After data pre-processing, several ML classifiers were trained and tested for several different feature combinations and data normalization methods. The XGB classifier achieved the best performance, with a F1-score of 98,36% and accuracy of 98,10%. This study can be extrapolated to other fruits and vegetables in order to allow a better management in the food industry, improve quality and prevent food waste.
LDCT Image Reconstruction on Edge Attention Graph Convolutional Network with Perceptual Loss and LoDoPaB-CT

Image reconstruction performs a protruding role in medical image analysis. Low-Dose CT (LDCT) scan images are a common diagnostic procedure to identify diseases in the human body. Recent scanners follow deep learning-based post-processing methods for low-dose imaging. Low-dose CT image reconstruction techniques deteriorate image quality, which has an impact on a physician's diagnosis. Therefore, this paper introduces a novel LDCT image reconstruction method based on the edge attention technique utilized in graph convolutional neural networks. The quality of the outcomes is measured through the perceptual loss function. Experimental assessments are shown on the LoDoPaB-CT benchmark dataset. It is demonstrated that the proposed method produced an improved high-quality image compared to both traditional and deep learning-based reconstruction methods qualitatively and quantitatively.
LipReadNet: A Deep Learning Approach to Lip Reading

LipReadNet is a deep learning approach to lip reading that aims to improve speech recognition technology for individuals with hearing impairments or in noisy environments. Lip reading has long been known as an effective method of communication for people who have hearing problems, and with the advancement of deep learning algorithms, it has become possible to automate the process of lip reading. The LipReadNet model has the potential to revolutionize the field of speech recognition technology, making it more accessible and useful for individuals with hearing impairments, as well as in scenarios where the audio signal is degraded or absent. The LipReadNet model comprises a 3D CNN and LSTM that is trained on large datasets of video and audio recordings. The model first extracts visual features from the mouth region of a person’s face, then combines these features with the corresponding audio signal to predict the spoken words. This approach is highly effective as it can recognize spoken words even in cases where the audio signal is corrupted or missing entirely. LipReadNet outperforms existing lip-reading models in terms of accuracy, robustness, and efficiency. The goal of a lip-reading project is to develop a system that can accurately recognize speech through visual cues, without the use of audio. Achieved accuracy of 93% in a lip-reading project which rely on different factors, such as quality of data and diversity of data of training, the choice of machine learning algorithms, and the evaluation metrics used to calculate the competence of the system.
Dynamic Drift-Adaptive Ensemble-Based Quality of Transmission Classification Framework in OTN

We introduce Dynamic Drift-Adaptive Ensemble-based Quality of Transmission (QoT) Classification Framework (DAEQoT) to effectively verify the feasibility of a lightpath while maintaining high prediction accuracy in a dynamic Optical Transport Network (OTN) scenario. This framework adopts the Early Drift Detection Method (EDDM) to identify any significant increase in the prediction error. Moreover, Ensemble Learning is implemented to enhance the accuracy of the Master QoT model by combining other Supportive QoT classifiers when an early drift warning is reported. Thus, prediction error and complete model retraining are mitigated. DAEQoT achieves the best accuracy of 98.56% and reduces the execution time up to 52.81% compared to state-of-the-art offline and online machine learning approaches.
Data-based harmonic impact quantification of multiple loads in a real medium-voltage network

This paper analyses a method to quantify harmonic contribution of multiple loads in a real medium-voltage network. The objective is to quantify the contributions of the individual subquarters of the Campus Lichtwiese of TU Darmstadt to the overall distortion using measurement data recorded in the network. The basis is provided by the Harmonic Impact Index, that can be developed with a data-driven approach and is a suitable method to quantify the harmonic contribution of more than two parties. Mathematically, this index is based on Partial Least Squares regression, which is briefly introduced. Moreover, an improvement of the regression is presented in this paper. By taking time-synchronised angle measurements into account, the accuracy of the regression model can be enhanced.
Robust Path Tracking and Obstacle Avoidance of Autonomous Ship using Stochastic Model Predictive Control

This paper presents a stochastic model predictive control (SMPC) approach for robust path tracking and obstacle avoidance of an autonomous ship. In real environment, state estimation error, environmental disturbance and uncertainties in the force generated by actuators contribute to the uncertainty of the model and controller. This uncertainty may make accurate modeling difficult and decrease controller performance, which may lead to a ship collision accident. SMPC is a promising solution to address the challenges associated with ship movement modeling and uncertainty. The linearized ship heading control model is used in the study, and state uncertainty propagation is performed to account for the ship's uncertainty. Chance constraints are applied to manage the probabilistic constraints for the uncertain state, and an auxiliary feedback controller is designed to overcome the uncertainty. The trade–off between conservativeness and risk is managed by a risk factor in the control design process. The proposed algorithm's effectiveness is verified through simulation results.
ETDA a tool for the development of e-tongues

This paper presents the Electronic Tongues Data Analytics (ETDA) tool for automatically extracting the relevant features from data obtained from an electronic tongue (e-tongue) system based on impedance spectroscopy measurements. The tool was implemented in Python and was created to assist with the development of e-tongues. This tool helps to quickly visualize the data from the experiments and can automatically create and calibrate the Principal Components Analysis (PCA) to fit the concentrations of a determined molecule present in the complex media.
The Anti-Windup Scheme for PID with a Switching Action Controller for Nonlinear Systems

The Proportional-Integral-Derivative with a Switching Action (PIDSA) controller, which incorporates PID and a switching action of sliding mode control (SMC), has been proposed to enhance the control performance of PID. However, the windup phenomenon can arise and cause excessive overshoot and instability when PIDSA is applied to systems with actuator saturation. This windup phenomenon is due to the integral term of PID and the integral terms of the double integral sliding surface. To address this issue, we propose anti-windup schemes specifically designed for PIDSA using a double integral sliding surface. For the integral term of PID, we use the back-calculation anti-windup scheme. However, this scheme alone cannot compensate for the windup effect caused by the integral term of PID. Therefore, we introduce a new sliding surface for the switching action that considers actuator saturation, and we propose the reset sliding surface scheme to match the sliding surface with the desired error dynamics of PID. To evaluate the effectiveness of the proposed scheme, we conduct experiments on a highly nonlinear system, a hydraulic vane motor. The experimental results demonstrate that our scheme effectively overcomes the windup problem of PIDSA.
Derivation of N-TH Order Cumulant Spectra

Higher-order spectra (HOS) provide the frequency-domain decomposition of higher-order moments by cross-frequency correlation. They establish the frequency-domain equivalent to correlation functions and form powerful representations for assessing nonlinear, non-Gaussian, or non-stationary systems and processes. HOS are subdivided into moment and cumulant spectra. While the latter provide a clear assessment of statistical dependence and favorable mathematical properties, cumulant spectra cannot be estimated directly. Their concept requires the identification and removal of spectra of lower order, analogously to their scalar-valued counterparts. So far, HOS applications have been based on third and fourth order and so has the derivation of cumulant spectra. Computational power, advanced methods, and new estimators put forward the interest in expanding HOS analysis to orders above four. This paper presents the combinatorial framework to define nth-order cumulant spectra in the frequency domain. On this basis, sixth-order spectral estimates are employed to differentiate two processes of same PSD and trispectrum.
Personal Mobility Safe Driving System with Knowledge Distillation

The global personal mobility market has been rapidly expanding due to its convenience. However, the increasing number of accidents involving personal mobility devices has become a growing concern, including falls, collisions with objects, and riders being struck by moving vehicles or objects. In this paper, we propose a deep learning-based safe driving system that considers both user and road images to address this issue. Our system employs CNN-based models to detect whether the user is 1) wearing a helmet and 2) looking ahead in user-side images. At the same time, the roadside image recognizes whether the user is 3) driving on the sidewalk and 4) near the intersection. These tasks are simultaneously performed in parallel to identify the overall situation, which is done by determining the final speed as the minimum speed of speed values extracted from all tasks. Additionally, we employ knowledge distillation techniques to compress the models and enable real-time inference on edge devices, resulting in a fast and accurate system that is well-suited to the characteristics of personal mobility.
A robust total Kalman filter algorithm with numerical evaluation

In this study, the observation model of Kalman Filter (KF) is extended to an errors-in-variables (EIV) model because the observations may exist in the design matrix of the observation model. Then, a robust total least squares method (RTLS) is introduced into the KF, and a robust total Kalman filter (RTKF) algorithm is derived. The RTKF is a simple, flexible and effective algorithm. It is simple because its computational formulae are similar to the computational formulae of a standard KF; it is flexible because it can be used in a wide range of applications; it is effective because the influence of outliers on estimated results is weakened. Finally, the simulated example of the indoor location and the empirical example of pseudorange differential positioning are used to demonstrate the performance of the RTKF algorithm. The results prove the validity, robustness, and reliability of the RTKF in dealing with the outliers that exist in both observation vector and design matrix of the EIV model. Furthermore, the results of the empirical example show that the RTKF improves the precision of a pseudorange differential positioning compared with KF and robust Kalman filter (RKF) algorithms regardless the observation model has outliers or not in this empirical example.
Sparse High-Dimensional Matrix-Valued Graphical Model Learning from Dependent Data

We consider the problem of inferring the conditional independence graph (CIG) of a sparse, high-dimensional, stationary matrix-variate Gaussian time series. All past work on matrix graphical models assume that i.i. d. observations of matrix-variate are available. Here we allow dependent observations. We consider a sparse-group lasso based frequency-domain formulation of the problem with a Kronecker-decomposable power spectral density (PSD), and solve it via an alternating direction method of multipliers (ADMM) approach. The problem is bi-convex which is solved via flip-flop optimization. We provide sufficient conditions for local convergence in the Frobenius norm of the inverse PSD estimators to the true value. This results also yields a rate of convergence. We illustrate our approach using numerical examples.
Photonic Multilayers for Broadband and Large Area Superchiral Surface Waves

We adopt genetic multi-objective optimization to design dielectric multilayers that support superchiral surface waves across a wide range of wavelengths. Our research reveals that it is possible to obtain extremely simple platforms that can significantly enhance the optical chirality in a wavelength range of hundreds of nanometers. Additionally, we demonstrate that this approach enhances the circular dichroism signal by more than two orders of magnitude when a layer of chiral molecules is deposited on top of the dielectric stack. As a result, our findings potentially expand the capabilities of chiral spectroscopies beyond what is usually possible with conventional circular dichroism measurements.
Design of Three-port Isolated Bidirectional Efficient EV Charging System with DPS Control

This paper presents an isolated three-port bidirectional charging system for electric vehicle (EV). This includes a power-factor correction (PFC) rectifier, a series resonant converter (SRC), and a blocking capacitor-based dual- active bridge (DAB) converter. The SRC is intended for fast charging, while the DAB converter is intended for slow charging. The three-port converter (TPC) reduces the number of components which ultimately minimizes the cost and volume of the converter. To manage the power flow of different ports, a dual-phase shift (DSP) control approach with frequency modulation has been implemented. In this work, SRC has been tested to obtain an output voltage of 200 V, while DAB has been tested for 50 V output. The simulation study has been carried out in MATLAB/Simulink environment.
Capacitor Trapped Current Fed ZCS-ZVS Push Pull DC-DC converter for renewable energy conversion

A current fed ZCS-ZVS push-pull converter has been analyzed and designed for renewable energy conversion applications. The topology maintains both zero current switching (ZCS) and (ZVS) on leading and trailing edges owing to the resonance between the trapped capacitor and the leakage inductance of the HF transformer, resulting in a near sinusoidal waveform at the primary of the HF transformer. The voltage spike across the power devices are suppressed effectively enabling the selection of low-voltage, low cost power devices. The proposed converter no auxiliary circuit is involved and since all the switches are ground referred, non-isolated driver with single power supply can be employed. A comprehensive study of 500 W highly efficient push pull converter and design of the proposed converter including steady-state and transient analysis has been reported.