Restoration of an active MV distribution grid with a battery ESS: A real case study

In order to improve power system operation, Battery Energy Storage Systems (BESSs) have been installed in high voltage/medium voltage stations by Distribution System Operators (DSOs) around the world. Support for restoration of MV distribution networks after a blackout or HV interruption is among the possible new functionalities of BESSs. With the aim to improve quality of service, the present paper investigates whether a BESS, installed in the HV/MV substation, can improve the restoration process indicators of a distribution grid. As a case study, an actual active distribution network of e-distribuzione, the main Italian DSO, has been explored. The existing network is located in central Italy. It supplies two municipalities of approximately 10,000 inhabitants and includes renewable generation plants. Several configurations are considered, based on: the state of the grid at blackout time; the BESS state of charge; and the involvement of Dispersed Generation (DG) in the restoration process. Three restoration plans (RPs) have been defined, involving the BESS alone, or in coordination with DG. A MATLAB®/Simulink® program has been designed to simulate the restoration process in each configuration and restoration plan. The results show that the BESS improves restoration process quality indicators in different simulated configurations, allowing the operation in controlled island mode of parts of distribution grids, during interruptions or blackout conditions. The defined restoration plans set the priority and the sequence of controlled island operations of parts of the grid to ensure a safe and better restoration. In conclusion, the results demonstrate that a BESS can be a valuable element towards an improved restoration procedure

Analytical Model of Cage Induction Machine Dedicated to the Study of the Inner Race Bearing Fault

This paper presents a new analytical model for inner bearing raceway defect. The model is based on the presentation of different machine inductances as Fourier series without any kind of reference frame transformation. The proposed approach shows that this model is able to give important features on the state of the motor. Simulation based on spectral analysis of stator current signal using Fast Fourier Transform (FFT) and experimental results are given to shed light on the usefulness of the proposed model

Network current quality enhancement under nonlinear and unbalanced load conditions using a four-wire inverter-based active shunt filter

The flow of a large current in the neutral conductor of a transmission system is one of the major problems caused by harmonic pollution. This current can assume excessive values and even exceed the current flowing in the phases which can be extremely dangerous both for the equipment and the safety of the personnel. Currently, the parallel or shunt active filter (SAF) or parallel active filter is considered as the most effective solution to mitigate harmonic pollution and restore a sinusoidal current waveform in electrical distribution networks. The SAF can be used to compensate for harmonic currents, as well as that of the reactive power. This paper proposes a SAF circuit based on a four-arm inverter topology. The designed SAF is shown to lead to better harmonic compensation with a reduced THD (Total Harmonic Distortion) level in the presence of nonlinear and unbalanced loads in the network. The other goal of this study is to eliminate the neutral current caused by the unbalance in the polluting loads connected to the distribution network, achieve a near-sinusoidal current waveform and protect the electric network equipment

Adaptive Filtering-Based Pseudo Open-Loop Three-Phase Grid-Synchronization Technique

International audienc

Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model

Recent studies have shown that robust diets recommended to patients by Dietician or an Artificial Intelligent automated medical diet based cloud system can increase longevity, protect against further disease, and improve the overall quality of life. However, medical personnel are yet to fully understand patient-dietician’s rationale of recommender system. This paper proposes a deep learning solution for health base medical dataset that automatically detects which food should be given to which patient base on the disease and other features like age, gender, weight, calories, protein, fat, sodium, fiber, cholesterol. This research framework is focused on implementing both machine and deep learning algorithms like, logistic regression, naive bayes, Recurrent Neural Network (RNN), Multilayer Perceptron (MLP), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM). The medical dataset collected through the internet and hospitals consists of 30 patient’s data with 13 features of different diseases and 1000 products. Product section has 8 features set. The features of these IoMT data were analyzed and further encoded before applying deep and machine and learning-based protocols. The performance of various machine learning and deep learning techniques was carried and the result proves that LSTM technique performs better than other scheme with respect to forecasting accuracy, recall, precision, and $F1$ -measures. We achieved 97.74% accuracy using LSTM deep learning model. Similarly 98% precision, 99% recall and $99\%~F1$ -measure for allowed class is achieved, and for not-allowed class precision is 89%, recall score is 73% and $F1$ Measure score is 80%

Soft-switching cells for Modular Multilevel Converters for efficient grid integration of renewable sources

The Modular Multilevel Converter (MMC) concept is a modern energy conversion structure that stands out for a number of interesting features that opens wide application chances in Power Systems, for example for efficient grid integration of renewable sources. In these high-voltage, high-power application fields, a high efficiency is mandatory. In this regard, an interesting and promising development opportunity could be to make soft-switching the elementary converters of the submodules (cells), half H-bridges or full H-bridges, obtaining at the same time the advantage of increasing the switching frequency. The-Active Resonant Commutated Pole Converter (ARCP) or the Auxiliary Quasi Resonant DC-link Inverter (AQRDCL) soft-switching topologies appear adequate for this purpose. This paper is dedicated to examining these development possibilities

Detailed energy analysis of a sheet-metal-forming press from electrical measurements

This paper presents a methodology that allows for the detection of the state of a sheet-metal-forming press, the parts being produced, their cadence, and the energy demand for each unit produced. For this purpose, only electrical measurements are used. The proposed analysis is conducted at the level of the press subsystems: main motor, transfer module, cushion, and auxiliary systems, and is intended to count, classify, and monitor the production of pressed parts. The power data are collected every 20 ms and show cyclic behavior, which is the basis for the presented methodology. A neural network (NN) based on heuristic rules is developed to estimate the press states. Then, the production period is determined from the power data using a least squares method to obtain normalized harmonic coefficients. These are the basis for a second NN dedicated to identifying the parts in production. The global error in estimating the parts being produced is under 1%. The resulting information could be handy in determining relevant information regarding the press behavior, such as energy per part, which is necessary in order to evaluate the energy performance of the press under different production conditions.Xunta de Galicia | Ref. IN854A 2020/0

Hybrid Modeling of Deformable Linear Objects for Their Cooperative Transportation by Teams of Quadrotors

his paper deals with the control of a team of unmanned air vehicles (UAVs), specifically quadrotors, for which their mission is the transportation of a deformable linear object (DLO), i.e., a cable, hose or similar object in quasi-stationary state, while cruising towards destination. Such missions have strong industrial applications in the transportation of hoses or power cables to specific locations, such as the emergency power or water supply in hazard situations such as fires or earthquake damaged structures. This control must be robust to withstand strong and sudden wind disturbances and remain stable after aggressive maneuvers, i.e., sharp changes of direction or acceleration. To cope with these, we have previously developed the online adaptation of the proportional derivative (PD) controllers of the quadrotors thrusters, implemented by a fuzzy logic rule system that experienced adaptation by a stochastic gradient rule. However, sagging conditions appearing when the transporting drones are too close or too far away induce singularities in the DLO catenary models, breaking apart the control system. The paper’s main contribution is the formulation of the hybrid selective model of the DLO sections as either catenaries or parabolas, which allows us to overcome these sagging conditions. We provide the specific decision rule to shift between DLO models. Simulation results demonstrate the performance of the proposed approach under stringent conditions.This work has been partially supported by spanish MICIN project PID2020-116346GB-I00, and project KK-2021/00070 of the Elkartek 2021 funding program of the Basque Government. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 777720

Continuous-Caliber Semiconductor Components

The needs in terms of power electronics has evolved and for high power applications, the increase of efficiency mainly goes through the increase of the voltage of the system. However, this is not possible with a conventional two-level topology because the semiconductor ranges are limited to 6.5 kV which approximatively corresponds to a blocking voltage of 3.6 kV. To overcome this semi-conductor limitation, multilevel topologies can be used and allow to extend the range of the DC bus input voltage while using smaller and more efficient components. Yet, the sizing of an optimal power converter with high efficiency and power density is hard to realize due to the discretization of semiconductor components calibers. Moreover, if we want to find the optimal value of the DC bus voltage for a specific application using an optimization algorithm, the use of continuous caliber for components will give better results. This paper aims to explain the used method to overcome this discretization by creating virtual components with exact suitable caliber, using the database parameters available from manufacturers. To do so, the used power converter losses and thermal models are recalled in order to specify the needed parameters. These parameters will be generated as to create the needed virtual component

Bayesian & AI driven Embedded Perception and Decision-making. Application to Autonomous Navigation in Complex, Dynamic, Uncertain and Human-populated Environments.Synoptic of Research Activity, Period 2004-20 and beyond

Robust perception & Decision-making for safe navigation in open and dynamic environments populated by human beings is an open and challenging scientific problem. Traditional approaches do not provide adequate solutions for these problems, mainly because these environments are partially unknown, open and subject to strong constraints to be satisfied (in particular high dynamicity and uncertainty). This means that the proposed solutions have to take simultaneously into account characteristics such as real-time processing, temporary occultation or false detections, dynamic changes in the scene, prediction of the future dynamic behaviors of the surrounding moving entities, continuous assessment of the collision risk, or decision-making for safe navigation. This research report presents how we have addressed this problem over the two last decades, as well as an outline of our Bayesian & IA approach for solving the Embedded Perception and Decision-making problems