id: 2368182021 password: ropec2021

In this talk we will discuss emerging topics in how artificial intelligence is currently being used to improve healthcare. Machine learning algorithms such as Bayesian networks, Random Forests, Deep Learning models, etc., are used to impact medicine in various steps, from early stage diagnosis to prognosis and pharmacogenomics. The future of precision medicine is indubitably linked to the intensive use of advanced analytics, alongside with the human and operational aspects on how we apply technology.

Traditionally, a feedback controller is designed under ideal assumptions that are unrealistic for real-work problems. Among these assumptions, traditional controllers are also designed based on an ideal plant model without considering uncertainties, disturbances, sensors, actuators, faults, imperfect communication channel for controller inputs and outputs, noises and other non-modeled dynamics which for real-life applications are effects that are constantly present and should be considered. Furthermore, control system design implemented with digital processors implies sampling and holding processes that can affect the performance; considering and compensating quantization effects of measured signals is a problem.
In recent years intelligent control, particularly neural control, has shown its ability to control complex systems subject to hard complications as mentioned above, even when the mathematical model of the system is unknown or if the application is complex; therefore, neural control is an obvious methodology for modeling and control of real-life systems. Particularly, this talk is focused on modeling and control of discrete-time unknown nonlinear delayed systems under real-life problems based on artificial neural networks. This talk includes a review of intelligent control growth, challenges and opportunities.

id: 3373959287 password: ropec2021

This paper presents an electromagnetic analysis of the bevel edge technique (BET) employed to reduce core losses in high voltage (HV) gapped-core shunt reactors. Authors analyzed this BET employing three-dimensional (3-D) finite element (FE) simulations. The magnetic anisotropy and the magnetic nonlinear properties of the grain oriented electrical steel are considered in the FE simulations. The authors validated their FE simulations computing the core losses in a real HV 5 MVAr three-phase shunt reactor without BET and comparing with the core losses measured in the laboratory. Validating the FE simulations of the HV shunt reactor without BET, the authors analyzed the core losses in the same HV shunt reactor with BET. In addition, authors analyzed the core loss effect produced by the bevel edge length in two real HV shunt reactors: a HV 25 MVA single-phase shunt reactor and in the original HV 5 MVAr three-phase shunt reactor. Finally, authors analyzed the bevel edge angle effect on the core losses of the HV 5 MVAr three-phase shunt reactor. Authors demonstrated and confirmed that the BET helps to reduce the core losses in HV shunt reactors. Finally, the authors concluded that the bevel edge length and the bevel edge angle have a slightly impact on the core losses of the HV shunt reactors.

Renewable energy sources (RESs), such as wind and solar energy, are an integral part of the current process of decarbonisation of electric power systems in the vast majority of countries. Due to the stochastic and variable nature of these resources, RESs could expose an electrical network to unexpected power outages. In this context, security studies are vital for the secure and day-to-day operation of the infrastructure. This article analyses the effects of integrating renewable energies into a power system from the perspectives of reliability and robustness. For this purpose, the IEEE 14-bus test system is modified, and two case studies are proposed with a high proportion of fossil-based power generation and a high share of renewable generation, respectively. In both cases, hourly load and electricity generation profiles are considered. The reliability study is completed using the Monte Carlo probabilistic method, and the robustness study is conducted by simulating cascading failures. These assessments are carried out in the two scenarios, where the electricity system without renewable energies is considered the base case. The effects of renewable energies are analysed comparatively in terms of Loss of Load Probability (LOLP), Expected Demand Not Supplied (EDNS) and Satisfied Demand (SD).

This paper presents a power flow method aimed to DC distribution grids including DC/DC transformers. Following first principles, the power transformers are modeled based on dual-active bridge (DAB) converters considering voltage and power controls. Results obtained from the developed power flow tool are validated against those furnished by the Simscape Electrical package of Simulink, demonstrating its suitable accuracy. The practicality of this novel formulation, to be used in the analysis of general DC distribution grids incorporating several series-connected DAB-based DC/DC transformers, is also demonstrated in this paper.

Renewable energies are gradually replacing conventional synchronous generators, which are responsible for supplying to the power grid of inertia damping properties. As a consequence, power grids with high level of renewable energies are more vulnerable to disruption than traditional ones. In this paper is proposed a nonlinear optimal controller for an inverter-based renewable energy system, which incorporates the dynamical behaviour similar to the conventional synchronus generator. The main contributions of this work are: the modeling and aplication of a nonlinear optimal controller in a power inverter such it dynamically behaves as a conventional synchronous generator, where the active and reactive power are regulated; and the dynamical modeling of the inertia that an inverter based generator posses and its corresponding response to react under power reference variations. Simulation results demonstrate the effectiveness of the methodology proposed in this work.

This paper proposes a new strategy to enhance the diagnosis accuracy of the rotor fault in the Wound Rotor Induction Machine (WRIM) which function under variable load conditions, like wind turbines system. An incipient isolation fault between two adjacent turns of the rotor winding of one phase afterward provokes a great number of Inter-turn short-circuits (ITSCs) due to the vibration produced by this unbalance between the three rotor phases. For this purpose, an early diagnosis of any incipient ITSC at different loads is prominent to get rid of subsequent degradation and minimize the maintenance costs. The monitoring condition (CM) system, based on the association of the Hilbert Transform (HT), Time-Synchronous- Averaging (TSA), and Motor Current Signature Analysis (MCSA), is elaborated. The proposed methodology has been built and verified using Matlab/Simulink. The obtained outcomes indicate that the developed technique can extract a small percentage of shorted turns in the rotor winding of the wound rotor machine, even under low or no-load.

Maximum Power Point Tracking (MPPT) algorithms are used to extract the maximum power from a Photovoltaic (PV) generation system with changes in the weather. This paper presents a Regula Falsi method used to calculate the voltage at the Maximum Power Point (MPP) in a PV panel. The proposed method is compared through simulations against the Perturb and Observe (P&O) method, which is typically used in the literature because of its simplicity, precision, and fast implementation. The results obtained show that the proposed method calculates the MPP with good accuracy and in shorter simulation times than the P&O method, even with changes in the weather, such as changes in the solar radiation and the temperature.

The integration of photovoltaic generation into distribution networks faces several challenges, being voltage fluctuations due to variability of output power one of the most critical issues. In this paper is compared the performance of three smart inverter functions: volt-var control, volt-watt control, and fixed power factor, regarding the reduction of voltage fluctuations along a radial distribution feeder. The metric chosen for the comparative analysis is the total number of tap operations performed by the step-voltage regulators held by the distribution system during a day. Quasi Static Time Series simulations were performed on the IEEE 123 Node Test Feeder with three photovoltaic systems connected to different nodes, using the software OpenDSS and Python. Analysis of the simulations demonstrates the effectiveness of volt-var and volt-watt control approaches in significantly reducing the number of tap operations by more than half.

Due to the demand of thermal energy in the world, it has become a necessity to increase the efficiency of concentrated solar power systems, the solar tracker systems, is one of the most important elements for such a task. This paper presents a novel control strategy for single-axis solar tracking systems. It incorporates the signals of a solar sensor of photodiodes and a shadow-based visual detection device to reduce the solar tracking error by means of a dual feedback closed-loop control, in a commercial parabolic trough collector.

Experimental results of four days outdoor solar tracking are presented as follow. The proposed dual feedback control is compared with a conventional single feedback control, and both loops employ an on-off control algorithm. The mean solar tracking errors were, respectively, 0.82° and 0.11° with the conventional closed-loop control with photodiodes and with the proposed dual control. Furthermore, with the proposed dual control, the solar tracking accuracy has been improved by over 80%.

This article aims to describe how the electric vehicle market operates in Costa Rica. Different government policies have been defined in order to promote the insertion of electric vehicles. With the existing public charging infrastructure is possible to travel between any two destinations in the country, and this type of vehicle are gaining popularity every day. However, there is still something that constrains a quicker change in the existing fleet, and that is the initial cost of electric vehicles when compared to conventional cars. For this reason this article analyzes the associated costs to several electric vehicles found in the Costa Rican market, and the costs of similar conventional cars to illustrate the user, which alternative can better suit their needs. The obtained results show that even though the higher initial cost, the electric vehicles are a better alternative in the long term due to a total lower cost of ownership.

Research develops a planning model with the aim of carrying out the prognosis of energy purchase that the distribution and marketing company is done through the use of energy demand information and with the penetration of renewable generation in the short and medium-term using a computational model of artificial neuronal networks in the MATLAB computational tool, the results obtained show the performance of this model with errors less than 1% both in training and prediction. For the respective testing of this algorithm, the historical data of 5 years of the "Electric Regional Enterprise Sur Centro C. A." was taken of the city of Cuenca in Ecuador.

id: 5918293670 password: ropec2021

Object search is a complex and demanding task, especially in an unknown or partially known environment; therefore, having an adequate strategy to enhance this task is essential. With recent advances in robotics systems, a single robot could perform complex autonomous object finding tasks, nevertheless, using a team of heterogeneous robots this task can be sped up by combining the individual capabilities of the robots. In this work, a strategy to search an object and reach its location is presented. The proposed strategy is based on the exploration of an unknown environment through the collaboration of an unmanned air vehicle (UAV) and a ground vehicle. To evaluate the proposed strategy, a virtual structured environment was built in which there is a UAV, equipped with a monocular camera pointing downwards, and a differential drive robot equipped with a depth sensor.

This article aboard a comparative of six different combinations of three detector and three matching Keypoints algorithms in an real images of aerial photogrammetry agriculture environment; evaluating quantitatively the results obtained, with the finally of purpose two combinations for two steps in Structure from motion specialized in aerial photogrammetry agriculture.

Two of the most used techniques to characterize color in common bean landraces have been spectrophotometry and color analysis in digital images. The main limitation in previous works has mainly been that data have been obtained from specific points of homogeneous regions or mean of regions. A particular characteristic of native bean populations is that they comprise not only seeds of different colors but also of heterogeneous colors. We propose a computer vision system based on the use of histograms to represent the color properties from joint probability distributions of acquired color spaces that come from digital images in RGB and CIE 1976 L*a*b*. We used 54 common bean landraces collected in different regions of the State of Oaxaca, Mexico. The classification accuracy of K-NN algorithm was 53.58%, 44.44%, and 53.80% with the spectrophotometer measures, RGB averages, and CIE 1976 L*a*b* averages respectively, while this same classifier achieved an average of 80% with histograms. Our results suggest that the two components regarding the chromaticity in CIE 1976 L*a*b* are enough to achieve the highest classification accuracy. Our proposal is not exclusive to classifying bean landraces; it might be used for fruit or vegetable color assessment.

The greenhouse fault diagnosis is a challenging task due to the interaction between variables of different phenomena: biological, physical, electrical and mechanical. In this article we propose a novel intelligent monitoring system to detect faults in a greenhouse that mimics a human operator to a certain extent, using a method based on deep learning and artificial vision. We design our algorithm for four novel fault detection architectures: Micro Output Manifold Fault Detection (MOMFD), Output Manifold Fault Detection (OMFD), Attention Fault Long Short Term Memory (AFLSTM) and Fault Long Short Term Memory (FLSTM). Then, we compare them with the well known Long Short Term Memory (LSTM). Results obtained from simulations of the greenhouse show that MOMFD presents the shortest inference time and the highest accuracy.

This work proposes a methodology to identify areas which may contain cemeteries by generating a grid over the terrain and then classify this grid with an ensemble of learning methods. The proposal is to characterize known cemeteries from satellite images information and building the training data. The learning methods are the Google Earth Engine’s implementation of Random Forests, Support Vector Machines and Gradient Tree Boost Classifier. Results show that using an ensemble of these learning algorithms can decrease the number of false positives in the classification. Furthermore, little or no tuning of the classifiers is required for the ensemble to achieve good accuracy.

One common damage in civil structures is the fatigue crack, where its detection in an incipient stage is vital because it can prevent catastrophic failures. This work proposes a new non-linear index based on the combination of statistical indices, principal component analysis, and Mahalanobis distance for detecting and quantifying the level of severity of fatigue cracks in a four-story building subjected to forced excitations. To evaluate the proposed vibration-based Structural Health Monitoring (SHM), elements with different levels of fatigue cracks, which are generated artificially, are introduced into the building. They represent light, moderate, and severe damage, i.e., 25%, 50%, and 75% of loss in a cross-sectional area of a beam, respectively. Results show that the proposal can make a reliable evaluation of the building condition. It can be considered a methodology with low complexity, low computational load, and reduced processing time. These conditions allow obtaining an early diagnosis, generating timely assignments for maintenance processes

One of the techniques for plant diseases diagnosis is the analysis of the spectral signatures in the vegetation. Among spectral analysis, there are methods for feature extraction to find out an early detection considering a significant number of samples of leaves in different growing stages. In this work, with multiple comparisons and statistical tests, the discrete wavelet and Fourier transforms are employed for the feature extraction of the spectral data. The decomposition of the spectral signatures by wavelet-based and frequency domain coefficients as features shows a promising base by the classification for plant disease detection in cucurbits plants.

The importance of automatic control, in a wide variety of applications, especially in the industrial sector, requires systems that perform multiple functions, which is why this work presents a reconfigurable monitoring system for control applications. The system is designed to perform the process of identification of systems, monitoring and control for the reference tracking and monitoring of the behavior of the parameters that define the motion dynamics. It should be noted that the system has a proportional-integral-derivative controller, in addition, it is capable of working in parallel for each process or individually, since it can be reconfigured from a laptop. System validation was performed by tracking the position path of a DC motor.

Abstract— There are practical cases in which the measured voltage and current signals of an electrical grid have many harmonics and oscillations with multiple frequencies and non-linear behavior. Traditionally, the electrical signal analysis tools are based in the Fourier Theory, ideal to test linear signals. Therefore, a time-frequency analysis based on the Wavelet Synchrosqueezed Transform (WSST) is proposed in this paper. Three voltage signals are tested: 1) A synthetic control signal, 2) Harmonic signal and 3) electromagnetic transient response. Finally, the WSST reduces the spectral spots clarifying the time-frequency plane, from which it is possible to identify the oscillation modes with more energy content.

In electrical systems, the transformers have an important role; therefore, their correct operation is fundamental. However, they can present malfunctions due to different types of faults. The short-circuited turns (SCTs) are one of the main causes of transformer damages, which can scale into most serious faults. In this regard, there are several works in literature to diagnose this type of fault through the analysis of vibration signals. In this work, the vibration signals are analyzed to diagnose the transformer condition, however, this analysis is not an easy task due to the embedded noise and no relevant information into the vibration signal. This problem is attacked using the MUSIC-empirical wavelet transform (MEWT), this technique allows to extract the relevant features from vibration signals. Additionally, three fractal dimension algorithms (FDAs); Katz, Sevcik, and Petrosian, are proposed and investigated as potential fault indicators. To test the proposed methodology, a modified transformer to emulate different SCTs fault conditions is used. These conditions are healthy, 5, 10, and 15 SCTs. The results show that MEWT removes properly noise and irrelevant information, allowing the identification of the fault condition using the FDAs as indicators.

id: 4991314019 password: ropec2021

One of the constant concerns in the wireless mobile devices, is to cover different radio communication services, with separated bands and each one with his own bandwidth. This represents a challenge for all radio communication technology devices, in this way, this work proposes a microstrip antenna based on a fractal structure to be installed into smart wireless mobile devices covering four bands, IMT bands, 3, 6, 7, and 10 GHz, describing the design, simulation, implementation, measurement and experimental results.

In conventional model predictive control (MPC) for single-phase 2-level shunt active power filter (SP-2L-SAPF), the voltage vector (VV) that will be applied at the next control cycle is selected by the evaluation of a cost function. However, the application of a single VV, either an active VV (AVV) or a zero VV (ZVV) during the whole control cycle results in a grid current with relatively high ripple. Furthermore, for proper implementation, very fast signal processing is required, which increases cost. Therefore, to overcome these drawbacks, a Modulated MPC (M2PC) scheme that uses two VVs in each control cycle is introduced. The improved control scheme predicts the current slopes of the all available VVs, then the algorithm calculates the optimal duty-cycle (ODC) of the AVVs. After that, the selection of the appropriate AVV is carried out by evaluating and minimizing the cost function as performed in MPC. Simulated results of the developed M2PC algorithm show a better performance and lower THD than those obtained with the conventional MPC for SP-2L-SAPFs.

This work addresses the problem of nonlinear optimal tracking control for a class of Euler-Lagrange mechanical systems, which can be described as the state-dependent coefficient factorization (SDCF), in order to solve the associated state-dependent Riccati equation derived from the nonlinear optimal tracking control problem. The effectiveness of the nonlinear optimal tracking control is demonstrated by means of simulation and real-time experimental results, specifically by applying the proposed control theory to the well-known rotary inverted pendulum, a highly nonlinear and non-minimum phase system with the objective of performing time-varying reference signal tracking.

Abstract—The trending for HVDC systems is to becoming MTDC systems with variable generation. Implementing effective and viable overcurrent protection schemes represent a challenge for these systems. The rudimentary process for DC fault interruption consists of opening the ACCB, but the interruption times for this process are unacceptable. Today, there are few DCCB options with interruption times of less than 30 ms however, their studies are limited in transmission lines (or cables), non-permanent faults and lack of selectivity. A permanent fault located at station output or bus, is not fully protected by a DCCB, since the AC system continues to feed this fault. This paper proposes a protection scheme based on a hybrid DCCB and auxiliary devices. The scheme demonstrates its ability to selectively interrupt station output and bus faults in MTDC systems with fault interruption time of 20 ms. The case studies to evaluate the performance are verified by modifying the Cigré MTDC-MMC grid implemented in PSCAD.

In this paper, the design and validation of a fault detection system based on high-gain observers are carried out. This system is applied to a distillation column reflux actuator, considering the distilled product, the boiler temperature, and the actuator status as the analysis variables. The system validation is carried out using experimental data from an EDF-1000 pilot plant real process.

This paper presents the design and hardware implementation considerations of an IGBT-based power cell to assemble voltage source inverters (VSIs) of different topologies in modular way. The design and selection of the main hardware components is explained. Moreover, the techniques for reducing stray inductances and EMI in the designing printed circuit board is given. For the design of the circuit board, local regulations for grid interconnection and international standards have been considered to obtain a safe and reliable electronic power cell. The developed hardware has been subjected to different tests by using a cost-effective digital system and AC electric motors as loads. Three VSIs topologies are evaluated: single-phase 2-level H-bridge VSI, three-phase 2-level VSI and a single-phase 5-level cascaded H-bridge VSI. Experimental results validate the theory and demonstrate an excellent performance, reliability, and high efficiency of the developed power cells for modular VSIs.

Voltage source PWM inverters are widely used inthe energy market but also in the industry as a motor drives. Inparticular, multilevel inverters allows to improve the performanceof those power systems regarding power quality and blockingvoltage. Classical multilevel topologies which have been wellstudied and are commercially available can be classified as:neutral point clamped, flying capacitor and cascaded multilevelinverters. Among these, cascade multilevel inverters provide someadvantages like modularity and power distribution losses. Inthis paper, the Single-Phase T-type cascade multilevel inverteris studied. The operating states are derive and different pulsewidth modulation strategies are proposed in order to evaluatethe performance of the inverter. The proposed strategies areevaluated by means of numerical simulations. A comparativeevaluation considering harmonic distortion and efficiency isperformed.

This paper reviews and discusses on-board battery charger solutions for plug-in hybrid vehicles and all-electric vehicles. The reduction or complete substitution of fossil fuels in transportation has led to electric vehicles or plug-in hybrid vehicles that have a battery pack that powers electric motors to provide traction. Therefore, on-board battery chargers are required that can be connected to a standard AC power source and provide adequate DC current and voltage to charge the battery. Achieving a high power factor, low harmonic content in the electrical grid, high efficiency, small size and low costs are essential in these applications.

In this paper, the modelling and control design for a double-inductor boost converter with power factor correction capability (PFC-DIBC) is proposed. The PFC-DIBC is coupled to the electrical grid by a rectification stage, formed by a full-bridge diode rectifier, then a high gain DC-DC DIBC stage feeds the DC load with a fixed voltage level. The main advantage of this configuration is the presence of the DC-DC DIBC which is capable to provide a high conversion ratio for the output voltage. The proposed controller is based on the average model of the system and consists of two main loops: a current tracking loop and a voltage regulation loop. The first is aimed to force the line current to track a sinusoidal current reference proportional to the grid voltage. Moreover, to assure an adequate tracking, a proportional plus resonant controller is designed, which is capable to deal with the harmonic distortion present in the electrical grid. The objective of the voltage regulation loop is to regulate the output voltage to a desired reference and to reach this objective a proportional-integral controller is proposed. Finally, to assess the performance of the proposed controller numerical simulations of the system using PSCAD software are presented.

Passivity-based control of a quadratic boost converter connected to a proton exchange membrane fuel cell (PEM) is presented in this study. The main purpose of the controller is to keep the load voltage constant through precise current regulation. As a result, two feedback loops are used for this purpose: a non-linear inner loop that uses the passive features of the system for current tracking and an outer loop that generates the current reference via a proportional-integral (PI) action over the output voltage. Additionally, a load estimator employing the immersion-invariance (I\&I) approach is also devised to improve the robustness of the inner current loop. Hence, an adaptive energy-based controller with asymptotic stability is achieved. Numerical simulations are carried out in order to assess the controller performance. The output voltage regulation is robust in terms of load variation and unregulated DC output voltage from the fuel-cell stack.