ABSTRACT. The Kijima maintenance model based on virtual ages of components of repairable systems is one of the most important models in the reliability theory. However, due to complexity of the resulting integral equation, it has not yet been possible to obtain an adequate expression for calculating the hazard rate for a wide class of distributions. Previously, an asymptotic solution of Kijima’s equation only for a narrow set of distribution functions, that are not appropriate for describing the system aging, has been obtained. In this paper, Kijima’s equation is asymptotically solved for the Weibull-type distributions and for an arbitrary value of the so-called depth of repair 0

ABSTRACT. Tests in rainy conditions are mostly used to determine the behavior of the dielectric elements against moisture. This project presents the development and construction of a sprinkler type rain chamber, which was designed according to the needs and dimensions of the high voltage laboratory of the “Universidad Politécnica Salesiana Sede Cuenca”. The guidelines established by the IEEE Std. 4-2013 are followed for both flow rates, pressures and water treatment to achieve the resistivity value of the normalized liquid for the different tests. The camera is built with fiberglass and tempered glass, with the respective hydraulic circuits to produce the precipitation. The project is complemented with a table of simulator prices and a behavior test of a pin type insulator in dry and wet conditions.

ABSTRACT. This paper proposes an algorithm for the control of the Matrix Converter (MC) based on the Singular Value Decomposition (SVD) modulation to improve functionalities in its operation. This algorithm find the modulation parameters to establish an output voltage at a magnitude and phase required, where the input reactive power is controllable independently of the voltage with some restrictions. This last one is described by a math expression, which depends on the apparent output power and controllable elements of the modulation. Simulations are developed with PSCAD/EMTDC software to demonstrate the effectiveness of the proposal.

ABSTRACT. The effectiveness of the application of LRM is disclosed through field tests to decrease the resistance of grounding in different configurations of grounding systems. In addition, a chemical electrode is implemented in the soil with greater resistivity to analyze its behavior with respect to other designs. The measurement of the resistivities of different soils, the modeling of each soil and the measurements of the grounding resistances are presented together with these methods. Therefore, with the results of this work and the existing equations for the calculation of the grounding resistance, it is possible to determine the value of the optimum grounding resistance to be obtained by applying LRM. This document aims to be a useful guide to the improvement of grounding systems for engineers and researchers who study, design and build innovative and effective grounding systems with the application of improved composites for safe electrical installations.

ABSTRACT. This work proposes a method for forecasting time series based on a model selection of kNN regressors. Our technique is simple but powerful, we propose to compose a single configuration space joining both time series parameters and kNN parameters, with the idea of performing a coupled global optimization of all parameters; then, we select a competitive model over that search space using random search and a cross- validation scheme. Our experimental results show that this strategy outperforms other complex approaches like Nearest Neighbor tuned by differential evolution (NNDE) or the Fuzzy Nearest Neighbor (FNN).

ABSTRACT. Abstract—This project shows the development of an embedded systems that monitors PM1, PM2 and PM10 dust particles in an intensive care room in a hospital. Results are communicated through the Orion Context Broker component (included on the internet of things chapter of fiware platform) and provided to the user by a cellphone app. The system informs about the size and concentration of dust particles suspended in the air surrounding patients on a given environment to help determine the risk of propagation of nosocomial diseases.

ABSTRACT. During brain Magnetic Resonance Imaging (MRI) analysis, image segmentation provides information for the measurement and visualization of anatomical structures of the brain. Currently, segmentation performed by human experts is the gold standard method for such task, but it presents bias and variability dependence of the observer, due to issues as imaging device configurations, complex anatomical shape of tissues and captured noise. In this paper, we introduce a new unsupervised segmentation algorithm for brain tissue segmentation, which incorporates prior knowledge of the brain structure and 3D features of the image, to tackle some of these problems. To evaluate our algorithm, we built a synthetic brain MRI database of 20 subjects, which is also described here. Our algorithm obtained better performance than other three popular state-of-the-art methods.

ABSTRACT. In Text-Independent speaker identification, the individual that produced some captured speech signal has to be identified without his collaboration, he might not even know that he is being the subject of an identification process. The system could not ask the individual to utter some specific word or phrase, which is precisely what is done in Text-Dependent speaker recognition. Text-Independent speaker identification is far more complicated since we cannot simply measure the similarity of an utterance of a word or phrase to another utterance made by the same speaker of the same word or phrase in which case we could use the dynamics of the speech signal. In this paper we search in the speech signal looking for voiced speech segments and estimate its first three formants, so we end up with a three-dimensional point cloud for each speaker of the collection of known speakers. To identify a speaker we have to measure the similarity of a point-cloud from an unknown speaker to the point-clouds that belong to known speakers, we do that by searching for local structures in the cloud in a way that is highly scalable and robust. We performed tests with both a collection of our own in Spanish and with the English Language Speech Database for Speaker Recognition (ELSDSR) from the Technical University of Denmark achieving results that improve recent published work with ELSDSR.

ABSTRACT. This paper presents a couple of digital architectures based on chaotic cellular automata for Compressed Sensing (CS) of electrocardiographic (ECG) signals. The first architecture has been designed taking as starting point the recent CS algorithm proposed in literature, called Cellular Automata Chaos with Original Signal Thresholding (CAC-OST). For its construction, we have split that algorithm into five simple digital modules. The second architecture, which we call Cellular Automata Chaos for Streaming Signals (CAC-SS), has been designed in such a way that can be adequate for streaming processing. These digital architectures are implemented in FPGA Cyclone IV from Altera® and compared against the state of the art ECG CS system. For each digital architecture we report the number of Logic Elements (LE), registers, memory bits and maximum frequency of operation. The compressed signal is recovered in Matlab® and compared with the original signal, and the error rate between recovered signal and original signal is calculated via Mean Squared Error (MSE). Our experimental results show that CAC-OST has better compression characteristics but at expenses of a greater hardware utilization, whereas CAC-SS is the simplest one among these architectures.

ABSTRACT. In this paper, the functioning and the operation of an electrowetting device to manipulate droplets is described. The prototype is built on a printed circuit board (PCB) and consists of an electrode array selected and energized by a microcontroller or switches. Each electrode can be manipulated to perform the sequence of steps required to carry out isothermal amplification. Laboratory tests have shown that droplets of 15 $\mu$L can be moved on the PCB surface. The system efficiency depends on the hydrophobic surface and the voltage applied to the electrodes as well as the switching frequency of the electrodes. This device is the first prototype of the desired biosensor under development.

ABSTRACT. Sudden cardiac death (SCD) is considered one of the main causes of death among people. Hence, an early prediction of an SCD event will allow saving people lives because they will receive timely medical procedures. In this paper, a methodology to predict SCD of an automatic manner using ECG signals, fractal dimension (FD), and artificial neural networks is presented. Three FD methods are investigated, Higuchi fractal dimension, Box dimension, and Katz fractal dimension. The effectiveness of the proposed methodology for predicting a SCD event is demonstrated using a database of 38 patients, 20 with SCD and 18 normal, provided by MIT- BIH (Boston's Beth Israel Hospital). The results show an accuracy of 91.4% 14 minutes prior to SCD event

ABSTRACT. Load flow formulations for direct current almost always are considered into the framework of integrated Alternating Current/Direct Current (AC/DC) power systems, but generally these formulations do not include the presence of loads and multiple generators in the DC power system. In this paper, the power flow problem for DC power systems is formulated and solved. The resulting nonlinear equations are solved by the Newton Raphson Method. Also, it is discussed the inclusion of several controlled voltage nodes and their treatment is described. The developed algorithm has been proven through an illustrative example with a five-node power system. Finally, concluding remarks of the algorithm developed are pointed out, and future work in this field is suggested.

ABSTRACT. The continuous growth and development of society have a direct correlation to the amount of energy required to satisfy its demand. As consequence, more interconnections of existing electrical networks are required, increasing the complexity on its operation. Thus, larger power systems are prone to experience inter-area oscillations, which are triggered by generators oscillating against each other from different geographic locations. Whether these so called inter-area oscillations are undamped, they could eventually lead to a system collapse. In this work, a Linear Quadratic Gaussian (LQG) control approach to damp inter-area oscillations out, which is coupled with a dynamic eigensystem realization algorithm (ERA), is proposed. Although these two concepts are well documented on the literature, the novelty presented here is its combination resulting on a fast and effective damping controller. The proposed architecture is implemented as a digital Power System Stabilizer (PSS) using the combination of the professional softwares DigSilent PowerFactory and Matlab. The presented controller is validated trough dynamic system simulations on the IEEE benchmark New England model. Additionally, a tutorial to implement the proposed controller is also presented.

ABSTRACT. In this paper, a decentralized direct model reference adaptive control (MRAC) with integral action is proposed in order to achieve the attitude control task for a quadrotor unmanned aerial vehicle (UAV). On the one hand, the nonlinear model for the quadrotor UAV is decomposed in the rotational and translational systems. Thus, a second order subsystem for each attitude angle is extracted from the rotational system dynamics. Then, under the assumption that a linear–in–the–parameters model for each subsystem can be defined, each subsystem is represented by a parameterized model from which a direct MRAC with integral action is designed. On the other hand, a PID controller is implemented for altitude control. The performance of our decentralized adaptive control with integral action scheme is validated via simulation results taking into account parametric variations, noise in the measurements and external perturbations and is compared versus a decentralized direct MRAC scheme.

ABSTRACT. This paper presents the instrumentation and modelling process of an experimental platform for aircraft pitch control. A complementary filter is designed to improve the angular position estimation based on inertial measurements. The platform was used for the implementation of different controllers in a wind tunnel facility: a Ziegler-Nichols PID and a loop shaping compensator.

The test bench allows the implementation of typical continuous-time linear and non-linear controllers. The plant identification process is designed to compute the parameters that meet the open loop time domain characteristics and the closed loop frequency characteristics. The time delay in the control loop is computed through an experiment and used for the loop shaping design process. The controllers are implemented and compared according to time domain performance. The best suited control is then analysed in terms of perturbation rejection and more demanding tests.

The article shows that the resulting test bench is suitable for the experimental evaluation of typical pitch angle control strategies. Due to the integration with a wind tunnel, the test bench is able to simulate a wide range of flight conditions. Including extreme conditions that would not be safe in real flight experiments.

ABSTRACT. In this paper the modal analysis and load-flow linear sensitivity techniques are investigated for voltage stability evaluation. Analysis modal is an elegant and very well accepted method. However, in this paper it is shown that the conclusions reached with this method are almost the same by applying the load-flow linear sensitivity technique. In order to show this hypothesis both methods are applied to the IEEE 14 and 30 bus systems. Main advantages and drawbacks of both techniques are pointed out in the paper.

ABSTRACT. In this work a state-feedback controller for pole placement is presented. In addition, a model predictive control (MPC) to minimize the magnitude of the controller input is also presented. The MPC use an observer to estimate the states of the plant and optimize a cost function to maintain the input within a pre-defined limit. The proposed approach is validate trough nonlinear simulations using the equivalent Nordic power system and where a Static Var Compensator is used as actuator. Widearea signals from Phasor Measurement Units (PMUs) are used to improve the damping of the system.

ABSTRACT. In this paper, a speed control of permanent magnet synchronous motor using bat algorithm, whale algorithm and cuckoo search algorithm is presented. Time domain specification of the speed response such as rise time, peak overshoot, undershoot, recovery time, settling time and steady state error is obtained and compared for the considered controllers. In addition, gains tuning of PI controller’s is optimized using the mentioned three optimization algorithms. In order to validate the effectiveness of the proposed controller, simulation is performed under variations of load condition and different values for set speed of the permanent magnet synchronous motor

ABSTRACT. This document presents a HIL based DC motor speed control. In this paradigm the controller is implemented as a physical system handled by a IGBT based on a PI control using a microcontroller’s PWM outputs. The controller receives the setpoint which is established based on the power system frequency. This is modelled using OPAL-RT Simulink® for the real time simulation allowing the motor to deliver an adjusted mechanical torque feeded into a synchronous generator coupled with the controlled CD motor which in this case is used as a prime mover. Therefore, the controller achieves to control the real power feeded into the simulated electrical power system to control its frequency.

ABSTRACT. This paper presents an asymptotically stable global controller design for distributed energy integration in electrical distribution networks using a three-phase voltage source converter (VSC). An invariant Park’s transformation is used to obtain the mathematical representation of the VSC in dq0 reference frame. To design of the proposed controller, interconnection and damping assignment passivity-based control (IDAPBC) theory is applied via a Hamiltonian representation for the open-loop dynamic as well as the desired closed-loop dynamic of the system. The control law obtained allows guaranteeing asymptotic stability properties in the sense of Lyapunov for closed-loop operation. To verify the robustness and effectiveness of the proposed controller a classic connection of a distributed generator with a VSC converter using an ideal voltage source in its DC side is employed. Simulation results show the capability of the proposed controller to support active and reactive power independently under unbalance voltage conditions and harmonic distortion as well as the possibility of using the VSC as a dynamic power factor corrector. Additionally, all simulation scenarios are compared to classic PI controllers to show the good dynamic performance of the proposed controller using IDA-PBC theory. MATLAB/SIMULINK software is employed as simulation environment.

ABSTRACT. In this work, the Stochastic Spiral Optimization (SSO) algorithm, which is an improvement of the deterministic spiral optimization (DSO), is introduced. SSO performance was evaluated with a group of five population-based optimization algorithms including the original DSO algorithm. Performance tests showed that the modifications on spiral optimization scheme allowed improving the exploration and exploitation skills with a wide range of control parameters, which produced lower optimization errors to a variety of standard functions. SSO admitted of selecting several combinations of its control parameters, which kept the optimization errors into an acceptable order. The SSO efficiency and effectiveness were noticeably increased, which was proven by the experimental results.

ABSTRACT. Nowadays linear programming is the most robust method to solve optimization problems as it is the only one that if there is an optimal point LP will find it, if not it will detect a problem with no feasible solutions. In practice many non linear programs are solved using linear approximations in the model. Non linearity can be present in the objective function and/or the constraints i.e. feasible region. The problem to deal with non linear constraints when the non linear constraint is convex has been already faced by converting the non linear constraint into a set of linear constraint. The problem which has not been faced is when the non linear constraint is not convex too. In this situation, the optimal solution could lead to a solution outside the feasible region. This contribution deals with the detection of such possible solutions before the LP solver is applied.

ABSTRACT. In this work, the first part presents a review of the characteristics of non-orthogonal multiple access techniques that will be used for fifth generation mobile communications systems. Additionally, an in-depth description of the construction of the code books for SCMA systems (Sparse Code Multiple Access) is made. In the final part of the work an example of SCMA codebooks considering 6 users, 4 radio resources and two coding dimensions is shown.

ABSTRACT. This paper presents the application of a physics-inspired algorithm based on the center of mass concept, called Bilevel Centers Algorithm (BCA), to deal with bilevel optimization problems. The center of mass is adopted for creating new directions in the bilevel continuous search space considering the objective function values of a set of randomly-chosen solutions in a hierarchical optimization structure. The performance of this approach is assessed by using representative test functions for bilevel optimization. The obtained results are compared against the state-of-the algorithm BLEAQ. The results based on accuracy and number of evaluations are competitive and promising.

ABSTRACT. Malware analysis using machine learning techniques has been the subject of study in recent years as a new alternative for the efficient detection of malicious behavior patterns in different operating systems. Recent advances in this area of research have used different algorithms in conjunction with information extraction techniques and feature selection using different types of data that converge with the same idea of improving several performance metrics. In this work is proposed the use of an assembly classifier, better known as Random Forest, that improves the performance of other well-known algorithms algorithms in the state of the art. A case study is presented using two different sets of malware that through pre-processing steps is improved the quality of training data for building the proposed classifier.

ABSTRACT. This paper was intended to describe a method for intelligent road monitoring by combining genetic algorithms and logistic regression applied to speed bumps detection. This method was structured by means of a Time and Feature Selection Analysis considering six classification techniques. Said alternatives were compared against a previous study analyzing a dataset with over 14,000 road samples. Obtained results suggested that genetic algorithms and logistic regression could achieve improvements in increasing accuracy and minimizing false-positives on the ROC curve. Current version of the proposal represents the end of the first stage of the project.

ABSTRACT. This paper presents an analysis of hard transistor switching of a step up/down converter for electric vehicle applications. An analysis is performed to obtain the switching sequence of the transistor bidirectional switches. In a continues current mode algorithm and operation are analyzed in a single stage and verified in a simulation performed in Saber; like-wise experimental are shown for verify the switching performance of the transistor converter wavetops/results.

ABSTRACT. This paper presents a comparison of different machine learning techniques for classification of the unbalance and damage Niquel-Metal Hydride (Ni-MH) battery cells used in hybrid electric vehicles (HEV) and electric vehicles (EV). The implemented linear and non-linear classification algorithms used in this study are: logistic regression (LR), k-nearest neighbors (k-NN), kernel space vector machine (KSVM), Gaussian naive Bayes (GNB) and a neural network (NN); the classifiers in this work used the principal component analysis (PCA) in dual variables to reduce the high dimensional data set. To evaluate the performance of the classifiers, experimental results and a detailed analysis of the confusion matrix are used where the effectiveness of the algorithms are demonstrated.

ABSTRACT. The aim of this work is to evaluate the utilization and size of the batteries and ultracapacitors in electric vehicles as a function of the powertrain topology. The maximum current demanded, the weight, the total stored energy as well as some other electric variables, are the main aspects that are taken into account in the evaluation. The analysis is derived from equivalent circuits of the batteries and ultracapacitors, which are used to relate the electric variables of load profile, with the stored energy and its dynamics. Design guidelines are derived from the dynamic analysis of the power demand, that provides minimum requirements of the arrays of the energy storage devices. The proposed design guidelines along with the performed analysis are evaluated using numerical simulation in every topology for three different driving cycles.

ABSTRACT. Although there are important efforts to electrify and diversify small vehicles, active safety on motorcycles and tricycles (also known as autorickshaw, tuk-tuk, mototaxi, etc.) has been relegated. For example, the electric tricycles marketed (and even internal combustion ones), don’t integrate an active safety system that prevents or mitigates the risk of rollover, despite how prone they are to such a situation; the concern for the increase in its commercialization is latent and unfortunately, there are very few scientific studies related. In this article, the obtaining and validation of a new rollover index for tricycles is presented and it is shown the effectiveness to predict and detect the risk even statically. In addition, a controller for the mitigation of the rollover risk and a Laplace-based stability analysis are presented, where the controller consists in performing a differential braking with the rear wheels; the effectiveness of the proposed strategy is illustrated with CarSim simulations.

ABSTRACT. This paper proposes an image classification system based on modified binary SVM (Support Vector Machines) structures. We design a 40 two-dimensional Gabor filter bank in order to perform convolution between each one of the filters and a given input image. This pre-processing step will result in a 40-feature vector for each image. These feature vectors will then be fed into the system for the training and testing processes. We also use a dataset of images that differ in background, illumination, scale and rotation conditions to test the effectiveness of our methods with real world data. After data standardization, we model a set of binary SVM to perform multi-class classification using the two major approaches found in the literature: One vs One SVM and One vs All SVM. This work provides a comparison between the two classification approaches and shows a classification accuracy rate above 90% for each class of objects and global accuracy rate results above 95% using three different types of SVM kernels.

ABSTRACT. In this paper, we fully design the hardware and software for system that sorts Mexican mangoes for exportation. We use a conveyor belt, a webcam and a presence sensor along with the AC motor controller and the pc serial communication board. An image is taken by the webcam whenever a mango is detected passing through the presence sensor. Then, the acquired image is converted to grayscale and thresholding is applied to segment the mango. A morphological opening operation is applied to remove white noise produced mainly because of the mango shadow. Finally, all the pixels in the mango surface are counted and the result is compared with our database registers in order to classify the mango into one of ten Mexican standard categories. Experimental results show that the average sorting accuracy rate of the system is 89.6%, taking 3.75 seconds to classify each mango.

ABSTRACT. In this article two algorithms are presented: one to add a watermark to a digital image and another to make the restoration of the image marked with the information stored in the watermark. This added watermark allows recovering manipulated or damaged information in digital images by up to $48\%$. This percentage is possible because compressed information of the original image and its respective parity codes are stored for recovery in the three least significant bits. From the tests carried out on 50 digital images, we show that it is possible to recover up to $48\% $

ABSTRACT. In this paper, we present a methodology for finding a set of useful parameters to identify traffic patterns. First, we perform motion detection in the scene by applying the background subtraction method. We compute the Euclidean distance of 3-dimensional pixels-vectors in the YCbCr colorspace and then apply thresholding to filter out small Euclidean distances that does not represent motion pixels. After that, we count the number of vehicles by implementing a detection line in one of the road lanes. We make sure that our system does not get false positive detections and counts the vehicles only when they completely pass through the detection line. Later, we determine the size of the vehicle by counting up all the pixels that passed through the detection line. According to the number of pixels, the vehicle is classified into two of the following categories: Big vehicle or Small vehicle. Finally, we determine the vehicle color by clustering the vehicle image into 7 different color categories. The color with the maximum number of occurrences in the image histogram is determined to be the vehicle color. Results shows that our system reaches a 100% of accuracy when counting vehicles and determining their size. However, the color determination process tends to present problems when images of vehicles of two or more colors are presented, but obtaining good results overall.

ABSTRACT. The availability of reliable data related with the behavior of the wind in a wind farm is very useful to determinate with accuracy some aspects as power curve of a wind energy turbine and the wind's speed in an interval of times. The precision in the prediction process of wind behavior is useful to reduce structural efforts in the wind-turbine rotor system, and even in the tower section. In practice, sensors are used to acquire data for monitoring wind farms which occasionally may tend to fail causing an incomplete information from the sensor. This work is focused on the imputation of missing data based on a combination of interpolation and regression models. Our experiments show this approach is useful for correlated time series, considering the direction and speed wind for 20 and 40 meters of height in wind farms located in the Isthmus of Tehuantepec in Oaxaca state in Mexico. Finally, the approach of combination methods is effective to solve the problem of missing data in the database of wind in wind farms.

ABSTRACT. In this paper, a low cost multipoint power quality monitoring system (PQMS) is developed. The system is versatile and consist of one or more standalone portable power monitoring devices with database server capability and local/remote visualization of the power quality indicators. The portable monitoring devices can measure, record and share information about power quality indicators as harmonics, THD and the power factor and a very important advantage of the proposed system is its capability of regularly record data for purposes of wave form reconstruction of voltage, current, active, reactive and apparent power of a 3-phase power line, for a long period of time. Each power monitoring device as also the capability of detect power quality disturbances, record the wave forms for signal reconstruction and generate alarms. The PQMS functionality can be improved, adapted and scaled depending on customer needs including research, educational, institutional or commercial purposes.

ABSTRACT. The main challenge associated with solar Photovoltaic power generation is its intermittent nature which highly dictates its performance. Since PV generation is mainly dependent on climatic parameters, it is necessary to have a mechanism for understanding and diagnosing the state of performance of the system at any given instance. To address this challenge, a deep neural network architecture is presented for instantaneous performance diagnosis. The proposed model enables to model and detect anomaly in any PV generation system with an accuracy of 98.3%. The output of this model is used as input to another neural network model for modeling the state of performance of the system. The novelty of this work lies in creating a solely data-driven model for diagnosing PV power performance.

ABSTRACT. Due to the opening of the energy market and agreements for the reduction of pollution emissions, the use of microgrids attracts more attention in the scientific community, but electricity administration has new challenges. This paper considers distributed generation as a main part to design a microgrid and the resources management is defined in a period through proposed dynamic economic dispatch approach. The inputs are obtained by the model predictive control algorithm considering variations of both pattern of consumption and generation systems capacity, including conventional and renewable energy sources. Furthermore, the proposed approach considers a stimulus program to customers involving a demand restriction and the costs of regeneration of the pollutants produced by conventional generation systems. The dispatch strategy seeks to reduce to the minimum the fuel cost of conventional generators, the energy transactions, the regeneration of polluted emissions and, finally, includes the benefit in electricity demand reduction satisfying all restrictions through mathematical programming strategy. The results exhibit the proposed approach effectiveness through a study case under different considerations.

ABSTRACT. This paper presents a single-phase multilevel inverter based on the cascade connection of a T-Type (TT) and a H-bridge (HB) converters. Each one of the converter generates a three-level output voltage. The proposed cascade multilevel inverter produces a 7-level output voltage which reduces the harmonic content, decreases the size of the grid-side filters and increases the efficiency of the inverter. The proposed inverter can be applied to active power injection using renewable energy sources. A modulation based on level-shifted sinusoidal PWM technique for the proposed converter is proposed. Also an efficiency analysis and THD are shown in order to validate both the proposed converter and modulation strategy. A comparative analysis with a 3-level T-Type inverter is developed to contrast the advantage of the proposed converter. Finally, numerical results are performed in order to validate the performance of the proposed multilevel converter.

ABSTRACT. This paper shows the development and implementation in a digital platform of a synchronization system for the discharge of three-phase alternative current through the technique of crossing by cosine applied to high inductive loads. The analog circuit, the programming algorithm and digital circuit, are validated and implemented experimentally.

ABSTRACT. Electrical impedance (EI) is a physical property of materials that can be used for elucidating directly or indirectly other physical parameters. In particular knowledge of EI properties of liquids has important implications for system identification, modelling, and control purposes in many areas of knowledge. One to the applications where EI can play an important role is tracing the propagation kinetics of liquids in porous media. This paper presents the results of experimental analysis of EI of liquids with different conductive properties to evaluate system identification procedures using data in the frequency range of 20 Hz to 30 MHz to obtain process models. The evaluation of the resulting process models suggests that it is possible to approximate the real frequency response of liquids with 98.22% average accuracy. The results suggest that a priori modelling of EI properties of liquids can be used to identify different types of liquids and can be used to facilitate the investigation of liquid propagation dynamics in porous media.

ABSTRACT. Chaotic systems with hidden attractors and theirs families (infinite number of equilibria, stable equilibria and without equilibria) are important in applications of engineering. Studies about hidden attractors in fractional order dynamical systems are limited. In this paper, we perform the analysis of fractional order systems with hidden attractors by using the Gründwald-Letnikov numerical integration method. In particular, hidden chaotic attractors with no-equilibria, stable equilibria and infinite number of equilibria are observed.

ABSTRACT. A simulation of an adaptive control and obtaining of the nonlinear modelling of a MIPR (mobile inverted pendulum robot) is presented in this paper, the model contains two variables (angle of wheel and angle of pendulum). The main objective is to implement an adaptive PID (proportional, integral derivative) controller using the GDM (gradient descendent method), to keep the pendulum in an unstable equilibrium point, the GDM is used for to optimize the gains of the PID controller. The parameters weight and length of the nonlinear model are varying in the time. The response of adaptive control is compared with a classic PID controller of constant gains. The system with two laws of control was implemented in the Matlab software, in this is showed that classic PID controller with constant gains can´t to control the MIPR, on the other hand the adaptive PID controller has good response.

ABSTRACT. On this paper, a novel power electronics converter with reduced input current ripple capability is evaluated for a photovoltaic (pv) energy source application. The mentioned converter possesses the ability of increasing the input voltage which is highly desirable since pv panels are dc power sources with low voltage generation. Besides, the high quality of the input current brings the benefit of lower oscillation, compared to the conventional boost converter, when a Maximum Power Point Tracking (MPPT) algorithm is implemented. Thus, the proposed converter possesses both characteristics, such a converter is derived from the connection of a conventional boost converter plus an enhanced Single-Ended Primary-Inductor Converter (sepic). Along the paper, the theoretical analysis is carried out as well as the performance when operated by means of the standard closed-loop Perturb & Observe (P&O) algorithm. Detail simulations proved the dynamics of the proposed converter.

ABSTRACT. This work proposes a Gain Scheduling Control to guarantee the maximum power transfer in a photovoltaic system even if variations in the resource solar are presented. This strategy improves the step-response characteristics of maximum power each instant. A case study of a solar generator with SEPIC converter is simulated and evaluated in comparison with P&O strategy. Results show that the proposed strategy control provide an improved response with the expected reduction of settling time and ripple in output power.

ABSTRACT. In this paper a dc-dc switching quadratic step-up converter for photovoltaic applications is presented. The converter is based on a noncascading structure I-IIC. A model of the converter, steady-state characteristics and a controller design to regulate the input voltage (photovoltaic module voltage) are given. For the regulation task, a multiloop control scheme is applied, where the inner loop uses the inductor current as feedback and the outer loop is implemented using a proportional-integrative controller. In the outer loop the photovoltaic module voltage is used to obtain the error signal. The input voltage regulation is used to control the injected power from the photovoltaic array.

ABSTRACT. This paper presents the principle of operation and the control algorithm in matrix converter topology used in an AC-DC converter for battery charger applications in electric vehicles. The operation of a matrix converter leg is described to achieve a Zero Voltage Transition (ZVT); idealized waveforms and steady-state analysis are explained together with simulation results in Saber.

ABSTRACT. This work reports on the design and implementation of a solar DC power supply which employs a Perturb and Observe Maximum Power Point Tracking algorithm. The device has been designed to be portable, highly efficient whilst being low cost. The power supply employs an ARDUINO MEGA embedded system to monitor and control two buck-boost converters that control the power flow from the solar panel to a battery and load with a measured efficiency of 85%