Filtering and signal extraction are essential ap-plications in some areas such as medical, electrical, pattern recognition, and other disciplines. For example, in regions of automatic detection, it is necessary to isolate specific signals to study them or accomplish the training process to reach a high detection rate. This paper proposes a new technique of signal extraction based on sparse representation and the Orthogonal Matching Pursuit algorithm, using discrete sine transform and discrete cosine transform to generate the dictionaries. Then, the signal is represented in the base of the given dictionary and limited to the desired bandwidth, extracting the desired information with almost no changes in phase and amplitude, reaching almost vertical slopes in lateral bands.
Strong ground motion signals are the main information source regarding the energy input that affects existing structures. Large earthquakes in some regions are rare, so strong seismic records are scarce in many countries and in many types of surface soil. When available, it is necessary to extract all possible information from those signals about seismic parameters for understanding the seismic impact on infrastructure and to improve some design parameters in local seismic codes. After the Pedernales Mw7.8 earthquake in Ecuador, several strong motion accelerograms were registered in different types of soils for the first time in the country, given the opportunity to apply updated signal analysis methods, to compute different kinds of linear and nonlinear response spectra, and to propose changes to the current Ecuadorian seismic code. Results of this analysis and some proposed upgrades to the current code are presented in this paper.
Voltage and current sensors are the first keys in smart electricity meter design. They must meet accuracy, security, and reliability. Hence, these sensors become sophisticated and expensive. This paper proposes a transducer not only to meet charge electricity billing, but also to acquire waveforms to evaluate adverse power quality phenomena. The proposed transducer is based on an optocoupler to isolate the electrical network from the meter. The transducer design uses a suggested method to exploit properly the current transfer ratio to meet the accuracy and linearity needed in the power quality context. In addition, a method to estimate a correction factor to compensate for the tolerance in the component values of the transducer is given. The proposed transducer has been validated using Oread Lite® software to simulate different case studies related to power quality phenomena. Results show that the proposed transducer has an adequate performance for smart electricity meters that can be extended to evaluate adverse power quality phenomena.
Early fault detection in wind turbines has positive impacts on the costs of repair and maintenance. If the damage is early detected, the damage can be repaired before it increases and generates a complete failure. In this work, a methodology based on convolutional neural networks and the time-frequency representation of vibration signals for the detection of imbalance and bearing faults is presented. In general, the methodology consists of the acquisition of vibration signals from two damage types (blade imbalance and bearing fault) and the healthy condition. Then, a spectrogram function is applied to get an image from the time-frequency plane of the vibration signals. This image is segmented and analyzed by the convolutional neural network to detect the wind turbine condition. A MATLAB graphic user interface is developed to implement the proposed methodology. Results demonstrate the usefulness of the proposal as 100% of accuracy in the diagnosis is achieved.
One of the most used approaches to synchronize power electronic converters to electrical power systems is the phase-locked loop (PLL). PLLs with adaptive characteristics such as the PLL based on a double second order generalized integrator (DSOGI - PLL) are widely used in the synchronization of three-phase power converters, due to their adaptive filtering characteristics and the improvement that is obtained by adding a sequence component calculator before the loop filter. However, the accuracy of the PLL measurements, and therefore the calculation of the phase angle, is affected during transients, so that the frequency, phase angle, and voltage calculated by the PLL are a function of the disturbance type and the architecture of the PLL. This article proposes an improved DSOGI-PLL with a frequency -lock loop based on a double generalized second order integrator (DSOGI-FLL) which is more stable and shows a faster response than the conventional DSOGI-PLL. The main idea is to combine the strengths of these two algorithms into one architecture that will be called DSOGI-PLL with FLL. The performance of the proposed PLL is verified and validated under the processor in the loop (PIL) approach.
In automated systems, there are tasks such as object pick and place. In this task, a vision system detects the coordinates of the work object. The robot uses these coordinates, link lengths, and joint values to implement inverse kinematics to move the robot to a point. The vision system obtains work object coordinates in its reference system; however, the robot needs to move to a point in its reference system. Changing the camera coordinate system to the robot coordinate system is necessary. We propose a crucial task methodology to compute this rigid transformation. This document proposes a method where a stereo vision system obtains the 3D coordinates of the center of a sphere in the robot end-effector. This way, we have two sets of points with different reference systems. We can find the transformation between robot and vision system references by finding the rigid transformation that reduces the Euclidean distance between the two sets of points. Because the real length of the link has an error derived in the manufacturing and assembly process, it is necessary to perform a robot calibration. The error obtained from adjusting both sets of points in the first experiment was 3.3136 mm, and after geometrical parameters compensation, this error was reduced to 1.9927 mm. It means a reduction of 39.86%.
Massive integration of inverter-based generation (IBG) has begun to displace conventional generation units. This type of generation does not provide support for frequency deviations, causing stability problems in weak power systems. In-verter control methods have emerged to extend IBG integration. Grid-forming control is a promising emerging technology that generates its own voltage signal and can regulate the frequency and voltage at the point of common coupling. The transition to a grid with very low or no inertia makes frequency regulation a challenge; nevertheless, grid forming technology makes it possible, besides being able to restore a power system from blackstart. Currently, conventional generation in hydroelectric and thermal power plants maintains a certain system stability; however, understanding what happens when moving towards an IBG-dominated grid is crucial in order to develop technologies that mitigate the shortcomings and enable a smooth transition. This changing grid landscape creates a wide range of challenges for system modeling, planning, stability and control. As an emerging technology, there is a lack of documentation and well-established generic models that can be used for testing. This paper presents the main theory and detailed mathematical modeling of a three-phase inverter, with emphasis on the control design and the capability to provide frequency support.
The PV systems present power losses due to the switching and conduction of the power semiconductors. Moreover, passive elements also produce power losses that reduce the efficiency. In general, the power losses vary along with the variations of the irradiance, thus, at low power, the efficiency is low while at rated power the efficiency is maximum. In order to increase the efficiency during low irradiance intervals, this paper proposes a method to improve the efficiency in a photovoltaic inverter by means of the implementation of a hybrid MOSFET-IGBT switch which has been implemented for the H5 topology. The proposed solution consists in the use of a MOSFET and an IGBT connected in parallel. The main idea is to take advantage of the lower losses of a MOSFET in low power and the lower losses of an IGBT in high power. The proposed idea is validated by means of simulations, demonstrating the feasibility of the hybrid switches. The results show an improvement in the European efficiency and the California Energy Commission (CEC) efficiency, in this case is the European efficiency where the improvement is higher, however the behavior of the results indicate the hybrid switch can achieve a higher efficiency with a higher power if it is compare with the using of only MOSFET or only IGBT.
In recent years, Electric Vehicles (EVs) have become a necessity more than a luxury, justified by the urgency of transitioning to a sustainable movability, being DC-DC and DC-AC converters the heart of this technology. Interleaved Buck DC-DC Converters are commonly used as energy buffers in this application, being high-power density circuits a critical aim in this type of converters. This paper presents a preliminary design assessment of an interleaved buck DC-DC converter with high reduction ratio, that uses a double magnetic together with a coupling capacitor to increase the reduction conversion ratio and power density. A brief survey around the steady-state principle of operation of this DC-DC circuit is presented together with a description of the initial setup towards the building of a 4 kW prototype.
This paper presents the use of the linear com-plementarity (LC) framework for power converter modeling, where non-linear characteristics of the converters are modeled (switched control signals and the conduction modes; continuous and discontinuous). The LC model is used in a model predictive control (MPC) scheme with a pulse-width modulation, where the MPC horizon is a duty cycle period. The MPC is solved by a non-linear optimization problem off-line, for finite values of current and voltage through parametric analysis, using the Nelder-Mead algorithm and the PATH solver. In this work, a parametric analysis is proposed by fitting the controller optimal duty cycle response through the general equation of the plane. The MPC is implemented in a microcontroller and applied to a boost converter, where the performance is tested in real-time using a Hardware-in-the-Loop (HIL) method.
This work presents an analysis of predictive control strategies for a permanent magnet synchronous machine and designed initially at the nominal operating regimen. The moti-vation of this work is based on applications where high-speed requirement and limited supply voltage are needed such as pow-ered by battery-pack. Three control strategies are analyzed and compared based on the following characteristics: Model-Based Predictive Control, avoided weight factor, and fixed frequency operation. The main contribution of this work yields in the behavior verification of the mentioned control strategies and the availability determination to work along to the field-weakening approach. Moreover, a combination of two strategies is presented generating a new contribution; this combination reduces the operating time interval while reduces the computational burden. The effectiveness of the analysis is experimentally validated.
This work presents the implementation and performance of a shooting method for rapid initialization of electrical power systems models implemented with the specialized power systems libraries of Simulink. We selected the numerical differen-tiation method as the shooting method since it does not need the mathematical model of the dynamic system explicitly, but only the state vector at the beginning and the end of the fundamental period. Additionally to the initialization, the method provides stability of the solution through the Floquet multipliers. We tested the method with a STATCOM based on a two-level inverter, and the results exhibit its correct performance in accuracy and computation time.
The improvement in Power Quality has been a concern for both the public and private sectors in recent years. One of the main problems within this topic is the appearance of anomalies or disturbances in the power supply, which represent sudden changes in the waveforms of the signals and cause severe damage to the utility grid. This paper presents a comparative study of different resolution levels for the analysis of eight types of Single Power Quality Disturbances using Multiresolution Analysis. A set of disturbances was generated in MATLAB through their mathematical models and the extracted wavelet-based features were normalized by Z-score. The results show that the use of nine resolution levels leads to an appropriate decomposition, since it allows obtaining a greater amount of information, without compromising the computational performance, which would facilitate a future classification process.
On this paper, a Savonius Wind Turbine prototype is proposed as a non-centralized option for low-scale power generation. Recent studies about Savonius wind generators have been developed in last years, as they are an attractive alternative to conventional horizontal axis wind turbine. The main advantage of vertical axis wind turbines is that they don't require additional wind orientation mechanisms, avoiding complexity of design related to stresses generated in the turbine vanes in events of abrupt changes in wind orientation. The methodology carried out in the manufacture of the prototype in the present work is based on the development of the mechanical design using the SolidWorks software, moving on to the calculations of the estimation of electrical power, that after a validation of those calculations with the design. The dimensions of the principal elements of the prototype are as follows: the blades are half section of circumference with diameter of 0.25 m and the endplates of the turbines have 0.5 m of diameter. To be able to estimate the wind potential of a region, the method used was a probability distribution that accurately describes the frequency (or probability) with which a specific value of wind speed occurs, called the Weibull distribution. The average speed in the region of Hermosillo, Sonora, based on a Global wind atlas, is 2.42 m/s. Considering the calculation of the weighted average power, the generated result is 15.98 W/m 2 , compared to the power generated only with at an average wind speed will is of 8.64 W/m 2 . The results show the importance of carrying out the study with the weighted average power.
Seismic waves are composed of a superposition of waves that differ in their frequencies, speed of propagation, and amplitude. In particular, primary seismic waves (P waves) travel faster than secondary seismic waves (S waves), which are more destructive. This property allows P waves to be detected first than the S waves, giving a chance to activate an alarm if considered so. This work presents a prototype to monitor the P waves of an earthquake at different geographical points. A radio communication link between a few locations is established, and the seismometers consist of Lehman pendulums. The signals from the seismographs are Fourier-transformed continuously and cross-correlated to discern between the presence of a seismic signal or noise. The system developed consisted of real-time multipoint communication in the UHF band. Three nodes were implemented to evaluate its performance. These consisted of two distant points transmitting data from seismic sensors, and a receiver located in the central node read those signals. This point also had its detector and performed signal processing and information visualization through a graphical interface. Experimentally, the multipoint communication permitted the continuous transmission of information between the different nodes in real-time, using test signals with programmed time delays, which agreed with those calculated by the algorithms, with an accuracy of tenths of milliseconds.
PMSM has been widely used in high-precision variable-speed applications, however, the control scheme demands normally a high dynamic performance under several operating contidions. Due to the non-linear nature of the PMSM, the use of an adaptive controller based on B-spline neural networks is proposed to determine the control signals. The proposed control technique through neural networks exhibits the best performance because it can be adapted to each operating condition, demanding low computational cost for an online operation, and considering non-linearities of the system. The performance of the proposed controller is evaluated in the presence of uncertainties. The results are compared with the conventional PI controller, optimized using whale optimization algorithm.
By using the Euler-Lagrange method, the non-linear mathematical model of the convey-crane is obtained. Using a tangential linearization, around the equilibrium point, the linear mathematical model is obtained in state space and transfer function forms. Using classical methods, a single-input single-output linear controller is designed for the output y = x + Lθ (translational projection of the pendulum position). Finally, the behavior of the nonlinear system, controlled through the linear controller, is simulated and satisfactory results are obtained.
Weapon identification has been a hot topic in the area of Object Recognition in recent years. However, its appli-cation has been virtually explored in social media. This work focuses on the detection of weapons in profiles that explicitly advocate their procession, both graphically and textually. This is a challenge, since access to a dataset is difficult; and once the samples are obtained, the dimensions and attributes of the images can vary significantly. In addition, the possession of a weapon does not imply that any offense or crime is being committed. To tackle these challenges, this manuscript presents a regularized adaptation of a Fast-Convolutional Neural Network (F-CNN) based on YOLO-V5, to merge and improve the results of the algorithm, along with a textual fingerprinting technique, to first corroborate if the intent of the post contains red flags of crime and violence. The results demonstrate that regularized adaptive models, mainly using Data Image Augmentation techniques, along with text classification, can provide better performance on unstructured data, such as those found in social media.
The aim of this contribution is to provide an introduction and overview for newcomers to the study of Noise Shaping type Successive Approximation Register (SAR) converters, reviewing a general description of the fundamentals of Noise Shaping SAR (NS-SAR). This paper addresses the difficulties (i.e., open problems), analyzes the fundamental challenges and summarizes the latest developments; including the main problems, which are primarily in the loop filter and its passive/active implementations, mismatch in the DAC elements, among others. Some possible solutions for the dynamic correction of CDAC mismatch are also presented. Finally, some trends and future challenges are discussed.
On this work, the performance of a Zinc-air battery with a novel structure powering a Bluetooth IoT node is described. Although Zinc-air batteries have the highest energy density of all actually available battery technologies, commercial zinc-air batteries are not used on wireless nodes because of their low capacity for enduring the short high-current pulses that occur when radio operation is active. Results show that the properties of the battery under test outperform standard commercial zinc-batteries and could be competitive with Li-Ion batteries performance, making it suitable for IoT applications.
