Dr. Espinosa-Neira, Eduardo

Smart meters play an important role in energy management systems as they provide essential parameters for real-time monitoring, protection, and control that enable informed decisions for the end-users and the utility grid. However, available systems are high-cost solutions with different hardware and software, which provide limited measuring parameters with certain accuracy. This work aims to develop and implement an innovative smart electricity meter (SEM) system that surpasses conventional designs by incorporating advanced features like noninvasive sensors, manual signal calibration, and flexible communication modes. The developed SEM supports real-time data transmission via IoT and provides superior accuracy in measuring harmonics and frequency, addressing key challenges in energy monitoring. This work contributes to real-time energy monitoring and energy management systems in residential and industrial applications.

The use of renewable energy sources (RESs) together with energy storage systems (ESSs) allows for smoothing power variations, thus improving power backup capabilities and power quality in the electric power grid. These applications require power converters to transfer energy between the renewable generator or energy storage and the power grid. In any case, the control algorithm of the power converter requires the synchronization method to provide a correct estimation of the instantaneous voltage of the power grid. This work provides engineers and researchers with an accessible platform at a low cost (less than USD 100) and a methodology for the experimental validation of digital synchronization algorithms as a step before their implementation in grid-connected equipment. The methodology evaluates the performance of the digital algorithms when there are variations in amplitude, frequency, phase, and harmonic content in the emulated three-phase power grid, as well as the execution times (tex), while a digital platform emulates the electrical signals and generates reference signals for the evaluation. To illustrate this proposal, two synchronization algorithms—SRF-PLL and DSOGI-PLL with a low-pass filter—are implemented in a digital controller and tested. The evaluation tool confirms the algorithms’ performance and shows that the execution time of DSOGI-PLL is 91% longer than that of SRF-PLL, which is well known in the literature.

The use of advanced modulation and control schemes for power converters, such as a Feedback Quantizer and Predictive Control, is widely studied in the literature. This work focuses on improving the closed-loop modulation scheme called Feedback Quantizer, which is applied to a three-phase voltage source inverter. This scheme has the natural behavior of mitigating harmonics at low frequencies, which are detrimental to electrical equipment such as transformers. This modulation scheme also provides good tracking for the voltage reference at the fundamental frequency. On the other hand, the disadvantage of this scheme is that it has a variable switching frequency, creating a harmonic spectrum in frequency dispersion, and it also needs a small sampling time to obtain good results. The proposed scheme to improve the modulation scheme is based on a Discrete Space Vector with virtual vectors to obtain a better approximation of the optimal vectors for use in the algorithm. The proposal improves the conventional scheme at a high sampling time (200 μs), obtaining a THD less than 2% in the load current, decreases the noise created by the conventional scheme, and provides a fixed switching frequency. Experimental tests demonstrate the correct operation of the proposed scheme.

In-depth research has been done in the literature on applying sophisticated modulation and control systems for power converters, such as Feedback Quantizer and Predictive Control. This research aims to enhance the Feedback Quantizer, a closed-loop modulation technique used in three-phase voltage source inverters. This method naturally reduces harmonics at low frequencies, which harms electrical machinery like transformers and AC motors. This modulation technique achieves an excellent follow-up of the fundamental voltage reference. One of the disadvantages of Feedback Quantizer are the low sampling time to achieve a good waveform quality; in addition, as it is not a carrier-based method, the switching frequency is variable, resulting in a dispersed harmonic spectrum. The three-phase voltage source inverter has eight valid states to monitor the voltage reference; the proposal consists of modifying the feedback quantizer modulation using virtual vectors by discrete space vectors, which achieves a better follow-up. The proposal considers a simplified implementation of discrete space vectors in such a way as to reduce the computational cost to obtain voltage and current waveforms with low WTHD, THD distortion, and a defined harmonic spectrum but with a high sampling time. The proposed modulation scheme obtains a THD of less than 2% in the load current, reducing the noise produced by the traditional scheme of Feedback Quantizer and using a fixed switching frequency for a sampling time of ( 200 μ s). The above is an improvement compared to the conventional Feedback Quantizer. Additionally, it was possible to reduce the mathematical operations of the algorithm by a factor of 5.38 by using strategies to simplify the algorithm of the suggested scheme. The proposed technique operates correctly, according to experimental tests.INDEX TERMS Feedback quantizer, discrete space vector modulation, total harmonic distortion, weighted total harmonic distortion, voltage source converter, modulation scheme.

Small grid-connected inverters are not friendly to the electrical grid, in the sense they do not take any action to support the grid when contingency events occur. For example, because of their relatively low power capacity, small grid-connected inverters are not designed to provide dynamic frequency support to the grid. On the other hand, it is well known that microgrids and weak grids including distributed generation would benefit significantly if all of the grid-connected converters could support and help against grid frequency disturbances. Within the family of small grid-connected converters, single-phase quasi-Z-source inverters (QZSI) have become an attractive topology, because they represent a reliable and economical alternative, and can be very efficient in applications that demand small or medium powers. However, a major disadvantage is that the control strategy must manage both direct current and alternating current variables through the same group of switches. The latter is a challenging task when implementing predictive control schemes. This paper proposes a finite control set model predictive control (FCS-MPC) strategy for a single- phase grid-supporting QZSI. The proposed predictive scheme can be easily integrated with a complementary control block to provide grid frequency support. Experimental results show evidence of the inverter operating under the proposed control strategy and providing grid frequency support, which demonstrates the feasibility of the proposal

The objective of this study is to explore the antecedents of the formation of entrepreneurial intention from a linear, causal and asymmetrical perspective. We have combined the Ajzen model applied to entrepreneurship, including two personality variables (self-confidence and creativity). This study involves a structural equation model based on partial least squares (PLS) and fuzzy set qualitative comparative analysis (fsQCA). All of the hypotheses were supported except for the influence of the variables subjective norms and self-confidence on EI. The PLS model explains 68.7% of the variance of EI. According to the fsQCA results, four models explain 88.1% of the existence of EI. The two models with the greatest degree of coverage are: Self-Confidence × Attitude Towards Entrepreneurial Behaviour × Subjective Norms and Perceived Behaviour Control × Creativity × Subjective Norms.

Cascaded H-bridge drives require using a significant-size capacitor on each cell to deal with the oscillatory power generated by the H-bridge inverter in the DC-link. This results in a bulky cell with reduced reliability due to the circulating second harmonic current through the DC-link capacitors. In this article, a control strategy based on a finite control set model predictive control and a proportional-resonant controller is proposed to compensate for the oscillatory power required by the H-bridge inverter through the cell’s input rectifier. With the proposed strategy, a DC-link second harmonic free operation is achieved, allowing for the possibility of reducing the capacitor size and, in consequence, the cell dimensions. The feasibility of the proposed control scheme is verified by experimental results in one cell of a cascade H-bridge inverter achieving an operation with a capacitance 141 times smaller than required by conventional control approaches for the same voltage ripple.

The use of controlled power converters has been extended for high power applications, stacking off-the-shelve semiconductors, and allowing the implementation of, among others, AC drives for medium voltages of 2.3 kV to 13.8 kV. For AC drives based on power cells assembled with three-phase diode rectifiers and cascaded H-bridge inverters, a sophisticated input multipulse transformer is required to reduce the grid voltage, provide isolation among the power cells, and compensate for low-frequency current harmonics generated by the diode-based rectifiers. However, this input multipulse transformer is bulky, heavy, and expensive and must be designed according to the number of power cells, not allowing total modularity of the AC drives based on cascade H-bridges. This study proposes and evaluates a control strategy based on a finite control set-model predictive control that emulates the harmonic cancellation performed by an input multipulse transformer in a cascade H-bridge topology. Hence, the proposed method requires conventional input transformers and replaces the three-phase diode rectifiers. As a result, greater modularity than the conventional multicell converter and improved AC overall input current with a THD as low as 2% with a unitary displacement power factor are achieved. In this case, each power cell manages its own DC voltage using a nonlinear control strategy, ensuring stable system operation for passive and regenerative loads. The experimental tests demonstrated the correct performance of the proposed scheme.

In this paper, we present an implementation of selective harmonic elimination modulation technique in a 27-Level asymmetric multilevel converter. The main issue in this kind of converters is the generation of the gating patterns to obtain an optimized AC voltage waveform. State-of-the art solutions use deep mathematical analysis in the frequency domain by means of the Fourier series, but they are mainly applied for two-level or symmetric multilevel converters. On the other hand, the modulation for asymmetric multilevel converters is mainly focused on nearest level control or nearest vector control. In this work, we propose a novel modulating technique that takes advantage of the switching angles optimization for a 27-level waveform. In fact, different set of solutions are obtained and presented in order to define the modulation index as well as the value of the switching angles for the multilevel waveform. A modulation index sweep was performed for the entire operating region of the converter, where it can be observed that the number of levels decreases when the modulation index is low, which are calculated in order to minimize the total harmonic distortion (THD) of the resulting voltage waveform. In order to validate the proposal, these results for different modulation indexes values are simulated, obtaining a THD < 5% for a modulation index 0.75 < M < 1.0. Finally, a small scale proof-of-concept prototype is implemented in order to validate the proposal. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

There is a need to ensure comfortable conditions for hospital staff and patients from the point of view of thermal comfort and air quality so that they do not affect their performance. We consider the need for hospital employees and patients to enjoy conditions of greater well-being during their stay. This is understood as a comfortable thermal sensation and adequate air quality, depending on the task they are performing. The contribution of this article is the formulation of the fundamentals of a system and platform for monitoring thermal comfort and Indoor Air Quality (IAQ) in hospitals, based on an Internet of Things platform composed of a low-cost sensor node network that is capable of measuring critical variables such as humidity, temperature, and Carbon Dioxide (CO2). As part of the platform, a multidimensional data model with an On-Line Analytical Processing (OLAP) approach is presented that offers query flexibility, data volume reduction, as well as a significant reduction in query response times. The experimental results confirm the suitability of the platform’s data model, which facilitates operational and strategic decision making in complex hospitals.