Instituto Nacional de Electricidad y Energías Limpias

Developing cost-effective and durable electrocatalysts is essential for advancing anion exchange membrane fuel cells (AEMFCs). This work evaluates Pd-based catalysts supported on β-MnO2, Vulcan carbon (C), and their physical blend (Pd/MnO2:Pd/C) as bifunctional electrodes for the oxygen reduction reaction (ORR) and hydrogen oxidation reaction (HOR). The catalysts were synthesized via chemical reduction and characterized by TGA, ICP-OES, TEM, BET, and XRD. Rotating disk electrode studies revealed that the hybrid exhibited superior activity and kinetics, with lower Tafel slopes and higher exchange current densities compared to the individual supports. In AEMFCs, the hybrid reached 128.0 mW cm−2 as a cathode and 221.7 mW cm−2 as an anode, outperforming individual components. This enhanced performance arises from the synergistic interaction between Pd nanoparticles and MnO2, where MnO2 modulates the catalyst’s microstructure and local reaction environment while the carbon phase ensures efficient electron transport. MnO2, although inactive for the HOR alone, acted as a structural spacer, enhancing mass transport and stability. Durability tests confirmed that the hybrid electrocatalyst retained over 99% of its initial activity after 3000 cycles. These results highlight the hybrid Pd/MnO2:Pd/C as a promising, bifunctional, and durable electrocatalyst for AEMFC applications.

Nowadays, wind turbines are one of the most popular devices for producing clean and renewable electric energy. The rotor blades catch the wind’s kinetic energy to produce rotational energy from the turbine and electric energy from the generator. In small-scale wind turbines, there are several methods to operate the blades to obtain the desired speed of rotation and power outputs. These methods include passive stall, active stall, and pitch control. Pitch control sets the angular position of the blades to face the wind to achieve a predefined relationship between turbine speed or power and wind velocity. Typically, conventional Proportional Integral (PI) controllers are used to set the angular position of the rotor blades or pitch angle. Nevertheless, the quality of speed or power regulation may vary substantially. This study introduces a rotor speed controller for a pitch-controlled small-scale wind turbine prototype based on fuzzy logic concepts. The basics of fuzzy systems required to implement this kind of controller are presented in detail to counteract the lack of such material in the technical literature. The knowledge base of the fuzzy speed controller is composed of Takagi–Sugeno–Kang (TSK) fuzzy inference rules that implement a dedicated PI controller for any desired interval of wind velocities. Each wind velocity interval is defined with a fuzzy set. Simulation experiments show that the TSK fuzzy PI speed controller can outperform the conventional PI controller in the speed and accuracy of response, stability, and robustness over the whole range of operation of the wind turbine prototype.

Lightning is the primary cause of outages for many high voltage transmission lines. One approach to reduce the frequency of these outages is the improvement of the grounding system using chemical ground enhancers to better disperse transient or fault currents into the soil. In this paper, the objective is to use graphene nano particles to improve the performance of commercial ground enhancers by exploration of 15 different commercial graphene nano particles used to reformulate 19 commercial ground enhancers and one natural ground enhancer. Incorporation of nano graphene particles is by high shear mixing method. The results show reduction in two of the commercial ground enhancers’ resistivity values by a factor of 60. Moreover, lightning impulse tests show that the use of graphene reduce deterioration of the ground enhancer, which can occur after high current events, thereby prolonging its efficacy. Due to the superior electrical conductivity and chemical stability of the graphene, the reformulated ground enhancers improve short-term performance in managing lightning current impulses, and provide a solution, which is likely to perform better over long periods. However, the compatibility of the constituent parts of the formulation is critical to long-term performance.

Las energías limpias son aquellas fuentes de energía y procesos de generación de electricidad cuyas emisiones o residuos, cuando los haya, no rebasen los umbrales establecidos en las disposiciones reglamentarias que para tal efecto se expidan. El INEL presenta información sobre el aprovechamiento de las fuentes de energía limpia para la generación de electricidad. El potencial de estas fuentes de energía está clasificado en potencial probado, potencial probable, potencial posible y potencial del recurso (potencial bruto). Además, cuentan con datos de carácter informativo sobre las zonas de protección ambiental, riesgo climático, riesgo geológico, riesgo social, entre otras. Esta información puede ser consultada a través de tablas, gráficas, fichas informativas y mapas interactivos.

ABSTRACT We propose probabilistic logic factored Markov decision processes (PL-fMDPs) as a behavior selection scheme for self-driving cars. Probabilistic logic combines logic programming with probability theory to achieve clear, rule-based knowledge descriptions of multivariate probability distributions, and a flexible mixture of deductive and probabilistic inferences. Factored Markov decision processes (fMDPs) are widely used to generate reward-optimal action policies for stochastic sequential decision problems. For evaluation, we developed a simulated self-driving car with reliable modules for behavior selection, perception, and control. The behavior selection module is composed of a two-level structure of four action policies obtained from PL-fMDPs. Three main tests were conducted focused on the selection of the appropriate actions in specific driving scenarios, and the overtaking of static obstacle vehicles and dynamic obstacle vehicles. We performed 520 repetitions of these tests. The self-driving car completed its task without collisions in 99.2% of the repetitions. Results show the suitability of the overall self-driving strategy and PL-fMDPs to construct safe action policies for self-driving cars.

Despite the fact that digital transformation introduces multiple advantages, it also introduces crucial security challenges, since it combines heterogeneous communications, the integration of digital devices, legacy technologies. In the case of power grid, in addition to damage to the availability, integrity and confidentiality of information; there may be manipulation and take control of assets through the infection of operational systems. In this context, powerful cybersecurity schemes and mechanisms that guarantee the safe transmission of information and the safe operation of assets are required. The goal is develop cyber security schemes and mechanisms based on intelligent cyber defense mechanisms that provide flexibility and self-learning capacity to support humans in the analysis and generation of containment measures against cyber-attacks. This paper presents the developed and validation of an Intrusion Detection and Prediction System (IDPS) based on individual classifiers and ensemble algorithms. The IDPS has demonstrated be an efficient countermeasure against several cyberattacks. The proposed IDPS uses J48 (decision tree), CLONAL-G (artificial immune system), bayesian classifier and ensemble algorithm and was validated with the KDDCup databases. The attacks in the data set are categorized into four attack types: DoS (denial-of-service attacks), R2L (root-to-local attacks), U2R (user-to-root attack), and Probe (probing attacks). The results show that the individual classifiers perform well for particular attack, so it was necessary to build an ensemble algorithm that combine the information from each classifier for better performance. The idea is not to rely on a single classifier for the decision, but rather individual information from different classifiers is combined to make the final decision.

The boost-boost converter presents interesting characteristics for photovoltaic applications, due to the nature of the system where we can increase the voltage in an adequate and controlled way according to the load, in combination with an optimal control system we can obtain a better efficiency of an autonomous photovoltaic system, taking into consideration that a solar panel within its most significant properties to operate within the most efficient way needs natural conditions of irradiation and temperature that are closest to the SCT measurement standard, otherwise efficiency will be lost, Therefore, it is necessary to jointly implement a control system together with an algorithm that allows it to always be in maximum power conditions (MPP). According to the above, this article addresses the low voltage photovoltaic application of an isolated system, using a boost converter and a PI control system, together with an algorithm for obtaining the maximum power point, using two different photovoltaic systems, with the objective of showing that with an essential PI control we can obtain good efficiency of the two systems even with disturbances, as if a more robust control were implemented in photovoltaic applications, saving both operation cost and implementation time. The I-V (current-voltage) characteristics similar to a non-linear source of the photovoltaic module, require the inclusion of linearization of the photovoltaic module and with this to be able to design the control of the system, the proper functioning of the designed control has been tested through mathematical modeling and simulation.