The present research develops the parametric estimation of a second-order transfer function in its standard form, employing metaheuristic algorithms. For the estimation, the step response with a known amplitude is used. The main contribution of this research is a general method for obtaining a second-order transfer function for any order stable systems via metaheuristic algorithms. Additionally, the Final Value Theorem is used as a restriction to improve the velocity search. The tests show three advantages in using the method proposed in this work concerning similar research and the exact estimation method. The first advantage is that using the Final Value Theorem accelerates the convergence of the metaheuristic algorithms, reducing the error by up to 10 times in the first iterations. The second advantage is that, unlike the analytical method, it is unnecessary to estimate the type of damping that the system has. Finally, the proposed method is adapted to systems of different orders, managing to calculate second-order transfer functions equivalent to higher and lower orders. Response signals to the step of systems of an electrical, mechanical and electromechanical nature were used. In addition, tests were carried out with simulated signals and real signals to observe the behavior of the proposed method. In all cases, transfer functions were obtained to estimate the behavior of the system in a precise way before changes in the input. In all tests, it was shown that the use of the Final Value Theorem presents advantages compared to the use of algorithms without restrictions. Finally, it was revealed that the Gray Wolf Algorithm has a better performance for parametric estimation compared to the Jaya algorithm with an error up to 50% lower.

This review examines advancements in smart home security through the integration of home automation technologies. Various security systems, including surveillance cameras, smart locks, and motion sensors, are analyzed, highlighting their effectiveness in enhancing home security. These systems enable users to monitor and control their homes in real-time, providing an additional layer of security. The document also examines how these security systems can enhance the quality of life for users by providing greater convenience and control over their domestic environment. The ability to receive instant alerts and access video recordings from anywhere allows users to respond quickly to unexpected situations, thereby increasing their sense of security and well-being. Additionally, the challenges and future trends in this field are addressed, emphasizing the importance of designing solutions that are intuitive and easy to use. As technology continues to evolve, it is crucial for developers and manufacturers to focus on creating products that seamlessly integrate into users’ daily lives, facilitating their adoption and use. This comprehensive state-of-the-art review, based on the Scopus database, provides a detailed overview of the current status and future potential of smart home security systems. It highlights how ongoing innovation in this field can lead to the development of more advanced and efficient solutions that not only protect homes but also enhance the overall user experience.

Over the last decades, the accelerated implementation of photovoltaic systems ( PVS ) has led to the creation of open circuit fault detection systems based on measurements made in completed facilities, growing by making the volume of data to be analyzed with each new installation, improving fault detection and location systems with various methods. In this article, an electronic adaptive device was developed that operates under a method based on the spectral analysis of signals using the Discrete Fourier Transform ( DFT ) and a classifier based on the k-Nearest Neighbor ( k-NN ) machine learning algorithm ( ML ) for the detection of Open Circuit Faults ( OCF ). The contribution of this work is that the entire photovoltaic array operated in conditions of radiance less than 10 W / m ² overnight with a red LED pulsed light applied on the photovoltaic array module furthest from the inverter. Under these operating conditions, the presence of an open circuit fault alters the variability in the impedances of the photovoltaic array under different fault locations in the systems compared to healthy systems without an open circuit fault, revealing that the predictability of the methodology shows values from 90% to 93% as the size of the photovoltaic system increases, concluding the effectiveness of the procedure.

Electrical power quality is one of the main elements in power generation systems. At the same time, it is one of the most significant challenges regarding stability and reliability. Due to different switching devices in this type of architecture, different kinds of power generators as well as non-linear loads are used for different industrial processes. A result of this is the need to classify and analyze Power Quality Disturbance (PQD) to prevent and analyze the degradation of the system reliability affected by the non-linear and non-stationary oscillatory nature. This paper presents a novel Multitasking Deep Neural Network (MDL) for the classification and analysis of multiple electrical disturbances. The characteristics are extracted using a specialized and adaptive methodology for non-stationary signals, namely, Empirical Mode Decomposition (EMD). The methodology’s design, development, and various performance tests are carried out with 28 different difficulties levels, such as severity, disturbance duration time, and noise in the 20 dB to 60 dB signal range. MDL was developed with a diverse data set in difficulty and noise, with a quantity of 4500 records of different samples of multiple electrical disturbances. The analysis and classification methodology has an average accuracy percentage of 95% with multiple disturbances. In addition, it has an average accuracy percentage of 90% in analyzing important signal aspects for studying electrical power quality such as the crest factor, per unit voltage analysis, Short-term Flicker Perceptibility (Pst), and Total Harmonic Distortion (THD), among others.

The transition to sustainable energy systems in the agroindustrial sector requires rigorous methodologies that enable a comprehensive and quantitative assessment of the technical and economic viability and environmental impact of photovoltaic integration. This study develops and validates a hybrid multi-criteria methodology structured in three phases: (i) analytical modeling of the load profile and preliminary sizing, (ii) advanced energy simulation using PVsyst for operational optimization and validation against empirical data, and (iii) environmental assessment using life cycle analysis (LCA) under ISO 14040/44 standards. The methodology is applied to a Cuban agroindustrial plant with an annual electricity demand of 290,870 kWh, resulting in the design of a 200 kWp photovoltaic system capable of supplying 291,513 kWh/year, thereby achieving total coverage of the electricity demand. The economic analysis yields an LCOE of 0.064 USD/kWh and an NPV of USD 139,408, while the environmental component allows for a mitigation of 113 t CO₂-eq/year. The robustness of the model is validated by comparison with historical records, yielding an MBE of $-0.65\%$, an RMSE of $2.87\%$, an MAPE of $2.62\%$, and an R² of $0.98$. This comprehensive approach demonstrates its superiority over previous methodologies by effectively integrating the three pillars of sustainability in an agroindustrial context, thus offering a scientifically sound, replicable, and adaptable tool for decision-making in advanced energy projects. The results position this methodology as a benchmark for future research and applications in emerging production scales.

Electrical power quality is one of the main elements in power generation systems. At the same time, it is one of the most significant challenges regarding stability and reliability. Due to different switching devices in this type of architecture, different kinds of power generators, and non-linear loads are used for different industrial processes. As a result of this, the need to classify and analyze Power quality disturbance (PQD) to prevent and analyze the degradation of the system reliability affected by the non-linear and non-stationary oscillatory nature. This paper presents A Novel Mul-titasking Deep Neural Network (MDL) for the Classification and Analysis of Multiple Electrical Disturbances. The characteristics are extracted with a specialized and adaptive methodology for non-stationary signals, Empirical Mode Decomposition (EMD). The methodology’s design, devel-opment, and various performance tests are carried out with 28 different difficulty levels, such as severity, disturbance duration time, and noise in the 20 dB to 60 dB signal range. MDL was devel-oped with a diverse data set in difficulty and noise, with a quantity of 4500 records of different samples of multiple electrical disturbances. The analysis and classification methodology has an average accuracy percentage of 95% with multiple disturbances. In addition, an average accuracy percentage of 90% in analyzing important signal aspects for studying electrical power quality such as crest factor, Per Unit voltage analysis, Short Term Flicker Perceptibility (Pst), and Total Harmonic Distortion (THD), among others.