The existence of failures in photovoltaic systems causes energy losses, security problems, and damage to its components. Therefore, it is necessary to develop monitoring systems to improve their productivity, reliability, efficiency, and safety. This work proposes a method for detecting and indicating short-circuit failure and partial shading present in grid-connected photovoltaic modules. The novelty of this proposal is the processing of voltage and current signals generated (ripple signals) by the electrical interaction between the photovoltaic string, the photovoltaic inverter, the condition of the modules, temperature, and irradiance. The magnitudes of specific frequency components are obtained from these electrical signals using DFT, and Cartesian coordinates are formed in a three-dimensional plane. Coordinates belonging to each of the string conditions are mostly located in different octants from this plane. This distribution allows an assessment of the modules state. Certain scattering is solved with a K-nearest neighbors algorithm. The effectiveness of the methodology was experimentally validated in two PVG with different characteristics. In each test case, the method was adaptable to the real conditions of the photovoltaic system. The results show effectiveness greater than 90% in the first evaluation levels, there is a slightly lower detection certainty in anomalous conditions with coordinates that are very close to each other. However, the system has 100% certainty detecting the presence of an abnormal condition. The method allows adaptation to different conditions, and takes advantage of the electrical signals derived from the actual performance of the used devices.

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.

This study introduces a new method to locate cracks in wind turbine blades using the support vector machine algorithm and the tangential vibration signal measured at the root blade in static conditions. The method was implemented in hardware and experimentally validated on 200 W wind turbine blades. The blade conditions were healthy, and transverse cracked at the root, midsection, and tip. The experimental procedure is easy, and only one low-cost piezoelectric accelerometer is needed, which is affordable and straightforward to install. The machine learning technique used requires a small dataset and low computing power. The results show exceptional performance, achieving an accuracy of 99.37% and a precision of 98.77%. This approach enhances the reliability of wind turbine blade monitoring. It provides a robust early detection and maintenance solution, improving operational efficiency and safety in wind energy production. K-nearest neighbors and decision trees are also used for comparison purposes.

Photovoltaic systems for pumping water, based on direct current powered motor pumps, have great application in small rural regions without electrical networks. In addition, these systems provide environmental benefits by replacing fossil fuels. However, these systems reduce their performance due to partial shading, which is magnified by the internal mismatch of the PV modules. This work proposes an intelligent, low-cost, and automatic method to mitigate these effects through the electrical reconfiguration of the PV array. Unlike other reported techniques, this method considers the pump head variations. For that, the global voltage and current supplied by the PV array to the motor pump subsystem are introduced to an artificial neural network and to a third-order equation, which locates the shaded PV module and detects the pump head, respectively. A connection control implements the optimal electrical rearrangement. The selection is based on the identified partial shading pattern and pump head. Finally, the switching matrix modifies the electrical connections between the PV modules on the PV array without changing the interconnection scheme, PV array dimension, or physical location of the PVMs. The proposed approach was implemented in a real PV water pumping system. Low-cost and commercial electronic devices were used. The experimental results show that the output power of the PV array increased by 8.43%, which maintains a more stable level of water extraction and, therefore, a constant flow level.

A firewood stove’s combustion chamber can withstand temperatures of 1500 °C. To prevent the deterioration of a firewood stove due to excessive heat, it is necessary to use thermal insulation materials that stop heat transfer to the walls. These materials must be economical and durable. This work examines the materials used in the construction of combustion chambers of firewood stoves in southern Mexico and Central America. This field study collects information and samples of materials used in the manufacture of firewood stoves. Heat transfer experiments are conducted, and the thermal properties of each material are analyzed. As a result, methodology and information is provided for the manufacture of future plancha-type firewood stoves used in the study area, such as pine wood (pinus chiapensis) which is mainly used as casing for firewood stoves in coniferous forest areas; in addition, the use of wood ash as thermal insulation material is proposed since it does not present direct costs and has a thermal conductivity between 0.10 and 0.20 W/m°C and a melting point greater than 1500 °C. The next layer proposed is hollow brick, a high-temperature-resistant material that can be used as support due to its mechanical strength and low thermal conductivity of 0.6 W/m°C. Finally, the use of calcium hydroxide as a coating material is proposed, applied in the form of a paste or paint to detail the imperfections of the combustion chamber construction as it resists temperatures above 1000 °C.

The response of the photovoltaic module (PVM) to a variable-frequency signal is analyzed using the equivalent circuit for alternating current. In this kind of analysis, a sinusoidal voltage signal of constant amplitude is injected into the PVM, and the input signal frequency is adjusted within a minimum-to-maximum range. However, the injection of a sinusoidal voltage signal does not reflect the behavior of the PVM subjected to pulsed light. Therefore, this paper evaluates a two-stage hybrid method to obtain the open-circuit voltage of a photovoltaic module subjected to fixed-frequency pulsed light. In the first stage (experimental), the PVM current under static conditions and the output voltage of the photovoltaic module subjected to pulsed light are obtained. In the second stage (simulation) of the hybrid method, the PVM output current and voltage data are processed. The results show that the difference between the simulated output voltage and the experimentally obtained voltage has a root mean square error of approximately zero. Additionally, the results for the total impedance of the equivalent circuit show linear behavior. Finally, the hybrid method is tested using monocrystalline and polycrystalline PVM manufacturing technologies.