The use of intelligent algorithms for global solar prediction is an ideal tool for research focused on the use of solar energy. Forecasting solar radiation supports different applications focused on the generation and transport of energy in places where there are no meteorological stations. Different solar radiation prediction techniques have been applied in different time horizons, such as neural networks (ANN) or machine learning (ML), with the latter being the most important. The support vector machine (SVM) is a classification method of the ML that is used to predict solar radiation. To obtain a better accuracy of prediction data, search optimization algorithms (SOA) such as genetic algorithms (GA) and the particle swarm optimization algorithm (PSO) were used to optimize the prediction accuracy by searching the model parameters. This article presents a review of different hybrid SVM models with SOA applied to obtain the best parameters to reduce the prediction error of solar radiation using meteorological variables. Research articles from the last 5 years on solar radiation prediction using SVM models and hybrid SMV optimized models with SOA were studied. The results show that SVM with GA presents a better performance than the classical SVM models using the Radial basis kernel function for prediction parameters.
Wind energy is an alternative to meet the growing energy demand and protect the environment; however, in places with limited wind resources, only the installation of small horizontal-axis wind turbines (SHAWTs) is profitable. At the height of these turbines, the wind is usually unstable with gusts and turbulence due to obstacles in its path such as buildings and trees. The pitch angle must be adaptable to guarantee nominal rotation speed, and it is commonly regulated with a proportional-integral-derivative (PID) feedback controller. This controller works well when the wind is stable, but not with drastic changes in wind speed. To correct this problem, this article introduces a PID controller with automatic adjustment of the gain values using a fuzzy logic controller (FLC). The PID gain adjustment allows an optimal response speed of the system for different wind conditions. The membership functions of the FLC are determined from a methodology that includes: The measurement of the wind speed at a calculated distance, a statistical analysis of the wind variability, and a dynamic analysis of the wind path. In this way, it is possible to anticipate the response of the actuator to the arrival of a gust of wind to the rotor. The algorithm is implemented in 14 kW SHAWTs where the difference in performance with a conventional controller is quantified. Satisfactory results were obtained, the electrical output increased by 7%, and the risk of rotor damage due to vibrations or mechanical fatigue was reduced by 20%.
Pipeline inspection gauges (PIGs) carry out automatic pipeline inspection with nondestructive testing (NDT) technologies like ultrasound, magnetic flux leakage, and eddy current. The ultrasonic straight beam allows technicians to determine the wall thickness of the pipeline through the time of flight diffraction (TOFD), providing the pipeline reconstruction and allowing the detection of several defects like dents or corrosion. If the pipeline is of a long distance, then the inspection process is automatic, and the fluid pressure pushes the PIG through the pipeline system. In this case, the PIG velocity and its axial alignment with the pipeline cannot be controlled. The PIG geometry, the pipeline deformations, and the girth welds cause a continuous chattering when the PIG is running, removing the transducers perpendicularity with the inspection points, which means that some echoes cannot be received. To reduce this problem, we propose a novel method to design a sensor carrier that takes into account the angularity and distance effects to acquire the straight beam echoes. The main advantage of our sensor carrier is that it can be used in concave and convex pipeline sections through geometric adjustments, which ensure that it is in contact with the inner pipe wall. Our improvement of the method is the characterization of the misalignment between the internal wall of the pipeline and the transducer. Later, we analyzed the conditions of the automatic pipeline inspection, the existing recommendations in state-of-the-art technology, and the different mechanical scenarios that may occur. For the mechanical design, we developed all the equations and rules. At the signal processing level, we set a fixed gain in the filtering step to obtain the echoes in a defined distance range without saturating the acquisition channels. For the validation, we compared through the mean squared error (MSE) our sensor carrier in a straight pipe section and a pipe elbow of steel versus other sensor carrier configurations. Finally, we present the design parameters for the development of the sensor carrier for different pipeline diameters.
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