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 the strongest renewable energy source developed in recent decades. Being systems that are directly connected to the grid of the electrical system, it is essential to use the maximum available power of the wind and obtain the maximum electrical power converted from the turbine. In this paper, the fundamental problem of the wind turbine is how to obtain at all times the maximum output power of the turbine in a wide range of wind speed. The randomness of the wind adds an intrinsic difficulty to be able to plan the available wind energy in advance. To solve this problem, it is not necessary to know the dynamic operation of the system; we must anticipate the control response to each one of the different probable scenarios. An expert control system can be used based on human knowledge and experience, which, through proper management of its variables and adequate control of criteria to manipulate stored data, provides a way to determine solutions. In other words, it is a model of the experience of professionals in this field. The more variables in the system are considered, the more complete the model will be, and the more information will be available for decision-making, with a more efficient system and higher results in power generation as a response. For this reason, the objective of this paper is to present expert systems developed in recent years and, thus, offer a control solution that approximates the conditions of different wind turbines.

Wind power is a renewable energy source that has been developed in recent years. Large turbines are increasingly seen. The advantage of generating electrical power in this way is that it can be connected to the grid, making it an economical and easily available source of energy. The fundamental problem of a wind turbine is the randomness in a wide range of wind speeds that determine the electrical energy generated, as well as abrupt changes in wind speed that make the system unstable and unsafe. A conventional control system based on a mathematical model is effective with moderate disturbances, but slow with very large oscillations such as those produced by turbulence. To solve this problem, expert control systems (ECS) are proposed, which are based on human experience and an adequate management of stored information of each of its variables, providing a way to determine solutions. This revision of recent years, mentions the expert systems developed to obtain the point of maximum power generation in a wind turbine with permanent magnet synchronous generator (PMSG) and, therefore, offers a control solution that adapts to the specifications of any wind turbine.

The population growth demands a greater generation of energy, an alternative is the use of small wind turbines, however, obtaining maximum wind power becomes the main challenge when there are drastic changes in wind speed. The angle of the blades rotates around its longitudinal axis to control the effect of the wind on the rotation of the turbine, a proportional-integral controller (PI) for this angle achieves stability and precision in a stable state but is not functional with severe alterations in wind speed, a different response time is necessary in both cases. This article proposes a novel pitch angle controller based on auto-tuning of PI gains, for which it uses a teaching–learning based optimization (TLBO) algorithm. The wind speed and the value of the magnitude of the change are used by the algorithm to determine the appropriate PI gains at different wind speeds, so it can adapt to any sudden change in wind speed. The effectiveness of the proposed method is verified by experimental results for a 14 KW permanent magnet synchronous generator (PMSG) wind turbine located at the Universidad Autónoma de Querétaro (UAQ), Mexico.

The inverted pendulum is one of the most significant problems in control theory and has been studied in control literatures. The nonlinear model is useful in the control design. In the present work, Volterra polynomial basis function networks have been used to identify a single inverted pendulum on a moving cart system. Here, the nonlinear model of the inverted pendulum has been implemented. The offline structure selection through orthogonal least square algorithm is used for the nonlinear system identification via the basis function selection of Volterra polynomial networks. The results show good matching between predicted and actual outputs.