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.INDEX TERMS Artificial neural network, fuzzy logic, genetic algorithms, wind power generation, control systems.
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%.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.