Abstract
Engineering students increase its motivation when they face exciting challenges. In this sense, renewable energies may be a motivational and attractive topic as it is seen as a contribution to a cleaner world. Specifically, wind energy plays nowadays a key role in the sustainability of the worldwide electric grid. But wind turbines (WT) are complex devices that need control systems to maintain the output power around the rated value. Control engineering students address this problem with a theoretical background on regulation, but they usually do not know how to improve the standard solutions. Artificial intelligence techniques, and specifically genetic algorithms (GA), have not received the due attention in electronics and industrial engineering yet, despite its utility as optimization tool. This evolutive optimization tool can be exploited to tune the parameters of the controllers and thus to optimize the operation of WTs. In this work, a problem-driven didactic proposal that can help student to learn optimized control techniques on WTs is presented. The students must work with a mathematical model of a WT. Once they have understood its performance, they design a PID controller to stabilize the output power. This regulator is manually tuned so they are aware of the complexity of this parameters adjustment for complex systems. Genetic algorithms are then presented as a powerful optimization tool to help them in this task. The proposal has been designed with the aim of achieving a set of didactic competences and learning objectives. To achieve it, a set of concepts are defined and scheduled in several sessions. In addition, a practice script has been develop to guide the students through the learning process.
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Acknowledgement
This work was partially supported by the Spanish Ministry of Science, Innovation and Universities under MCI/AEI/FEDER Project number RTI2018-094902-B-C21.
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Sierra-García, J.E., Santos, M. (2022). Wind Turbines Control Optimization: A Problem-Driven Proposal to Learn Genetic Algorithms. In: Gude Prego, J.J., de la Puerta, J.G., García Bringas, P., Quintián, H., Corchado, E. (eds) 14th International Conference on Computational Intelligence in Security for Information Systems and 12th International Conference on European Transnational Educational (CISIS 2021 and ICEUTE 2021). CISIS - ICEUTE 2021. Advances in Intelligent Systems and Computing, vol 1400. Springer, Cham. https://doi.org/10.1007/978-3-030-87872-6_36
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