Abstract
The article describes a hybrid power plant control system based on machine learning technologies. The hybrid power plant is based on solar panels and a wind generator. This article suggests using a neural network to track the maximum power point for more efficient charge control. This is a method of regulating the battery charge to increase the amount of electricity received. Neural network technologies are used to control the distribution of electricity produced in a hybrid power plant. The neural network controls the controllers that control the process and provide an effective increase in current only when the output voltage of the solar panel is higher than the battery voltage. A multilayer neural network was used to implement an intelligent control system with a training procedure based on the backpropagation algorithm. After training, the system finds the maximum power point and takes into account the current battery charge to redistribute voltage and current, which leads to an increase in system power compared to the circuit on traditional controllers.
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Gozhyj, A., Bidyuk, P., Matsuki, Y., Nechakhin, V., Kalinina, I., Shchesiuk, O. (2021). Hybrid Power Plant Control System Based on Machine Learning Methods. In: Shakhovska, N., Medykovskyy, M.O. (eds) Advances in Intelligent Systems and Computing V. CSIT 2020. Advances in Intelligent Systems and Computing, vol 1293. Springer, Cham. https://doi.org/10.1007/978-3-030-63270-0_17
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DOI: https://doi.org/10.1007/978-3-030-63270-0_17
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