Inverse Optimal Control Using Metaheuristics of Hydropower Plant Model via Forecasting Based on the Feature Engineering

Marlene A. Perez-Villalpando, Kelly J. Gurubel Tun, Carlos A. Arellano-Muro and Fernando Fausto
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Marlene A. Perez-Villalpando: School of Engineering and Technological Innovation, Campus Tonalá, University of Guadalajara, Guadalajara 45425, Jalisco, Mexico
Kelly J. Gurubel Tun: School of Engineering and Technological Innovation, Campus Tonalá, University of Guadalajara, Guadalajara 45425, Jalisco, Mexico
Carlos A. Arellano-Muro: Western Institute of Technology and Higher Education, Tlaquepaque 45640, Jalisco, Mexico
Fernando Fausto: Departamento de Electrónica, Universidad de Guadalajara, Centro Universitario de Ciencias Exactas e Ingenierías, Guadalajara 44430, Jalisco, Mexico

Energies, 2021, vol. 14, issue 21, 1-18

Abstract: Optimal operation of hydropower plants (HP) is a crucial task for the control of several variables involved in the power generation process, including hydraulic level and power generation rate. In general, there are three main problems that an optimal operation approach must address: (i) maintaining a hydraulic head level which satisfies the energy demand at a given time, (ii) regulating operation to match with certain established conditions, even in the presence of system’s parametric variations, and (iii) managing external disturbances at the system’s input. To address these problems, in this paper we propose an approach for optimal hydraulic level tracking based on an Inverse Optimal Controller (IOC), devised with the purpose of regulating power generation rates on a specific HP infrastructure. The Closed–Loop System (CLS) has been simulated using data collected from the HP through a whole year of operation as a tracking reference. Furthermore, to combat parametric variations, an accumulative action is incorporated into the control scheme. In addition, a Recurrent Neural Network (RNN) based on Feature Engineering (FE) techniques has been implemented to aid the system in the prediction and management of external perturbations. Besides, a landslide is simulated, causing the system’s response to show a deviation in reference tracking, which is corrected through the control action. Afterward, the RNN is including of the aforementioned system, where the trajectories tracking deviation is not perceptible, at the hand of, a better response with respect to use a single scheme. The results show the robustness of the proposed control scheme despite climatic variations and landslides in the reservoir operation process. This proposed combined scheme shows good performance in presence of parametric variations and external perturbations.

Keywords: Inverse Optimal Control; Feature Engineering application; forecasting; recurrent high order neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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