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

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...

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Autores principales:	Marlene A. Perez-Villalpando, Kelly J. Gurubel Tun, Carlos A. Arellano-Muro, Fernando Fausto
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Inverse Optimal Control Feature Engineering application forecasting recurrent high order neural network Technology T
Acceso en línea:	https://doaj.org/article/c0a9cfc647fa4dfc8b8f430d44c4ee40
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spelling	oai:doaj.org-article:c0a9cfc647fa4dfc8b8f430d44c4ee402021-11-11T16:04:34ZInverse Optimal Control Using Metaheuristics of Hydropower Plant Model via Forecasting Based on the Feature Engineering10.3390/en142173561996-1073https://doaj.org/article/c0a9cfc647fa4dfc8b8f430d44c4ee402021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7356https://doaj.org/toc/1996-1073Optimal 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.Marlene A. Perez-VillalpandoKelly J. Gurubel TunCarlos A. Arellano-MuroFernando FaustoMDPI AGarticleInverse Optimal ControlFeature Engineering applicationforecastingrecurrent high order neural networkTechnologyTENEnergies, Vol 14, Iss 7356, p 7356 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Inverse Optimal Control Feature Engineering application forecasting recurrent high order neural network Technology T
spellingShingle	Inverse Optimal Control Feature Engineering application forecasting recurrent high order neural network Technology T Marlene A. Perez-Villalpando Kelly J. Gurubel Tun Carlos A. Arellano-Muro Fernando Fausto Inverse Optimal Control Using Metaheuristics of Hydropower Plant Model via Forecasting Based on the Feature Engineering
description	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.
format	article
author	Marlene A. Perez-Villalpando Kelly J. Gurubel Tun Carlos A. Arellano-Muro Fernando Fausto
author_facet	Marlene A. Perez-Villalpando Kelly J. Gurubel Tun Carlos A. Arellano-Muro Fernando Fausto
author_sort	Marlene A. Perez-Villalpando
title	Inverse Optimal Control Using Metaheuristics of Hydropower Plant Model via Forecasting Based on the Feature Engineering
title_short	Inverse Optimal Control Using Metaheuristics of Hydropower Plant Model via Forecasting Based on the Feature Engineering
title_full	Inverse Optimal Control Using Metaheuristics of Hydropower Plant Model via Forecasting Based on the Feature Engineering
title_fullStr	Inverse Optimal Control Using Metaheuristics of Hydropower Plant Model via Forecasting Based on the Feature Engineering
title_full_unstemmed	Inverse Optimal Control Using Metaheuristics of Hydropower Plant Model via Forecasting Based on the Feature Engineering
title_sort	inverse optimal control using metaheuristics of hydropower plant model via forecasting based on the feature engineering
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c0a9cfc647fa4dfc8b8f430d44c4ee40
work_keys_str_mv	AT marleneaperezvillalpando inverseoptimalcontrolusingmetaheuristicsofhydropowerplantmodelviaforecastingbasedonthefeatureengineering AT kellyjgurubeltun inverseoptimalcontrolusingmetaheuristicsofhydropowerplantmodelviaforecastingbasedonthefeatureengineering AT carlosaarellanomuro inverseoptimalcontrolusingmetaheuristicsofhydropowerplantmodelviaforecastingbasedonthefeatureengineering AT fernandofausto inverseoptimalcontrolusingmetaheuristicsofhydropowerplantmodelviaforecastingbasedonthefeatureengineering
_version_	1718432437705375744

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

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