Control Systems Analysis and Design Labs with Educational Plants

Sotnikova, Margarita V.; Zhabko, Natalia A.; Lepikhin, Timur

doi:10.3182/20120619-3-ru-2024.00088

Cited by 5 publications

(3 citation statements)

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“…By deriving the discrete time set of equations, the behavior of control variable can be predicted at next sampling time k. The proposed methodology is based on the fact that the slope of the PV array power curve is zero at the predicted MPP, positive on the left and negative on the right of the predicted MPP. Predicted behavior of control variables at the next sampling time k + 1 can be described by a discrete-time set of equations by the following [27][28][29]:…”

Section: Model Predictive Controller Schemementioning

confidence: 99%

An Adaptive Maximum Power Output Sustaining System for a Photovoltaic Power Plant Based on a Robust Predictive Control Approach

Elzein

Петренко

2020

Izv. vysh. ucheb

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Photovoltaic power plants have non-linear voltage-current characteristic, with specific maximum power point, which depends on operating conditions, viz. irradiation and temperature. In targeting the maximum power, it is by far known that the photovoltaic arrays have to operate at the maximum power point despite unpredicted weather changes. For this reason the controllers of all photovoltaic power electronic converters employ some method for maximum power point tracking. This paper makes an emphasis on model predictive controller as a control method for controlling the maximum power point tracking through the utilization of the well-known algorithm namely the Perturb and Observe technique. Further, during the advanced stages of this research study, the model will compare the results obtained for tracking the maximum power point from model predictive controller and a PID-controller as they are integrated Perturb and Observe algorithm. The obtained results will verify that the adaptive PID-controller Perturb and Observe algorithm has limitation for tracking the precise MPP during the transient insulation conditions. As compared to the proposed adaptive/modified model predictive controller for Perturb and Observe algorithm we illustrate that by adopting this method we will get to establish more accurate and efficient results of the obtained power in any dynamic environmental conditions: advantages as on regulation time (six times under the accepted experimental conditions) and by the number of fluctuations.

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Section: Model Predictive Controller Schemementioning

confidence: 99%

An Adaptive Maximum Power Output Sustaining System for a Photovoltaic Power Plant Based on a Robust Predictive Control Approach

Elzein

Петренко

2020

Izv. vysh. ucheb

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“…Al pasar de los estudios, la planta MAGLEV se convirtió en un desafío para la ingeniería debido a su dinámica no lineal altamente inestable, provocando que se construyeran plantas de experimentación en muchas universidades. En la actualidad MAGLEV es reconocida como una planta de experimentación clásica en los sistemas de control [7].…”

Section: Introduccciónunclassified

“…Además, el estudiante tiene la oportunidad de probar diferentes estrategias de control y de realizar experimentos de identificación de sistemas reales. Las plantas didácticas son invaluables para la enseñanza de sistemas de control [7].…”

Section: Introduccciónunclassified

Modelado y control de un sistema de levitación magnética

2021

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En este artículo se realizó el modelado y control de un sistema de levitación magnética ( MAGLEV). Para ello se construyó un prototipo con materiales de bajo costo con el objetivo de realizar prácticas experimentales para la enseñaanza de sistemas de control en el programa de Ingeniería Física. Los resultados muestran que es posible utilizar el sistema para controlar la posición de una bola mediante una estrategia de control lineal.

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Design of Ideological and Political Online Education System Based on Neural Network

Quan

2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Control Systems Analysis and Design Labs with Educational Plants

Cited by 5 publications

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An Adaptive Maximum Power Output Sustaining System for a Photovoltaic Power Plant Based on a Robust Predictive Control Approach

An Adaptive Maximum Power Output Sustaining System for a Photovoltaic Power Plant Based on a Robust Predictive Control Approach

Modelado y control de un sistema de levitación magnética

Design of Ideological and Political Online Education System Based on Neural Network

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