Dynamic Modeling and Robust Control by ADRC of Grid‐Connected Hybrid PV‐Wind Energy Conversion System

Aboudrar, Imad; Hani, Soumia El; Heyine, Mohamed Saleck; Naseri, Nisrine

doi:10.1155/2019/8362921

Cited by 20 publications

(13 citation statements)

References 29 publications

(34 reference statements)

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“…The error obtained by the observer is given by:

e = z - \overset{z}{true}

its dynamics is then written as:

\overset{e}{true} = ((), A - italicLC) e

with

A - italicLC = ([], \begin{array}{c} - β_{01} & 1 & 0 & \dots & 0 \\ - β_{02} & 0 & 1 & \dots & 0 \\ ⋮ & ⋮ & ⋮ & ⋱ & ⋮ \\ - β_{0 n} & 0 & 0 & \dots & 1 \\ - β_{0 ((), n + 1)} & 0 & 0 & \dots & 0 \end{array})

To obtain a good functioning of the observer (

e \to 0

when

t \to \infty

), the

L

matrix gains should be chosen in the way that

((), A - italicLC)

constitutes a Hurwitz matrix, which means that its polynomial characteristic poles of

P_{ESO} ((), s)

all have strictly negative parts 43

P_{ESO} ((), s) = \det ((), {sI}_{n + 1} - ((), A - italicLC)) = s^{n + 1} + β_{01} s^{n} + β_{02} s^{n - 1} + \dots + β_{0 n} s + β_{0 ((), n + 1)}

Generally, the pole placement technique is used to determine the observer's gains.…”

Section: Mathematical Analysis Of the Proposed Strategiesmentioning

confidence: 99%

LVRT capability enhancement of a grid connected three phase PV system by ADRC and DSOGI FLL

Aboudrar

Hani

Mediouni

et al. 2021

Int Trans Electr Energ Syst

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Summary Low voltage ride‐through is considered as one of the key technologies for the operation of grid‐connected photovoltaic power generation systems. At present, the most used control methods are based mainly on PI controllers, and their robustness results are still unsatisfactory. In contrast, this article introduces a new robust control strategy that uses the compensation effect of “total disturbance” before affecting the system; this control strategy is called Active Disturbance Rejection Control (ADRC) method. In addition, most of the controllers use the standard SRF PLL to achieve the phase lock and grid synchronization. However, when the grid voltage is distorted or other unbalanced, it is necessary to use the positive sequence voltage component of the fundamental wave as a reference, but the component of the negative sequence cannot be effectively filtered because of its narrow bandwidth, and then, accurate phase lock cannot be achieved, which may cause a failure. In this purpose, a Double Second Order Generalized Integrator based Frequency Locked Loop (DSOGI‐FLL) is proposed and used to improve the system operating performance and non‐off‐line capability. In order to confirm the robustness of the proposed control strategies, a multiple simulations tests were done by utilizing the Matlab/Simulink software. The results have demonstrated the superiority of the proposed methods and their robustness to symmetrical and unsymmetrical faults.

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“…The error obtained by the observer is given by:

e = z - \overset{z}{true}

its dynamics is then written as:

\overset{e}{true} = ((), A - italicLC) e

with

A - italicLC = ([], \begin{array}{c} - β_{01} & 1 & 0 & \dots & 0 \\ - β_{02} & 0 & 1 & \dots & 0 \\ ⋮ & ⋮ & ⋮ & ⋱ & ⋮ \\ - β_{0 n} & 0 & 0 & \dots & 1 \\ - β_{0 ((), n + 1)} & 0 & 0 & \dots & 0 \end{array})

To obtain a good functioning of the observer (

e \to 0

when

t \to \infty

), the

L

matrix gains should be chosen in the way that

((), A - italicLC)

constitutes a Hurwitz matrix, which means that its polynomial characteristic poles of

P_{ESO} ((), s)

all have strictly negative parts 43

P_{ESO} ((), s) = \det ((), {sI}_{n + 1} - ((), A - italicLC)) = s^{n + 1} + β_{01} s^{n} + β_{02} s^{n - 1} + \dots + β_{0 n} s + β_{0 ((), n + 1)}

Generally, the pole placement technique is used to determine the observer's gains.…”

Section: Mathematical Analysis Of the Proposed Strategiesmentioning

confidence: 99%

LVRT capability enhancement of a grid connected three phase PV system by ADRC and DSOGI FLL

Aboudrar

Hani

Mediouni

et al. 2021

Int Trans Electr Energ Syst

Self Cite

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show abstract

“…For our case, the system is of the first order. In general, the canonical form of the first-order system is [33]…”

Section: Active Disturbance Rejection Controllermentioning

confidence: 99%

Active Disturbance Rejection Control for a Five-Level Cascaded H-Bridge Inverter Fed Induction Motor Sensorless Field-Oriented

Bourhichi

Oukassi

Bahir

et al. 2021

Mathematical Problems in Engineering

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In this paper, a robust vector control sensorless based on three Active Disturbance Rejection Controllers (ADRC) for an induction motor fed by a five-level cascaded H-bridge was developed. The estimation of rotor position is achieved using a Luenberger estimator. The proposed model considers the disturbances and the inaccuracies of the system and compensates them using the extended state observer of ADRC. Moreover, a five-level cascaded H-bridge inverter is the best topology to minimize the torque ripples and to improve the current output of IMunder control. The suggested control system was simulated under different conditions using MATLAB-Simulink. The results demonstrate the effectiveness and the robustness of the proposed system against disturbances such as variations of parameters and load torque.

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“…Photovoltaic technology is an excellent RES for controlling electromagnetic energy production from such a large centralized facility to a large number of tiny decentralized and distributed systems, reducing environmental impact and increasing resource production in remote places. The costs of photovoltaic components are rapidly decreasing, enabling photovoltaic systems more affordable than traditional energy sources [7]. Lithium battery packages and an energy storage stack make up the powerful engine of a typical proton electrolyte membrane hybrid hydrogen fuel cell.…”

Section: Introductionmentioning

confidence: 99%

Artificial Deep Neural Network in Hybrid PV System for Controlling the Power Management

Sahoo

Amirthalakshmi²,

Ramesh³

et al. 2022

International Journal of Photoenergy

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The analysis of different components of a grid-linked hybrid energy system (HES) comprising a photovoltaic (PV) system is presented in this work. Due to the increase of the population and industries, power consumption is increasing every day. Due to environmental issues, traditional power plants alone are insufficient to supply customer demand. In this case, the most important thing is to discover another approach to meet customer demands. Most wealthy countries are now concentrating their efforts on developing sustainable materials and investing considerable amounts of money in product development. Wind, solar, fuel cells, and hydro/water resources are among the most environmentally benign renewable sources. To control the variability of PV generation, this sort of application necessitates the usage of energy storage systems (ESSs). Lithium-ion (Li-ion) batteries are the most often used ESSs; however, they have a short lifespan due to the applied stress. Hybrid energy storage systems (HESSs) started to evolve as a way to decrease the pressure on Li-ion batteries and increase their lifetime. This study represents a great power management technique for a PV system with Li-ion batteries and supercapacitor (SC) HESS based on an artificial neural network. The effectiveness of the suggested power management technique is demonstrated and validated using a conventional PV system. Computational models with short-term and long-term durations were used to illustrate their effectiveness. The findings reveal that Li-ion battery dynamical stress and peak value are reduced, resulting in longer battery life.

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Dynamic Modeling and Robust Control by ADRC of Grid‐Connected Hybrid PV‐Wind Energy Conversion System

Cited by 20 publications

References 29 publications

LVRT capability enhancement of a grid connected three phase PV system by ADRC and DSOGI FLL

LVRT capability enhancement of a grid connected three phase PV system by ADRC and DSOGI FLL

Active Disturbance Rejection Control for a Five-Level Cascaded H-Bridge Inverter Fed Induction Motor Sensorless Field-Oriented

Artificial Deep Neural Network in Hybrid PV System for Controlling the Power Management

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