Design and implementation of partial feedback linearizing controller for grid-connected fuel cell systems

This paper proposes a control methodology based on feedback linearization for a doubly fed induction generator (DFIG) incorporating the magnetic saturation. The feedback linearization algebraically converts a nonlinear system model into a linear model, allowing the use of linear control techniques. Feedback linearization control depends on the model of the system and is therefore sensitive to parameter variations. The doubly fed induction generator (DFIG) operating under the magnetic saturation conditions results in the nonlinear variation of magnetizing inductance, which affects the performance of the control algorithm. From this stand point, on the basis of the dynamic model of the doubly fed induction generator considering magnetic saturation, the feedback linearizing control technique has been formulated. The mathematical model of the doubly fed induction generator, integrating the magnetic saturation has been formulated in the stator flux-oriented reference frame with rotor current and stator magnetizing current as state variables. Simulation studies demonstrate that the inclusion of magnetic saturation in the feedback linearization control of the doubly fed induction generator model increases its accuracy and results in a more efficient and reliable synthesis of the control algorithm.

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“…where L � d|ψ s |/d|i ms | which represents the slope of magnetization characteristics. Substitution of equation (13) in equation (12) yields…”

Section: Stator Voltage Equationsmentioning

confidence: 99%

“…e feedback linearization control (FBLC) is widely used as a nonlinear control strategy in power electronics and drives [7][8][9]. It has been implemented in excitation controllers of synchronous generators [10][11][12] and also in control inputs for voltage source inverters in renewable power application [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Formulation of Feedback Linearizing Control of Doubly Fed Induction Generator Including Magnetic Saturation Effects

Jose

Chattopadhyay

2020

Mathematical Problems in Engineering

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“…Various types of linear controllers for micro-grid inverters including classical controllers, Proportional Resonant (PR) controllers, and Linear Quadratic Gaussian (LQG) controllers were reported [6][7][8]. Nonlinear controllers for grid-connected inverter systems (GCIS) such as sliding mode, feedback linearization, and hysteresis controllers have been proposed in [9][10][11][12][13]. In [14], a current-control is proposed for voltage-source inverters using the H∞ robust control technique.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Approximation Control of PV Grid-Connected Inverters

et al. 2021

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Three-phase inverters are widely used in grid-connected renewable energy systems. This paper presents a new control methodology for grid-connected inverters using an adaptive fuzzy control (AFC) technique. The implementation of the proposed controller does not need prior knowledge of the system mathematical model. The capabilities of the fuzzy system in approximating the nonlinear functions of the grid-connected inverter system are exploited to design the controller. The proposed controller is capable to achieve the control objectives in the presence of both parametric and modelling uncertainties. The control objectives are to regulate the grid power factor and the dc output voltage of the photovoltaic systems. The closed-loop system stability and the updating laws of the controller parameters are determined via Lyapunov analysis. The proposed controller is simulated under different system disturbances, parameters, and modelling uncertainties to validate the effectiveness of the designed controller. For evaluation, the proposed controller is compared with conventional proportional-integral (PI) controller and Takagi–Sugeno–Kang-type probabilistic fuzzy neural network controller (TSKPFNN). The results demonstrated that the proposed AFC showed better performance in terms of response and reduced fluctuations compared to conventional PI controllers and TSKPFNN controllers.

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“…Consequently, the FC/ELZ system, as electrochemical energy storage, has been frequently adopted in hybrid energy systems to balance the power against the intermittency and uncertainty of RESs and the variations of load [2][3][4][5][6][7][8][9][10][11][12]. In [3][4][5], the hybrid energy system including FC/ELZ was investigated and some systematic controllers were proposed to compensate the intermittent power of the wind or photovoltaic (PV) energies in a standalone system.…”

Section: Introductionmentioning

confidence: 99%

Control of grid‐forming application for fuel cell/electrolyser system

Quan

Dou

et al. 2020

IET Renewable Power Generation

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With the rapid development of renewable energy sources (RESs), adopting power-to-gas to improve the penetration of RESs is promising. This study designs a grid-forming application for the fuel cell/electrolyser (FC/ELZ) system to supply the frequency support for the grid as well as the voltage source. The detailed control methods including the DC, AC voltage controls and the power synchronisation control are designed for the FC/ELZ system so that it can operate independently without any other energy sources. Several simulation results are provided to demonstrate the effectiveness of the designed grid-forming application of the FC/ELZ system.

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Design and implementation of partial feedback linearizing controller for grid-connected fuel cell systems

Cited by 25 publications

References 25 publications

Mathematical Formulation of Feedback Linearizing Control of Doubly Fed Induction Generator Including Magnetic Saturation Effects

Mathematical Formulation of Feedback Linearizing Control of Doubly Fed Induction Generator Including Magnetic Saturation Effects

Adaptive Fuzzy Approximation Control of PV Grid-Connected Inverters

Control of grid‐forming application for fuel cell/electrolyser system

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