Design of Backstepping Direct Power Control for Three-Phase PWM Rectifier

Wai, Rong‐Jong; Yang, Yan

doi:10.1109/tia.2019.2893832

Cited by 68 publications

(24 citation statements)

References 32 publications

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“…Moreover, the current command of the slave inverter is given by the current of the master inverter. For a fair comparison, suitable control parameters for the conventional PIC framework are selected via the Bode-plot analysis [34] to get a control response to be similar to the one controlled by the designed ATSMC and the proposed AFNNISMC at the nominal case. The steady-state experimental results of the parallelinverter system controlled by the PIC, the designed ATSMC, and the proposed AFNNISMC under a resistive load (Rl=12.5Ω) are depicted in Fig.…”

Section: B Experimental Verificationmentioning

confidence: 99%

Design of Adaptive Fuzzy-Neural-Network-Imitating Sliding-Mode Control for Parallel-Inverter System in Islanded Micro-Grid

Yang

Wai

2021

IEEE Access

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In this study, an adaptive fuzzy-neural-network-imitating sliding-mode control (AFNNISMC) is developed for a parallel-inverter system in an islanded micro-grid (MG) via a master-slave current sharing strategy. For ensuring the system-level stability, an entire dynamic model is constructed by viewing the parallel-inverter system as a whole. First, a total sliding-mode control (TSMC) scheme, and the TSMC plus an adaptive observer to form an adaptive TSMC (ATSMC) framework are designed for the parallelinverter system. Then, a four-layer fuzzy neural network (FNN) is investigated to imitate the TSMC law to improve the system robustness, overcome the drawback of the dependence on detailed system dynamics, and deal with the chattering phenomena caused by the TSMC. According to the Lyapunov stability theorem and the projection algorithm, network parameters in the FNN are regulated online by employing the approximation error between the FNN and the TSMC law to ensure the convergence of the network and the stability of the control system. Thereby, the performance of high power quality and high-precision current sharing between inverters can be guaranteed even if system uncertainties exist. Moreover, the proposed AFNNISMC system can achieve the seamless disconnection and re-connection of slave inverters from and into an energized parallel-inverter system, which improves the redundancy and operation flexibility. In addition, numerical simulations and experimental results are given to demonstrate the feasibility and effectiveness of the proposed AFNNISMC scheme. Furthermore, performance comparisons with the ATSMC strategy and a conventional proportional-integral control (PIC) framework are provided to verify the superiority of the proposed scheme.INDEX TERMS Total sliding-mode control (TSMC), fuzzy neural network (FNN), adaptive control, parallel-inverter system, islanded micro-grid (MG), master-slave current sharing.

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Section: B Experimental Verificationmentioning

confidence: 99%

Design of Adaptive Fuzzy-Neural-Network-Imitating Sliding-Mode Control for Parallel-Inverter System in Islanded Micro-Grid

Yang

Wai

2021

IEEE Access

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“…Moreover, the author in [41] created different lookup tables to store different combinations of DC/AC inverter switches based on active and reactive power variation to obtain a higher power factor and reduce total harmonic distortion. Although the lookup table is a simple technique and results in a faster response, it causes varying switching frequency, which might cause large power fluctuation [42]. Application of lookup table to intermittent resources such as solar PV might be not proper because of uncertainties that complicate finding an accurate estimation of output power which is the essence of our proposed methodology.…”

Section: Lookup Table Based Controlmentioning

confidence: 99%

A Novel Cooperative Control Strategy to Mitigate PV Power Fluctuation and Increase its Penetration

Alsharif

2021

IEEE Access

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This paper proposes a novel optimization-based power sharing control strategy to moderate PV power intermittency and increase its penetration. Such a control strategy aims to minimize the total PV power sensitivity to the light change by re-distributing the demand power among available PV units such that the demanding power met with minimal variation. PV plant is coupled with the grid utility, and it should maintain a specific amount of power determined by the grid operator. However, due to the PV power sensitivity to the light, the delivered power at the coupling point fluctuates and leads to undesirable responses such as grid frequency excursion, stability problems, and unoptimized power generation. To address such issues, we formulate an optimization problem to reduce the total PV power sensitivity by selecting the optimal reference voltage for each PV panel. We define the power sensitivity as the rate of power change to light fluctuation to be the objective function of the proposed optimization problem, and the selected reference voltages for all PV panels are the decision variables. Compared to other conventional power-sharing techniques such as the same utilization level, droop control, and lookup table, MATLAB simulation results verify the contribution of the proposed algorithm's performance superiority in PV power sensitivity reduction, grid stability assurance, and power generation enhancement. Moreover, when there is insufficient PV power to meet the grid operator's demand, the proposed algorithm automatically sets the entire PV plant to work at its MPP without switching circuits.

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“…Robustness feature is also ensured with proper gain tuning. Previously, backstepping has been applied to control several power electronic systems [19][20][21][22][23][24][25] and other engineering applications. Earlier works focused on the grid integration of VSCs, where output current and its dynamics are known.…”

Section: Introductionmentioning

confidence: 99%

Robust backstepping output voltage controller for standalone voltage‐sourced converters

Achlerkar

Panigrahi

2020

IET Renewable Power Generation

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Design of Backstepping Direct Power Control for Three-Phase PWM Rectifier

Cited by 68 publications

References 32 publications

Design of Adaptive Fuzzy-Neural-Network-Imitating Sliding-Mode Control for Parallel-Inverter System in Islanded Micro-Grid

Design of Adaptive Fuzzy-Neural-Network-Imitating Sliding-Mode Control for Parallel-Inverter System in Islanded Micro-Grid

A Novel Cooperative Control Strategy to Mitigate PV Power Fluctuation and Increase its Penetration

Robust backstepping output voltage controller for standalone voltage‐sourced converters

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