Limitations of Voltage-Oriented PI Current Control of Grid-Connected PWM Rectifiers With $LCL$ Filters

Dannehl, J.; Wessels, C.; Fuchs, Friedrich W.

doi:10.1109/tie.2008.2008774

Cited by 667 publications

(327 citation statements)

References 25 publications

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“…9 shows the results. One of the result curves is for the neural controller, another is for a tuned PI conventional GCC controller (Luo et al, 2009;Carrasco et al, 2006;Dannehl et al, 2009), and a third is for a "direct-current vector control" structure (DCC, (Li et al, 2010(Li et al, , 2011). The PI controllers were tuned until the controller performance was acceptable (Li et al, 2011).…”

Section: Comparison Of Neural Controller With Conventional Standard Amentioning

confidence: 99%

“…Limitations of these methods are that they can have slow response times to changing reference commands, can take considerable time to settle down from oscillating around the target reference state (Dannehl et al, 2009), and have difficulty recovering from short-circuit faults in either the generator or the power-grid. Hence neural-network based solutions have been proposed to overcome these difficulties, in this control problem and related ones (Qiao et al, 2008b(Qiao et al, , 2009aLi et al, 2012;Venayagamoorthy et al, 2002;Park et al, 2004;Qiao et al, 2008a;Venayagamoorthy et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

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An adaptive recurrent neural-network controller using a stabilization matrix and predictive inputs to solve a tracking problem under disturbances

Fairbank¹,

Li²,

Fu³

et al. 2014

Neural Networks

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractWe present a recurrent neural-network (RNN) controller designed to solve the tracking problem for control systems. We demonstrate that a major difficulty in training any RNN is the problem of exploding gradients, and we propose a solution to this in the case of tracking problems, by introducing a stabilization matrix and by using carefully constrained context units. This solution allows us to achieve consistently lower training errors, and hence allows us to more easily introduce adaptive capabilities. The resulting RNN is one that has been trained off-line to be rapidly adaptive to changing plant conditions and changing tracking targets.The case study we use is a renewable-energy generator application; that of producing an efficient controller for a three-phase grid-connected converter. The controller we produce can cope with random variation of system parameters and fluctuating grid voltages. It produces tracking control with almost instantaneous response to changing reference states, and virtually zero oscil-$ This work was supported in part by the U.S. National Science Foundation under Grant EECS 1059265/1102159, the Mary K. Finley Missouri Endowment, and the Missouri S&T Intelligent Systems Center.Email addresses: michael.fairbank@virgin.net (Michael Fairbank), sli@eng.ua.edu (Shuhui Li), xfu@crimson.ua.edu (Xingang Fu), E.Alonso@city.ac.uk (Eduardo Alonso), dwunsch@mst.edu (Donald Wunsch) Preprint submitted to Neural Networks September 23, 2013 lation. This compares very favorably to the classical proportional integrator (PI) controllers, which we show produce a much slower response and settling time. In addition, the RNN we propose exhibits better learning stability and convergence properties, and can exhibit faster adaptation, than is achievable with adaptive critic designs.

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Section: Comparison Of Neural Controller With Conventional Standard Amentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An adaptive recurrent neural-network controller using a stabilization matrix and predictive inputs to solve a tracking problem under disturbances

Fairbank¹,

Li²,

Fu³

et al. 2014

Neural Networks

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“…6 i L1 0°)1 1[ 59, 60] 1 for a low value of a [11,12]; lead filter [11]; notch filter [11,14] No extra sensor for i L1 control \ (1 Require accurate system information i L2 ±180°)1 Notch filter 1 for a high value of a [11,12,26]; lowpass filter [11]; notch filter [11,16] No extra sensor for i L2 control…”

Section: Digital Control Delay Impactmentioning

confidence: 99%

“…In summary, for low x res , it is possible to use i L1 closed-loop control, while it is hard for the i L2 closed-loop control to be stable. For high x res , i L1 closed-loop control is no longer stable, while i L2 closed-loop control works well [11,12,26,[61][62][63].…”

Section: Digital Control Delay Impactmentioning

confidence: 99%

“…Filter-based AD improves stability by adding a digital filter next to the current controller [11][12][13][14][15][16]. For feedbackbased AD, the state used for feedback can be the capacitor current [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32], the capacitor voltage [33][34][35][36], the inverterside current [37], the grid current [38][39][40][41] or a combination of multiple states [42][43][44][45][46][47][48][49][50].…”

Section: Introductionmentioning

confidence: 99%

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LCL-resonance damping strategies for grid-connected inverters with LCL filters: a comprehensive review

Xie

2017

J. Mod. Power Syst. Clean Energy

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Grid-connected LCL-filtered inverters are commonly used for distributed power generators. The LCL resonance should be treated properly. Recently, many strategies have been used to damp the resonance, but the relationships between different damping strategies have not been thoroughly investigated. Thus, this study analyses the essential mechanisms of LCL-resonance damping and reviews state-of-the-art resonance damping strategies. Existing resonance damping strategies are classified into those with single-state and multi-state feedback. Singlestate feedback strategies damp the LCL resonance using feedback of a voltage or current state at the resonance frequency. Multi-state feedback strategies are summarized as zero-placement and pole-placement strategies, where the zero-placement strategy configures the zeros of a novel state combined by multi-state feedback, while the poleplacement strategy aims to assign the closed-loop poles freely. Based on these mechanisms, an investigation of single-state and multi-state feedback is presented, including detailed comparisons of the existing strategies. Finally, some future research directions that can improve LCL-filtered inverter performance and minimize their implementation costs are summarized.

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PID Control System Design for Electrical Drives and Power Converters

Wang¹,

Chai

Yoo

et al. 2014

PID and Predictive Control of Electrical Drives and Power Converters Using Matlab®/Simulink®

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Limitations of Voltage-Oriented PI Current Control of Grid-Connected PWM Rectifiers With $LCL$ Filters

Cited by 667 publications

References 25 publications

An adaptive recurrent neural-network controller using a stabilization matrix and predictive inputs to solve a tracking problem under disturbances

An adaptive recurrent neural-network controller using a stabilization matrix and predictive inputs to solve a tracking problem under disturbances

LCL-resonance damping strategies for grid-connected inverters with LCL filters: a comprehensive review

PID Control System Design for Electrical Drives and Power Converters

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