Adaptive Neurofuzzy Inference System Least-Mean-Square-Based Control Algorithm for DSTATCOM

Badoni, Manoj; Singh, Alka; Singh, Bhim

doi:10.1109/tii.2016.2516823

Cited by 76 publications

(35 citation statements)

References 22 publications

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“…[16]. The disadvantage with fixed value of "μ" is that the LMS algorithm is not able to achieve minimum static error with fast convergence rate [17].…”

Section: Adaptive Filter Using Lms Algorithmmentioning

confidence: 99%

Adaptive filter based current controller for Shunt Active filter

Sriranjani¹,

Jayalalitha²

2018

IJET

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This paper deals with the harmonic mitigation and reactive power compensation using Shunt Active filter. The performance of the Shunt active filter depends on the design and current control technique for generating a reference current. The design of the filter involves the iterative procedure for choosing the filter components so that it compensates the harmonics and reactive power for maximum load condition. The current controller method adopted for indirect current control technique where the reference signal is similar to the supply current signal. The Adaptive filter used in current controller mitigates harmonics and reactive power and voltage control loop of SAF balancing the active power between the load and supply unit. The learning rate is fixed by repeated simulation. The SAF active filter is tested in four load condition ie., nonlinear load(37% Total Harmonic Distortion(THD)),highly nonlinear load(>100% THD), linear load(<8% THD, power factor(pf) <0.5) and both nonlinear and linear load(37% THD and 0.7 pf). The study is carried out in MATLAB Simulink and the results are presented.

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“…[16]. The disadvantage with fixed value of "μ" is that the LMS algorithm is not able to achieve minimum static error with fast convergence rate [17].…”

Section: Adaptive Filter Using Lms Algorithmmentioning

confidence: 99%

Adaptive filter based current controller for Shunt Active filter

Sriranjani¹,

Jayalalitha²

2018

IJET

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“…In recent years, there has been an increasing interest in least squares adaptive control for both linear and nonlinear systems . In most continuous‐time applications of least squares adaptive control, the parametric model with a regressor is first filtered to remove the time derivatives of plant states, which not only increases the order of the controller but also degrades the estimation performance .…”

Section: Introductionmentioning

confidence: 99%

“…21 In recent years, there has been an increasing interest in least squares adaptive control for both linear and nonlinear systems. [22][23][24][25][26][27][28][29][30][31][32][33][34] In most continuous-time applications of least squares adaptive control, the parametric model with a regressor is first filtered to remove the time derivatives of plant states, which not only increases the order of the controller but also degrades the estimation performance. 22 An integrated least squares-based identification and adaptive control approach was proposed for a class of nonlinear systems with linear-in-the-parameters (LIP) uncertainties in the work of Krstic,22 where the time derivatives from the parametric model are absorbed into the parameter estimation such that regressor filtering is not necessary.…”

Section: Introductionmentioning

confidence: 99%

On parameter convergence in least squares identification and adaptive control

Sun

2019

Intl J Robust & Nonlinear

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Summary Least squares estimation is appealing in performance and robustness improvements of adaptive control. A strict condition termed persistent excitation (PE) needs to be satisfied to achieve parameter convergence in least squares estimation. This paper proposes a least squares identification and adaptive control strategy to achieve parameter convergence without the PE condition. A modified modeling error that utilizes online historical data together with instant data is constructed as additional feedback to update parameter estimates, and an integral transformation is introduced to avoid the time derivation of plant states in the modified modeling error. On the basis of these results, a regressor filtering–free least squares estimation law is proposed to guarantee exponential parameter convergence by an interval excitation condition, which is much weaker than the PE condition. And then, an identification‐based indirect adaptive control law is proposed to establish exponential stability of the closed‐loop system under the interval excitation condition. Illustrative results considering both identification and control problems have verified the effectiveness and superiority of the proposed approach.

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“…Shunt compensator provides suitable load compensation to prevent distorted currents entering the utility grid. Also, there is an increased interest in the development of advanced and fast digital control algorithms to achieve the desired power quality standards.…”

Section: Introductionmentioning

confidence: 99%

“…[2][3][4] Two review papers 5,6 present different configurations and controllers for shunt compensator. Advanced soft computing controllers based on artificial neural network, 7-9 fuzzy techniques, 10 adaline, 11 and adaptive neuro fuzzy inference system 12,13 are interesting but complex. Recently, a number of new controllers [14][15][16][17][18][19] based on repetitive computation and updating of weights such as anti Hebbian, 14 least means square-based technique, 15 adaptive notch-based control 16 and recursive techniques, 17,18 and other signal cancelation techniques 19 have gained immense popularity.…”

Section: Introductionmentioning

confidence: 99%

Design, analysis, and implementation of proportional-resonant filters for shunt compensation

Chittora

Singh

2017

Int Trans Electr Energ Syst

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Summary Recent technological advances in power electronics has led to a substantial increase in nonlinear loads. This has led to deterioration in quality of power in distribution networks and sparked interest in power quality issues and its solutions. This paper discusses the issue of harmonics, which is a power quality problem: their estimation and subsequent control and mitigation using a shunt compensator. The control of shunt compensator using proportional‐resonant (PR) controllers has been designed and implemented. Literature review presents a wide range of control techniques and proportional‐integral (PI) controllers are the simplest and most commonly used in industries. Recently introduced PR controllers have been presented as an alternative to PI controllers. These controllers are believed to overcome the shortcomings of the conventional PI controller, which include the presence of steady‐state errors and the need for transformation in three‐phase systems using the synchronous dq frame. The added advantages of PR controllers include the possibility of implementation for simultaneous detection and extraction of harmonic components without requiring excessive computational efforts. This paper highlights the application of PR controller for harmonic estimation and their reduction in the utility grid using shunt compensator. Closed loop control using PR controllers as filters has been demonstrated using simulation as well as experimental results. Extensive results are presented for different loading conditions, and they demonstrate the application of PR controllers in grid‐interfaced converter for power quality improvement.

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Adaptive Neurofuzzy Inference System Least-Mean-Square-Based Control Algorithm for DSTATCOM

Cited by 76 publications

References 22 publications

Adaptive filter based current controller for Shunt Active filter

Adaptive filter based current controller for Shunt Active filter

On parameter convergence in least squares identification and adaptive control

Design, analysis, and implementation of proportional-resonant filters for shunt compensation

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