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Performance Enhancement of DVR Using Adaptive Neural Fuzzy and Extreme Learning Machine-Based Control Strategy

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Abstract

This paper proposes adaptive voltage control strategies for a three-wire dynamic voltage restorer based on a self-adaptive fuzzy neural network for estimation of fundamental weight components and optimal extreme learning machine control for smooth voltage regulation. The SAFNN overcomes the sequential tuning process of classical ANFIS controllers by simultaneously optimizing the structure and parameters of unknown system dynamics. The construction of SAFNN does not require control base rules in the initial phase and this enhances the learning capability to track the approximated voltage error. The proposed SAFNN employs the linear transformation of the input signal to optimize the required fuzzy rules and speed up the online learning process to achieve the higher accuracy of the control results. The meta-algorithms whale optimization algorithm and Harris hawks optimization is employed for online dynamic behavior identification. The SAFNN-WOA optimizes the membership function to estimate direct and quadrature axis voltage for prediction performance in terms of fast-tracking predefined signals, fewer oscillations, and voltage compensation ability. The dc-link voltage is accurately estimated by employing OELM-HHO and provides improved power quality performance during voltage disturbances. The OELM has the inherent feature of randomly generating the initial weights which effectively evaluates the output weight during the online training procedure and enhances the learning rate. The statistical tools MSE, RMSE, ME, SD, and R during the training stage were evaluated as 168.9052, 12.9964, − 5.5524e−07, 12.9965 and during the testing stage the value are 168.9611, 12.9985, − 0.11566, 12.9986 and 0.78654. These statistical assessment values confirm the goodness-of-fit of the predictive model. The result shows that the proposed hybrid controls SAFNN-WOA and OELM-HHO-based DVR significantly achieves the prediction accuracy for both training and test dataset. Nevertheless, the proposed intelligent DVR model outperforms the other state-of-the-art methods.

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Abbreviations

DVR:

Dynamic voltage restorer

SAFNN:

Self-adaptive fuzzy neural network

OELM:

Optimal extreme learning machine

ELM:

Extreme learning machine

ANFIS:

Adaptive neuro-fuzzy inference system

WOA:

Whale optimization algorithm

HHO:

Harris hawks optimization

MFs:

Membership function

MSE:

Mean square error

RMSE:

Root mean square error

ME:

Mean error

SD:

Standard deviation

R:

Coefficient of correlation

PQ:

Power quality

PI:

Proportional integral

FNNs:

Fuzzy and neural networks

PCC:

Point of common coupling

VSC:

Voltage source converter

FIS:

Fuzzy inference system

NN:

Neural network

SOFC:

Self-organizing fuzzy controller

AFC:

Enhanced adaptive fuzzy control

FAE:

Fuzzy approximation error

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Funding

Under Science and Engineering Research Board-New Delhi Project (Extra Mural Research Funding Scheme), Grant No.SB/S3/EECE/030/2016, Dated 17/08/2016.

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Authors and Affiliations

  1. Department of Electrical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, 395007, India

    Prashant Kumar, Sabha Raj Arya & Khyati D. Mistry

Authors
  1. Prashant Kumar
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  2. Sabha Raj Arya
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  3. Khyati D. Mistry
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Contributions

The author contributed to the research design, results in the evaluation and elaboration of the manuscript. The main contribution is that the proposed SAFNN model is integrated with WOA to build an online fuzzy inference system that automatically extracts the fuzzy rules for the estimation of the fundamental component under non-ideal grid voltage. The OELM based on the HHO controller is implemented for the fast estimation and mitigation of harmonics in the distribution system. Solution techniques are proposed to enhance its performance based on the intelligent control scheme for fast-tracking the reference signal and reducing the complexity of the control system. The intelligent DVR control scheme shows excellent under dynamic operation at different load conditions.

Corresponding author

Correspondence to Sabha Raj Arya.

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Conflict of interest

The authors declare that they have no conflict of interest to publish this paper.

Appendices

Appendix 1

AC mains: 415 V (L-L), 50 Hz; Rs = 0.01 Ω, Ls = 2 mH; load current (iL) = 21A; Ripple filters Rf = 6Ω, Cf = 10 μF; Interfacing inductor Lf = 1.3 mH; DC bus capacitor Cdc = 3300 μF; DC bus voltage Vdc = 300 V; AC bus voltage (Vt) = 339 V; load: 18 kVA (0.8 p.f. lagg.), sample time (ts) = 20 µs.

Appendix 2: Parameters Setting for Optimization Algorithm

Main three common control Parameters includes Number of search agents: 30 and Max number of iterations = 1000. WOA and HHO Training samples: 8000 dataset, Testing samples: 2000 dataset. Training Algorithm: WOA, a = [0,2], b = [− 1,1], Membership Function (Gaussian Type): Clusters = 15 and Number of fuzzy rules = 15.

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Kumar, P., Arya, S.R. & Mistry, K.D. Performance Enhancement of DVR Using Adaptive Neural Fuzzy and Extreme Learning Machine-Based Control Strategy. Int. J. Fuzzy Syst. 24, 3416–3430 (2022). https://doi.org/10.1007/s40815-022-01265-4

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