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DVR Using Randomized Self-Structuring Fuzzy and Recurrent Probabilistic Fuzzy Neural-Based Controller

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Abstract

This article proposes a machine learning-based dynamic voltage restorer (DVR) control strategy for addressing the conventional design procedure of fuzzy logic that needs human expertise to decide membership functions. A randomized evolving Takagi–Sugeno (ReTSK) machine learning approach is proposed for the estimation of fundamental weight components from the polluted grid for enhanced DVR compensating capability. A recurrent probabilistic fuzzy neural network (RPFNN) control is employed for encountering the manual parameters tuning approach of a proportional-integral controller that depends on the optimized coefficients during severe voltage disturbances. The outlined approaches demonstrate robust performance by improving the DC- and AC-link voltage regulation with parametric variations and disturbances. The recommended RPFNN control provides a better response during the transitory state in terms of performance indicators like rise time (0.15 s), settle time (0.08 s), overshoot (3.3%), undershoot (3.3%) and recovery time (0.42 s). Advanced meta-algorithms like Seagull and Rat Swarm Optimization are employed for the self-tuning of the controller’s parameters and compared with competitive algorithms like Moth-Flame Optimization, Spotted Hyena Optimizer and Harris Hawks Optimization. The results of best-fitted forecasting models ReTSK are evaluated by using statistical performance indices (MSE, RMSE, ME, SD and R), while RPFNN are assessed by (MSE, RMSE MAE, MAPE, SI and R). The performance results confirm the validation of the developed control strategies which emphasize their relevance.

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Authors and Affiliations

  1. Department of Electrical Engineering, S. V. National Institute of Technology, Surat, Gujarat, 395007, India

    Sabha Raj Arya, Khyati D Mistry & Prashant Kumar

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  1. Sabha Raj Arya
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  2. Khyati D Mistry
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  3. Prashant Kumar
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Correspondence to Sabha Raj Arya.

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Appendices

Appendix A

Simulation System Parameters

Supply mains: 410 V(L-L), 50 Hz; grid impedance (Rs and Ls) = 0.01 Ω, 2mH, respectively; load current (iL) = 21A; filter: Rf = 6 Ω, Cf = 10 μF; interface inductor Lf = 1.3mH; dc bus capacitor Cdc = 3300 μF; dc bus voltage Vdc = 300 V; AC bus voltage (Vt) = 335 V; load:18kVA (0.8 p.f. lag.), sample time (ts) = 20 µs.

ReTSK-SOA Data: Parameters considered for simulation are total epochs (1000), type/number of MFs (Gaussian), and learning method (SOA). The parameters of the proposed model after training with SOA are given as the number of neurodes = 126, linear and nonlinear variables are 60 and 90, respectively, the total variables = 150, training data pairs = 63,751 and fuzzy rules = 15. The K-FCM is utilized for data clustering in this method as it requires less parameters to find the optimal global solution for various inputs.

Appendix B

Experimental Parameters

Polluted supply voltage: 110 VL-L, 50 Hz; 0.353 kVA load; 2 A load current (iL); DVR interface two winding transformer: 4 kVA, 125/125 V; dc bus voltage (Vdc) = 60 V; capacitor at dc bus (Cdc) = 4700 μF; and interfacing inductor (Lf) = 0.5mH; switching ripple filtering elements: Rf = 10 Ω and Cf = 120μF.

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Arya, S.R., Mistry, K.D. & Kumar, P. DVR Using Randomized Self-Structuring Fuzzy and Recurrent Probabilistic Fuzzy Neural-Based Controller. J. Inst. Eng. India Ser. B (2024). https://doi.org/10.1007/s40031-023-00973-1

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