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A novel nature-inspired maximum power point tracking (MPPT) controller based on ACO-ANN algorithm for photovoltaic (PV) system fed arc welding machines

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Abstract

In this paper, a metaheuristic optimized multilayer feed‐forward artificial neural network (ANN) controller is proposed to extract the maximum power from available solar energy for a three-phase shunt active power filter (APF) grid connected photovoltaic (PV) system supplying an arc welding machine. Firstly, in order to improve the maximum power point (MPP) delivered by PV arrays and to overcome the drawbacks in the conventional MPPT method under irradiation variation, a hybrid MPPT controller is designed, in which the input parameters include the PV array voltage and current, and the output parameter is the duty cycle of the DC/DC boost converter. The proposed approach abbreviated as ANN-ACO MPPT controller is based on an ant colony optimization (ACO) algorithm which is useful to train the developed ANN and to evolve the connection weights and biases to get the optimal values of duty cycle converter corresponding to the MPP of a PV array. Secondly, aiming to meet the various grid requirements such as power quality improvement, distortion free signals etc., a three-phase shunt APF is utilized, and a direct power control algorithm is designed for distributing the solar energy between the DC-link capacitor, arc welding machine and the AC grid. Finally, the performance of proposed control system is confirmed by simulation tests on a 12.2 kW PV system. Both simulation and experimental results have demonstrated that the deigned ANN-ACO MPPT controller can provide a better MPP tracking with a faster speed and a high robustness with a minimal steady-state oscillation than those obtained with the conventional INC method. Also, with the use of a three-phase shunt APF, all the power fluctuations from the arc welding machine disturbances are damped out and the output active and reactive power become controllable.

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Acknowledgements

The first author thanks the LEPCI Laboratory of the University of Ferhat Abbas Setif-1, Algeria for the special support that made possible the preparation of this work.

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Authors and Affiliations

  1. Research Center in Industrial Technologies (CRTI), P.O. Box 64, 16014, Cheraga, Algiers, Algeria

    Badreddine Babes, Amar Boutaghane & Noureddine Hamouda

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  1. Badreddine Babes
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  2. Amar Boutaghane
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  3. Noureddine Hamouda
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Correspondence to Badreddine Babes.

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Appendices

Appendix

Design parameters of DC-DC boost converter

The Switching frequency of DC-DC boost circuit is Fs1 = 12 kHz. The inductor of the DC-DC boost converter is L = 3 mH, and the capacitor is Cpv = 100 µF, respectively.

Design parameters of three-phase shunt APF

The DC link voltage of shunt APF is Vdc = 600 V, the AC inductor of shunt APF is LF = 3 mH, the DC voltage PI regulator parameters are Kp1 = 0.15 and Ki1 = 20.

The DC-link capacitance of shunt APF is Cdc = 1100 µF, the line to line grid voltage is Vg = 220 V (RMS), the grid frequency is fg = 50 Hz, Line parameters are Rg = 0.023 Ω and Lg = 3 mH, respectively.

Design parameters of arc welding machine

Three-phase bridge rectifiers with C1 = 300 pF, output filter parameters are L0 = 8 µH and C0 = 4 µF, respectively. The output resistor is R0 = 0.2 Ω. The turns Ratio of HFT is N = 16. The switching frequency of full bridge buck circuit is Fs2 = 50 kHz. The output DC voltage of arc welding machine is Vw = 20 V. the arc voltage PI regulator parameters are Kp2 = 0.05 and Ki2, = 3, respectively. The hysteresis band of welding current regulator is ∆I = 0.1A.

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Babes, B., Boutaghane, A. & Hamouda, N. A novel nature-inspired maximum power point tracking (MPPT) controller based on ACO-ANN algorithm for photovoltaic (PV) system fed arc welding machines. Neural Comput & Applic 34, 299–317 (2022). https://doi.org/10.1007/s00521-021-06393-w

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