Indonesian Journal of Electrical Engineering and Computer Science Vol. 26, No. 2, May 2022, pp. 629~638 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v26.i2.pp629-638  629 Journal homepage: http://ijeecs.iaescore.com Grey wolf optimization-recurrent neural network based maximum power point tracking for photovoltaic application Arumbu Venkadasamy Prathaban, Dhandapani Karthikeyan Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India Article Info ABSTRACT Article history: Received Oct 24, 2021 Revised Feb 21, 2022 Accepted Mar 16, 2022 To increase the photovoltaic (PV) power-generation conversion, maximum power point tracking (MPPT) is the primary concern. This works explains about the grey wolf optimization (GWO-RNN)-based hybrid MPPT method to get quick and maximum photovoltaic (PV) power with zero oscillation tracking. The GWO–RNN based MPPT method doesn’t need additional sensor for measuring irradiance and temperature variables. The NLT is used for the multi-level inverter (MLI) control strategy to achieve less harmonics distraction and less switching losses with better voltage and current profile. This employed methodology brings remarkable aspects in the PV boosting potential extraction. A GWO–RNN controlled LUO converter is a zero-output harmonic agreement impedance matching interface that is MPPT is performed by placing the PV modules between the load regulator power circuit and the load regulator power circuit. To actualize the proposed hybrid GWO–RNN model for the PV system, perturb and observe, RNN, ant colony optimization, and artificial bee colony MPPT techniques are employed. The MATLAB interfaced dSPACE interface is used to finish the hands-on validation of the intended grid-integrated PV system. The obtained results eloquently support the appropriate design of higher-performance control algorithms. Keywords: Control algorithms GWO-RNN Hybrid MPPT LUO converter Photovoltaic system This is an open access article under the CC BY-SA license. Corresponding Author: Dhandapani Karthikeyan Department of Electrical and Electronics Engineering SRM Institute of Science & Technology Kattankulathur, Tamil Nadu, India Email: karthipncl@gmail.com 1. INTRODUCTION The photovoltaic (PV) system has made significant progress in recent years. Under the global power point tracking condition, maximum power point trackers (MPPTs) play a critical role in ensuring superior PV energy generation. Many MPPT control techniques are available, and they are used according on the applications. The fuzzy logic-based MPPT technique plays an essential role in this regard. It offers a simpler architecture and durable design that can tackle the PV power system's uncertainties and nonlinearity difficulties. Traditional there is discussion of MPPT procedures such as trouble and observe (T and O), hill climbing (HC), and increase of conductance (INC) [1]–[4]. T and O and HC approaches are easier to implement in hardware, but they include large oscillations close to the maximum power point (MPP), resulting in power losses. The increase of conductance approaches exacts and adaptable under a variety of atmospheric circumstances, and it also includes modelling and practical complexity. Under inconsistent solar irradiation and for the determination of right perturbation size, the aforementioned procedures are inefficient and traditional. Several research groups are aiming to reduce the cost, improve tracking, and increase the reliability of PV-based industrial sectors and applications. The main goal of hybrid techniques and methodologies is to