978-1-6654-1741-9/22/$31.00 ©2022 IEEE Using ANN based MPPT controller to increase PV central performance Elaid Bouchetob Faculté des hydrocarbures et de la chimie, Laboratoire d’électrification des entreprise industrielles, LREEI Université de M’hamed Bouguerra, Boumerdes, Algeria e.bouchetob@univ-boumerdes.dz Bouchra Nadji Faculté des hydrocarbures et de la chimie, Laboratoire d’électrification des entreprise industrielles, LREEI Université de M’hamed Bouguerra, Boumerdes, Algeria b.nadji@univ-boumerdes.dz Abstract—Recently, searches for renewable energy and artificial intelligence have grown in popularity. Photovoltaic (PV) technology is an ecologically beneficial and cost-effective source of power, since it requires fewer components for maintenance than other green energy sources. Slow reaction to rapid variations in solar temperature and irradiance may render the MPP incapable of tracking. In addition, as a consequence of the deficiency of conventional approaches (P&O, IC, etc.), artificial intelligence (AI) methods such as (ANN, FLC, ANFIS, etc.) are becoming increasingly popular and are used to monitor the MPP in PV systems. Each of these methods has benefits. Utilizing the benefits of the DC-DC converter and the ANN-based MPPT controller, our work proposes a novel research and design to improve the performance and efficiency of the Ghardaia SKTM installation (Module 1) by leveraging the DC-DC converter and the MPPT controller. Keywords— PV system; ANN; MPPT; DC-DC converter; simulink. I. INTRODUCTION Investors and academics are developing renewable energy technologies to balance the energy shortfall and reduce pollution caused by fossil fuel emissions [1]. Photovoltaic (PV) technology is a low-cost, ecologically beneficial, and low- maintenance energy source [2]. Non-linear panel characteristics are caused by variables like solar radiation, temperature, and shading (I-V and P-V). In spite of this, the final set of curves reveals just a single peak power point, designated MPP. Several strategies and procedures may be used to increase the PV tracking efficiency, but maximum power point tracking (MPPT) has been rated the most effective and popular. Additionally, P&O[5] is being developed as part of these strategies. fractional open-circuit voltage[6], IC[7], NN[8], and FLC[9]. Artificial intelligence offers a more exact and quick reaction to changes in irradiance and temperature as compared to traditional MPPT methods such as P&O (IC, etc.). [10] In comparison, conventional MPPT approaches include: An artificial neural network, often known as an ANN, is a piece of software for computers that makes use of many forms of intelligence to rapidly link Irradiance and Temperature to output (Vmpp)[11]. The approach known as maximum power point tracking (MPPT), which is based on artificial neural networks (ANNs), makes use of the benefits offered by ANNs, including their noise rejection capabilities and the fact that they do not require any prior knowledge of physical factors that are important to PV systems. Module 1 is the primary focus of our study on this paper. We designed an entirely new connection by making use of artificial intelligence in order to increase the quantity of energy that is supplied into the inverter. Our goal was to increase the amount of power that was available. The output power of an ANN-based MPPT controller was measured and compared to the actual power generated on a cloudy winter day for the purpose of this investigation. II. MATERIAL AND METHODE A. Present Ghardaia SKTM central Sonelgaz Algeria started exploiting electric energy from Ghardaia SKTM PV central in 2015; this last consists of 8 modules with 4 different technologies of PV panels (Mono- crystalline, Multi crystalline, Amorphous, and Couch mince). The modules (1, 5 and 7) use the technology "mono- crystalline’’ with (105, 105, 255) KW of power production in successive; the modules (2, 6 and 8) use the technology "Multi- crystalline" with (98, 98, and 263) KW of power in successive; and module 3 uses the "Thin layers" technology with 100 KW of power. Finally, module 5 uses the "Amorphous" technology with 100 KW of power. The output of those modules has connected with eight inverters. B. Proposed design Module 1 has 420 panels. Every 20 panels are linked in series in a channel, resulting in an output voltage of 607 V at MPP. The parallel connection of 21 channels in a single junction box with an output current of 173A at MPP yields a module output power of 105 kW. However, we presented a new matrix in which 10 panels are connected in series for each channel (output at MPP is 303.5 V), and the junction box contains 42 channels with 346 A at MPP. Figure 1 illustrates both designs: 2022 2nd International Conference on Advanced Electrical Engineering (ICAEE) | 978-1-6654-1741-9/22/$31.00 ©2022 IEEE | DOI: 10.1109/ICAEE53772.2022.9961982 Authorized licensed use limited to: Consortium - Algeria (CERIST). Downloaded on December 24,2022 at 22:54:11 UTC from IEEE Xplore. Restrictions apply.