Optimal Performance of Photovoltaic-Powered Water Pumping System

• Published in: Mathematics (Basel) 2023-02, Vol.11 (3), p.731
• Main Authors: Altimania, Mohammad R., Elsonbaty, Nadia A., Enany, Mohamed A., Gamil, Mahmoud M., Alzahrani, Saeed, Alraddadi, Musfer Hasan, Alsulami, Ruwaybih, Alhartomi, Mohammad, Alghuson, Moahd, Alatawi, Fares, Mosaad, Mohamed I.
• Format: Article
• Language: English
• Subjects: adaptive neuro-fuzzy inference system; Adaptive systems; Alternative energy sources; Artificial neural networks; Efficiency; Energy storage; Flow velocity; Food science; Fuzzy logic; Induction electric motors; induction motor; Induction motors; Industrial applications; Inverters; Irradiance; Mathematical analysis; Maximization; maximum power point tracking; Maximum power tracking; Optimization; Optimization techniques; optimum operation; photo-voltaic; Photovoltaic cells; Power factor; Pumping; Renewable energy sources; Renewable resources; Solar energy; Solar energy industry; Technology application; water pumping system
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math11030731

Photovoltaic (PV) systems are one of the promising renewable energy sources that have many industrial applications; one of them is water pumping systems. This paper proposes a new application of a PV system for water pumping using a three-phase induction motor while maximizing the daily quantity of water pumped while considering maximizing both the efficiency of the three-phase induction motor and the harvested power from the PV system. This harvesting is performed through maximum power point tracking (MPPT) of the PV system. The proposed technique is applied to a PV-powered 3 phase induction motor water pumping system (PV-IMWPS) at any operating point. Firstly, an analytical approach is offered to find the optimal firing pattern of the inverter (V-F) for the motor through optimal flux control. This flux control is presented for maximizing the pump flow rate while achieving MPPT for the PV system and maximum efficiency of the motor at any irradiance and temperature. The provided analytical optimal flux control is compared to a fixed flux one to ascertain its effectiveness. The obtained feature of the suggested optimal flux control validates a significant improvement in the system performances, including the daily pumped quantity, motor power factor, and system efficiency. Then converting the data from the first analytical step into an intelligent approach using an adaptive neuro-fuzzy inference system (ANFIS). This ANFIS is trained offline with the input (irradiance and temperature) while the output is the inverter pattern to enhance the performance of the proposed pumping system, PV-IMWPS.