Developed and Intelligent Structure of a Control for PV Water Treatment System

• Published in: Energies (Basel) Vol. 16; no. 18; p. 6540
• Main Authors: Zitouni, Naoufel, Gammoudi, Rabiaa, Attafi, Rim, Mezgahni, Dhafer
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023
• Subjects: Deep learning; Energy; Force and energy; Infection control; IRFOC (indirect rotor field-oriented control); MPPT (maximum power point tracking); Photovoltaic cells; Purification; PVG (photovoltaic generator); SCV (Static converter); Simulation; SVPWM (space vector pulse width modulation); UV lamp (ultraviolet lamp); Water; Water quality; Water treatment; Tunisia
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en16186540

The subject of this work is a UV-irradiated water disinfection prototype intended for use in rural areas where access to water is difficult. Given the favorable climatic conditions of our country, the use of photovoltaic panels as a source of energy is particularly interesting, and has relevance in regions with a similar climate. PV energy being a fluctuating source that influences water disinfection operations, we have developed a database to distribute the energy available to the loads (UV lamps, electric pumps) in order to ensure a better quality of the water. This database is used in deep learning to model water disinfection phenomena. This method is able to adjust the speed instructions of the motor pump (therefore the flow rate) and the UV irradiation according to the energy available to ensure optimal water quality. Several other techniques have been implemented to control the instructions generated by the deep learning developed, to control the motor, the inverter and the DC/DC converter (IRFOC, SVPWM, sliding mode). All these approaches are tested in real time and they represent good results in terms of water treatment control. The effectiveness of these types of control is proven by the results obtained.