Modeling and Performance Analysis of a Grid-Connected Photovoltaic System with Advanced Controller considering Varying Environmental Conditions

This paper presents a mathematical model of 255 kW grid-connected solar photovoltaic (SPV) system. To study the performance characteristics SPV system, new hybrid adaptive grasshopper optimization algorithm with recurrent neural network (AGO-RNN) control technique was implemented. Furthermore, power quality at point common coupling (PCC) has been studied using conventional (PSO) and proposed AGO-RNN controllers. The PV system were analyzed under varying environmental (variable irradiance temperature) conditions considering 3 different cases such as (i) standard test (STC), (ii) variable radiation constant temperature, (iii) temperature. For each case, total harmonic distortion (THD) calculated technique, results compared particle swarm technique. is initially developed connected to three-level NPC inverter, an MPPT-based perturbation observation algorithm. Later, controlled by pulse width modulation (PWM) controller then integrated main grid PCC. advantage this exploiting separate DC-DC converter between module inverter. Finally, simulated on MATLAB for analyzing based its I-V P-V characteristics, inverter voltage, power, gird current, factor, THD conditions. simulation demonstrate that current magnitude SPVGC are improved cutting-edge PSO in all three scenarios, value less than 1.6%, which within permitted limits IEC 61727 standards.