Automation intelligence photovoltaic system for power and voltage issues based on Black Hole Optimization algorithm with FOPID

Abstract

As voltage and power issues across automation intelligence photovoltaic system, the efficient of Fractional Order Proportional Integral Derivative (FOPID) controller can be the primary approach to preventing unstable the current and voltage sensors, and this strategy is considered as an automation intelligent computing, in providing an automated intelligent photovoltaic computing system solution to select the appropriate FOPID gains for the most cases with four cases, many challenges are bound to be faced: (1) design an automated intelligent photovoltaic system including scalability and precision toward voltage and power issues for prioritizing current and voltage sensors, and (2) technical measured and simulated including the gap between convergences and an accurate matching amongst four cases considering all FOPID gains. Based on previous studies, no study had provided a solution for automation intelligent photovoltaic system during these gains settings issues. This study aimed to propose a novel automation intelligence photovoltaic system for voltage and power issues across current and voltage sensors based on Black Hole Optimization (BHO) algorithm to provide an efficient automation intelligence system using FOPID gains. The batteries, including three thermal model for three batteries have met the importance intelligent batteries in the automated intelligence PV system, must be augmented to develop system. Four cases for voltage and power issues conclude for the three PV panels. In the first case, a new automated intelligent PVs Panels between lowest and upper bounds is generated on the basis of the automated system with select fifteen gains for tuning and different bounds for other cases acquired via intelligent BHO algorithm. The execution of automated intelligent system possesses the precision and scalability presentation within tuned BHO algorithm. The objective validation results which indicate that the case 1 is a better case.