Multi control adaptive fractional orderPIDcontrol approach forPV/wind connected grid system

Abstract

Summary This paper introduces a novel genetic optimize multi‐control adaptive fractional order PID (AFOPID) for Photovoltaic (PV) and Wind connected grid system. The proposed AFOPID controller is optimized by a genetic algorithm (GA) to initialize the controller parameters. The renewable energy sources are mathematically modeled using multi control approach (MCA). In the proposed work, the MCA involves the maximum power point tracking (MPPT), dc link voltage, and current control and quadrature axis modeling. Furthermore, the current control functionalities are performed through an adaptive approach of AFOPID, where the controlling parameters are updated by measured error at every instant. The idea behind the research is to improve the tracking efficiency by introducing better control in order to gain maximum power from the source with minimized total harmonic distortion (THD). The proposed control scheme is tested using computer‐aided experimentation by varying the output of renewable energy sources, inverter uncertainty, and grid voltage variations. The results are benchmarked against the conventional fuzzy logic controllers, fractional‐order PID, and PI controllers. Moreover, to evaluate the effectiveness of the proposed controller, the MCA‐AFOPID is compared with ant colony optimization (ACO) and particle swarm optimization (PSO) optimized FOPID controller respectively. The proposed controller outperforms as compared to other controller.

PMU-Based FOPID Controller of Large-Scale Wind-PV Farms for LFO Damping in Smart Grid

“…The FOPID controllers are based on fractional-order calculus, which is a famous mathematical concept that generalizes conventional integer-order calculus into arbitrary orders. This issue has a history of more than 300 years, yet its application in different fields has been realized only recently due to its implementation complexity in reality [22][23][24][25][26][27][28][29][30].…”

“…Fractional differential equations based on fractionalorder calculus have emerged as one of the most important areas of interdisciplinary interest in recent years [20][21][22][23][24][25]. The fractional-order differentiator, which can be denoted by a general fundamental operator as a general form of differential and integral operators, is defined as follows [22]:…”

“…The conventional PID controller has three variables (K P , K I , and K D ) while the FOPID has five variables (K P , K I , K D , λ, and δ). The ability to manage model uncertainties in nonlinear applications, more flexibility in tuning, and high disturbance rejection are other advantages of the FOPID controller [20,24]. The fractional-differential-equation of this type of controller is expressed as follows [22,29]:…”

“…One of the types of modern controllers that have been studied recently is the fractional-order proportional-integralderivative (FOPID) controller, which is the general structure of proportional-integral-derivative (PID) controllers [22]. The FOPID controller is mathematically based on the fractional-order calculus [22][23][24][25][26][27][28][29][30]. Due to the inherent characteristics of the FOPID controllers, these types of controllers have a wide stability region.…”

African Vulture Optimization Algorithm-Based PI Controllers for Performance Enhancement of Hybrid Renewable-Energy Systems

“…This converter setup allows these systems to use the maximum power available, regardless of the environmental conditions. The error between the output PV/wind system's voltage and the MPPT algorithm's reference voltage is used to control the converter's electronic switches, which are minimized by using lots of controllers such as slide-mode control [30], fuzzy logic [31], feedback linearization [32], a proportional-integral-derivative (PID) controller [33], conventional proportional plus integral (PI) [34], etc. Nonetheless, using certain controllers in the industry is limited due to practice and sophisticated computations.…”