Driver Training Based Optimized Fractional Order PI-PDF Controller for Frequency Stabilization of Diverse Hybrid Power System

Abstract

This work provides an enhanced novel cascaded controller-based frequency stabilization of a two-region interconnected power system incorporating electric vehicles. The proposed controller combines a cascade structure comprising a fractional-order proportional integrator and a proportional derivative with a filter term to handle the frequency regulation challenges of a hybrid power system integrated with renewable energy sources. Driver training-based optimization, an advanced stochastic meta-heuristic method based on human learning, is employed to optimize the gains of the proposed cascaded controller. The performance of the proposed novel controller was compared to that of other control methods. In addition, the results of driver training-based optimization are compared to those of other recent meta-heuristic algorithms, such as the imperialist competitive algorithm and jellyfish swarm optimization. The suggested controller and design technique have been evaluated and validated under a variety of loading circumstances and scenarios, as well as their resistance to power system parameter uncertainties. The results indicate the new controller’s steady operation and frequency regulation capability with an optimal controller coefficient and without the prerequisite for a complex layout procedure.

Cascaded Fractional-Order Controller-Based Load Frequency Regulation for Diverse Multigeneration Sources Incorporated with Nuclear Power Plant

“…These states are estimated based on the cost function. Twenty-five (25) unrestricted mathematical assessment functions are applied to examine the performance of the presented SGO algorithm, along with six others that regularly used metaheuristics for assessment [53]. Furthermore, the suggested SGO's capability is evaluated using advanced real-real-life challenges on the latest CEC, such as CEC 2020, with the SGO demonstrating remarkable results in haggling with these inspiring optimization challenges [53].…”

“…Mathematically, the αth fractional-order (FO) derivative, denoted as S α , can be represented as follows [25]:…”

“…The FOI-FOPIDD2 controller gains have been established by reducing the cost function through the utilization of the SGO. The settling period is shortened, and high oscillations are swiftly suppressed using an ITSE-based cost function [25,33,37]: The FOI-FOPIDD2 controller gains are subject to the following restrictions.…”

“…Due to advances in computing power that enable simulation and accurate implementation of the fractional-order controller, the use of fractional-based controllers has recently drawn more attention in power engineering issues, specifically for power optimization control. The FOPID and their modification have been utilized in [23][24][25] as a secondary LFC to improve the frequency stability of the interconnected two region power systems. Numerous cascaded controller forms [26][27][28] have been used to improve frequency persistence in PSs.…”

“…These states are estimated based on the cost function. Twenty-five (25) unrestricted mathematical assessment functions are applied to examine the performance of the presented SGO algorithm, along with six others that regularly used metaheuristics for assessment 2…”