Driver training based optimized fractional order PI-PDF controller for frequency stabilization of diverse hybrid power system
Document Type
Article
Publication Date
4-1-2023
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.
Keywords
renewable energy resources, optimization techniques, fractional order controller, power system, load frequency control, heuristic techniques, driver training-based optimization
Divisions
sch_ecs
Funders
"Young Talent Sub-project of Ningbo Yongjiang Talent Introduction Programme (20100859001)
Publication Title
Fractal and Fractional
Volume
7
Issue
4
Publisher
MDPI
Publisher Location
ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND