Abstract
This study proposes a new optimization technique, known as the eagle strategy arithmetic optimization algorithm (ESAOA), to address the limitations of the original algorithm called arithmetic optimization algorithm (AOA). ESAOA is suggested to enhance the implementation of the original AOA. It includes an eagle strategy to avoid premature convergence and increase the populations’ efficacy to reach the optimum solution. The improved algorithm is utilized to fine-tune the parameters of the fractional-order proportional-integral-derivative (FOPID) and the PID controllers for supporting the frequency stability of a hybrid two-area multi-sources power system. Here, each area composites a combination of conventional power plants (i.e., thermal-hydro-gas) and renewable energy sources (i.e., wind farm and solar farm). Furthermore, the superiority of the proposed algorithm has been validated based on 23 benchmark functions. Then, the superiority of the proposed FOPID-based ESAOA algorithm is verified through a comparison of its performance with other controller performances (i.e., PID-based AOA, PID-based ESAOA, and PID-based teaching learning-based optimization TLBO) under different operating conditions. Furthermore, the system nonlinearities, system uncertainties, high renewable power penetration, and control time delay has been considered to ensure the effectiveness of the proposed FOPID based on the ES-AOA algorithm. All simulation results elucidate that the domination in favor of the proposed FOPID-based ES-AOA algorithm in enhancing the frequency stability effectually will guarantee a reliable performance.
Highlights
Introduction published maps and institutional affilWith the rapid growth of the global population, the establishment of new power plants becomes essential to supply all citizens’ electrical requirements
This study proposes a novel improved algorithm derived from the arithmetic optimization algorithm (AOA), which is called eagle strategy arithmetic optimization algorithm (ESAOA) to select the optimum PID controller parameters in the secondary control loop
This study proposed a recently improved meta-heuristic known as ESAOA that was selected meticulously according to its merits
Summary
This section introduces the construction of the studied two-area interconnected power plant in detail. Here,tothe to generator the wind turbine generator obtaining random wind output power oscillations. The captured output power of the wind is responsible for obtaining random wind output power oscillations. + 1 turbine that penetrated in area-1, A signifies the rotor swept area in m , ρ signifies the air density (nominally 1.22 represents output power wind turbine that penetrated in coeffiarea-1, 3), V Pwt represents thethe rated wind speedofinthe m/s,. Shows the random output power of wind energy.
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