Abstract

In light of the critical need for reliable power systems, this research aims to determine the best configuration of proportional, integral, and derivative (PID) controllers for a single-machine infinite bus (SMIB) system. There has been an effort to discover faster, more efficient, and resource-conservative optimization methods since conventional procedures can need more frequently changing working conditions. By filling in the gaps left by previous assessments, this study hopes to increase the accuracy of PID controllers inside the SMIB framework based on the general-purpose simulation system (GPSS). Hybrid butterfly particle swarm optimization (HBPSO) is one of its innovative strategies. The HBPSO algorithm is a huge step forward in developing a more stable and damping system as it optimizes the PID controller's gain settings and overall design. Extensive GPSS simulations prove that the HBPSO-optimized PID controller is functional. The controller significantly improved over previous methods, including the firefly proportional, integral, and derivative power system stabilizer (FPID-PSS) and biogeography-based optimization algorithm (BBO) based PID systems, with a settling time of only 10.10 seconds. There are a lot of cases when the system's operational dependability and transient stability are greatly enhanced by this substantially quicker reaction time. By guiding unstable and weakly damped eigenvalues toward a more desirable stable zone, eigenvalue analysis supports the claim that the PID-modified power system stabilizer (MPSS) model based on HBPSO has better damping performance and dynamic stability. The optimization of PID controllers in SMIB systems using the HBPSO algorithm has proven useful. This technology uniquely optimizes electrical grid systems, leading to more stable, efficient, and reliable future electrical distribution networks, and sets a new standard for attenuation and robustness.

Keyword

BOA, GPSS, HBPSO, PID, PSO, PSS, SMIB.

Cite this article

Kirange YK, Nema P.Optimization of PID controller parameters for an SMIB system using a hybrid butterfly particle swarm optimization approach. International Journal of Advanced Technology and Engineering Exploration. 2024;11(113):529-551. DOI:10.19101/IJATEE.2023.10102286

Refference

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