A novel approach for model order reduction of interconnected power systems is proposed, using the concept of interim reduced models (IRM) alongside geometric mean optimization (GMO). By refining the solution space through balanced residualization, this method significantly enhances both the accuracy and efficiency of complex system simulations.
The ever-growing complexity of modern power systems presents considerable challenges for modeling, analysis, and control. Efficiency is of utmost importance, particularly as systems upscale and existing methodologies struggle to keep pace. The newly proposed method leverages BRM to determine the initial reduction models, coupled with the GMO algorithm to fine-tune these models effectively. This approach mitigates potential inaccuracies caused by random selection of optimization parameters, thereby enhancing the overall efficacy of model order reductions.
The IRM concept introduced allows for strategic identification of decision variables and their respective boundaries, creating a focused framework for optimization. This ensures not just viability of the results but also promotes stability across the interconnected networks analyzed. Through rigorous assessments involving three complex interconnected multiple test systems, the proposed technique is validated to show significant advancements over traditional methods, showcasing reduced error indices and improved performance metrics.
Published on March 9, 2025, by researchers at Amrita Vishwa Vidyapeetham, this study demonstrates the versatility of the proposed methods, not only within the power systems domain but extending to other advanced engineering applications, including robotics and biomedical systems.
Key elements of this investigation include the application of Hankel singular values (HSV) to determine reduced model orders, with the study emphasizing how these methodologies outperform established approaches, as evidenced through comparative evaluations of step responses and Bode diagrams.
The findings suggest this innovative modeling technique retains original system characteristics, faithfully preserving transient and steady-state responses, all the more impressive considering its computational merit. Key metrics such as integral squared error (ISE) and root mean square error (RMSE) reaffirm the reliability of IRM-based approaches.
Overall, this research opens avenues for not just quicker simulations but powerful control design strategies, potentially applicable to systems like wind turbines and electric vehicles. The authors envisage extending this technology to encompass fractional-order and multi-input multi-output systems, reflecting the boundless opportunities for enhancement within engineering practices.
Future research could explore more integrated approaches, possibly merging multiple meta-heuristic algorithms to tackle unique modeling challenges, as no single algorithm guarantees optimal results across varied systems. By systematically refining how search spaces are defined and exploited, researchers aim to innovate continuously within the modeling framework.
Transparent and well-defined methods such as these position the discipline for heightened accuracy without the trade-offs typically associated with similar undertakings. Embracing such advancements promises to bolster the fidelity of system responses across diverse applications well beyond traditional power systems.