arXiv subjects

Prashanth N A

Publications and source records attributed to Prashanth N A.

2 recordsLinked to original sources

arXiv2026

STATCOM-Based Flicker Mitigation in Electric Arc Furnace Systems Using DQ-PI and Lyapunov-Based Nonlinear Control

Electric arc furnaces inject stochastic reactive-power swings that cause voltage flicker at the point of common coupling. This paper compares two STATCOM inner controllers for an 11~kV, 50~Hz furnace feeder: a split-frequency DQ-PI design and a Lyapunov-based nonlinear design whose integral state is retained in the storage function, giving global asymptotic stability of the nominal unsaturated current-error subsystem. Gains for both controllers were tuned by a frozen metaheuristic campaign and then evaluated over 600~s with an IEC~61000-4-15 digital flickermeter. A post-campaign audit showed that the stochastic arc realization used for the original comparison was periodic, so the two selected gain sets were frozen without retuning or reselection and evaluated on ten independent stochastic realizations that had been specified and archived before the STO-ENS validation campaign, together with a paired uncompensated case for each realization. Both controllers reduce the mean short-term flicker severity by about 62% relative to the STO-ENS uncompensated baseline, and no statistically detectable difference in mean flicker severity is found between them. They separate instead in realization sensitivity: the Lyapunov standard deviation across arcs is roughly one third that of DQ-PI. Controller ordering inferred from a single stochastic trajectory is therefore not a reliable measure of stochastic robustness, and ensemble validation is necessary.

eess.SY↗

arXiv2026

REMBGO for Evaluation-Limited Optimization: CEC2022 Benchmarking and EAF-STATCOM Generalization

Metaheuristic comparisons are often reported at evaluation budgets far larger than an expensive engineering simulation can afford. This paper presents REMBGO, an evaluation-budget-aware refinement of EMBGO. Its contribution is not any single operator: it is the integration of population preservation, scale-normalized exploration and recovery mechanisms whose parent-relative benefit is concentrated under restricted function-evaluation budgets. REMBGO is evaluated on CEC2022 at a primary, application-aligned budget of 1040 function evaluations, with a surrounding budget ladder reported descriptively to characterise budget dependence rather than as a family of controlled hypotheses. A component-removal study finds no individual mechanism whose removal survives multiplicity correction, consistent with an integrated rather than a single-operator contribution. The algorithm is then applied to EAF--STATCOM controller tuning, and the resulting frozen gain vectors are transferred without retuning to twenty pre-specified unseen stochastic arc realizations ($210$ fixed-gain simulations, all feasible). The selected gain-vector main effect accounts for only $3.6$--$5.8\%$ of the crossed validation variation, against approximately $61.4\%$ for interaction plus residual, and the matched REMBGO--EMBGO paired differences are controller-dependent in sign and not statistically significant. REMBGO is therefore presented as a resource-aware refinement whose benefit must be stated together with the evaluation budget, not as a universally superior optimizer.

cs.NE↗