. 2025 Jan 21;15:2641. doi: 10.1038/s41598-025-85393-5

Table 2.

Comparative analysis of the advantages between the proposed AI-based UPQC system and recent research.

Feature	Proposed AI-UPQC System	Recent Research Works
Control Strategy	ANN with Lyapunov optimization for both shunt and series APF control	NARMA-L2 Controller and PI Controller with Adaptive Lizard Algorithm⁵
Power Quality Improvement	Reduces THD below 1%, VUF from 1.5–0.8%, Unity power factor correction	Only partial improvement in THD and power factor using conventional methods⁵
Response Time	40% faster dynamic response with optimized control	Limited by fixed algorithm configurations⁵
Adaptability	Real-time adaptation to varying load conditions using AI-driven control	Requires initial configuration and manual parameter tuning⁵
Implementation Complexity	Plug-and-play capability with model-free control	Complex configuration requirements and control parameter settings³¹
Integration Capability	Compatible with renewable energy sources and smart grid systems	Limited integration capabilities with modern power systems²⁹
Cost Effectiveness	Reduced maintenance costs and improved energy efficiency (95%)	Higher operational costs due to fixed control algorithms³⁰
Stability	Enhanced system stability through Lyapunov optimization	Stability issues under varying load conditions^29,34