This study introduces a novel method to tackle the significant problem of power quality deterioration caused by nonlinear power electronic converter loads. In this project, a novel concept was introduced: integrating an Adaptive NeuroFuzzy Inference System (ANFIS) into the control system of a Unified Power Quality Conditioner (UPQC). The limitations of conventional PI controllers in regulating the DC-link capacitor voltage are effectively overcome through the intelligent adaptation and learning capabilities of the ANFIS-based controller. Extensive simulations conducted in MATLAB/Simulink provide a comprehensive evaluation of the ANFIS-controlled UPQC's performance. The results demonstrate the efficacy of our approach in mitigating power quality challenges, including voltage and current distortions. The intelligent controller outperforms classical controllers while introducing the capability for subjective decision-making based on acquired knowledge. This research significantly contributes to the advancement of power quality management by offering a viable and intelligent solution for UPQC devices. The ANFISbased method shows great potential for improving the dependability and effectiveness of power distribution networks, especially when dealing with nonlinear loads. These findings emphasize the need to explore and implement smart control strategies in power systems to maintain high-quality electrical power for both residential and industrial users.
A Novel ANFIS-Controlled Unified Power Quality Conditioner (UPQC) for Enhanced Power Quality
31.07.2024
1842484 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
BASE | 2020
|UPQC: An Exhaustive Solution to Improve Power Quality
IEEE | 2020
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