Integrating Cloud-Based Power Factor Correction with ANFIS-Controlled UPQC for Power Quality Enhancement

– The increasing presence of nonlinear loads in electrical networks has resulted in power quality degradation, including voltage instability, reactive power losses, and harmonic distortions. Traditional power factor correction (PFC) methods and unified power quality conditioners (UPQC) often rely on static or PI-based controllers, which have limitations in dynamic response and adaptability. This research proposes an integrated approach combining cloud-based automated power factor correction (APFC) and an adaptive neuro-fuzzy inference system (ANFIS)-controlled UPQC to optimize power quality. Cloud computing enables real-time monitoring and data-driven optimization, while ANFIS ensures intelligent compensation for voltage and current distortions. MATLAB/Simulink simulations depicts that the proposed system efficiently maintains voltage stability and reduced total harmonic distortion (THD) compared to conventional methods. The results highlight the advantages of leveraging cloud-based intelligence and AI-driven control strategies in modern power distribution networks.