The growing use of power electronic equipment, driven by increasing energy demand, has intensified issues related to harmonic distortion and poor power quality in electrical systems. Active Power Filters (APFs) have proven effective in mitigating such distortions, and this study introduces an advanced approach using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to enhance Pulse Width Modulation (PWM) control within APFs. The proposed ANFIS-based method efficiently handles asymmetric selective harmonic current mitigation by combining the adaptive learning capability of neural networks with the decision-making flexibility of fuzzy logic. The developed model is implemented in MATLAB to evaluate its performance. Simulation results demonstrate that the proposed ANFIS-based technique significantly improves power quality by reducing total harmonic distortion (THD) from 3.59% to 0.15%. Furthermore, the system achieves voltage outputs of 300 V at a 5-level inverter and 500 V at a 7-level inverter, with corresponding current values of 6 A and 10 A, respectively. Compared with conventional Artificial Neural Network (ANN)-based approaches, the proposed method achieves superior accuracy, stability, and harmonic suppression. These findings highlight ANFIS as a reliable and intelligent control solution for advanced power electronic applications, promoting improved efficiency and system dependability in modern power networks.
ANFIS, ANN, Fuzzy, Asymmetric, Harmonic, PWM, Active, Power Filters, APFs.
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