Hybrid AI–GA–DQN Optimization for Harmonic Mitigation in Smart Inverters
DOI:
https://doi.org/10.32492/nucleus.v5i2.5202Keywords:
Inverter, Harmonic Mitigation, Genetic Algorithm, Deep Q-Network, Artificial Intelligence, Mathematical ModelingAbstract
This paper proposes a hybrid Artificial Intelligence–Genetic Algorithm–Deep Q-Network (AI–GA–DQN) control framework for inverter systems to minimize harmonic distortion and enhance dynamic voltage regulation. A mathematical state-space model incorporating harmonic dynamics is formulated, and a hybrid optimization approach is applied to adaptively tune modulation indices and switching parameters. The GA provides global parameter optimization, while DQN enables reinforcement learning-based adaptation under variable load conditions. MATLAB/Simulink simulations show that the proposed controller achieves a 75.6% reduction in Total Harmonic Distortion (THD), improves power factor to 0.995, and enhances system efficiency to 97.8%, compared with conventional PWM and GA-only controllers. The results confirm the effectiveness of the AI–GA–DQN strategy in achieving real-time harmonic suppression and superior transient performance in intelligent power converters.
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