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Optimization of a Low-Power Inverter for AI-Based Solar Power Systems Using Reinforcement Learning a) Department of Electrical Engineering Education, Universitas Negeri Medan, Medan, Indonesia. Abstract The rapid growth of rooftop photovoltaic (PV) installations has intensified the demand for compact, high-efficiency inverters capable of maintaining power quality under variable irradiance. This study proposes a reinforcement-learning-driven control strategy for a low-power single-phase inverter that interfaces residential PV arrays with local AC loads. A Deep Q-Network (DQN) agent is trained to minimize total harmonic distortion (THD) and switching losses while tracking a sinusoidal reference under wide-ranging solar conditions. The research follows a two-stage methodology. First, a detailed MATLAB/Simulink model of the inverter-incorporating real PV I-V data and an L-filter-is used to train the DQN offline with experience replay and prioritized sampling. Second, the learned policy is ported to a 32-bit microcontroller and validated on a 500 W hardware prototype fed by a 1 kW PV array and programmable DC source. Experimental results demonstrate that the proposed controller achieves a THD of 1.8 % at rated power, outperforming conventional PI (3.9 %) and model-predictive control (2.4 %) baselines. Efficiency improves by 2.7 % owing to optimized switching sequences, and dynamic response time to irradiance steps (800 →- 300 W/m2) is reduced by 35 %. Reliability tests over a 72-hour duty cycle confirm stable operation without thermal derating. These findings indicate that reinforcement learning can deliver high power quality and energy savings in low-power solar inverters, supporting the broader adoption of AI-based control in distributed renewable energy systems. Keywords: Low-power inverter- Reinforcement learning- Solar PV- Total harmonic distortion- Energy efficiency Topic: Sciences, Engineering and Material Science |
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