研究目的
Investigating the performance of a neural network controller for a three-phase grid-connected Photovoltaic system to enhance power quality and ensure maximum power extraction with unity power factor.
研究成果
The neural network controller demonstrates superior performance compared to PI controllers, with faster response times, lower overshoot, and reduced THD. It effectively adapts to changes in solar irradiance, ensuring high power quality and unity power factor.
研究不足
The study is based on simulation results under specific conditions (temperature of 25°C and varying solar irradiance). Real-world application may present additional challenges not covered in the simulation.
1：Experimental Design and Method Selection:
The study employs a neural network controller for grid current control in a PV system, replacing traditional PI controllers. The Levenberg Marquardt algorithm is used for training the neural network.
2：Sample Selection and Data Sources:
Training and validation data for the neural controller are obtained through simulations of the system with PI controllers under varying solar irradiance conditions.
3：List of Experimental Equipment and Materials:
The system includes a PV array, a boost converter with Maximum Power Point Tracking (MPPT), a three-phase power inverter, and an RL filter.
4：Experimental Procedures and Operational Workflow:
The neural network controller is trained using simulation data. The performance of the neural controller is compared with that of PI controllers under the same conditions.
5：Data Analysis Methods:
The performance is evaluated based on response time, overshoot, and Total Harmonic Distortion (THD) of the grid current.
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