NN trining for DVR control using d modification

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NN trining for DVR control using d modification

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Neural Network Training for DVR Control Using d-Modification

Neural networks (NNs) have become a powerful tool for controlling dynamic systems, including Dynamic Voltage Restorers (DVRs). DVRs are essential in power systems for mitigating voltage disturbances, ensuring stable power quality. Traditional control methods may struggle with nonlinearities and uncertainties in real-world power systems, making adaptive approaches like neural networks particularly useful.

One effective technique for improving NN-based control is the d-modification method. This approach enhances the robustness of adaptive control by preventing parameter drift—a common issue in online training where weights may grow excessively. The d-modification technique introduces a damping term that penalizes large parameter updates, stabilizing the learning process without significantly degrading performance.

When applied to DVR control, the NN can be trained to adjust voltage compensation dynamically based on real-time measurements. The d-modification ensures that the network adapts to varying load conditions while maintaining stability. This makes the control system more reliable under disturbances like voltage sags, swells, or harmonic distortions.

The training process involves adjusting the NN weights using an adaptive law that incorporates the d-modification term. This term balances learning speed and stability, ensuring smooth convergence even in the presence of noise or unmodeled dynamics. The result is a robust DVR controller that can handle real-world uncertainties effectively.

By leveraging neural networks with d-modification, power engineers can develop more resilient voltage regulation systems. This approach not only enhances performance but also reduces the need for extensive system modeling, making it a practical solution for modern power grids.