Neural Network Training for DVR Control Using d-Modification Technique

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 and ensuring stable power quality. Traditional control methods often struggle with nonlinearities and uncertainties in real-world power systems, making adaptive approaches like neural networks particularly valuable for real-time voltage compensation.

The d-modification method significantly enhances NN-based control robustness by preventing parameter drift—a common issue in online training where weights may grow excessively. In code implementation, this technique introduces a damping term that penalizes large parameter updates through a modification to the weight update rule: Δw = -η(∂E/∂w) - σw, where σ is the damping coefficient. This stabilizes the learning process without significantly degrading performance.

When applied to DVR control, the neural network can be trained to dynamically adjust voltage compensation based on real-time measurements. The training algorithm typically involves feedforward computation of voltage error and backpropagation with d-modification to update weights. The d-modification ensures the network adapts to varying load conditions while maintaining stability, making the control system more reliable under disturbances like voltage sags, swells, or harmonic distortions.

The training process implements an adaptive law incorporating the d-modification term, which balances learning speed and stability. Key functions in the implementation include: real-time voltage sampling, error calculation between reference and measured voltages, and the modified weight update procedure. This approach ensures smooth convergence even with measurement noise or unmodeled dynamics, resulting in a robust DVR controller that effectively handles real-world uncertainties.

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. The implementation typically requires defining appropriate network architecture (number of layers and neurons), selecting activation functions, and tuning the damping coefficient σ for optimal performance.