Dynamic Optimal Adjustment of Power Supply Load in Distribution Networks Based on Adaptive Neural Networks

Due to the low efficiency of fixed-point calculation of output power for dynamic optimization of power supply load in distribution networks, the adjustment frequency has significantly decreased. Therefore, an analysis of dynamic optimization and adjustment of power supply load in distribution networks based on adaptive neural networks is proposed. According to the current measurement requirements, a multi-level approach will be adopted to adjust the phase sequence balance parameters of user loads and enhance adjustment efficiency. Constructing an adaptive neural network dynamic optimization model for power supply load in distribution networks, dynamically adjusting the output power load by adjusting weights, and capturing the power supply load of the distribution network. By adopting the method of comparing and verifying network losses, combined with adaptive neural network technology, considering various factors such as power supply load, weather conditions, equipment status, etc., an optimization objective function and constraint function are constructed to achieve dynamic optimization and adjustment of power supply load in the distribution network. The test results show that the proposed optimization method targets four groups of 30, 60, 90, and 120 people, and the final optimization adjustment frequency is relatively high. This indicates that with the assistance and support of adaptive technology, the designed dynamic optimization adjustment strategy for power load is more reliable, efficient, and has achieved better practical application results, completing the application verification.