Abstract
Voltage stability in power distribution systems is crucial for preventing system inefficiencies and equipment damage, particularly when integrating Distributed Generation (DG). This study proposes an advanced method for minimizing voltage deviation in distribution feeders by optimizing the size and location of DG units using a hybrid Artificial Neural Network (ANN) and Fuzzy Logic approach. The ANN is employed to predict optimal DG placement and sizing, while Fuzzy Logic addresses the uncertainties within the distribution network. The proposed method is validated on a standard IEEE 33-bus distribution system, demonstrating significant improvements in voltage regulation and power loss reduction compared to conventional techniques. In addressing inconsistent power supply issues caused by voltage deviations, this study also focuses on optimizing DG deployment to stabilize voltage within the 0.95 to 1.05 per unit range. An intelligent algorithm is developed to identify weak buses and calculate voltage/current deviations, aiming to enhance overall power stability. Simulation results reveal that the voltage at bus 1, initially at 0.930 per unit, improves to 1.019 per unit with the intelligent algorithm, reducing the voltage deviation from 98.41% to 82.01%. Similarly, bus 8's voltage is stabilized within the desired range, underscoring the algorithm's effectiveness in improving distribution network stability.