Analysis of photovoltaic penetration on voltage stability in the electrical distribution system of manabí using neural networks: a practical case study approach

Abstract The integration of photovoltaic generation into distribution networks enhances energy sustainability but poses challenges for voltage stability. This study analyses voltage stability in the Manabí distribution system using a static model with different levels of photovoltaic penetration. The experiments highlight the importance of voltage stability indices, determined using artificial neural networks with a 10-neuron structure in each hidden layer of the multilayer perceptron architecture. The scaled conjugate gradient training algorithm exhibits superior learning performance, achieving a mean square error of 5.6231E-05. The results confirm that voltage stability indices effectively determine the most resilient nodes for photovoltaic integration, with voltage variations ranging from 0.05% to 0.12% in distributed installations and from 0.04% to 0.05% in centralized locations. These findings validate the usefulness of voltage stability indices for assessing system stability and optimizing the placement of photovoltaic generation in distribution networks.