Predicting Voltage Changes in Low-Voltage Secondary Networks using Deep Neural Networks

High penetrations of residential solar PV can cause voltage issues on low-voltage (LV) secondary networks. Distribution utility planners often utilize model-based power flow solvers to address these voltage issues and accommodate more PV installations without disrupting the customers already connected to the system. These model-based results are computationally expensive and often prone to errors. In this paper, two novel deep learning-based model-free algorithms are proposed that can predict the change in voltages for PV installations without any inherent network information of the system. These algorithms will only use the real power (P), reactive power (Q), and voltage (V) data from Advanced Metering Infrastructure (AMI) to calculate the change in voltages for an additional PV installation for any customer location in the LV secondary network. Both algorithms are tested on three datasets of two feeders and compared to the conventional model-based methods and existing model-free methods. The proposed methods are also applied to estimate the locational PV hosting capacity for both feeders and have shown better accuracies compared to an existing model-free method. Results show that data filtering or pre-processing can improve the model performance if the testing data point exists in the training dataset used for that model.