Power Flow Approximations for Multiphase Distribution Networks using Gaussian Processes

• URL: http://arxiv.org/abs/2504.21260v1
• Date: Wed, 30 Apr 2025 02:26:31 GMT
• Title: Power Flow Approximations for Multiphase Distribution Networks using Gaussian Processes
• Authors: Daniel Glover, Parikshit Pareek, Deepjyoti Deka, Anamika Dubey,
• Abstract summary: This study introduces a data-driven power flow method based on Gaussian Processes (GPs) to approximate the multiphase power flow model.<n> Simulation results using the IEEE 123-bus and 8500-node distribution test feeders demonstrate that the trained GP model can reliably predict the nonlinear power flow solutions.<n>We also conduct a comparative analysis of the training efficiency and testing performance of the proposed GP-based power flow approximator against a deep neural network-based approximator.
• Score: 0.2812395851874055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning-based approaches are increasingly leveraged to manage and coordinate the operation of grid-edge resources in active power distribution networks. Among these, model-based techniques stand out for their superior data efficiency and robustness compared to model-free methods. However, effective model learning requires a learning-based approximator for the underlying power flow model. This study extends existing work by introducing a data-driven power flow method based on Gaussian Processes (GPs) to approximate the multiphase power flow model, by mapping net load injections to nodal voltages. Simulation results using the IEEE 123-bus and 8500-node distribution test feeders demonstrate that the trained GP model can reliably predict the nonlinear power flow solutions with minimal training data. We also conduct a comparative analysis of the training efficiency and testing performance of the proposed GP-based power flow approximator against a deep neural network-based approximator, highlighting the advantages of our data-efficient approach. Results over realistic operating conditions show that despite an 85% reduction in the training sample size (corresponding to a 92.8% improvement in training time), GP models produce a 99.9% relative reduction in mean absolute error compared to the baselines of deep neural networks.

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