Feeder Load Balancing using Neural Network

TL;DR

This paper introduces a neural network-based method for optimal reconfiguration of distribution feeders to achieve phase balancing, addressing a complex combinatorial optimization problem in power systems.

Contribution

It presents a novel neural network approach for feeder reconfiguration to improve phase balance, demonstrated with real and simulated data.

Findings

Effective phase balancing achieved in test cases.

Neural network method outperforms traditional approaches.

Applicable to real-world distribution systems.

Abstract

The distribution system problems, such as planning, loss minimization, and energy restoration, usually involve the phase balancing or network reconfiguration procedures. The determination of an optimal phase balance is, in general, a combinatorial optimization problem. This paper proposes optimal reconfiguration of the phase balancing using the neural network, to switch on and off the different switches, allowing the three phases supply by the transformer to the end-users to be balanced. This paper presents the application examples of the proposed method using the real and simulated test data.