Feeder Load Balancing using Neural Network
This addresses phase balancing in distribution systems for utility operators, but appears incremental as it applies neural networks to an existing problem without claiming major breakthroughs.
The paper tackled the combinatorial optimization problem of phase balancing in distribution systems by proposing an optimal reconfiguration method using neural networks to switch on and off different switches, achieving balanced three-phase supply to end-users, with application examples demonstrated using real and simulated test data.
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.