Detection of missing power meter readings using artificial neural networks

In a power distribution network, network topology information is essential for an efficient operation of the network. This information is not accurately available, due to uninformed changes that happen from time to time, or uncertain meter readings. Reliable prediction of system status is a highly demanded functionality of smart energy systems, which can enable users or human operators to react quickly to potential future system changes. This paper presents potential of artificial neural networks to determine missing power meter readings in medium voltage (MV) networks where the number of on-line measurements is limited, and state estimation relies heavily on estimates of power injections. The applicability of the approach is demonstrated through simulation using supervisory control and data acquisition and smart meter measurements recorded from an actual MV distribution network. Results are showing that artificial neural networks can have 100% measurements detection accuracy.