Mylinking, a company that specializes in network traffic visibility, network data visibility, and network packet visibility, is taking a step forward in diagnosing faults in active distribution networks. They have introduced real-time fault detection that leverages statistical inference to improve the accuracy and speed of fault diagnosis.

Active distribution networks are complex systems that integrate renewable energy sources, distributed generators, and energy storage devices. Managing these networks is a challenging task, which requires advanced tools and techniques to ensure reliable and efficient operation. Fault diagnosis is an essential part of managing active distribution networks. It is critical to identify the location and type of faults quickly to prevent further damage and restore power supply as soon as possible.

Traditional fault diagnosis techniques rely on the analysis of voltage and current signals collected from the network. However, these signals may be affected by noise, distortion, and other factors that can result in inaccurate fault detection. To overcome these limitations, Mylinking has proposed a new approach that uses synchrophasor data and covariance index to locate faults accurately and quickly.

Synchrophasors are time-synchronized measurements of voltage and current phasors that provide a high-resolution view of the power system dynamics. By leveraging synchrophasor data, Mylinking can detect and diagnose faults in real-time. The covariance index is a statistical measure that characterizes the correlation between synchrophasor data at different locations in the network. It helps to identify the direction of fault propagation and narrow down the location of the fault.