LoCoVolt: Distributed Detection of Broken Meters in Smart Grids through Stream Processing

Paper in proceeding, 2018

Smart Grids and Advanced Metering Infrastructures are rapidly replacing traditional energy grids.
The cumulative computational power of their IT devices, which can be leveraged to continuously monitor the state of the grid, is nonetheless vastly underused.

This paper provides evidence of the potential of streaming analysis run at smart grid devices.
We propose a structural component, which we name \name{} (Local Comparison of Voltages), that is able to detect in a distributed fashion malfunctioning smart meters, which report erroneous information about the power quality. This is achieved by comparing the voltage readings of meters that, because of their proximity in the network, are expected to report readings following similar trends. Having this information can allow utilities to react promptly and thus increase timeliness, quality and safety of their services to society and, implicitly, their business value.
As we show, based on our implementation on Apache Flink and the evaluation conducted with resource-constrained hardware (i.e., with capacity similar to that of hardware in smart grids) and data from a real-world network, the streaming paradigm can deliver efficient and effective monitoring tools and thus achieve the desired goals with almost no additional computational cost.