Algorithm could help detect and reduce power grid faults
The team at Binghamton University in New York state have shown that the Singular Spectrum Analysis (SSA) algorithm may be the best tool to help remotely detect and locate power grid faults.
“Theoretically, the SSA algorithm is an optimal approach for accurate and quick detection. However, it has not been adopted in real-world engineering applications. We adapted and improved the algorithm for the new application in power grid areas,” said Yu Chen, associate professor of electrical and computer Engineering at Binghamton University, and co-author of a paper with Zekun Yang, Ning Zhou and Aleksey Polunchenko.
The redundancy in the network provides stability but is complex and filled with vulnerable points. Beyond the simple tree limb taking out a wire in a windstorm, hackers can break in and change how electricity flows subtly, which can have a cascading and potentially catastrophic effect on infrastructure. Currently, the time and location of anomalies within the grid is determined by well-known formulas such as the Event Start Time (EST) algorithm, which calculates differing arrival times of power changes in different geographic locations. Even though the differences are incredibly small, they are enough to triangulate the location of changes.
The Binghamton team used simulation data generated by the Power System Tool box to prove that the SSA algorithm is faster and more robust at finding changes in the power grid from generator or transmission line problems. SSA may even be used to predict problem spots in the future.
“At the current stage, the algorithm can only detect and locate problems, and it cannot predict future problems,” said Chen. “It laid a solid foundation for the next step: prediction. Being able to detect subtle changes in the power grid promptly, our approach has the potential to predict future problems by including a power system model.”
Despite the confirmation of SSA’s effectiveness, fine tuning is still needed, including more ways to gather accurate geolocations of problems, more simulation testing and real-world data collection to validate the algorithm and polish it to cope with more realistic scenarios.
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