Case Studies: Utilities
Real-Time Monitoring of Power Transmission
Problem
Data-overwhelm in monitoring critical power systems
Solution
LSTM prediction and detection of line and bus voltage sags
Results
- Real-time monitoring
- Adapted power generation output to timely downstream line conditions and usage
Innovate or Stagnate
Real Results
A multi-state power utility deployed Federally mandated sensors on their power transmission grid, but the volume and velocity of data was overwhelming for both centralized database systems and human analysts, leading to usage and transmission analyses that were weeks or even months behind the occurrence of events on the grid and did not allow for capacity planning or preventative maintenance.
APPROACH
We employed several deep-learning models from LSTM time-series prediction to CNNs to analyze specific lines and buses in a distributed fashion. The output from these models aggregated to whole grid models that could be used to identify critical patterns such as voltage sags or usage fluctuation and returned results on an hourly basis.
RESULT
The solution increased operator visibility of the performance of the transmission system both globally (across the grid) and locally (for specific lines or busses), enabling improved generation allocation and timely preventative maintenance to decrease capital costs.
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