Power blackouts have a major impact on the supply and production chain, damage assets and wasted product, and even cause accidents.
Being able to anticipate blackouts can significantly reduce their impact.
It is possible to anticipate power blackouts by looking for weak signals that prelude significant events. Finding these week signals requires capturing data at the highest resolution possible and performing spectral analysis on the data.
The most significant part of the data consists of the complete tri-phasic electrical waveform data. Resolution can go up to 20 kHz: 20,000 points per second per waveform. It means that a multi-month history is typically at the petabyte scale.
A data lake is a typical solution as storing data in files is simple. Data warehousing solutions are just not cost-effective for these data volumes.
However, a data lake cannot be queryied. This means that once the data is captured, computations (for example, FFT) need to be done ad hoc, adding to latency and cost.
Quasar can capture the complete waveform data at the highest resolution directly from the MQTT channel.
Each waveform is stored in a separate table, organized in a timeseries. Tagging allows for multiple tables to be queried at the same time.
Feature extraction is done by Quasar and does not require a dedicated program. Quasar has built aggregation functions that can work directly on the data, leveraging the SIMD capabilities of modern processors. For example, Quasar can transform alternate current in direct current in real-time.
Data scientists can get the data exactly how they need it when they need it without going through any additional steps. This dramatically increases research speed.
For monitoring purposes, electrical data can be visualized in real time by leveraging Grafana and the Quasar Grafana connector.
Quasar is used both for live data and historical data, making model deployments smooth.