Power black out detection

Case Study


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.

In a nutshell, what Quasar delivered

  • Setup of complete electrical waveform data capture in less than a week
  • Powerful and flexible analysis as well as short feedback loop enable finding patterns of precursors faster
  • Future proof solution thanks to built-in scalability


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.

The solution

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.


  • Significantly accelerate research speed to find weak signals indicating power blackout, preventing millions of dollars of damage and waste
  • Building and deploying models is frictionless, leading to a quick feedback loop to improve the accuracy and thus the gains
  • Limitless historical capabilities, giving potentially unlimited accuracy
  • Outstanding TCO: extracting features does not require writing custom programs and uses less CPU. Up to 20X less storage space is needed compared to a typical data warehousing solution and up to 1,000X faster feature extraction compared to data lakes

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