Blazing innovation to prevent wildfires


- Optimized Development Environment (Cloud Based)
- Machine Learning Algorithm


Traditional data acquisition methods for electric systems analyse levels of voltage and current. Although fairly accurate, inherent safety and technical issues are associated with their use. They are connected to the larger grid and thus susceptible to electrical anomalies that may arise. Sometimes, these sensors fail and fail violently, causing collateral damage such as neighbouring equipment failure and even wildfires.

The tools’ limitations are governed by their design and the laws of physics. For example, a current transformer, used to measure the amperage or load, has a saturation point beyond which it will no longer measure with any degree of accuracy.

Our client designed a line of optical, field-effect sensors to measure the needed variables of the electric power grid. It was a complete theoretical shift from the traditional measurement methods. Their design requires advanced analytics to compensate for the environment in which it is deployed. The challenge that they faced was the impact of temperature on the DC measurement, which caused inaccurate AC RMS values. Temperature sensitivity is not unique to optical sensing, but solving this in optics, proved to be a highly specific challenge that Altruistic’s community of data scientists worked to address.


A dramatic technological shift is underway in the energy sector. Faced with unprecedented energy demand, utility companies need to solve grid visibility issues. With advanced sensors, these companies can track data around the overall health of their systems. Getting the data is only the start. These companies need to find efficient and safe methods to gather the data.

Altruistic- using AI to predict wildfires on electric grids


Leaders in innovation, they harnessed the power of open innovation to create a solution in the fastest and most cost-effective manner available to them, partnering with Altruistic. Altruistic and our community of data scientists built an algorithm to provide a working mathematical model that can remove the DC-induced signal error. At the same time, the model keeps the e-field induced signal and maintains the correction as a function of temperature. The result was a model with never-before-seen levels of accuracy.

Electrical fires account for an estimated 51,000 fires each year, nearly 500 deaths, more than 1,400 injuries, and $1.3 billion in property damage. By embracing the technological shift in the Utility Sector, Altruistic is driving forward innovation to optimise power, accelerate an industry and save lives!


increase in development cycle speed

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