Nyquis, a US-based energy technology startup, has raised $6.3 million in seed funding to improve electricity grid monitoring and help utilities detect potential wildfire risks. The round was co-led by Gigascale Capital and South Park Commons, with participation from Allison Pickens Ventures, Friale, Sovereign’s Capital, Pioneer Fund, Script Capital and Lost Coast Ventures.
The company develops high-resolution monitoring systems that help electricity providers identify early signs of equipment failure, electrical faults and possible wildfire ignitions. Its technology also helps grid operators understand where additional electricity demand can be supported by existing infrastructure.
The investment comes as electricity demand continues to increase, driven by data centres, electric vehicles, manufacturing and wider electrification. At the same time, utilities face growing pressure to improve grid reliability and reduce wildfire risks.
Why Power Grid Monitoring Needs to Improve
Electricity distribution networks cover a large part of the power grid, but many operators still have limited visibility into what is happening across their power lines.
Traditional monitoring equipment mainly measures how much current is flowing through a line. However, some dangerous electrical faults do not generate enough current to trigger conventional protection systems.
For example, a tree branch that touches a power line, or an old electrical connection can cause an arc to develop even without reaching the current level that normally activates automatic protection systems. Such incidents can go unnoticed, thus increasing the risk of blackouts and wildfires.
Utilities usually rely on manual inspections and information coming from other monitoring systems to detect problems. To prevent wildfires, some utilities also cut off power in some areas when there is a risk of a fire.
Nyquis has come up with a solution to the problem by introducing Nyquis Node, a monitoring system which can be installed in already existing sub-stations. The firm states that it takes approximately 15 minutes to install the device that can do without switching off the power supply or rewiring.
The system combines high-resolution electrical sensors, precise time synchronisation and edge computing. It continuously records electrical waveforms and analyses the data locally, allowing it to identify warning signs that conventional equipment may miss.
Nyquis uses machine learning models developed by co-founder and CTO Steve A. Bukowski, a former principal investigator at Idaho National Laboratory. The models analyse voltage and current measurements captured thousands of times per second.
According to the company, its models have been trained on more than 200,000 simulated faults and can identify electrical anomalies involving less than one amp of current.
Early Deployment Detects Hidden Electrical Faults
Nyquis has already deployed its technology with Otero County Electric Cooperative in New Mexico.
During a windstorm, the system detected more than 50 high-impedance fault events caused by tree branches repeatedly contacting power lines. The cooperative’s existing monitoring equipment had not detected these events.
Nyquis identified four locations where the problems were occurring. After repair crews addressed three of them, the warning signals at those locations stopped. The fourth location continued to show activity, helping operators identify where further attention was needed.
This deployment demonstrates how continuous monitoring could help utilities detect electrical hazards earlier and check whether repairs have resolved the problems.
