Millions of data points for Simplifying Decision Making

Getting access to data was the first step. Making sense of it comes next.

The journey towards digital transformation does not end with data access. We recognize that interfacing ageing transformers is just the beginning; the real innovation comes in applying our data for solving real world problems towards a smarter and more sustainable future.
To help you get started, we have packaged some of these innovations as ready for use applications.

Grid frequency

Local non-invasive measurement of grid frequency for an efficient look on the heartbeat of your electrical grid.


Harmonics are part of grid load patterns and important to asset health.

Load Profile

Understanding peak loads is good for keeping a healthy grid. Keep track for analysing how power consumption patterns affect transformer performance.

Early Fault Detection

Continuous monitoring of transformer core, winding and structure so you can stay one step ahead on which components need your attention the most.

Transformer Capacity (Dynamic Thermal Rating)

Transformers are underutilised in a world of increasing grid congestion. Our enhanced IEEE/IEC models give an accurate view on what your transformer’s capacity is at any given time.

Accelerated Ageing and Loss of Life

Identifying and extending asset lifespan is now an active part of asset management strategies. Get exact numbers of actual asset usage.

Overload Forecast

Predictive analytics to know what to expect and plan ahead on informed decisions.

Gridwide Transparency

See your grid through transformers

There is a blind spot today in low voltage grids. Load profiles, distributed grid frequency and thermal environments of transformer kiosks are not easy to get access to.

We have developed algorithms to measure load profiles, analyse grid harmonics, and calculate grid frequency. All based on data harvested through transformers.

Combining our syncronised datapoints across your grid gives unprecedented data basis for analysing grid response to an increasing electricity consumption.

Dynamic Thermal Rating

Capacity and overload forecasting for any transformer

As electrification of our society is increasing across all sectors of society, the heydays of overdimensioned grids are diminishing. Our grids are getting congested, and this is happening at a time where transformer costs and lead times are rising.

We have to optimise the transformers we have, which means we have to better understand the capacity limits of our assets.

Oktogrid™ has developed a thermal model for measuring and forecasting safe transformer capacity. Our model is based on IEC transformer loading guide standards with machine learning based optimisations adapting the model to match the exact transformer it is being used for.

Loss of Life

Tracking accelerated transformer ageing

Knowing the loss of life of an asset is a crucial element for optimising asset usage and forecasting reinvestments.

The capacity limits of a transformer are heavily dependent on environmental factors such as temperature, wind, solar radiation, and if in kiosks, the thermal environment within these enclosures.

Keeping a check on how these factors affect loss of life is dependet of having a good understanding of transformer capacity and to kow when overloads occur.

Based on our thermal models, Oktogrid™ has developed a method for tracking transformer loss of life. Combining these with load profiles, external factors, and maintenance needs, the natural life of the transformer can be followed and unusual patterns detected.

Early fault detection of winding failure

As high voltage transformers are ageing it becomes increasingly difficult to follow a reactive or time-based maintenance strategy. Having a significant part of utility fleets moving into their end of life, it also becomes increasingly ressourceful to use oil analysis or electrical testing as a precautionary method.

There is a need for better tactics to answer the inevitable question of when to perform advanced performance analysis and diagnostics.

As a state-of-the-art method, Oktogrid™ has developed an advanced machine learning method for very early detection of winding insulation and structural failure of transformers.

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