Sensegrid - a technology platform for monitoring and analysing real-time grid data.

The electrical power grid is critical infrastructure in any modern society, and the utilities which own and operate this infrastructure are currently facing unprecedented change. Decentralized energy production, intermittent renewable energy production and a general electrification represent a paradigm-shift for how electric utilities operate and plan their grids. Today, most power grids are operated based on static safety limits and experience-based models and forecasts, with relatively few sources of real-time data. In the Sensegrid project we have developed a power grid monitoring platform based on a network of overhead line sensors. By instrumenting the overhead lines, we can get a full digital representation of the grid. The monitoring platform consist of a high voltage line sensor collecting real time measurements which is sent to our secure cloud server, where the data is structured, combined with external data sources, processed and presented in a user oriented interface. Transmission capacity in the grid is presented as historic data, real time data and predictions, which gives the grid owners a complete overview and a way to manage the true capacity of their grid. The Sensegrid project has also developed drone installations of the sensor, in order to perform installations on critical lines without any disturbance to the grid. The hypotheses at project start on use of the sensor for fault detection and capacity control are confirmed through several pilot installations, and several grid companies are now adapting the technology around the world.

In the project the next generation neuron design has been completed and tested. This neuron is providing real time measurements of the status and health of the grid which enables many valuable use cases for the grid operators. The data and results generated by the project reveals bottlenecks and weaknesses in the grid, opening up new possibilities for increasing reliability of supply, nationally as well as internationally. Dynamic Line Rating increases the capacity of existing grid by up to 40%, which reduces drastically the need for building more power lines. The solution developed is providing both real time monitoring and projecting of future capacity. Similarly we have developed real time monitoring and projection of ice on the grid. This gives a method for the grid owners to manage ice on their grid and minimize downtime. The project has also resulted in Heimdall Power being able to install the neuron by our autonomous drone, without any disturbance to the grid.

The electrical power grid is critical infrastructure in any modern society, and the utilities which own and operate this infrastructure are currently facing unprecedented change. Decentralized energy production, intermittent renewable energy production and a general electrification represent a paradigm-shift for how electric utilities operate and plan their grids. Today, most power grids are operated based on static safety limits and experience-based models and forecasts, with relatively few sources of real-time data. Sensegrid is a project whose goal is to research and develop a technology platform for sensing, monitoring and analyzing real-time grid data, providing insight and decision support for transmission and distribution system operators. The project is centered around the development of an overhead line sensor which can be installed on power lines, measuring its condition. The data produced by these sensors provide a unique opportunity to develop new tools which have previously not been possible to realize. One of these is Dynamic Line Rating (DLR) of lines, which allows the system operator to maximize the capacity of their grid, without compromising safety, when environmental conditions allow it. The project will also examine new solutions for fault detection, ice monitoring and prediction and grid planning. By researching and developing these solutions, existing infrastructure be utilized more efficiently, failures can be avoided and detected immediately and localized with higher accuracy, maintenance can be planned better, and grid investments can be postponed or avoided. The main research challenge is related to building accurate and reliable data models which can be used in grid operation based on real-time data. Given the importance of the grid as critical infrastructure, the tools and models developed need to have tight statistical tolerance limits, with a high level of confidence, if they are to be used for dynamic grid operations.