Line Dashboards
Line-level analysis
The four line dashboards contain aggregated data on the line. This section provides a brief introduction to how data is aggregated and used in order to provide insight into the condition of the line.
Power lines differ from other power system assets in their great extent and exposure to environmental conditions. A single power line can pass through valleys and mountains, across roads and rivers, yet it still needs to be operated and analysed as a single component. All lines will contain a few spans that are dimensioning for the whole due to their low ground clearance or exposure to environmental conditions. By instrumenting these spans, and observing the aggregated data produced from them, TSOs and DSOs can have full control over their power lines.
Illustration of power line extent & environmental loads along different spans
Instrumenting the lines
Heimdall Power always works together with the customer to identify the placement and amount of neurons necessary to provide the operator with the full insight needed to conduct line-level analysis. With a fully instrumented line, operators are immediately notified of dangerous situations, eliminating guesswork. Operational decisions can be tested and verified through observation and lines are utilized to their full capacity.
By monitoring the critical spans of the line, it can be monitored and operated as a whole
Aggregation and analysis
Neuron data is sent to our cloud-hosted solution where it is associated with a specific span and power line, along with contextual data on the operational limits of the power line, line properties and weather data from the area. These are used as input to run models for
Sag
Clearance
Dynamic Line Rating
Line events
Ice detection
Vibrations
Line Forecasting (DLR & Wire Temperature)
Observational data (current, conductor temperature, vibrations-) is aggregated across multiple spans and in time. For the selected time interval, the system will by default present the MAX observed wire temperature and the MAX observed load.
However, the following aggregation functions are supported for observational data, and can be plotted by the user.
Function | How it’s calculated | When to use |
|---|---|---|
Average (AVG) | Integral of time series divided by the size of the time range | Eliminating noise or downsampling raw data from lines with e.g. highly variable load curves |
Max | The highest value of all stored data points in aggregation interval | Examining thermal loading and identifying critical spans |
Min | The lowest value of all stored data points in aggregation interval | Comparing spans |
Aggregation of Dynamic Line Rating values
Heimdall Power's approach to DLR is based on the leading industry standards (CIGRE & IEEE) in combination with live data from the power line. By leveraging the theoretical strengths of the standards with observations from the grid, we have designed a proven DLR model with extremely high accuracy.
To produce accurate and statistically valid ampacity and temperature forecasts for monitored lines, DLR needs to be calculated individually for each monitored span. The span with the lowest rating becomes the dimensioning span for the line. DLR values presented in Heimdall Cloud are always aggregated by minimum, with a temporal resolution of 1 hour. This ensures that the weakest span is always accounted for while ensuring safe and consistent results for benchmarking and rating line performance and utilization.
For more details see Line Forecasting (Archied)archived