For TSOs and grid asset owning utilities, risk management is central to operating overhead lines. Risks can arise from exceeding mechanical or thermal limits of the conductor. They can also relate to public safety, particularly if excessive sag reduces clearance to the ground. These realities explain why operators have traditionally taken a conservative approach. As highlighted by the catastrophic blackout event in the Spanish peninsula last year, the consequences of failure can be severe.
At the same time, electrification of industry and transport, growing renewables integration and permitting bottlenecks are driving the need to extract more capacity from existing infrastructure. Rather than waiting for new lines to be built, operators are turning to grid enhancing technologies such as Dynamic Line Rating.
At its core, DLR determines in real time how much current a transmission line can safely carry based on actual environmental and operating conditions, rather than static assumptions. Effective risk management in this context depends on understanding those conditions accurately. The greater the data certainty, the more confidently operators can increase capacity without compromising safety. Where data is less certain, more conservative decisions must prevail.
This balance between observability and risk can be visualised as a pyramid. As operators climb higher and gather more reliable data, uncertainty decreases and operational confidence rises.
At the lowest level of the pyramid, DLR models rely entirely on software. External data such as weather forecasts, ambient temperatures and terrain maps are combined to simulate line behavior. This approach requires no field installations and can be deployed quickly and at relatively low cost.
However, important limitations remain. Wind is the most influential parameter in DLR calculations because it plays a major role in conductor cooling. Even small changes in wind speed can significantly increase the maximum current a conductor can safely carry. Yet wind conditions are volatile and highly localised, particularly at low speeds, making accurate estimation challenging.
To manage this uncertainty, some sensorless solutions adopt a conservative fixed wind assumption, often around 0.5 m/s. While this reduces the risk of overestimating cooling, it also limits the ability to benefit from favourable wind conditions. Without direct measurements, potential network capacity is effectively underestimated.
Moving up the pyramid introduces sensors, though not necessarily mounted on the conductor of interest. These systems measure parameters such as conductor temperature, solar radiation or sag locally. Direct temperature measurement can provide a fair representation of real time thermal conditions.
Nevertheless, wind often remains inferred from external weather stations or broad assumptions. Given its importance in predicting thermal behaviour, this introduces continued uncertainty. A more sophisticated approach uses Optical Ground Wire technology combined with Distributed Acoustic Sensing to estimate wind conditions based on measured oscillations of the ground wire. Wind induced vibrations are translated into wind speed estimates, which can then be used in thermal calculations.
This represents a significant improvement over modelling alone. However, wind is measured at the height of the ground wire, not at the height of the phase conductor. Terrain and vegetation can influence wind conditions at lower levels, and sag or ground clearance is not directly measured. While uncertainty is reduced, extrapolation and incomplete observations remain part of the process.
At the highest level lies direct phase monitoring. A sensor mounted on the conductor measures how the line oscillates under wind loading as well as its temperature. Using this data, an advanced model calculates conductor geometry changes under thermal expansion and wind cooling effects.
Sag, and therefore clearance, is measured directly to ensure safe distances from the ground and obstacles. Span averaged wind measurements enable accurate cooling calculations across the full conductor span, avoiding the risk of relying on unrepresentative local readings.
By measuring both sag and wind, operators gain full visibility of real time operating conditions and the boundaries of the operational envelope. Uncertainty is reduced to a level that allows capacity to be maximised without imposing conservative assumptions to compensate for missing data.
Direct phase monitoring removes the need for extrapolation and external weather data. Measured data from the conductor can also be integrated into forecasting models to anticipate capacity several hours ahead.
The path up the DLR pyramid is therefore a progression in data certainty. For TSOs and utilities, the question is not whether to consider Dynamic Line Rating, but how high they are prepared to climb in order to balance safety and capacity with confidence.
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