Ampacity

Abstract: Real-time power line rating may be provided. First, sensor data may be received corresponding to a conductor of a power line. The sensor data may provide real-time weather conditions for the conductor's environment. The sensor data may be received from a sensor device configured to collect the sensor data. The sensor data may correspond to the weather conditions at a location of the sensor device on the power line. Next, design limitations for the power line having the conductor may be received. The conductor of the power line may have a design ampacity based upon the design limitations and assumed weather conditions for the conductor's environment. Then a dynamic ampacity may be calculated for the power line based upon the received sensor data and the received design limitations for the power line. The power line may then be operated according to the calculated dynamic ampacity instead of the design ampacity.

Abstract: For the first time a Real Time Thermal Rating System has been developed for overhead transmission lines using actual meteorological data and real-time conductor temperatures and line loadings. This System provides, on a probability basis, much higher ampacity ratings than those derived from conventional methods. As a portion of this System, a steady state thermal rating procedure is presented in the companion paper, which includes forced convection heat transfer equations taking into account the effect of wind turbulence or gustiness, conductor yaw (wind direction), conductor height above ground, smooth versus rough (stranded) conductor surfaces, the proximity of conductors in a bundle, and conductor pitch. A natural convective heat equation is developed for stranded conductors. The conductor temperature is solved directly without resorting to an iterative solution.

Abstract: This paper presents an overview of the state of the art on the research on Dynamic Line Rating forecasting. It is directed at researchers and decision-makers in the renewable energy and smart grids domain, and in particular at members of both the power system and meteorological community. Its aim is to explain the details of one aspect of the complex interconnection between the environment and power systems. The ampacity of a conductor is defined as the maximum constant current which will meet the design, security and safety criteria of a particular line on which the conductor is used. Dynamic Line Rating (DLR) is a technology used to dynamically increase the ampacity of electric overhead transmission lines. It is based on the observation that the ampacity of an overhead line is determined by its ability to dissipate into the environment the heat produced by Joule effect. This in turn is dependent on environmental conditions such as the value of ambient temperature, solar radiation, and wind speed and direction. Currently, conservative static seasonal estimations of meteorological values are used to determine ampacity. In a DLR framework, the ampacity is estimated in real time or quasi-real time using sensors on the line that measure conductor temperature, tension, sag or environmental parameters such as wind speed and air temperature. Because of the conservative assumptions used to calculate static seasonal ampacity limits and the variability of weather parameters, DLRs are considerably higher than static seasonal ratings. The latent transmission capacity made available by DLRs means the operation time of equipment can be extended, especially in the current power system scenario, where power injections from Intermittent Renewable Sources (IRS) put stress on the existing infrastructure. DLR can represent a solution for accommodating higher renewable production whilst minimizing or postponing network reinforcements. On the other hand, the variability of DLR with respect to static seasonal ratings makes it particularly difficult to exploit, which explains the slow take-up rate of this technology. In order to facilitate the integration of DLR into power system operations, research has been launched into DLR forecasting, following a similar avenue to IRS production forecasting, i.e. based on a mix of statistical methods and meteorological forecasts. The development of reliable DLR forecasts will no doubt be seen as a necessary step for integrating DLR into power system management and reaping the expected benefits.