Prediction of overhead transmission line ampacity by stochastic and deterministic models

Hall, J.F.; Deb, A.K.

doi:10.1109/61.4319

Cited by 66 publications

(18 citation statements)

References 12 publications

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“…There are different techniques to increase this capacity: determine meteorological conditions by means of deterministic [3] or probabilistic [4] methods, up to the newest innovations in smart grids and line parameters real-time monitoring: temperature, sag, tilt, power, current and weather conditions [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Comparison between IEEE and CIGRE Thermal Behaviour Standards and Measured Temperature on a 132-kV Overhead Power Line

et al. 2015

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This paper presents the steady and dynamic thermal balances of an overhead power line proposed by CIGRE (Technical Brochure 601, 2014) and IEEE (Std.738, 2012) standards. The estimated temperatures calculated by the standards are compared with the averaged conductor temperature obtained every 8 min during a year. The conductor is a LA 280 Hawk type, used in a 132-kV overhead line. The steady and dynamic state comparison shows that the number of cases with deviations to conductor temperatures higher than 5 • C decreases from around 20% to 15% when the dynamic analysis is used. As some of the most critical variables are magnitude and direction of the wind speed, ambient temperature and solar radiation, their influence on the conductor temperature is studied. Both standards give similar results with slight differences due to the different way to calculate the solar radiation and convection. Considering the wind, both standards provide better results for the estimated conductor temperature as the wind speed increases and the angle with the line is closer to 90 • . In addition, if the theoretical radiation is replaced by that measured with the pyranometer, the number of samples with deviations higher than 5 • C is reduced from around 15% to 5%.

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Section: Introductionmentioning

confidence: 99%

Comparison between IEEE and CIGRE Thermal Behaviour Standards and Measured Temperature on a 132-kV Overhead Power Line

et al. 2015

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“…This challenge has already been highlighted in the relevant literature, such as the pioneering works by Hall and Deb [8], Douglass [9] and Foss [10]. In today's context, the time scales involved are in line with electricity markets where most operational decisions are made the day before operation: DLR forecasts should employ lead times roughly between 12 and 36 or 54 h. Forecasts should also be available with a resolution specified by the users' needs (from minutes to hours).…”

Section: Introductionmentioning

confidence: 94%

Forecasting for dynamic line rating

Michiorri

Nguyen

Alessandrini

et al. 2015

Renewable and Sustainable Energy Reviews

133

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International audienceThis 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

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“…Probabilistic STRs account for the variability and stochastic nature of ambient weather conditions. They ensue from statistical analyses, cumulative distribution functions and risk tolerances [2,10,17,20]. Statistical evaluation of such thermal ratings entails knowledge of environmental parameters along the line conductor.…”

Section: Transmission Line Thermal Ratingmentioning

confidence: 99%

Quantification of gains and risks of static thermal rating based on typical meteorological year

Heckenbergerová

Musilek

Filimonenkov

2013

International Journal of Electrical Power & Energy Systems

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Prediction of overhead transmission line ampacity by stochastic and deterministic models

Cited by 66 publications

References 12 publications

Comparison between IEEE and CIGRE Thermal Behaviour Standards and Measured Temperature on a 132-kV Overhead Power Line

Comparison between IEEE and CIGRE Thermal Behaviour Standards and Measured Temperature on a 132-kV Overhead Power Line

Forecasting for dynamic line rating

Quantification of gains and risks of static thermal rating based on typical meteorological year

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