Ampacity studies on 49 degrees C-rated transmission line

Chisholm, William A.; Barrett, J.S.

doi:10.1109/61.25635

Cited by 16 publications

(10 citation statements)

References 13 publications

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“…For example, on hot summer days with low wind speeds, the standard deviation of the wind angle is typically about 451 or more [56]. In such situations, the effective yaw angle of the wind is set between 351 and 451 (depending on user practices) irrespective of the average wind direction [57].…”

Section: Wind Direction (And Its Angle With Lines)mentioning

confidence: 99%

Forecasting for dynamic line rating

Michiorri

Nguyen

Alessandrini

et al. 2015

Renewable and Sustainable Energy Reviews

134

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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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Section: Wind Direction (And Its Angle With Lines)mentioning

confidence: 99%

Forecasting for dynamic line rating

Michiorri

Nguyen

Alessandrini

et al. 2015

Renewable and Sustainable Energy Reviews

134

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“…Once taken out of travelers, conductor movements along the line with two or more spans of unequal length are restrained by suspension insulator swings [24] [25]. With care, this enhances the accuracy of predicted high-temperature sags compared to rather poor uncertainties of ±20 C° at 100°C when the ruling span method is used [6] [7][24] [25]. Another factor in sag calculations for older lines is that, once the conductors have been fixed by insulator clamps, and long-term creep sets in, the insulators no longer hang vertical at the original stringing temperature.…”

Section: A Tension Equalization and The Ruling Span Approachmentioning

confidence: 99%

“…Conductor-mounted SONAR sensors [25] offer the possibility of measuring distance to vegetation below the line. This configuration has a disadvantage: Normal conductor rotation means that the sensor is only pointing downward to the minimum clearance for a small fraction of the time.…”

Section: B Continuous Level Monitoring With Sonarmentioning

confidence: 99%

“…Predicted displacements of conductor attachments along the line using the ruling span method can be large; sometimes they exceed 3 m (10 ft) in lines with mixed short and long spans over a normal range of operating temperature. Once taken out of travelers, conductor movements along the line with two or more spans of unequal length are restrained by suspension insulator swings [24] [25]. With care, this enhances the accuracy of predicted high-temperature sags compared to rather poor uncertainties of ±20 C° at 100°C when the ruling span method is used [6] [7][24] [25].…”

Section: A Tension Equalization and The Ruling Span Approachmentioning

confidence: 99%

See 1 more Smart Citation

Key Considerations for the Selection of Dynamic Thermal Line Rating Systems

Black

Chisholm²

2015

IEEE Trans. Power Delivery

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Recent reviews have shown that many methods can be used for estimating the dynamic thermal capacity of overhead transmission lines. A number of approaches are described and key features of each system are organized. Data from field trials provide a unique basis for assessing the variation in ten different parameters. Characteristics of distributed measurement systems are underscored and contrasted against point measurements systems.

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“…Studies about the validation of thermal models of conductors are not common in the literature; and the authors of this contribution are aware of only three of them. In [6] an idle "Aluminum Conductor Steel Reinforced" (ACSR) line was monitored under constant current, and the weather conditions were recorded. Regardless of a very coarse data averaging, the study found good agreement between the measurements and the model predictions.…”

Section: Introductionmentioning

confidence: 99%