Overhead line real-time rating estimation algorithm: Description and validation

Michiorri, Andrea; Taylor, Phil; Jupe, Samuel

doi:10.1243/09576509jpe859

Cited by 30 publications

(29 citation statements)

References 16 publications

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“…Another test was carried out by Nuon in 2004 [42] and consisted of a fiber-optic-based temperature monitoring system for electric cables, power transformers and overhead lines. In recent years, the application of DLRs has been studied and tested, particularly in the UK, for accommodating new wind power generation by Scottish and Southern Energy [43], Iberdrola Scottish Power [44][45][46] and Northern Ireland Electricity [47], and also the Belgian ELIA [48]. The situation is different for solar radiation, as few dedicated applications exist.…”

Section: Historical and Practical Perspectivesmentioning

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: Historical and Practical Perspectivesmentioning

confidence: 99%

Forecasting for dynamic line rating

Michiorri

Nguyen

Alessandrini

et al. 2015

Renewable and Sustainable Energy Reviews

134

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“…The overhead line model used to calculate ratings in the UK methodology is the same as that used by Michiorri, Taylor, and Jupe in [3].…”

Section: A Overhead Line Modelmentioning

confidence: 99%

“…Diagram of the heat balance within a conductor This energy balance is described by Michiorri, Taylor, and Jupe in their 2009 article [3]: (1) where q s , q c , and q r are the heating through solar radiation, cooling through convection, and cooling through radiation, respectively, and T c is core temperature of the conductor. I 2 R(T c ) represents the internal heating due to line current from the Joule Effect.…”

mentioning

confidence: 99%

A Comparison of Real-Time Thermal Rating Systems in the U.S. and the U.K.

Greenwood

Gentle

Myers

et al. 2014

IEEE Trans. Power Delivery

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract-Real-Time Thermal Rating is a smart grid technology that allows the rating of electrical conductors to be increased based on local weather conditions. Overhead lines are conventionally given a conservative, constant seasonal rating based on seasonal and regional worst case scenarios rather than actual, say, local hourly weather predictions. This paper provides a report of two pioneering schemes-one in the United States of America and one in the United Kingdom-in which Real-Time Thermal Ratings have been applied. Thereby, we demonstrate that observing the local weather conditions in real time leads to additional capacity and safer operation. Secondly, we critically compare both approaches and discuss their limitations. In doing so, we arrive at novel insights which will inform and improve future Real-Time Thermal Rating projects.

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“…In this project, a novel DTR system was developed and trialled in open-loop control mode on a single 7 km 132kV overhead line circuit. The system was developed using thermal state estimation techniques, based on the Monte Carlo method [13]. This offered the following benefits: (i) an economically viable solution for distribution network applications; and (ii) reduced intrusion to the power system.…”

Section: Outline Of Preceding Research and Development Projectmentioning

confidence: 99%

Application of a dynamic thermal rating system to a 132kV distribution network

Jupe¹,

Kadar

Murphy

et al. 2011

2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies

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This paper presents learning points from the application of a dynamic thermal rating (DTR) system to a 132kV distribution network. The DTR system incorporates over 90km of 132kV overhead lines and is being deployed to facilitate the connection of in excess of 200 MW of wind generation. The benefits and principles of this DTR system are stated, and initial results from the deployment are presented.The system architecture is discussed and the process by which it evolved from a single-circuit field trial to a geographically disperse system, covering seven meshed circuits, is described. Key elements of the DTR system approach include (i) the system architecture (using intelligent remote terminal units (RTUs) to process and condition the system data); (ii) the system data requirements; (iii) the methodology used for locating the meteorological stations (which underpin the DTR system); (iv) the use of direct conductor temperature sensors to provide additional thermal visibility of the network; and (v) the requirements to ensure that long-term system reliability is maintained, whilst also considering the repeatability of the system for other network areas. The experiences described in this paper may serve as a useful reference for any other DNOs looking to adopt DTR systems.

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Overhead line real-time rating estimation algorithm: Description and validation

Cited by 30 publications

References 16 publications

Forecasting for dynamic line rating

Forecasting for dynamic line rating

A Comparison of Real-Time Thermal Rating Systems in the U.S. and the U.K.

Application of a dynamic thermal rating system to a 132kV distribution network

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