Identification of Critical Spans for Monitoring Systems in Dynamic Thermal Rating

Matus, Marcelo; Sáez, Doris; Favley, M.; Suazo-Martínez, Carlos; Moya, Javier Carretero; Jiménez‐Estévez, Guillermo; Palma‐Behnke, Rodrigo; Olguin, G.; Jorquera, P.

doi:10.1109/tpwrd.2012.2185254

Cited by 85 publications

(58 citation statements)

References 21 publications

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“…At 80%, the number of overloading cases has been increased with a total of 192 for test 1 and 32 for test 2. It is in light of this point that line [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] was the first to be chosen to implement the DLR. This line has a base case ampacity of 0.5 kA with a resistance of 0.11 Ω/km.…”

Section: Determining Suitable Dlr Candidatesmentioning

confidence: 99%

“…In [13], authors have developed a statistical model using measured data in a transmission line and demonstrated the potential of DLR in a wind rich power network. The conductor sag based dynamic line rating models have also been investigated in the published literature [14][15]. Furthermore, potential of DLR to reduce system losses have also been demonstrated in [16], assuming all transmission lines have DLR capability.…”

Section: Introductionmentioning

confidence: 99%

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Comparative analysis of dynamic line rating models and feasibility to minimise energy losses in wind rich power networks

Simms

Meegahapola

2013

Energy Conversion and Management

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Wind power generation has indicated an exponential increase during last two decades and existing transmission network infrastructure is increasingly becoming inadequate to transmit remotely generated wind power to load centres in the network. The dynamic line rating (DLR) is one of the viable solutions to improve the transmission line ampacity during high wind penetration without investing on an additional transmission network. The main objective of this study is to identify the basic differences between two main line rating standards, since transmission network service providers (TNSPs) heavily depend on these two standards when developing their line rating models. Therefore, a parameter level comparison between two line rating models is a timely requirement, in particular for high wind conditions. Study has shown that roughness factor causes a significant difference between both standards. In particular, the IEEE model indicates more conservative approach due to this parameter. In addition, solar heat-gain calculation has also resulted in significant difference in ampacity ratings between two standards. A case study was developed considering a wind rich network and it has shown that by implementing DLR in wind rich regions, it can effectively reduce line overloading incidents and accommodate wind power flows in the network without any curtailment. Moreover, ability of DLR to reduce network energy losses is also demonstrated and emphasised the importance of selecting suitable DLR candidates to minimise energy losses in the network. © 2013 Elsevier Ltd. All rights reserved. AbstractWind power generation has indicated an exponential increase during last two decades and existing transmission network infrastructure is increasingly becoming inadequate to transmit remotely generated wind power to load centres in the network. The dynamic line rating (DLR) is one of the viable solutions to improve the transmission line ampacity during high wind penetration without investing on an additional transmission network. The main objective of this study is to identify the basic differences between two main line rating standards, since transmission network service providers (TNSPs) heavily depend on these two standards when developing their line rating models. Therefore, a parameter level comparison between two line rating models is a timely requirement, in particular for high wind conditions. Study has shown that roughness factor causes a significant difference between both standards. In particular, the IEEE model indicates more conservative approach due to this parameter. In addition, solar heatgain calculation has also resulted in significant difference in ampacity ratings between two standards. A case study was developed considering a wind rich network and it has shown that by implementing DLR in wind rich regions, it can effectively reduce line overloading incidents and accommodate wind power flows in the network without any curtailment. Moreover, ability of DLR to reduce network energy losses is also demonstrated ...

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Section: Determining Suitable Dlr Candidatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative analysis of dynamic line rating models and feasibility to minimise energy losses in wind rich power networks

Simms

Meegahapola

2013

Energy Conversion and Management

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“…A number of sources agree that the true thermal capacity of a transmission line is considerably higher than the rated values [8][9][10][11][12] since ratings are calculated under the worst case weather assumption although such operating conditions occurs rarely in practice. It is possible to exploit this property by using dynamic line ratings (DLR) which model the thermal limit of transmission lines as stochastically varying function of internal and external real time operating conditions such as ambient temperature, level of loading, intermittent effects, and sag.…”

Section: Introductionmentioning

confidence: 99%

“…The two immediate challenges of implementing the dynamic line rating methods presented in [8][9][10][11] are the need for an online, smart monitoring system to capture real time variation and the modelling of uncertainty in constraints in optimal scheduling. While uncertainty in optimization variables can be accounted for by stochastic optimization techniques, uncertainty in constraints is more challenging to model since analytical constrained optimization techniques only allow fixed constraints.…”

Section: Introductionmentioning

confidence: 99%

Assessment of post-contingency congestion risk of wind power with asset dynamic ratings

Banerjee

Jayaweera

Islam

2015

International Journal of Electrical Power & Energy Systems

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a b s t r a c tLarge scale integration of wind power can be deterred by congestion following an outage that results in constrained network capacity. Post outage congestion can be mitigated by the application of event control strategies; however they may not always benefit large wind farms. This paper investigates this problem in detail and proposes an advanced mathematical framework to model network congestion as functions of stochastic limits of network assets to capture post contingency risk of network congestion resulting through the constrained network capacity that limits high penetration of wind. The benefit of this approach is that it can limit the generation to be curtailed or re-dispatched by dynamically enhancing the network latent capacity in the event of outages or as per the need. The uniqueness of the proposed mathematical model is that it converts conventional thermal constraints to dynamic constraints by using a discretized stochastic penalty function with quadratic approximation of constraint relaxation penalty. The case study results with large and small network models suggest that the following an outage, wind utilization under dynamic line rating can be increased considerably if the wind power producers maintain around a 15% margin of operation. IntroductionNetwork congestion is an undesirable result of insufficient capacity being available on a network to transport electricity from generation to loads. It leads to highly variable locational marginal prices (LMP) at nodes usually with high prices at load points which are affected by congestion compared to those which are not. A number of publications have used LMP as an indicator of network congestion [1][2][3]. In systems with large amount of wind power, network congestion hinders effective integration and utilization of wind as extra wind generated has to be curtailed thereby leading to uncertainty in revenue for wind power producers. The dynamic nature of wind results in large variations in power output over a short period of time, which makes effective utilization of wind an even bigger challenge in congested networks.Network congestion has a greater impact in networks under contingency. When a contingency occurs in a branch, the remaining branches in the network can experience greater loading and be at a higher risk of network congestion [4][5][6][7]. While traditional security analysis uses the N À 1 criterion this does not account for variation in output of wind leading to post contingency congestion and curtailment of wind. Therefore, even when a network seems to have no congestion and utilizes wind effectively, there is a high risk that any contingency will drastically change the situation.A number of sources agree that the true thermal capacity of a transmission line is considerably higher than the rated values [8-12] since ratings are calculated under the worst case weather assumption although such operating conditions occurs rarely in practice. It is possible to exploit this property by using dynamic line ratings (DLR) which model ...

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“…These works cover a wide range of methods including sag measurement, thermal models for the conductor ether by including the effect of all weather parameters, weather model (WM), or by using just the conductor temperature, when it is higher than the ambient temperature by 10 degrees or more, which is known as conductor temperature model (CTM). Determination of the line section with the highest temperature, the critical span, has also been considered in many research works and field studies [11]. The availability of new measurement technologies such as GPS and PMU has resulted in developing new methods for DLR.…”

Section: Introductionmentioning

confidence: 99%

Field Measurement Based PLS Model for Dynamic Rating of Overhead Lines in Wind Intensive Areas

Abdelkader

Morrow

Fu³

et al. 2013

REPQJ

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Abstract. This paper presents results of lab tests, field measurements and mathematical modelling for the Dynamic Rating (DR) of Over Head Transmission Line (OHTL) in a wind intensive area. In this paper, DR is done by developing a statistical line model. This model is based on Partial Least Squares (PLS) multi regression. DR provides extra capacity to the line, over the traditional seasonal static rating, which makes it possible to defer the need for reinforcement the existing network or building new lines.

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Identification of Critical Spans for Monitoring Systems in Dynamic Thermal Rating

Cited by 85 publications

References 21 publications

Comparative analysis of dynamic line rating models and feasibility to minimise energy losses in wind rich power networks

Comparative analysis of dynamic line rating models and feasibility to minimise energy losses in wind rich power networks

Assessment of post-contingency congestion risk of wind power with asset dynamic ratings

Field Measurement Based PLS Model for Dynamic Rating of Overhead Lines in Wind Intensive Areas

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