Forecast ratio and security analysis of rating forecasting methods in an overhead line

Albizu, I.; Fernández, E.; Mazón, A.J.; Alberdi, Rafael

doi:10.1049/iet-gtd.2016.1649

Cited by 12 publications

(4 citation statements)

References 13 publications

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“…In [6], the authors define some indicators of the security of line ratings. Indicators such as security confidence level, maximum temperature exceedance, and temperature increase in the worst 1 % cases, are applied to several line rating forecasting methods.…”

Section: A Methods Proposed For the Adaptive Static Line Ratingmentioning

confidence: 99%

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Adaptive Static Line Rating for Systems With HTLS Conductors

Albizu

Fernández

Alberdi

et al. 2018

IEEE Trans. Power Delivery

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Operation and planning of a power system are constrained by the rating of power lines. Usually, the static line rating is used for system operation and planning. The static line rating defined for an electric grid uses the same conservative weather assumptions for the whole grid regardless of the location of each line or its maximum-allowable conductor temperature. A separate analysis of the weather magnitudes measured in a pilot line shows how favorable air temperature and solar heating compensate for unfavorable wind speed. However, this compensation is limited for high maximumallowable conductor temperatures. As a result, the risk of the static line rating exceeding this maximum temperature is higher for HTLS conductors. An adaptive static line rating is proposed to control the assumed risk. The wind speed assumption for the static rating is reduced for higher maximum-allowable conductor temperature.

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Section: A Methods Proposed For the Adaptive Static Line Ratingmentioning

confidence: 99%

“…DLR monitoring systems provide information about the current state of lines, but grid operation requires predictions of the grid condition several hours in advance. This is why DLR forecasting methods are being developed [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Static Line Rating for Systems With HTLS Conductors

Albizu

Fernández

Alberdi

et al. 2018

IEEE Trans. Power Delivery

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“…Saatloo et al [23] proposed hierarchical extreme learning machine-enabled short-term DLR forecasting based on meteorological parameters. Albizu et al [24] compared the results of many DLR forecasting methods, focusing on appropriate forecast ratios and safety indicators. Because of their lower maximum temperature exceedance and adequate overestimate ranges, the SLR and selective ambient-adjusted ratings were found to be the most stable.…”

Section: A Literature Surveymentioning

confidence: 99%

Decomposition-Based Stacked Bagging Boosting Ensemble for Dynamic Line Rating Forecasting

Ahmadi

Taheri

Ghorbani

et al. 2023

IEEE Trans. Power Delivery

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Effective exploitation of overhead transmission lines needs reliable and precise dynamic line rating forecasting. High-accuracy dynamic line rating forecasting, in particular, is an important short-term method for coping with grid congestion, enhancing grid stability, and accommodating high renewable energy penetration. Due to the non-stationarity and stochasticity of the meteorological variables, a single model is often not sufficient to accurately predict the dynamic line rating. Herein, a new stacked bagging boosting ensemble is developed based on multivariate empirical mode decomposition to overcome single models' restrictions and increase the dynamic line rating forecasting performance. The developed ensemble is utilized on the data gathered from a 400 kV aluminum conductor steel-reinforced overhead power line with a length of 32.85 Km between Ghadamgah and Binalood wind farms, located in the northeast of Iran. The simulation results substantiate that the proposed ensemble can capture meteorological variables' non-linear characteristics, yielding more accurate yet robust to noisy data forecasts than single forecasting models.

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“…[13] and [14] based the prediction on the use of time series techniques. And [15] worked applying statistical prediction.…”

Section: Introductionmentioning

confidence: 99%

Improvement of safety operating conditions in overhead conductors based on ampacity modeling using artificial neural networks

Martinez

Alberdi

Fernández

et al. 2019

2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

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Thermal ratings are usually considered for planning the operating conditions for overhead lines and are usually obtained with static parameters. These conditions can be improved using dynamic ratings based on the region weather forecasts, and this improvement can be ever higher when a local prediction is performed at the point where the line is located. In this work, a model based on artificial neural networks techniques is applied to predict the ampacity property of a transmission overhead line, in order to adjust and optimize the operation point of the grid under safety conditions. These predictions are calculated for a time horizon of 24 hours and are validated with actual conditions of a real overhead line monitored by sensors. With the conclusion that applying the selected model, the operational security of the conductor can be improved, passing from a 17.82% of overheating conditions to only a 3.91%.

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Forecast ratio and security analysis of rating forecasting methods in an overhead line

Cited by 12 publications

References 13 publications

Adaptive Static Line Rating for Systems With HTLS Conductors

Adaptive Static Line Rating for Systems With HTLS Conductors

Decomposition-Based Stacked Bagging Boosting Ensemble for Dynamic Line Rating Forecasting

Improvement of safety operating conditions in overhead conductors based on ampacity modeling using artificial neural networks

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