Hierarchical Extreme Learning Machine Enabled Dynamic Line Rating Forecasting

Mansour-Saatloo, Amin; Moradzadeh, Arash; Moayyed, Hamed; Mohammadpourfard, Mostafa; Mohammadi‐Ivatloo, Behnam

doi:10.1109/jsyst.2021.3128213

Cited by 12 publications

(3 citation statements)

References 37 publications

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“…Its purpose is to provide up-to-date information about the actual thermal line rating on a continuous basis [61]. As a result, the network operator can have a better insight into the actual line rating using various methodologies [62][63][64][65]. This rating depending on weather conditions can be higher or lower than the static line rating [66].…”

Section: Dynamic Line Ratingmentioning

confidence: 99%

Modelling Smart Grid Technologies in Optimisation Problems for Electricity Grids

et al. 2023

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The decarbonisation of the electricity grid is expected to create new electricity flows. As a result, it may require that network planners make a significant amount of investments in the electricity grids over the coming decades so as to allow the accommodation of these new flows in a way that both the thermal and voltage network constraints are respected. These investments may include a portfolio of infrastructure assets consisting of traditional technologies and smart grid technologies. One associated key challenge is the presence of uncertainty around the location, the timing, and the amount of new demand or generation connections. This uncertainty unavoidably introduces risk into the investment decision-making process as it may lead to inefficient investments and inevitably give rise to excessive investment costs. Smart grid technologies have properties that enable them to be regarded as investment options, which can allow network planners to hedge against the aforementioned uncertainty. This paper focuses on key smart technologies by providing a critical literature review and presenting the latest mathematical modelling that describes their operation.

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Section: Dynamic Line Ratingmentioning

confidence: 99%

Modelling Smart Grid Technologies in Optimisation Problems for Electricity Grids

et al. 2023

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“…Minguez [22] demonstrated a promising DLR method based on ML techniques for reducing wind farm outages caused by excess power generation. 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.…”

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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“…This is important because several issues and directions for further development remain open for the full deployment of DLR systems, such as the selection of transmission lines, the location and the number of sensors, or their type and reliability level [4][5][6]. In recent years, there have been aspirations to build novel DLR models based on neural networks and other intelligent techniques [18][19][20][21][22]. These models can predict the line rating and forecast the ampacity hours ahead.…”

Section: Motivation Of the Papermentioning

confidence: 99%

Performance Analysis of a Dynamic Line Rating System Based on Project Experiences

et al. 2022

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This paper aims to demonstrate the performance and reliability analysis of a dynamic line rating (DLR) system at the Bulgarian demonstration site of the FLEXITRANSTORE project. As part of the project, various manufacturers’ different line monitoring DLR sensors and weather stations were installed on a 110 kV double-circuit overhead line (OHL). These devices provided input parameters to the DLR system based on objective measurements. This paper used statistical tools to examine the reliability and accuracy of installed devices, thus making products from different manufacturers comparable. In addition, two independent line monitoring and DLR models have been developed: the black-box and extended white-box models. The performances of the two models were analyzed for the same input parameters and compared to the field measurements. Based on the presented results, the reliability and accuracy of the applied weather stations of different companies were almost the same. This conclusion cannot be said for DLR line monitoring sensors, where the devices could be differentiated based on reliability and measurement accuracy results. In terms of models, the usability of the extended white-box model seemed to be limited in certain weather conditions, implicating a more significant role for soft-computing-based DLR models in the future. In addition to the results, root causes for the errors and future directions that may provide a framework for further research are also presented.

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Hierarchical Extreme Learning Machine Enabled Dynamic Line Rating Forecasting

Cited by 12 publications

References 37 publications

Modelling Smart Grid Technologies in Optimisation Problems for Electricity Grids

Modelling Smart Grid Technologies in Optimisation Problems for Electricity Grids

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

Performance Analysis of a Dynamic Line Rating System Based on Project Experiences

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