Dynamic Line Rating Forecasting Based on Integrated Factorized Ornstein–Uhlenbeck Processes

Madadi, Sajad; Mohammadi‐Ivatloo, Behnam; Tohidi, Sajjad

doi:10.1109/tpwrd.2019.2929694

Cited by 29 publications

(9 citation statements)

References 27 publications

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“…It is known that wind speed has a stronger impact in terms of DLR rating than ambient temperature or solar radiation [ 9 ], so the effect of wind speed was studied in this paper. As shown in Figure 3 , two SF 0147 fans (variable speed, 50 W, Orbegozo, Murcia, Spain) were used to simulate variable wind speed conditions.…”

Section: Methodsmentioning

confidence: 99%

“…DLR can be useful for the integration of intermittent renewable energy sources into existing power networks [ 8 ]. Intermittent renewable energy sources (IRES) cannot provide constant additional power, making it difficult for system operators to justify the investment required to expand current transmission lines [ 9 ]. Dynamic line rating (DLR) allows for the maximum utilization of the conductor, that is, to operate at the maximum ampacity or current that it can withstand without exceeding the upper allowed temperature of the conductor [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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On-Line Core Losses Determination in ACSR Conductors for DLR Applications

et al. 2022

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Dynamic line rating (DLR) is a method that focuses on dynamically determining the maximum allowable current of power lines, while ensuring they operate within safe limits. DLR needs to monitor the temperature and current of the line in real-time, as well as the weather variables in the surroundings of the power line. DLR approaches also require determining the AC resistance of the power line conductors, which is a key parameter that enables it to determine Joule and core losses. This paper presents an approach for an on-line alternating current (AC) resistance estimation of aluminum conductor steel-reinforced (ACSR) conductors to determine the DLR capability of such conductors from real-time conductor and meteorological parameter measurements. For this purpose, conductors with one, two and three layers of aluminum strands are analyzed in detail. Based on the experimental results presented in this paper, two possible approaches are proposed.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On-Line Core Losses Determination in ACSR Conductors for DLR Applications

et al. 2022

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“…Furthermore, the authors of [10, 17] explained how wave height and wind speed affect maintenance scheduling and availability for the offshore wind farms, respectively. Furthermore, methods such as quanitle regression [18], spectrum analysis [19], integrated factorised Ornstein–Uhlenbeck processes [20] and Markov chain [21] started finding application in time series forecasting, though these techniques application to offshore weather forecasting are limited.…”

Section: Related Work Of Forecasting Using Data‐driven Methodsmentioning

confidence: 99%

Data‐driven weather forecasting models performance comparison for improving offshore wind turbine availability and maintenance

Pandit

Kolios

Infield

2020

IET Renewable Power Generation

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Wind energy is an attractive alternative to conventional sources of electricity generation due to its effectively zero carbon emissions. Wind power is highly dependent on wind speed and operations offshore are affected by wave height; these together called turbine weather datasets that are variable and intermittent over various timescales and signify offshore weather conditions. In contrast to onshore wind, offshore wind requires improved forecasting since unfavourable weather prevents repair and maintenance activities. Delayed repair results in increased downtime and reduced wind farm availability and energy yield. This paper proposes two data-driven models for long-term weather conditions forecasting to improve the wind farm availability and support operation and maintenance (O&M) decision-making process. These two data-driven approaches are Long Short-Term Memory Network, abbreviated as LSTM, and Markov chain. A LSTM is an artificial recurrent neural network (RNN), capable of learning long-term dependencies within a sequence of data and is typically used to avoid the long-term dependency problem. While, Markov is another data-driven stochastic model, which assumes that, the future states depend only on the current states, not on the events that occurred before. The readily available weather datasets are obtained from FINO3 database to train and validate the performance of these data-driven models. A performance comparison between these weather forecasted models would be carried out to determine which approach is most accurate and suitable for improving offshore wind turbine availability and support maintenance activities. The full paper outlines the weakness and strength associated with proposed models in relations to offshore wind farms operational activities.

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“…Several examples of data-driven DLR forecasting models can be found in previous literature. The uncertainty and variability attributed to DLR forecasting are tackled in [14] using the integrated factorized Ornstein-Uhlenbeck technique. The potential of DLR in increasing the ampacity of 130 kV transmission lines and facilitating the large-scale penetration of wind power is investigated in [15].…”

Section: Introductionmentioning

confidence: 99%

A dagging‐based deep learning framework for transmission line flexibility assessment

Morteza

Sadipour

Fard

et al. 2022

IET Renewable Power Gen

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Uncertainty in renewable energy generation, energy consumption, and electricity prices, as well as transmission congestion, pose a number of problems in modern power grids, necessitating stability on the supply, grid, and demand sides. Grid-side stability can be achieved by dynamic line rating (DLR) forecasting, which reliably predicts the overall current carrying potential of overhead transmission lines. Long short-term memory proved beneficiary in this field, owing to its ability to learn highly variable and uncertain data. To empower this network to tackle the non-stationary nature of meteorological parameters, a novel machine learning (ML) architecture based on Dagging technique is proposed and tested on the data collected from a 400 kV overhead transmission line. Simulation results corroborate that the proposed Dagging-based stacked LSTM can successfully handle the non-stationary issue and outperform the decomposition-based technique, as the state-ofthe-art algorithm, for various forecasting horizons. The results confirm the generalizability of models with an application in forecasting DLR over the line without utilizing additional sensors and communication networks. Moreover, the proposed model is compared to several ML architectures, including support vector machines (SVM), random forest (RF), and multi-layer perceptron (MLP) in a comprehensive benchmark study. The introduced algorithm outperforms MLP by 3.4%, RF by 9.4%, and SVM by 6.7% in terms of average prediction accuracy.

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Dynamic Line Rating Forecasting Based on Integrated Factorized Ornstein–Uhlenbeck Processes

Cited by 29 publications

References 27 publications

On-Line Core Losses Determination in ACSR Conductors for DLR Applications

On-Line Core Losses Determination in ACSR Conductors for DLR Applications

Data‐driven weather forecasting models performance comparison for improving offshore wind turbine availability and maintenance

A dagging‐based deep learning framework for transmission line flexibility assessment

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