Improving DTR assessment by means of PCA applied to wind data

Bosisio, Alessandro; Berizzi, Alberto; Le, Duong; Bassi, Fabio; Giannuzzi, Giorgio Maria

doi:10.1016/j.epsr.2019.02.028

Cited by 26 publications

(5 citation statements)

References 17 publications

(20 reference statements)

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“…The forecast data is augmented with real-time weather information from local weather stations placed along the overhead lines (Carlini, 2020). Moreover, principal component analysis (PCA) method is utilized, given the large volume of data provided by real-time measurements and weather forecasts, with a space-time model based on clustering, extrapolation and interpolation techniques (Bosisio et al, 2019).…”

Section: Wide-area Weather-based Indirect Dlr Systemsmentioning

confidence: 99%

Indirect weather‐based approaches for increasing power transfer capabilities of electrical transmission networks

Keyvani

Whelan

Doddy

et al. 2023

WIREs Energy & Environment

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Dynamic line rating (DLR) systems are recognized as a cost-effective and socially accepted asset for relieving network congestion and uprating existing transmission systems, based upon accessing additional weather-dependent capacity of overhead lines. Although direct and indirect DLR methods are available, utilization of indirect weather-based approaches, that is, sensors are not installed on the conductor, are of increasing interest due to fast installation times, that is, no requirement for line outages and lower capital costs, with achievable potential for wide-area implementation. An extensive review is presented on the components and requirements of such systems, including weather stations, forecasting models, downscaling and DLR calculations, overhead line and conductor thermal models, and communication platforms. In addition, the features of practical instances of these systems are briefly reviewed. Moreover, a systematic approach is introduced for statistical evaluation of the high-level DLR potential across an entire region, as well as an assessment of the line-level DLR ampacities within an electrical grid, based on (weather forecasting) reanalysis data. The proposed methodology can disclose available additional capacity as part of early-stage planning for wide-area DLR systems. The island of Ireland and the 110 kV network of the Republic of Ireland (ROI) power system are considered as the study cases, with comparison made against seasonal static ratings and ambient temperature adjusted line rating methods.

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Section: Wide-area Weather-based Indirect Dlr Systemsmentioning

confidence: 99%

Indirect weather‐based approaches for increasing power transfer capabilities of electrical transmission networks

Keyvani

Whelan

Doddy

et al. 2023

WIREs Energy & Environment

View full text Add to dashboard Cite

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“…As such, steady-state ampacity is predicted using wind simulations coupled with meteorological records. Bosisio et al [20] built a step-by-step method for evaluating all of the stochastic processes of atmospheric variables usable for DLR forecasting. Albizu et al [21] suggested an adaptive SLR for static line rating situations that could reach a maximum temperature limit.…”

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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“…e PCA algorithm calculates the eigenvalues of the input variable matrix, finds the variance corresponding to each input variable, and then determines the PC according to the cumulative value [28]. e main steps are as follows.…”

Section: Meteorological Feature Extraction Based On Principalmentioning

confidence: 99%

A Hybrid Method for Ultrashort-Term Wind Power Prediction considering Meteorological Features and Seasonal Information

Zhou

Sun

et al. 2020

Mathematical Problems in Engineering

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High-precision wind power prediction is important for the planning, economics, and security maintenance of a power grid. Meteorological features and seasonal information are strongly related to wind power prediction. This paper proposes a hybrid method for ultrashort-term wind power prediction considering meteorological features (wind direction, wind speed, temperature, atmospheric pressure, and humidity) and seasonal information. The wind power data are decomposed into stationary subsequences using the ensemble empirical mode decomposition (EEMD). The principal component analysis (PCA) is used to reduce the redundant meteorological features and the algorithm complexity. With the stationary subsequences and extracted meteorological features data as inputs, the long short-term memory (LSTM) network is used to complete the wind power prediction. Finally, the seasonal autoregressive integrated moving average (SARIMA) is innovatively used to fit seasonal features (quarterly and monthly) of wind power and reconstruct the prediction results of LSTM. The proposed method is used to predict 15-minute wind power. In this study, three datasets were collected from a windfarm in Laizhou to validate the prediction performance of the proposed method. The experimental results showed that the prediction accuracy was significantly improved when meteorological features were considered and further improved with seasonal correction.

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Improving DTR assessment by means of PCA applied to wind data

Cited by 26 publications

References 17 publications

Indirect weather‐based approaches for increasing power transfer capabilities of electrical transmission networks

Indirect weather‐based approaches for increasing power transfer capabilities of electrical transmission networks

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

A Hybrid Method for Ultrashort-Term Wind Power Prediction considering Meteorological Features and Seasonal Information

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