Improvement of Wind Energy Production through HVDC Systems

Brenna, Morris; Foiadelli, Federica; Longo, Michela; Zaninelli, Dario

doi:10.3390/en10020157

Cited by 20 publications

(14 citation statements)

References 30 publications

(15 reference statements)

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“…More and more, voltage source converters (VSC) tend to be preferred over LCC owing to their ability to independently control the reactive power flow, the faster dynamic response and the possibility to connect to weak AC networks [44,45]. These are typical situations in networks with substantial introduction of renewable energy [46]. LCC tend to be dedicated to high-power HVDC links with stable networks.…”

Section: Discussionmentioning

confidence: 99%

DC Model Cable under Polarity Inversion and Thermal Gradient: Build-Up of Design-Related Space Charge

Adi¹,

Teyssèdre

et al. 2017

Technologies

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Abstract:In the field of energy transport, High-Voltage DC (HVDC) technologies are booming at present due to the more flexible power converter solutions along with needs to bring electrical energy from distributed production areas to consumption sites and to strengthen large-scale energy networks. These developments go with challenges in qualifying insulating materials embedded in those systems and in the design of insulations relying on stress distribution. Our purpose in this communication is to illustrate how far the field distribution in DC insulation systems can be anticipated based on conductivity data gathered as a function of temperature and electric field. Transient currents and conductivity estimates as a function of temperature and field were recorded on miniaturized HVDC power cables with construction of 1.5 mm thick crosslinked polyethylene (XLPE) insulation. Outputs of the conductivity model are compared to measured field distributions using space charge measurements techniques. It is shown that some features of the field distribution on model cables put under thermal gradient can be anticipated based on conductivity data. However, space charge build-up can induce substantial electric field strengthening when materials are not well controlled.

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Section: Discussionmentioning

confidence: 99%

DC Model Cable under Polarity Inversion and Thermal Gradient: Build-Up of Design-Related Space Charge

Adi¹,

Teyssèdre

et al. 2017

Technologies

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“…The typical power rating of the turbine side converter is 5-10 MW. Collection systems mostly utilize the 33-36 kV AC to gather the energy from the wind farm [5,30]. High voltage submarine cables connect the wind turbine output to the offshore platform.…”

Section: Hvdc Offshore Technologies Comparisonmentioning

confidence: 99%

“…Each investment must be assessed in terms of resource evaluation, grid connection and operation, which applies to deep offshore projects, in particular [2,3]. To improve the cost-effectiveness of offshore designs, it will be essential to implement a technology optimizing architecture at a system level [4,5].…”

Section: Introductionmentioning

confidence: 99%

Evolution of the HVDC Link Connecting Offshore Wind Farms to Onshore Power Systems

Ryndzionek

Sienkiewicz

2020

Energies

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This paper presents an overview of the DC link development and evolution dedicated to HVDC structure for connecting offshore wind power plants to onshore power systems. The growing demand for the green energy has forced investors in power industry to look for resources further out at sea. Hence, the development of power electronics and industrial engineering has enabled offshore wind farms to be situated further from the shore and in deeper waters. However, their development will require, among other technologies, DC-DC conversion systems. The advantages of HVDC over HVAC technology in relation to transmission distance are given. The different HVDC configurations and topologies of HVDC converters are elucidated. In this context, the HVDC grids are a promising alternative for the expansion of the existing AC grid.

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“…Wind power is currently one of the most important renewable energies in the world [1] in terms of penetration in the electric power system [2,3], economic impact and annual growth rate [4], both onshore [5] and offshore [6]. Electric power generation is usually carried out in large wind farms [7,8] far from urban centers [9,10], though, in the last few years, urban wind power generation is also gaining impulse [11], including its use in smart grids [12].…”

Section: Motivationmentioning

confidence: 99%

Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data

et al. 2017

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Wind Power Ramp Events (WPREs) are large fluctuations of wind power in a short time interval, which lead to strong, undesirable variations in the electric power produced by a wind farm. Its accurate prediction is important in the effort of efficiently integrating wind energy in the electric system, without affecting considerably its stability, robustness and resilience. In this paper, we tackle the problem of predicting WPREs by applying Machine Learning (ML) regression techniques. Our approach consists of using variables from atmospheric reanalysis data as predictive inputs for the learning machine, which opens the possibility of hybridizing numerical-physical weather models with ML techniques for WPREs prediction in real systems. Specifically, we have explored the feasibility of a number of state-of-the-art ML regression techniques, such as support vector regression, artificial neural networks (multi-layer perceptrons and extreme learning machines) and Gaussian processes to solve the problem. Furthermore, the ERA-Interim reanalysis from the European Center for Medium-Range Weather Forecasts is the one used in this paper because of its accuracy and high resolution (in both spatial and temporal domains). Aiming at validating the feasibility of our predicting approach, we have carried out an extensive experimental work using real data from three wind farms in Spain, discussing the performance of the different ML regression tested in this wind power ramp event prediction problem.

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Improvement of Wind Energy Production through HVDC Systems

Cited by 20 publications

References 30 publications

DC Model Cable under Polarity Inversion and Thermal Gradient: Build-Up of Design-Related Space Charge

DC Model Cable under Polarity Inversion and Thermal Gradient: Build-Up of Design-Related Space Charge

Evolution of the HVDC Link Connecting Offshore Wind Farms to Onshore Power Systems

Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data

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