Special Issue "Large Grid-Connected Wind Turbines"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy".

Deadline for manuscript submissions: 15 July 2018

Special Issue Editors

Guest Editor
Dr. S M Muyeen

Faculty of Science and Engineering, School of Electrical Engineering and Computing, Curtin University, Perth, Western Australia 6102, Australia
Website | E-Mail
Interests: renewable energy; energy storage; smart grid; power system; control applications in power system
Guest Editor
Prof. Dr. Frede Blaabjerg
Highly Cited - Clarivate Analytics (formerly Thomson Reuters)

Department of Energy Technology, Aalborg University, Aalborg 9220, Denmark
Website | E-Mail
Fax: +45 9815 1411
Interests: power electronics and its applications in motor drives; wind turbines; PV systems; harmonics; reliability of power electronic systems

Special Issue Information

Dear Colleagues,

The renewable energy penetration rate has increased rapidly since the last decade. Some countries are already generating more than 50% of their required energy from renewable sources and they are moving towards 100% renewables. Among the different renewable sources, such as wind, solar, biomass/biogas, tidal, geothermal, etc., wind energy is playing a vital role in the energy market and is competing with the traditional power industry. 10 MW class wind turbines will be available commercially in the near future and growth will continue.

To maximize the energy production from wind turbines, and transfer this power to the power grid, different types of power electronic converters are being used presently as interfacing devices. With increased turbine size, grid interfacing technologies are getting more complex. Large wind turbines are, not only supplying grid power, but are also supposed to provide some ancillary services to the grid. Controller and filter design tasks are becoming more complex. System stability is becoming a headache for transmission and distribution operators, when large scale wind farms are connected with existing weak networks. The energy storage system appears as a crucial part of grid tied to large scale wind turbine generator systems. This Special Issue aims to collect important works addressing the stability, variability, and scalability of large-scale, wind-turbine, grid-interfacing techniques and challenges.

Assoc. Prof. Dr. S. M. Muyeen
Prof. Dr. Frede Blaabjerg
Guest Editors

Manuscript Submission Information

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Keywords

  • Wind Turbine Generator System
  • Offshore Wind Farm
  • Energy Storage System
  • Grid Interfacing Power Converters
  • Power Quality
  • Fault Ride Through
  • Synthetic Inertia

Published Papers (2 papers)

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Research

Open AccessArticle Wind Power Forecasting Using Multi-Objective Evolutionary Algorithms for Wavelet Neural Network-Optimized Prediction Intervals
Appl. Sci. 2018, 8(2), 185; doi:10.3390/app8020185
Received: 12 December 2017 / Revised: 22 January 2018 / Accepted: 25 January 2018 / Published: 26 January 2018
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Abstract
The intermittency of renewable energy will increase the uncertainty of the power system, so it is necessary to predict the short-term wind power, after which the electrical power system can operate reliably and safely. Unlike the traditional point forecasting, the purpose of this
[...] Read more.
The intermittency of renewable energy will increase the uncertainty of the power system, so it is necessary to predict the short-term wind power, after which the electrical power system can operate reliably and safely. Unlike the traditional point forecasting, the purpose of this study is to quantify the potential uncertainties of wind power and to construct prediction intervals (PIs) and prediction models using wavelet neural network (WNN). Lower upper bound estimation (LUBE) of the PIs is achieved by minimizing a multi-objective function covering both interval width and coverage probabilities. Considering the influence of the points out of the PIs to shorten the width of PIs without compromising coverage probability, a new, improved, multi-objective artificial bee colony (MOABC) algorithm combining multi-objective evolutionary knowledge, called EKMOABC, is proposed for the optimization of the forecasting model. In this paper, some comparative simulations are carried out and the results show that the proposed model and algorithm can achieve higher quality PIs for wind power forecasting. Taking into account the intermittency of renewable energy, such a type of wind power forecast can actually provide a more reliable reference for dispatching of the power system. Full article
(This article belongs to the Special Issue Large Grid-Connected Wind Turbines)
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Open AccessArticle Stability Augmentation of a Grid-Connected Wind Farm by Fuzzy-Logic-Controlled DFIG-Based Wind Turbines
Appl. Sci. 2018, 8(1), 20; doi:10.3390/app8010020
Received: 30 November 2017 / Revised: 17 December 2017 / Accepted: 19 December 2017 / Published: 24 December 2017
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Abstract
Wind farm (WF) grid codes require wind generators to have low voltage ride through (LVRT) capability, which means that normal power production should be resumed quickly once the nominal grid voltage has been recovered. However, WFs with fixed-speed wind turbines with squirrel cage
[...] Read more.
Wind farm (WF) grid codes require wind generators to have low voltage ride through (LVRT) capability, which means that normal power production should be resumed quickly once the nominal grid voltage has been recovered. However, WFs with fixed-speed wind turbines with squirrel cage induction generators (FSWT-SCIGs) have failed to fulfill the LVRT requirement, which has a significant impact on power system stability. On the other hand, variable-speed wind turbines with doubly fed induction generators (VSWT-DFIGs) have sufficient LVRT augmentation capability and can control the active and reactive power delivered to the grid. However, the DFIG is more expensive than the SCIG due to its AC/DC/AC converter. Therefore, the combined use of SCIGs and DFIGs in a WF could be an effective solution. The design of the rotor-side converter (RSC) controller is crucial because the RSC controller contributes to the system stability. The cascaded control strategy based on four conventional PI controllers is widely used to control the RSC of the DFIG, which can inject only a small amount of reactive power during fault conditions. Therefore, the conventional strategy can stabilize the lower rating of the SCIG. In the present paper, a new control strategy based on fuzzy logic is proposed in the RSC controller of the DFIG in order to enhance the LVRT capability of the SCIG in a WF. The proposed fuzzy logic controller (FLC) is used to control the reactive power delivered to the grid during fault conditions. Moreover, reactive power injection can be increased in the proposed control strategy. Extensive simulations executed in the PSCAD/EMTDC environment for both the proposed and conventional PI controllers of the RSC of the DFIG reveal that the proposed control strategy can stabilize the higher rating of the SCIG. Full article
(This article belongs to the Special Issue Large Grid-Connected Wind Turbines)
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