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Journal articles on the topic 'Wind energy conversion systems – Canada'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Papadopoulos, M. "Book Review: Wind Energy Conversion Systems." International Journal of Electrical Engineering & Education 29, no. 3 (July 1992): 264. http://dx.doi.org/10.1177/002072099202900309.

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2

Yates, D. A. "Book Review: Wind Energy Conversion Systems." International Journal of Mechanical Engineering Education 22, no. 1 (January 1994): 76–77. http://dx.doi.org/10.1177/030641909402200112.

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3

Wagner, Hermann-Josef. "Introduction to wind energy systems(*)." EPJ Web of Conferences 246 (2020): 00004. http://dx.doi.org/10.1051/epjconf/202024600004.

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This article presents the basic concepts of wind energy and deals with the physics and mechanics of operation. It describes the conversion of wind energy into rotation of turbine, and the critical parameters governing the efficiency of this conversion. After that it presents an overview of various parts and components of windmills. The connection to the electrical grid, the world status of wind energy use for electricity production, the cost situation and research and development needs are further aspects which will be considered.

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4

Wagner, Hermann-Josef. "Introduction to wind energy systems." EPJ Web of Conferences 189 (2018): 00005. http://dx.doi.org/10.1051/epjconf/201818900005.

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This article presents the basic concepts of wind energy and deals with the physics and mechanics of operation. It describes the conversion of wind energy into the rotation of a turbine, and the critical parameters governing the eﬃciency of this conversion. After that it presents an overview of the various parts and component of windmills. The connection to the electrical grid, the world status of wind energy use for electricity production, the cost situation and research and development needs are further aspects which will be considered.

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5

Alhmoud, Lina, and Hussein Al-Zoubi. "IoT Applications in Wind Energy Conversion Systems." Open Engineering 9, no. 1 (November 2, 2019): 490–99. http://dx.doi.org/10.1515/eng-2019-0061.

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AbstractRenewable energy reliability has been the main agenda nowadays, where the internet of things (IoT) is a crucial research direction with a lot of opportunities for improvement and challenging work. Data obtained from IoT is converted into actionable information to improve wind turbine performance, driving wind energy cost down and reducing risk. However, the implementation in IoT is a challenging task because the wind turbine system level and component level need real-time control. So, this paper is dedicated to investigating wind resource assessment and lifetime estimation of wind power modules using IoT. To illustrate this issue, a model is built with sub-models of an aerodynamic rotor connected directly to a multi-pole variable speed permanent magnet synchronous generator (PMSG) with variable speed control, pitch angle control and full-scale converter connected to the grid. Besides, a large number of various sensors for measurement of wind parameters are integrated with IoT. Simulations are constructed with Matlab/Simulink and IoT ’Thingspeak’ Mathworks web service. IoT has proved to increase the reliability of measurement strategies, monitoring accuracy, and quality assurance.

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6

Freitas, Walmir, Ahmed Faheem Zobaa, Jose C. M. Vieira, and James S. McConnach. "Issues related to wind energy conversion systems." International Journal of Energy Technology and Policy 3, no. 4 (2005): 313. http://dx.doi.org/10.1504/ijetp.2005.008397.

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7

Han, Ying Hua. "Grid Integration of Wind Energy Conversion Systems." Renewable Energy 21, no. 3-4 (November 2000): 607–8. http://dx.doi.org/10.1016/s0960-1481(00)00042-2.

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8

Chen, Zhe. "Special Issue on “Wind Energy Conversion Systems”." Applied Sciences 9, no. 16 (August 9, 2019): 3258. http://dx.doi.org/10.3390/app9163258.

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A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: describe briefly the main methods or treatments applied; (3) Results: summarize the article’s main findings; and (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article; it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.

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9

Xie, Kaigui, and Roy Billinton. "Energy and reliability benefits of wind energy conversion systems." Renewable Energy 36, no. 7 (July 2011): 1983–88. http://dx.doi.org/10.1016/j.renene.2010.12.011.

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10

Perera, Sinhara M. H. D., Ghanim Putrus, Michael Conlon, Mahinsasa Narayana, and Keith Sunderland. "Wind Energy Harvesting and Conversion Systems: A Technical Review." Energies 15, no. 24 (December 8, 2022): 9299. http://dx.doi.org/10.3390/en15249299.

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Wind energy harvesting for electricity generation has a significant role in overcoming the challenges involved with climate change and the energy resource implications involved with population growth and political unrest. Indeed, there has been significant growth in wind energy capacity worldwide with turbine capacity growing significantly over the last two decades. This confidence is echoed in the wind power market and global wind energy statistics. However, wind energy capture and utilisation has always been challenging. Appreciation of the wind as a resource makes for difficulties in modelling and the sensitivities of how the wind resource maps to energy production results in an energy harvesting opportunity. An opportunity that is dependent on different system parameters, namely the wind as a resource, technology and system synergies in realizing an optimal wind energy harvest. This paper presents a thorough review of the state of the art concerning the realization of optimal wind energy harvesting and utilisation. The wind energy resource and, more specifically, the influence of wind speed and wind energy resource forecasting are considered in conjunction with technological considerations and how system optimization can realise more effective operational efficiencies. Moreover, non-technological issues affecting wind energy harvesting are also considered. These include standards and regulatory implications with higher levels of grid integration and higher system non-synchronous penetration (SNSP). The review concludes that hybrid forecasting techniques enable a more accurate and predictable resource appreciation and that a hybrid power system that employs a multi-objective optimization approach is most suitable in achieving an optimal configuration for maximum energy harvesting.

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11

Karaki, S. H., R. B. Chedid, and R. Ramadan. "Probabilistic performance assessment of wind energy conversion systems." IEEE Transactions on Energy Conversion 14, no. 2 (June 1999): 217–24. http://dx.doi.org/10.1109/60.766986.

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12

Dulley, T. A. C. "A Guide to Small Wind Energy Conversion Systems." IEE Review 35, no. 1 (1989): 36. http://dx.doi.org/10.1049/ir:19890016.

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13

Hodges, Laurent. "A guide to small wind energy conversion systems." Physics Teacher 26, no. 7 (October 1988): 481. http://dx.doi.org/10.1119/1.2342587.

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14

Kamal, Elkhatib, Abdel Aitouche, Reza Ghorbani, and Mireille Bayart. "Robust nonlinear control of wind energy conversion systems." International Journal of Electrical Power & Energy Systems 44, no. 1 (January 2013): 202–9. http://dx.doi.org/10.1016/j.ijepes.2012.07.009.

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15

B S, Yogananda, and Dr K. Thippeswamy. "Improvement of Power Quality in Wind Energy Conversion Systems." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 12–20. http://dx.doi.org/10.22214/ijraset.2022.41877.

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Abstract: Wind Energy Conversion Systems (WECS) show variability in their output power as a result of changing their main engines (wind speed). This introduces a new grid uncertainty factor and poses many challenges to electricity system designers and utilities in terms of grid network integrity, ie power system security, power system stability and power quality. This paper discusses the various challenges of wind energy when integrated into the grid and identifies different mitigation strategies for its smooth integration. Keywords: wind energy system, Power quality, Power filters, Reactive Power, controllers

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16

Djahbar, Abdelkader, Abdallah Zegaoui, and Michel Aillerie. "Multiphase Wind Energy Conversion Systems Based on Matrix Converter." Automatika 57, no. 2 (January 2016): 396–404. http://dx.doi.org/10.7305/automatika.2016.10.1313.

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17

Amar, Fathi Ben, and Mustapha Elamouri. "Wind Energy Conversion Systems Adapted to the Tunisian Sites." Smart Grid and Renewable Energy 04, no. 01 (2013): 57–68. http://dx.doi.org/10.4236/sgre.2013.41009.

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18

Karaki, S. H., R. B. Chedid, and R. Ramadan. "Probabilistic production costing of diesel-wind energy conversion systems." IEEE Transactions on Energy Conversion 15, no. 3 (2000): 284–89. http://dx.doi.org/10.1109/60.875494.

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19

Milligan, M. R. "Utility-scale wind energy conversion systems: modeling and applications." IEEE Power Engineering Review 19, no. 11 (November 1999): 13–14. http://dx.doi.org/10.1109/mper.1999.799636.

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20

Olaofe, Zaccheus O. "Application of neural network to wind energy conversion systems." International Journal of Renewable Energy Technology 4, no. 3 (2013): 265. http://dx.doi.org/10.1504/ijret.2013.054758.

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21

Li, Gong, and Jing Shi. "Applications of Bayesian methods in wind energy conversion systems." Renewable Energy 43 (July 2012): 1–8. http://dx.doi.org/10.1016/j.renene.2011.12.006.

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22

Fan, Lingling, Zhixin Miao, Subbaraya Yuvarajan, and Rajesh Kavasseri. "Hybrid modeling of DFIGs for wind energy conversion systems." Simulation Modelling Practice and Theory 18, no. 7 (August 2010): 1032–45. http://dx.doi.org/10.1016/j.simpat.2010.04.002.

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23

Kazemi, Yousef, and Mohammad Mahdi Rezaei. "A grid forming control for wind energy conversion systems." Energy Reports 9 (December 2023): 2016–26. http://dx.doi.org/10.1016/j.egyr.2023.01.037.

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24

Naba, Agus, and Ahmad Nadhir. "Power Curve Based-Fuzzy Wind Speed Estimation in Wind Energy Conversion Systems." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 1 (January 20, 2018): 76–87. http://dx.doi.org/10.20965/jaciii.2018.p0076.

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Availability of wind speed information is of great importance for maximization of wind energy extraction in wind energy conversion systems. The wind speed is commonly obtained from a direct measurement employing a number of anemometers installed surrounding the wind turbine. In this paper a sensorless fuzzy wind speed estimator is proposed. The estimator is easy to build without any training or optimization. It works based on the fuzzy logic principles heuristically inferred from the typical wind turbine power curve. The wind speed estimation using the proposed estimator was simulated during the operation of a squirrel-cage induction generator-based wind energy conversion system. The performance of the proposed estimator was verified by the well estimated wind speed obtained under the wind speed variation.

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25

Roy, S. "Optimal planning of wind energy conversion systems over an energy scenario." IEEE Transactions on Energy Conversion 12, no. 3 (1997): 248–54. http://dx.doi.org/10.1109/60.629710.

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26

Billinton, Roy, and Yi Gao. "Multistate Wind Energy Conversion System Models for Adequacy Assessment of Generating Systems Incorporating Wind Energy." IEEE Transactions on Energy Conversion 23, no. 1 (March 2008): 163–70. http://dx.doi.org/10.1109/tec.2006.882415.

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27

Nguyen, Hoa Minh. "Nonlinear Feedback Linearization Control for Wind Generators in Hybrid Wind Energy Conversion Systems." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 16 (September 7, 2021): 493–501. http://dx.doi.org/10.37394/23203.2021.16.45.

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This paper deals with the optimal power extraction of wind generators in hybrid wind energy conversion systems. The proposed control technique is the nonlinear exact feedback linearization which is able to give satisfactory performances over a broad spectrum of operating points. The main contribution of the paper is the successful dealing with the most challenging task in the design of nonlinear feedback linearization controllers for wind energy conversion systems, which is the transform and manipulation of state-dependent high-order nonlinear power coefficients presented in wind turbines. In other words, this paper addresses the full-order highly nonlinear power coefficients functions instead of using approximated low-order functions as in previous works in literature. The obtained nonlinear controller is able to cope with the time-varying nature of wind turbines and maintain the optimal power conversion points. Moreover the nonlinear feedback linearization control performance is also compared to that of traditional perturbation and observation based maximum power point tracking and classical PI control. The numerical simulation outcomes show that the proposed nonlinear controller outperform those two traditional controllers in terms of maximum gained power and transient specifications.

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28

Nazir, Muhammad Shahzad, Yeqin Wang, Muhammad Bilal, Hafiz M. Sohail, Athraa Ali Kadhem, H. M. Rashid Nazir, Ahmed N. Abdalla, and Yongheng Ma. "Comparison of Small-Scale Wind Energy Conversion Systems: Economic Indexes." Clean Technologies 2, no. 2 (April 3, 2020): 144–55. http://dx.doi.org/10.3390/cleantechnol2020010.

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Wind energy is considered as one of the most prominent sources of energy for sustainable development. This technology is of interest owing to its capability to produce clean, eco-friendly, and cost-effective energy for small-scale users and rural areas where grid power availability is insufficient. Wind power generation has developed rapidly in the past decade and is expected to play a vital role in the economic development of countries. Therefore, studying dominant economic factors is crucial to properly approach public and private financing for this emerging technology, as industrial growth and energy demands may outpace further economic studies earlier than expected. In this study, a strategy-focused method for performing economic analysis on wind energy based on financial net present value, levelized cost of energy, internal rate of return, and investment recovery period is presented. Numerical and simulation results depict the most optimal and economical system from a 3 and a 10 kW wind energy conversion system (WECS). Moreover, the aforementioned criteria are used to determine which WECS range is the most suitable investment with the shortest payback period. Finally, an economically viable and profitable wind energy system is recommended.

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29

R Ramesh,, M. Sravani Durga. "Implementation of PSO MPPT Technique for Wind Energy Conversion Systems." International Journal for Modern Trends in Science and Technology, no. 8 (August 7, 2020): 100–103. http://dx.doi.org/10.46501/ijmtst060818.

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One major advantage of renewable energy is that it is sustainable and will never run out. They provide clean energy because they are non-pollutant and non-contributor to greenhouse effects and global warming. Renewable energy facilities generally require less maintenance than traditional generators. Their fuel being derived from natural and available resources reduces the costs of operation. The proposed PSO algorithm uses the dc current as the perturbing variable. The algorithm detects sudden wind speed changes indirectly through the dc-link voltage slope. The voltage slope is also used to enhance the tracking speed of the algorithm and to prevent the generator from stalling under rapid wind speed slow down conditions. The proposed method uses two modes of operation: A PSO mode with adaptive step size under slow wind speed fluctuation conditions, and a prediction mode employed under fast wind speed change conditions. The dc-link capacitor voltage slope reflects the acceleration information of the generator, which is then used to predict the next step size and direction of the current command.

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30

Tan, Jian Ding, Clifford Choe Wei Chang, Mohammad Arif Sobhan Bhuiyan, Khairun Nisa’ Minhad, and Kharudin Ali. "Advancements of wind energy conversion systems for low-wind urban environments: A review." Energy Reports 8 (November 2022): 3406–14. http://dx.doi.org/10.1016/j.egyr.2022.02.153.

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31

Mansouri, Majdi, Radhia Fezai, Mohamed Trabelsi, Hajji Mansour, Hazem Nounou, and Mohamed Nounou. "Enhanced Gaussian Process Regression for Diagnosing Wind Energy Conversion Systems." IFAC-PapersOnLine 55, no. 6 (2022): 673–78. http://dx.doi.org/10.1016/j.ifacol.2022.07.205.

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32

Cortes-Vega, David, Fernando Ornelas-Tellez, and Juan Anzurez-Marin. "Nonlinear Optimal Control for PMSG-Based Wind Energy Conversion Systems." IEEE Latin America Transactions 19, no. 7 (July 2021): 1191–98. http://dx.doi.org/10.1109/tla.2021.9461848.

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33

TALAT, L. A. "RELIABILITY AND COST BENEFITS ASSESSMENT OF WIND ENERGY CONVERSION SYSTEMS." JES. Journal of Engineering Sciences 39, no. 6 (November 1, 2011): 1463–73. http://dx.doi.org/10.21608/jesaun.2011.129442.

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34

Park, Jang-Hyun. "Adaptive self-structuring fuzzy controller of wind energy conversion systems." Journal of Korean Institute of Intelligent Systems 23, no. 2 (April 25, 2013): 151–57. http://dx.doi.org/10.5391/jkiis.2013.23.2.151.

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35

McDonald, Ronan. "Book Review: A Guide to Small Wind Energy Conversion Systems." International Journal of Electrical Engineering & Education 25, no. 1 (January 1988): 84–85. http://dx.doi.org/10.1177/002072098802500123.

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36

Shikha, T. S. Bhatti, and D. P. Kothari. "Wind Energy Conversion Systems as a Distributed Source of Generation." Journal of Energy Engineering 129, no. 3 (December 2003): 69–80. http://dx.doi.org/10.1061/(asce)0733-9402(2003)129:3(69).

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37

Karaki, S. H., R. B. Chedid, and R. Ramadan. "Probabilistic performance assessment of autonomous solar-wind energy conversion systems." IEEE Transactions on Energy Conversion 14, no. 3 (1999): 766–72. http://dx.doi.org/10.1109/60.790949.

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38

Chedid, R., F. Mrad, and M. Basma. "Intelligent control of a class of wind energy conversion systems." IEEE Transactions on Energy Conversion 14, no. 4 (1999): 1597–604. http://dx.doi.org/10.1109/60.815111.

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39

Koay, Ying Ying, Jian Ding Tan, Siaw Paw Koh, Kok Hen Chong, Sieh Kiong Tiong, and Janaka Ekanayake. "Optimization of wind energy conversion systems – an artificial intelligent approach." International Journal of Power Electronics and Drive Systems (IJPEDS) 11, no. 2 (June 1, 2020): 1040. http://dx.doi.org/10.11591/ijpeds.v11.i2.pp1040-1046.

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The environmentally friendly wind energy conversion system has become one of the most studied branches of sustainable energy. Like many other power generator, maximum power point tracking is an easy yet effective way to boost the efficiency of the conversion system. In this research, a modified Electromagnetism-like Mechanism Algorithm (EM) is proposed for the maximum power point tracking (MPPT) scheme of a micro-wind energy conversion system (WECS). In contrast with the random search steps used in a conventional EM, modified EM is enhanced with a Split, Probe, and Compare (SPC-EM) feature which ensures solutions with higher accuracies quicker by not having to scrutinize the search in details at the beginning stages of the iterations. Experiments and simulations are carried to test the SPC-EM in tracking the maximum power point under different wind profiles. Results indicate that the performance of the modified EM showed significant improvement over the conventional EM in the benchmarking. It can thus be concluded that based on the simulations, the SPC-EM performs well as an MPPT scheme in a micro-WECS.

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40

Talebi, Nasser, Mohammad Ali Sadrnia, and Ahmad Darabi. "Dynamic response of wind energy conversion systems under various faults." International Journal of Engineering Systems Modelling and Simulation 7, no. 2 (2015): 80. http://dx.doi.org/10.1504/ijesms.2015.068651.

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41

Singh, Bhim, and Shailendra Sharma. "Voltage and frequency controllers for standalone wind energy conversion systems." IET Renewable Power Generation 8, no. 6 (August 2014): 707–21. http://dx.doi.org/10.1049/iet-rpg.2013.0186.

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42

Sachan, Ayushi, Akhilesh Kumar Gupta, and Paulson Samuel. "A Review of MPPT Algorithms Employedin Wind Energy Conversion Systems." Journal of Green Engineering 6, no. 4 (2017): 385–402. http://dx.doi.org/10.13052/jge1904-4720.643.

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43

El-Fouly, T. H. M., E. F. El-Saadany, M. M. A. Salama, T. H. M. El-Fouly, E. F. El-Saadany, and M. M. A. Salama. "Grey Predictor for Wind Energy Conversion Systems Output Power Prediction." IEEE Transactions on Power Systems 21, no. 3 (August 2006): 1450–52. http://dx.doi.org/10.1109/tpwrs.2006.879246.

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44

Mauricio, J. M., A. Marano, A. Gomez-Exposito, and J. L. Martinez Ramos. "Frequency Regulation Contribution Through Variable-Speed Wind Energy Conversion Systems." IEEE Transactions on Power Systems 24, no. 1 (February 2009): 173–80. http://dx.doi.org/10.1109/tpwrs.2008.2009398.

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45

Arifujjaman, Md, M. T. Iqbal, and J. E. Quaicoe. "Performance Comparison of Grid Connected Small Wind Energy Conversion Systems." Wind Engineering 33, no. 1 (January 2009): 1–17. http://dx.doi.org/10.1260/0309-524x.33.1.1.

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46

Jayashri, R., and R. P. Kumudini Devi. "Rotor Speed Stability of Grid Connected Wind Energy Conversion Systems." Wind Engineering 31, no. 6 (December 2007): 475–85. http://dx.doi.org/10.1260/030952407784079726.

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47

Kamal, Elkhatib, Abdel Aitouche, Reza Ghorbani, and Mireille Bayart. "Fuzzy Scheduler Fault-Tolerant Control for Wind Energy Conversion Systems." IEEE Transactions on Control Systems Technology 22, no. 1 (January 2014): 119–31. http://dx.doi.org/10.1109/tcst.2013.2246162.

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48

Leon, Andres Enrique, Jorge Alberto Solsona, and Juan Manuel Mauricio. "Subsynchronous resonance mitigation using variable-speed wind energy conversion systems." IET Generation, Transmission & Distribution 7, no. 5 (May 1, 2013): 511–25. http://dx.doi.org/10.1049/iet-gtd.2012.0357.

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49

Haghani, Adel, Minjia Krueger, Torsten Jeinsch, Steven X. Ding, and Peter Engel. "Data-Driven Multimode Fault Detection for Wind Energy Conversion Systems." IFAC-PapersOnLine 48, no. 21 (2015): 633–38. http://dx.doi.org/10.1016/j.ifacol.2015.09.597.

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50

Yang, Qinmin, Xuguo Jiao, Qingshun Luo, Qi Chen, and Youxian Sun. "L1 adaptive pitch angle controller of wind energy conversion systems." ISA Transactions 103 (August 2020): 28–36. http://dx.doi.org/10.1016/j.isatra.2020.04.001.

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