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    Modelling Type 1 and 2 Wind Turbines based on IEC 61400-27-1: Transient Response under Voltage Dips

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    [EN] Wind power plants depend greatly on weather conditions, thus being considered intermittent, uncertain and non-dispatchable. Due to the massive integration of this energy resource in the recent decades, it is important that transmission and distribution system operators are able to model their electrical behaviour in terms of steady-state power flow, transient dynamic stability, and short-circuit currents. Consequently, in 2015, the International Electrotechnical Commission published Standard IEC 61400-27-1, which includes generic models for wind power generation in order to estimate the electrical characteristics of wind turbines at the connection point. This paper presents, describes and details the models for wind turbine topologies Types 1 and 2 following IEC 61400-27-1 for electrical simulation purposes, including the values for the parameters for the different subsystems. A hardware-in-the-loop combined with a real-time simulator is also used to analyse the response of such wind turbine topologies under voltage dips. The evolution of active and reactive powers is discussed, together with the wind turbine rotor and generator rotational speeds.This work was partially supported by the Spanish Ministry of Economy and Competitiveness and the European Union -FEDER Funds, ENE2016-78214-C2-1-R-; and the Spanish Ministry of Education, Culture and Sports -ref. FPU16/04282-.García-Sánchez, TM.; Muñoz-Benavente, I.; Gómez-Lázaro, E.; Fernández-Guillamón, A. (2020). Modelling Type 1 and 2 Wind Turbines based on IEC 61400-27-1: Transient Response under Voltage Dips. Energies. 13(16):1-19. https://doi.org/10.3390/en13164078S1191316Fernández-Guillamón, A., Villena-Lapaz, J., Vigueras-Rodríguez, A., García-Sánchez, T., & Molina-García, Á. (2018). An Adaptive Frequency Strategy for Variable Speed Wind Turbines: Application to High Wind Integration Into Power Systems. Energies, 11(6), 1436. doi:10.3390/en11061436Fernández-Guillamón, A., Das, K., Cutululis, N. A., & Molina-García, Á. (2019). Offshore Wind Power Integration into Future Power Systems: Overview and Trends. Journal of Marine Science and Engineering, 7(11), 399. doi:10.3390/jmse7110399Fernández-Guillamón, A., Gómez-Lázaro, E., Muljadi, E., & Molina-García, Á. (2019). Power systems with high renewable energy sources: A review of inertia and frequency control strategies over time. Renewable and Sustainable Energy Reviews, 115, 109369. doi:10.1016/j.rser.2019.109369Cardozo, C., van Ackooij, W., & Capely, L. (2018). Cutting plane approaches for frequency constrained economic dispatch problems. Electric Power Systems Research, 156, 54-63. doi:10.1016/j.epsr.2017.11.001Fernández-Guillamón, A., Martínez-Lucas, G., Molina-García, Á., & Sarasua, J. I. (2020). An Adaptive Control Scheme for Variable Speed Wind Turbines Providing Frequency Regulation in Isolated Power Systems with Thermal Generation. Energies, 13(13), 3369. doi:10.3390/en13133369Global Wind Report 2019https://gwec.net/global-wind-report-2019/Muñoz-Benavente, I., Hansen, A. D., Gómez-Lázaro, E., García-Sánchez, T., Fernández-Guillamón, A., & Molina-García, Á. (2019). Impact of Combined Demand-Response and Wind Power Plant Participation in Frequency Control for Multi-Area Power Systems. Energies, 12(9), 1687. doi:10.3390/en12091687Villena-Ruiz, R., Lorenzo-Bonache, A., Honrubia-Escribano, A., Jiménez-Buendía, F., & Gómez-Lázaro, E. (2019). Implementation of IEC 61400-27-1 Type 3 Model: Performance Analysis under Different Modeling Approaches. Energies, 12(14), 2690. doi:10.3390/en12142690Kumar, D., & Chatterjee, K. (2016). A review of conventional and advanced MPPT algorithms for wind energy systems. Renewable and Sustainable Energy Reviews, 55, 957-970. doi:10.1016/j.rser.2015.11.013Hansen, A. D., Iov, F., Blaabjerg, F., & Hansen, L. H. (2004). Review of Contemporary Wind Turbine Concepts and Their Market Penetration. Wind Engineering, 28(3), 247-263. doi:10.1260/0309524041590099Liang, X. (2017). Emerging Power Quality Challenges Due to Integration of Renewable Energy Sources. IEEE Transactions on Industry Applications, 53(2), 855-866. doi:10.1109/tia.2016.2626253Calif, R., & Schmitt, F. G. (2014). Multiscaling and joint multiscaling description of the atmospheric wind speed and the aggregate power output from a wind farm. Nonlinear Processes in Geophysics, 21(2), 379-392. doi:10.5194/npg-21-379-2014Calif, R., Schmitt, F. G., & Huang, Y. (2013). Multifractal description of wind power fluctuations using arbitrary order Hilbert spectral analysis. Physica A: Statistical Mechanics and its Applications, 392(18), 4106-4120. doi:10.1016/j.physa.2013.04.038Fernández‐Guillamón, A., Vigueras‐Rodríguez, A., & Molina‐García, Á. (2019). Analysis of power system inertia estimation in high wind power plant integration scenarios. IET Renewable Power Generation, 13(15), 2807-2816. doi:10.1049/iet-rpg.2019.0220Heredia, F.-J., Cuadrado, M. D., & Corchero, C. (2018). On optimal participation in the electricity markets of wind power plants with battery energy storage systems. Computers & Operations Research, 96, 316-329. doi:10.1016/j.cor.2018.03.004Zhang, W., & Fang, K. (2017). Controlling active power of wind farms to participate in load frequency control of power systems. IET Generation, Transmission & Distribution, 11(9), 2194-2203. doi:10.1049/iet-gtd.2016.1471Honrubia-Escribano, A., Gómez-Lázaro, E., Fortmann, J., Sørensen, P., & Martin-Martinez, S. (2018). Generic dynamic wind turbine models for power system stability analysis: A comprehensive review. Renewable and Sustainable Energy Reviews, 81, 1939-1952. doi:10.1016/j.rser.2017.06.005Moschitta, A., Carbone, P., & Muscas, C. (2011). Generalized Likelihood Ratio Test for Voltage Dip Detection. IEEE Transactions on Instrumentation and Measurement, 60(5), 1644-1653. doi:10.1109/tim.2011.2113110Moschitta, A., Carbone, P., & Muscas, C. (2012). Performance Comparison of Advanced Techniques for Voltage Dip Detection. IEEE Transactions on Instrumentation and Measurement, 61(5), 1494-1502. doi:10.1109/tim.2012.2183436Gallo, D., Landi, C., Luiso, M., & Fiorucci, E. (2014). Survey on Voltage Dip Measurements in Standard Framework. IEEE Transactions on Instrumentation and Measurement, 63(2), 374-387. doi:10.1109/tim.2013.2278996Ipinnimo, O., Chowdhury, S., Chowdhury, S. P., & Mitra, J. (2013). A review of voltage dip mitigation techniques with distributed generation in electricity networks. Electric Power Systems Research, 103, 28-36. doi:10.1016/j.epsr.2013.05.004Hossain, M. J., Pota, H. R., Ugrinovskii, V. A., & Ramos, R. A. (2010). Simultaneous STATCOM and Pitch Angle Control for Improved LVRT Capability of Fixed-Speed Wind Turbines. IEEE Transactions on Sustainable Energy, 1(3), 142-151. doi:10.1109/tste.2010.2054118Hossain, M. J., Pota, H. R., & Ramos, R. A. (2011). Robust STATCOM control for the stabilisation of fixed-speed wind turbines during low voltages. Renewable Energy, 36(11), 2897-2905. doi:10.1016/j.renene.2011.04.010Hossain, M. J., Pota, H. R., & Ramos, R. A. (2012). Improved low-voltage-ride-through capability of fixed-speed wind turbines using decentralised control of STATCOM with energy storage system. IET Generation, Transmission & Distribution, 6(8), 719. doi:10.1049/iet-gtd.2011.0537Wessels, C., Hoffmann, N., Molinas, M., & Fuchs, F. W. (2013). StatCom control at wind farms with fixed-speed induction generators under asymmetrical grid faults. IEEE Transactions on Industrial Electronics, 60(7), 2864-2873. doi:10.1109/tie.2012.2233694Obando-Montaño, A., Carrillo, C., Cidrás, J., & Díaz-Dorado, E. (2014). A STATCOM with Supercapacitors for Low-Voltage Ride-Through in Fixed-Speed Wind Turbines. Energies, 7(9), 5922-5952. doi:10.3390/en7095922Moghadasi, A., Sarwat, A., & Guerrero, J. M. (2016). A comprehensive review of low-voltage-ride-through methods for fixed-speed wind power generators. Renewable and Sustainable Energy Reviews, 55, 823-839. doi:10.1016/j.rser.2015.11.020Heydari-doostabad, H., Khalghani, M. R., & Khooban, M. H. (2016). A novel control system design to improve LVRT capability of fixed speed wind turbines using STATCOM in presence of voltage fault. International Journal of Electrical Power & Energy Systems, 77, 280-286. doi:10.1016/j.ijepes.2015.11.011Fortmann, J., Engelhardt, S., Kretschmann, J., Feltes, C., & Erlich, I. (2014). New Generic Model of DFG-Based Wind Turbines for RMS-Type Simulation. IEEE Transactions on Energy Conversion, 29(1), 110-118. doi:10.1109/tec.2013.2287251Goksu, O., Altin, M., Fortmann, J., & Sorensen, P. E. (2016). Field Validation of IEC 61400-27-1 Wind Generation Type 3 Model With Plant Power Factor Controller. IEEE Transactions on Energy Conversion, 31(3), 1170-1178. doi:10.1109/tec.2016.2540006Honrubia-Escribano, A., Jiménez-Buendía, F., Gómez-Lázaro, E., & Fortmann, J. (2016). Validation of Generic Models for Variable Speed Operation Wind Turbines Following the Recent Guidelines Issued by IEC 61400-27. Energies, 9(12), 1048. doi:10.3390/en9121048Honrubia-Escribano, A., Jimenez-Buendia, F., Gomez-Lazaro, E., & Fortmann, J. (2018). Field Validation of a Standard Type 3 Wind Turbine Model for Power System Stability, According to the Requirements Imposed by IEC 61400-27-1. IEEE Transactions on Energy Conversion, 33(1), 137-145. doi:10.1109/tec.2017.2737703Lorenzo-Bonache, A., Honrubia-Escribano, A., Jiménez-Buendía, F., Molina-García, Á., & Gómez-Lázaro, E. (2017). Generic Type 3 Wind Turbine Model Based on IEC 61400-27-1: Parameter Analysis and Transient Response under Voltage Dips. Energies, 10(9), 1441. doi:10.3390/en10091441Honrubia-Escribano, A., Jiménez-Buendía, F., Sosa-Avendaño, J. L., Gartmann, P., Frahm, S., Fortmann, J., … Gómez-Lázaro, E. (2019). Fault-Ride Trough Validation of IEC 61400-27-1 Type 3 and Type 4 Models of Different Wind Turbine Manufacturers. Energies, 12(16), 3039. doi:10.3390/en12163039Wang, L., Zhang, Z., Long, H., Xu, J., & Liu, R. (2017). Wind Turbine Gearbox Failure Identification With Deep Neural Networks. IEEE Transactions on Industrial Informatics, 13(3), 1360-1368. doi:10.1109/tii.2016.2607179Hansen, A. D., & Hansen, L. H. (2007). Wind turbine concept market penetration over 10 years (1995–2004). Wind Energy, 10(1), 81-97. doi:10.1002/we.210IEC 61400-27-1. Electrical Simulation Models—Wind Turbines; Technical Reporthttps://webstore.iec.ch/publication/21811Vázquez-Hernández, C., Serrano-González, J., & Centeno, G. (2017). A Market-Based Analysis on the Main Characteristics of Gearboxes Used in Onshore Wind Turbines. Energies, 10(11), 1686. doi:10.3390/en10111686Duong, M., Grimaccia, F., Leva, S., Mussetta, M., & Le, K. (2015). Improving Transient Stability in a Grid-Connected Squirrel-Cage Induction Generator Wind Turbine System Using a Fuzzy Logic Controller. Energies, 8(7), 6328-6349. doi:10.3390/en8076328Cheng, M., & Zhu, Y. (2014). The state of the art of wind energy conversion systems and technologies: A review. Energy Conversion and Management, 88, 332-347. doi:10.1016/j.enconman.2014.08.037Pinar Pérez, J. M., García Márquez, F. P., Tobias, A., & Papaelias, M. (2013). Wind turbine reliability analysis. Renewable and Sustainable Energy Reviews, 23, 463-472. doi:10.1016/j.rser.2013.03.018Sumathi, S., Ashok Kumar, L., & Surekha, P. (2015). Wind Energy Conversion Systems. Green Energy and Technology, 247-307. doi:10.1007/978-3-319-14941-7_4Fernández-Guillamón, A., Sarasúa, J. I., Chazarra, M., Vigueras-Rodríguez, A., Fernández-Muñoz, D., & Molina-García, Á. (2020). Frequency control analysis based on unit commitment schemes with high wind power integration: A Spanish isolated power system case study. International Journal of Electrical Power & Energy Systems, 121, 106044. doi:10.1016/j.ijepes.2020.106044Liu, J., Gao, Y., Geng, S., & Wu, L. (2017). Nonlinear Control of Variable Speed Wind Turbines via Fuzzy Techniques. IEEE Access, 5, 27-34. doi:10.1109/access.2016.2599542Margaris, I. D., Hansen, A. D., Sørensen, P., & Hatziargyriou, N. D. (2010). Illustration of Modern Wind Turbine Ancillary Services. Energies, 3(6), 1290-1302. doi:10.3390/en3061290Wan, S., Cheng, K., Sheng, X., & Wang, X. (2019). Characteristic Analysis of DFIG Wind Turbine under Blade Mass Imbalance Fault in View of Wind Speed Spatiotemporal Distribution. Energies, 12(16), 3178. doi:10.3390/en12163178Boukhezzar, B., & Siguerdidjane, H. (2011). Nonlinear Control of a Variable-Speed Wind Turbine Using a Two-Mass Model. IEEE Transactions on Energy Conversion, 26(1), 149-162. doi:10.1109/tec.2010.2090155Chu, Yuan, Hu, Pan, & Pan. (2019). Comparative Analysis of Identification Methods for Mechanical Dynamics of Large-Scale Wind Turbine. Energies, 12(18), 3429. doi:10.3390/en12183429Villena-Ruiz, R., Honrubia-Escribano, A., Fortmann, J., & Gómez-Lázaro, E. (2020). Field validation of a standard Type 3 wind turbine model implemented in DIgSILENT-PowerFactory following IEC 61400-27-1 guidelines. International Journal of Electrical Power & Energy Systems, 116, 105553. doi:10.1016/j.ijepes.2019.105553Ekanayake, J. B., Holdsworth, L., & Jenkins, N. (2003). Comparison of 5th order and 3rd order machine models for doubly fed induction generator (DFIG) wind turbines. Electric Power Systems Research, 67(3), 207-215. doi:10.1016/s0378-7796(03)00109-3Brandl, R. (2017). Operational Range of Several Interface Algorithms for Different Power Hardware-In-The-Loop Setups. Energies, 10(12), 1946. doi:10.3390/en10121946Matar, M., Karimi, H., Etemadi, A., & Iravani, R. (2012). A High Performance Real-Time Simulator for Controllers Hardware-in-the-Loop Testing. Energies, 5(6), 1713-1733. doi:10.3390/en506171

    Transmission System Reliability: Monitoring and Analysis

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    This book gives an overview of existing data analysis methods to analyse the dynamic data obtained from full scale testing, with their advantages and drawbacks. The overview of full scale testing and dynamic data analysis is limited to energy performance characterization of either building components or whole buildings. The methods range from averaging and regression methods to dynamic approaches based on system identification techniques. These methods are discussed in relation to their application in following in situ measurements: -measurement of thermal transmittance of building components based on heat flux meters; -measurement of thermal and solar transmittance of building components tested in outdoor calorimetric test cells; -measurement of heat transfer coefficient and solar aperture of whole buildings based on co-heating or transient heating tests; -characterisation of the energy performance of whole buildings based on energy use monitoring

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