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Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following:  Fuzziness in smart grids computing  Emerging techniques for renewable energy forecasting  Robust and proactive solution of optimal smart grids operation  Fuzzy-based smart grids monitoring and control frameworks  Granular computing for uncertainty management in smart grids  Self-organizing and decentralized paradigms for information processing

voltage regulation --- smart grid --- decentralized control architecture --- multi-agent systems --- t-SNE algorithm --- numerical weather prediction --- data preprocessing --- data visualization --- wind power generation --- partial discharge --- gas insulated switchgear --- case-based reasoning --- data matching --- variational autoencoder --- DSHW --- TBATS --- NN-AR --- time-series clustering --- decentral smart grid control (DSGC) --- interpretable and accurate DSGC-stability prediction --- data mining --- computational intelligence --- fuzzy rule-based classifiers --- multi-objective evolutionary optimization --- power systems resilience --- dynamic Bayesian network --- Markov model --- probabilistic modeling --- resilience models

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The book contains the research contributions belonging to the Special Issue "Numerical Simulation of Wind Turbines", published in 2020-2021. They consist of 15 original research papers and 1 editorial. Different topics are discussed, from innovative design solutions for large and small wind turbine to control, from advanced simulation techniques to noise prediction. The variety of methods used in the research contributions testifies the need for a holistic approach to the design and simulation of modern wind turbines and will be able to stimulate the interest of the wind energy community.

large-scale wind turbine balde --- computational aeroacoustics --- sound source detection --- low Mach number turbulent flows --- NACA0012 airfoil --- fluid–structure interaction --- wind turbine --- atmospheric boundary layer --- composite materials --- gusts --- wind energy --- actuator line method --- wind turbine simulation --- regularization kernel --- small wind turbine (SWT) --- computational fluid dynamics (CFD) --- composites --- fluid–structure interaction (FSI) --- VAWT --- gurney flap --- CFD --- RBF --- power augmentation --- Darrieus --- turbulence --- experiments --- turbine wake --- turbine size --- large-eddy simulation --- actuator surface model --- wind turbine wake --- actuator disk model --- dynamic mode decomposition --- coherent structures --- wake meandering --- vertical axis wind turbine (VAWT) --- Savonius turbine --- deformable blades --- power coefficient --- blade load --- fluid-structure interaction (FSI) --- uncertainty quantification --- blade damage --- AEP --- winglet --- computational fluid dynamics (CFD), wind energy --- renewable energy --- rotor blade --- tip vortices --- aerodynamics --- ansys fluent --- savonius turbine --- icewind turbine --- static torque --- three-dimensional simulation --- Delayed DES --- H-Darrieus --- micro wind power generation --- wind turbine control --- load mitigation --- individual pitch control --- lifting line free vortex wake --- vortex methods --- pitch --- stall --- engineering codes --- n/a --- fluid-structure interaction

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Power network operators are rapidly incorporating wind power generation into their power grids to meet the widely accepted carbon neutrality targets and facilitate the transition from conventional fossil-fuel energy sources to clean and low-carbon renewable energy sources. Complex stability issues, such as frequency, voltage, and oscillatory instability, are frequently reported in the power grids of many countries and regions (e.g., Germany, Denmark, Ireland, and South Australia) due to the substantially increased wind power generation. Control techniques, such as virtual/emulated inertia and damping controls, could be developed to address these stability issues, and additional devices, such as energy storage systems, can also be deployed to mitigate the adverse impact of high wind power generation on various system stability problems. Moreover, other wind power integration aspects, such as capacity planning and the short- and long-term forecasting of wind power generation, also require careful attention to ensure grid security and reliability. This book includes fourteen novel research articles published in this Energies Special Issue on Wind Power Integration into Power Systems: Stability and Control Aspects, with topics ranging from stability and control to system capacity planning and forecasting.

DFIG --- ES --- virtual inertia control --- capacity allocation --- fuzzy logic controller --- wind power generation --- multi-model predictive control --- fuzzy clustering --- virtual synchronous generator --- doubly fed induction generator --- sub-synchronous resonance --- impedance modeling --- renewable energy sources (RESs) --- regional RoCoF --- model-based operational planning --- linear sensitivity-based method (LSM) --- cumulant-based method (CBM) --- collaborative capacity planning --- distributed wind power (DWP) --- energy storage system (ESS) --- optimization --- variable-structure copula --- Reynolds-averaged Navier–Stokes method --- wind turbine wake model --- 3D aerodynamic model --- turbulence model --- correction modules --- hybrid prediction model --- wavelet decomposition --- long short-term memory --- scenario analysis --- weak grids --- full-converter wind --- active power output --- control parameters --- subsynchronous oscillation --- eigenvalue analysis --- doubly fed induction generator (DFIG) --- wind generation --- frequency control --- artificial neural network (ANN) --- error following forget gate-based long short-term memory --- ultra-short-term prediction --- wind power --- load frequency control (LFC) --- wind farm --- particle swarm optimization --- kinetic energy --- inertial response --- low inertia --- the center of inertia --- frequency response metrics --- wind integration --- PSS/E --- FORTRAN --- electromechanical dynamics --- FCWG dynamics --- strong interaction --- electromechanical loop correlation ratio (ELCR) --- FCWG dynamic correlation ratio (FDCR) --- quasi- electromechanical loop correlation ratio (QELCR) --- permanent magnet synchronous generator (PMSG) --- supercapacitor energy storage (SCES) --- rotor overspeed control --- low voltage ride through (LVRT) --- capacity configuration of SCES --- n/a --- Reynolds-averaged Navier-Stokes method