A Real-Time Heterogeneous Emulator of a High-Fidelity Utility-Scale Variable-Speed Variable-Pitch Wind Turbine

Moness, Mohammed; Osama, Muhammad; Moustafa, Ahmed M.

doi:10.1109/tii.2017.2723960

Cited by 15 publications

(6 citation statements)

References 31 publications

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“…Most realtime capable WT models contain a simple rotor model based on its performance curves, a single-mass or two-mass drive train, a generator model along with the converter system [77,[95][96][97][98]. More accurate approaches implement the BEM rotor model or couple FAST to the simulation framework [99,100], thus considering the total inflow on the rotor surface [101].…”

Section: Real-time Applicationsmentioning

confidence: 99%

Analytical three‐dimensional wind flow model for real‐time wind farm simulation

Kipke

Chhor

2020

IET Renewable Power Generation

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Section: Real-time Applicationsmentioning

confidence: 99%

Analytical three‐dimensional wind flow model for real‐time wind farm simulation

Kipke

Chhor

2020

IET Renewable Power Generation

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“…Among various types of renewable energy generations, wind turbines attract more attention due to their lower net cost [14]. The wind turbine emulator replicates the static and dynamic behavior of a real wind turbine and provides the real-time hardware-based simulations [15][16]. The performance of wind energy conversion, and the operation of control schemes can be analyzed without the need for a controlled environment in terms of wind profiles and other environmental conditions.…”

Section: Wind Turbine Emulatormentioning

confidence: 99%

Smart Microgrid Educational Laboratory: An Integrated Electric and Communications Infrastructures Platform

et al. 2020

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Microgrids as the local area power systems are changing the power system landscape due to their potential of offering a viable solution for integrating renewable energy resources into the main grid. From the operational point of view, microgrids should have the appropriate power electronic interfaces, control schemes, as well as monitoring and automation infrastructures to provide the required flexibility and meet the related IEEE 1547 standard requirements. This paper describes some of the efforts made in the smart microgrid educational laboratory to provide these facilities and create a real-world conditions needed to conduct researches and teach the related courses. Laboratory works not only increase the practical skills of the students but also can motivate them to pursue theoretical courses with more passion. The introduced facilities are somehow unique for the integration of both electric and communication infrastructures which overcome the shortcomings of not considering data transfer challenges in the studies. Complete hardware design of power plant components, and incorporation of solar photovoltaic (PV) and two types of wind turbine generations are some of the efforts made to bring the real-world conditions in the laboratory. In the load-related side, dynamic behaviors of the various types of motors are modeled. To demonstrate some of the laboratory applications, some experimental studies have been carried out. The results show that this laboratory model is useful to provide insightful perspectives about the microgrid future.

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“…data processing. For instance, we have applied GPUs to accelerate explicit-state model checking [11,43], bisimilarity checking [42], the reconstruction of genetic networks [12], wind turbine emulation [30], metaheuristic SAT solving [44], and SAT-based test generation [33]. Recently, we introduced SIGmA [34,35] as the first SAT simplification preprocessor to exploit GPUs.…”

Section: Introductionmentioning

confidence: 99%

SAT Solving with GPU Accelerated Inprocessing

Osama

Wijs

Biere

2021

Tools and Algorithms for the Construction and Analysis of Systems

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Since 2013, the leading SAT solvers in the SAT competition all use inprocessing, which unlike preprocessing, interleaves search with simplifications. However, applying inprocessing frequently can still be a bottle neck, i.e., for hard or large formulas. In this work, we introduce the first attempt to parallelize inprocessing on GPU architectures. As memory is a scarce resource in GPUs, we present new space-efficient data structures and devise a data-parallel garbage collector. It runs in parallel on the GPU to reduce memory consumption and improves memory access locality. Our new parallel variable elimination algorithm is twice as fast as previous work. In experiments our new solver ParaFROST solves many benchmarks faster on the GPU than its sequential counterparts.

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A Real-Time Heterogeneous Emulator of a High-Fidelity Utility-Scale Variable-Speed Variable-Pitch Wind Turbine

Cited by 15 publications

References 31 publications

Analytical three‐dimensional wind flow model for real‐time wind farm simulation

Analytical three‐dimensional wind flow model for real‐time wind farm simulation

Smart Microgrid Educational Laboratory: An Integrated Electric and Communications Infrastructures Platform

SAT Solving with GPU Accelerated Inprocessing

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