Power Electronics Technology for Large-Scale Renewable Energy Generation

Blaabjerg, Frede; Yang, Yongheng; Kim, Katherine A.; Rodríguez, José

doi:10.1109/jproc.2023.3253165

Cited by 94 publications

(30 citation statements)

References 75 publications

(104 reference statements)

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“…Among the different alternatives, wind energy has become one of the main technologies to compete with conventional energy sources, making this form of renewable energy a significant component of current and future energy supply systems. Given the sustained technological growth, the cumulative installed capacity increased exponentially to reach about 850 GW in 2021 [ 1 ], and nowadays, large wind turbines (WTs) (with capacities of up to 8–16 MW) are widely being installed in power distribution networks [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, every year is becoming more critical to guaranteeing efficient, reliable, and safe wind power generation. Reliability is an important index as it could lead to a further reduction in the overall costs of renewable energy resources, given the long-term investment they imply and their expensive reparation costs [ 3 , 4 ]. Hence, the structural integrity of WTs should be guaranteed [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

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Autonomous Sensor System for Low-Capacity Wind Turbine Blade Vibration Measurement

Muxica,

Rivera,

Orchard

et al. 2024

Sensors

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This paper presents the design, implementation, and validation of an on-blade sensor system for remote vibration measurement for low-capacity wind turbines. The autonomous sensor system was deployed on three wind turbines, with one of them operating in harsh weather conditions in the far south of Chile. The system recorded the acceleration response of the blades in the flapwise and edgewise directions, data that could be used for extracting the dynamic characteristics of the blades, information useful for damage diagnosis and prognosis. The proposed sensor system demonstrated reliable data acquisition and transmission from wind turbines in remote locations, proving the ability to create a fully autonomous system capable of recording data for monitoring and evaluating the state of health of wind turbine blades for extended periods without human intervention. The data collected by the sensor system presented in this study can serve as a foundation for developing vibration-based strategies for real-time structural health monitoring.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autonomous Sensor System for Low-Capacity Wind Turbine Blade Vibration Measurement

Muxica,

Rivera,

Orchard

et al. 2024

Sensors

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“…The European Network of Transmission System Operator for Electricity (ENTSO-E) reports both the decrease of traditional SGs and the increase of GFL inverters as the cause of the reduction in the stability of the power system [3]. Accordingly, extensive efforts have been dedicated to the advancement of GFM control, which can provide inertia and damping to the grid [4]. The inverter with a GFM control usually performs as a voltage source at the point of common coupling (PCC), as shown in Figure 1b.…”

Section: Introductionmentioning

confidence: 99%

Small-Signal Stability of Hybrid Inverters with Grid-Following and Grid-Forming Controls

Ji,

Liu,

Jiang

et al. 2024

Energies

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In the modern power grid, characterized by the increased penetration of power electronics and extensive utilization of renewable energy, inverter-based power plants play a pivotal role as the principal interface of renewable energy sources (RESs) and the grid. Considering the stability characteristics of grid-following (GFL) inverters when the grid is relatively weak, the application of grid-forming (GFM) controls becomes imperative in enhancing the stability of the entire power plant. Thus, there is an urgent need for suitable and effective models to study the interaction and stability of the paralleled inverters employing GFL and GFM controls. Thus, the small-signal modeling with full-order state-space model and eigenvalues analysis are presented in this paper. First, the small-signal state-space model of the individual GFL and GFM inverters is obtained, considering the control loop, interaction, reference frame, transmissions, and time delays. Then, the models of the individual inverter are extended to the hybrid inverters to study the effects of the GFM inverters on the small-signal stability of the entire system. And the impacts of the inertia and damping are analyzed by the eigenvalues of the state-transition matrix. A case comprising three parallel GFL inverters and two GFL inverters with one GFM inverter, respectively, are studied to examine the effectiveness and accuracy of the model. Finally, the stability margin obtained from the eigenvalue analysis of the entire system is verified by time-domain simulations.

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“…Wang and L. Nordström are with the Department of Electrical Engineering, KTH Royal Institute of Technology, Stockholm, Sweden (e-mail: xiongfei, larsno@kth.se). 1 The simulation and data acquisition platform, the TensorFlow implementation of the InvNet framework, and the database supporting the findings of this work are all publicly available at https://github.com/superrabbit2023/InvNet. before [1], [2].…”

Section: Introductionmentioning

confidence: 99%

“…1 The simulation and data acquisition platform, the TensorFlow implementation of the InvNet framework, and the database supporting the findings of this work are all publicly available at https://github.com/superrabbit2023/InvNet. before [1], [2]. These inverters at the grid edge are ubiquitously needed to integrate renewable energy resources, battery storage systems, loads, and more, to the future power grid.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning at the Grid Edge: Data-Driven Impedance Models for Model-Free Inverters

Li,

Liao,

Zhao

et al. 2024

IEEE Trans. Power Electron.

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The future electric grid is supported by a vast number of smart inverters interfacing with distributed energy resources at the edge. These inverters' dynamics are typically characterized as impedances, which are crucial for ensuring grid stability and resiliency. However, the physical implementation of these inverters may change significantly from inverters to inverters and may be kept confidential. Existing analytical impedance models require a complete and precise understanding of system parameters. They can hardly capture the complete electrical behaviors when the inverters are performing complex functions. Online impedance measurements for many inverters across multiple operating points are not scalable. To address these issues, we present InvNet, a machine learning framework to systematically evaluate the effectiveness of data-driven methods for modeling inverter impedance patterns across a wide operation range, even with limited impedance data. Leveraging transfer learning, the InvNet can extrapolate from physics-based models to real-world ones and from one inverter to another with very limited data. This framework demonstrates machine learning as a powerful tool for modeling and analyzing black-box characteristics of grid-tied inverter systems that cannot be accurately described by traditional analytical methods, such as inverters under model predictive control. Comprehensive evaluations were conducted to verify the effectiveness of the InvNet in various scenarios. All data and models were open-sourced 1 .

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Power Electronics Technology for Large-Scale Renewable Energy Generation

Cited by 94 publications

References 75 publications

Autonomous Sensor System for Low-Capacity Wind Turbine Blade Vibration Measurement

Autonomous Sensor System for Low-Capacity Wind Turbine Blade Vibration Measurement

Small-Signal Stability of Hybrid Inverters with Grid-Following and Grid-Forming Controls

Machine Learning at the Grid Edge: Data-Driven Impedance Models for Model-Free Inverters

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