A data-driven physics-informed stochastic framework for hurricane-induced risk estimation of transmission tower-line systems under a changing climate

Snaiki, Reda; Parida, Siddharth S.

doi:10.1016/j.engstruct.2023.115673

Cited by 12 publications

(2 citation statements)

References 56 publications

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“…The low RMSE values highlight the superior control capacities of the MPC model which can satisfy several control objectives while accounting for the system constraints compared to standard controllers, such as the PID model. In this section, the previous scenario will be again repeated but with a more severe sea state condition which could arise due to the effects of storm events or climate change (i.e., with a significant wave height H s = 4 [m] and a peak spectral period T p = 10 [s]) [45][46][47]. Only the simulation results related to the platform roll and pitch along with its roll and pitch velocities will be shown here since comparable simulation results as the previous scenario was obtained.…”

Section: Quantitative Comparison Of the Controllers' Performancesmentioning

confidence: 99%

“…In this section, the previous scenario will be again repeated but with a more severe sea state condition which could arise due to the effects of storm events or climate change (i.e., with a significant wave height 𝐻 = 4 [m] and a peak spectral period 𝑇 = 10 [s] ) [45][46][47]. Only the simulation results related to the platform roll and pitch along with its roll and pitch velocities will be shown here since comparable simulation results as the previous scenario was obtained.…”

Section: Mpc Performance Under Severe Environmental Conditionsmentioning

confidence: 99%

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Real-Time Repositioning of Floating Wind Turbines Using Model Predictive Control for Position and Power Regulation

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2023

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As offshore wind capacity could grow substantially in the coming years, floating offshore wind turbines (FOWTs) are particularly expected to make a significant contribution to the anticipated global installed capacity. However, FOWTs are prone to several issues due partly to environmental perturbations and their system configuration which affect their performances and jeopardize their structural integrity. Therefore, advanced control mechanisms are required to ensure good performance and operation of FOWTs. In this study, a model predictive control (MPC) is proposed to regulate FOWTs’ power, reposition their platforms to reach predefined target positions and ensure their structural stability. An efficient nonlinear state space model is used as the internal MPC predictive model. The control strategy is based on the direct manipulation of the thrust force using three control inputs, namely the yaw angle, the collective blade pitch angle, and the generator torque without the necessity of additional actuators. The proposed controller accounts for the environmental perturbations and satisfies the system constraints to ensure good performance and operation of the FOWTs. A realistic scenario for a 5-MW reference wind turbine, modeled using OpenFAST and Simulink, has been provided to demonstrate the robustness of the proposed MPC controller. Furthermore, the comparison of the MPC model and a proportional-integral-derivative (PID) model to satisfy the three predefined objectives indicates the superior performances of the MPC controller.

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Section: Quantitative Comparison Of the Controllers' Performancesmentioning

confidence: 99%

Section: Mpc Performance Under Severe Environmental Conditionsmentioning

confidence: 99%

Real-Time Repositioning of Floating Wind Turbines Using Model Predictive Control for Position and Power Regulation

Jard

Snaiki

2023

Wind

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Fragility Modeling of Power Grid Infrastructure for Addressing Climate Change Risks and Adaptation

Karagiannakis,

Panteli,

Argyroudis

2024

WIREs Climate Change

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The resilience of electric power grids is threatened by natural hazards. Climate‐related hazards are becoming more frequent and intense due to climate change. Statistical analyses clearly demonstrate a rise in the number of incidents (power failures) and their consequences in recent years. Therefore, it is of utmost importance to understand and quantify the resilience of the infrastructure to external stressors, which is essential for developing efficient climate change adaptation strategies. To accomplish this, robust fragility and other vulnerability models are necessary. These models are employed to assess the level of asset damage and to quantify losses for given hazard intensity measures. In this context, a comprehensive literature review is carried out to shed light on existing fragility models specific to the transmission network, distribution network, and substations. The review is organized into three main sections: damage assessment, fragility curves, and recommendations for climate change adaptation. The first section provides a comprehensive review of past incidents, their causes, and failure modes. The second section reviews analytical and empirical fragility models, emphasizing the need for further research on compound and non‐compound hazards, especially windstorms, floods, lightning, and wildfires. Finally, the third section examines risk mitigation and adaptation strategies in the context of climate change. This review aims to improve the understanding of approaches to enhance the resilience of power grid assets in the face of climate change. These insights are valuable to various stakeholders, including risk analysts and policymakers, who are involved in risk modeling and developing adaptation strategies.

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A novel hybrid machine learning model for rapid assessment of wave and storm surge responses over an extended coastal region

Saviz Naeini,

Snaiki

2024

Coastal Engineering

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A data-driven physics-informed stochastic framework for hurricane-induced risk estimation of transmission tower-line systems under a changing climate

Cited by 12 publications

References 56 publications

Real-Time Repositioning of Floating Wind Turbines Using Model Predictive Control for Position and Power Regulation

Real-Time Repositioning of Floating Wind Turbines Using Model Predictive Control for Position and Power Regulation

Fragility Modeling of Power Grid Infrastructure for Addressing Climate Change Risks and Adaptation

A novel hybrid machine learning model for rapid assessment of wave and storm surge responses over an extended coastal region

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