Physics-informed optimization of robust control system to enhance power efficiency of renewable energy: Application to wind turbine

Junejo, Allah Rakhio; Gilal, Nauman Ullah; Doh, Jaehyeok

doi:10.1016/j.energy.2022.125667

Cited by 9 publications

(3 citation statements)

References 34 publications

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“…The USA and China have recorded a raise in wind energy installation. In [31], the authors presented a physics-informed optimization approach for modeling and controlling the wind turbine energy system connected to a standalone site with the aim to improving the energy efficacy of wind turbine. Zhang et al [32] suggested a multi-objective optimization and configuration of wind turbine/solar energy system including energy storage system.…”

Section: Wind Turbine Energymentioning

confidence: 99%

Overview: Using Hybrid Energy System for Electricity Production Based on the Optimization Methods

Saib,

Bayındır,

Vadi

2024

Gazi University Journal of Science

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Renewable energy systems are mostly used in the world due to their inexhaustible and non-polluting production. As a result of a large utilization of these energy sources in different areas, the electricity production rate is increasing every day. Previous studies clarified uses, modeling, configuration, energy management operation, and optimization objectives based on different energy sources. For this reason, this paper focuses on an overview of multi energy systems as renewable and conventional power sources with the integration of an energy storage system coupled to the on-off electrical network. Furthermore, a survey is done regarding global energy production, configuration energy systems, energy storage systems, power management strategies, and optimization methods based on different hybrid energy systems. Multiple optimization approaches have been implemented to reach the global best solution for the hybrid power systems. To ensure the best optimization result, it is preferable to take hybrid optimization methods into consideration. These methods have been invented recently and have proved their efficacy and performance mainly in power systems.

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Section: Wind Turbine Energymentioning

confidence: 99%

Overview: Using Hybrid Energy System for Electricity Production Based on the Optimization Methods

Saib,

Bayındır,

Vadi

2024

Gazi University Journal of Science

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“…More recently, physics-informed AI and AI inverse design models have been reported for various applications in electrical energy fields. [46][47][48] For example, physicsinformed AI models were developed for photovoltaic and solar energy, [49][50][51] wind energy, [52,53] power flow [54,55] and power system management. [56,57] Figure 5 illustrates the principles and procedures of physics-informed AI inverse design for TENG systems, encompassing theoretical analysis, the selection of conductive and dielectric materials, and the design of the contact interface.…”

Section: Inverse Design Of Tengs By Physics-informed Aimentioning

confidence: 99%

Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

Jiao,

Wang,

Alavi

2023

Advanced Materials

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Triboelectric nanogenerators offer an environmentally friendly approach to harvesting energy from mechanical excitations. This capability has made them widely sought‐after as an efficient, renewable, and sustainable energy source, with the potential to decrease reliance on traditional fossil fuels. However, developing triboelectric nanogenerators with specific output remains a challenge mainly due to the uncertainties associated with their complex designs for real‐life applications. Artificial intelligence‐enabled inverse design is a powerful tool to realize performance‐oriented triboelectric nanogenerators. This is an emerging scientific direction that can address the concerns about the design and optimization of triboelectric nanogenerators leading to a next generation nanogenerator systems. This perspective paper aims at reviewing the principal analysis of triboelectricity, summarizing the current challenges of designing and optimizing triboelectric nanogenerators, and highlighting the physics‐informed inverse design strategies to develop triboelectric nanogenerators. Strategic inverse design is particularly discussed in the contexts of expanding the four‐mode analytical models by physics‐informed artificial intelligence, discovering new conductive and dielectric materials, and optimizing contact interfaces. Various potential development levels of artificial intelligence‐enhanced triboelectric nanogenerators are delineated. Finally, the potential of physics‐informed artificial intelligence inverse design to propel triboelectric nanogenerators from prototypes to multifunctional intelligent systems for real‐life applications is discussed.

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“…For the long-term performance assessment of FOWTs, a blend of physical experiments, numerical methods, and data-driven techniques is essential. Many scenarios cannot be numerically modeled due to high computational demands, complexity, reliance on assumptions, and the need for detailed, high-quality input data [5,6]. The rise of machine learning (ML) represents a big leap forward in computational efficiency and scalability [7].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Pitch Response of Floating Offshore Wind Turbines with a Deep Learning Model

Barooni,

Velioglu Sogut

2024

Clean Technol.

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The design and optimization of floating offshore wind turbines (FOWTs) pose significant challenges, stemming from the complex interplay among aerodynamics, hydrodynamics, structural dynamics, and control systems. In this context, this study introduces an innovative method for forecasting the dynamic behavior of FOWTs under various conditions by merging Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU) network. This model outperforms traditional numerical models by delivering precise and efficient predictions of dynamic FOWT responses. It adeptly handles computational complexities and reduces processing duration, while maintaining flexibility and effectively managing nonlinear dynamics. The model’s prowess is showcased through an analysis of a spar-type FOWT in a multivariate parallel time series dataset using the CNN–GRU structure. The outcomes are notably promising, underscoring the model’s proficiency in accurately forecasting the performance of FOWTs.

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Physics-informed optimization of robust control system to enhance power efficiency of renewable energy: Application to wind turbine

Cited by 9 publications

References 34 publications

Overview: Using Hybrid Energy System for Electricity Production Based on the Optimization Methods

Overview: Using Hybrid Energy System for Electricity Production Based on the Optimization Methods

Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

Forecasting Pitch Response of Floating Offshore Wind Turbines with a Deep Learning Model

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