Clean and environment-friendly energy harvesting are of prime interest today as it is one of the key enablers in achieving the Sustainable Development Goals (SDGs) as well as accelerates social progress and enhances living standards. India, the second-most populous nation with a population of 1.353 billion, is one of the largest consumers of fossil fuels in the world which is responsible for global warming. An ever-increasing population is projected until 2050, and consequently, the energy demand in the upcoming decades will be co-accelerated by the rapid industrial growth. The Ministry of New and Renewable Energy (MNRE) with the support of National Institution for Transforming India (NITI) Aayog is working to achieve the Indian Government's target of attaining 175 GW through renewable energy resources. Many Indian states are currently increasing their renewable energy capacity in an objective to meet future energy demand. The review paper discusses in-depth about the three Indian states, namely Karnataka, Gujarat, Tamil Nadu, which pioneers the renewable energy production in India. The global energy scenario was discussed in detail with Indian contrast. Further, the barriers to the development of renewable energy generation and policies of the Indian government are discussed in detail to promote renewable energy generation throughout India as well as globally since the challenges are similar for other nations. This study analyzed various prospects of the country in renewable energy which has been done in a purpose to help the scholars, researchers, and policymakers of the nation, as it gives an insight into the present renewable energy scenario of the country.

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A Comprehensive Review on Renewable Energy Development, Challenges, and Policies of Leading Indian States With an International Perspective

Offshore wind is a source of clean, renewable energy of great potential value to the power industry in the context of a low carbon society. Rapid development of offshore wind energy depends on a good understanding of technical issues related to offshore wind turbines, which is spurring ongoing research and development programmes. Foundations of offshore wind turbines present one of the main challenges in offshore wind turbine design. This paper reviews the present state of knowledge concerning geotechnical and structural issues affecting foundation types under consideration for the support structures of offshore wind turbines, and provides recommendations for future research and development.

/pdf/foundations-of-offshore-wind-turbines-a-review-3yvgufr6nj.pdf

Foundations of offshore wind turbines: A review

A review on optimization of composite structures Part I: Laminated composites

The apple target recognition algorithm is one of the core technologies of the apple picking robot. However, most of the existing apple detection algorithms cannot distinguish between the apples that are occluded by tree branches and occluded by other apples. The apples, grasping end-effector and mechanical picking arm of the robot are very likely to be damaged if the algorithm is directly applied to the picking robot. Based on this practical problem, in order to automatically recognize the graspable and ungraspable apples in an apple tree image, a light-weight apple targets detection method was proposed for picking robot using improved YOLOv5s. Firstly, BottleneckCSP module was improved designed to BottleneckCSP-2 module which was used to replace the BottleneckCSP module in backbone architecture of original YOLOv5s network. Secondly, SE module, which belonged to the visual attention mechanism network, was inserted to the proposed improved backbone network. Thirdly, the bonding fusion mode of feature maps, which were inputs to the target detection layer of medium size in the original YOLOv5s network, were improved. Finally, the initial anchor box size of the original network was improved. The experimental results indicated that the graspable apples, which were unoccluded or only occluded by tree leaves, and the ungraspable apples, which were occluded by tree branches or occluded by other fruits, could be identified effectively using the proposed improved network model in this study. Specifically, the recognition recall, precision, mAP and F1 were 91.48%, 83.83%, 86.75% and 87.49%, respectively. The average recognition time was 0.015 s per image. Contrasted with original YOLOv5s, YOLOv3, YOLOv4 and EfficientDet-D0 model, the mAP of the proposed improved YOLOv5s model increased by 5.05%, 14.95%, 4.74% and 6.75% respectively, the size of the model compressed by 9.29%, 94.6%, 94.8% and 15.3% respectively. The average recognition speeds per image of the proposed improved YOLOv5s model were 2.53, 1.13 and 3.53 times of EfficientDet-D0, YOLOv4 and YOLOv3 and model, respectively. The proposed method can provide technical support for the real-time accurate detection of multiple fruit targets for the apple picking robot.

A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5

https://link.springer.com/content/pdf/10.1057%2Fjors.1994..pdf

Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method

Petri-net-based dynamic scheduling of flexible manufacturing system via deep reinforcement learning with graph convolutional network

This paper studies reliability-based design optimization (RBDO) of a 5-MW wind turbine blade for designing reliable as well as economical wind turbine blades. A novel dynamic wind load uncertainty model has been developed using 249 groups of wind data to consider wind load variation over a large spatiotemporal range. The probability of fatigue failure during a 20-year service life is estimated using the uncertainty model in the RBDO process and is reduced to meet a desired target reliability. Meanwhile, the cost of composite materials used in the blade is minimized by optimizing the composite laminate thicknesses of the blade. In order to obtain the RBDO optimum design efficiently, deterministic design optimization (DDO) of the 5-MW wind turbine blade is carried out first using the mean wind load obtained from the wind load uncertainty model. The RBDO is then initiated from the DDO optimum. During the RBDO iterations, fatigue hotspots for RBDO are identified among the laminate section points. For an efficient RBDO process, surrogate models of 10-min fatigue damages D10 at the hotspots are accurately created using the Kriging method. Using the wind load uncertainty model and surrogate models, probability of fatigue failure during a 20-year lifespan at the hotspots and the design sensitivities are calculated at given design points. Using the probability of fatigue failure and design sensitivity, RBDO of the 5-MW wind turbine blade has been successfully carried out, satisfying the target probability of failure of 2.275 %.

/pdf/reliability-based-design-optimization-of-wind-turbine-blades-1emzu3vm91.pdf

Reliability-based design optimization of wind turbine blades for fatigue life under dynamic wind load uncertainty

The paper presents a detailed review of the state-of-the-art research activities on structural reliability analysis of wind turbines between the 1990s and 2017. We describe the reliability methods including the first- and second-order reliability methods and the simulation reliability methods and show the procedure for and application areas of structural reliability analysis of wind turbines. Further, we critically review the various structural reliability studies on rotor blades, bottom-fixed support structures, floating systems and mechanical and electrical components. Finally, future applications of structural reliability methods to wind turbine designs are discussed.

https://www.mdpi.com/1996-1073/10/12/2099/pdf

Structural Reliability Analysis of Wind Turbines: A Review

A comprehensive fatigue analysis framework for composite wind turbine blades has been developed. It includes variable wind loads from wind field simulation and aerodynamic analysis, stress prediction by finite element analysis, and fatigue damage evaluation based on the resulting fatigue data. The variable wind load is represented by a joint distribution of mean wind speed and turbulence intensity. In order to simulate realistic wind loads applied on the blade while maintaining affordable computational time, the sectional surface pressure fields obtained from the potential flow aerodynamics model XFOIL are transformed to match the lift, drag, and moment coefficients obtained using AeroDyn. Thus, the modified pressure distribution includes the effect of dynamic stall, rotation, and wake effects on the blade aerodynamics. A high-fidelity finite element blade model, in which the design of composite materials can be easily tailored, has been parameterized for detailed stress analysis. The non-proportional multi-axial complex stress states are involved when calculating 10-min fatigue damage of section points through laminate thickness. The annual fatigue damage is calculated based on the 10-min fatigue damage and the joint distribution of 10-min mean wind speed and 10-min turbulence intensity. Consequently, the blade fatigue effect due to not only the mean wind speed and the atmospheric turbulence in the short term, but also the wind load variation in a large spatiotemporal range, can be investigated. The developed fatigue analysis framework can facilitate reliability analysis and reliability-based design optimization of composite wind turbine blades.

Weifei Hu

Papers

Petri-net-based dynamic scheduling of flexible manufacturing system via deep reinforcement learning with graph convolutional network

Reliability-based design optimization of wind turbine blades for fatigue life under dynamic wind load uncertainty

Structural Reliability Analysis of Wind Turbines: A Review

Integrating variable wind load, aerodynamic, and structural analyses towards accurate fatigue life prediction in composite wind turbine blades

TBM performance prediction with Bayesian optimization and automated machine learning