Wind Energy Reliability Analysis based on Monte Carlo Simulation Method

Abdusamad, Khaled B.

doi:10.21467/proceedings.4.41

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…Specifically in the realm of wind turbines, applying MC simulations allows modeling of uncertainties related to wind conditions and material properties. This method provides a comprehensive perspective on the reliability of the system in a spectrum of scenarios [33], [35], [36]. Additionally, Weibull analysis proves to be a common practice for scrutinizing the distribution of wind speeds [35].…”

Section: B Numerical Simulationsmentioning

confidence: 99%

“…This method provides a comprehensive perspective on the reliability of the system in a spectrum of scenarios [33], [35], [36]. Additionally, Weibull analysis proves to be a common practice for scrutinizing the distribution of wind speeds [35]. Engineers leverage this method by fitting a Weibull distribution to the observed wind speed data, thereby estimating the likelihood of various wind speed occurrences.…”

Section: B Numerical Simulationsmentioning

confidence: 99%

“…In this area, Abdusamad [35] employed a robust approach based on MC simulation to assess the reliability of wind energy systems. The simulation utilizes Weibull distribution to model the wind speed.…”

Section: B Numerical Simulationsmentioning

confidence: 99%

See 2 more Smart Citations

Review on the Advancements in Wind Turbine Blade Inspection: Integrating Drone and Deep Learning Technologies for Enhanced Defect Detection

Memari,

Shakya,

Shekaramiz

et al. 2024

IEEE Access

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The increasing demand for wind power requires more frequent inspections to identify defects in the Wind Turbine Blades (WTBs). These defects, if not detected, can compromise the structural integrity and safety of wind turbines. As WTBs are crucial and costly components, they may suffer material degradation and fatigue failure, which affects their performance and safety. Thus, the urgency for efficient and regular monitoring to maintain their structural integrity is greater than ever. This review paper explores innovative methods in fatigue testing, damage detection, and structural reliability in WTBs, focusing on the use of recent inspection methods, including those that take advantage of drones. Drones are used to identify defects such as cracks, erosion, and coating irregularities using high-resolution imagery with the onboard cameras. Various investigators have developed novel data-driven approaches, incorporating machine learning and deep learning, to accurately identify these defects. Although deep learning-based image processing has been successful in other public infrastructure contexts, its application to wind turbine inspection from aerial images presents unique challenges. This paper also highlights the critical role of failure inspection in enhancing the operational integrity of WTBs, showcasing state-of-the-art deep learning techniques that are pivotal for identifying and analyzing failures in WTBs from images captured by drones. The paper provides insights into the latest developments in using drone imagery for blade defect detection, contrasting this method with traditional non-destructive techniques. This approach could significantly transform the wind energy industry by offering a more efficient, automated, and precise way of ensuring the structural health of wind turbines. Unlike previous studies that predominantly focus on isolated aspects such as inspection or fatigue, this review paper not only integrates the three major aspects of WTBs integrity in terms of aerial inspection, image processing using machine learning, and structural integrity of the blade but also undertakes an extensive examination of the prevailing methodologies in the field, pinpointing crucial gaps and challenges. It provides a detailed review of existing research, covering various areas including automated inspection, image processing techniques, fatigue analysis, and the reliability of wind turbines. This approach enriches the discourse by offering a multifaceted perspective on WTB maintenance, thereby advancing the understanding of operational integrity within the field of wind energy.

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Section: B Numerical Simulationsmentioning

confidence: 99%

Section: B Numerical Simulationsmentioning

confidence: 99%

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Review on the Advancements in Wind Turbine Blade Inspection: Integrating Drone and Deep Learning Technologies for Enhanced Defect Detection

Memari,

Shakya,

Shekaramiz

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Hence, Monte Carlo simulation plays a crucial role in analyzing the reliability of wind energy systems. This assessment assists in identifying potential concerns and devising effective maintenance strategies (Abdusamad, 2018).…”

Section: Introductionmentioning

confidence: 99%

Maximizing Wind Turbine Efficiency: Monte Carlo Simulation Based on Cost and Energy Loss Analysis for Optimal Preventive Maintenance

EDDOUH,

DAYA,

EL OTMANI

et al. 2024

Management and Production Engineering Review

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In response to the urgent need for sustainable energy, this study addresses a critical challenge in wind turbine optimization. It focuses on developing a nuanced preventive maintenance strategy to minimize costs and mitigate energy losses. Within this framework, our paper introduces a novel approach employing a Monte Carlo simulation to identify the optimal preventive maintenance frequency, striking a balance between cost efficiency and energy loss mitigation. The results show, that grouped maintenance approach, pinpointing an optimal frequency of 93 months. This strategic configuration minimizes costs to $9997 while concurrently maintaining an average energy loss of 32.014 MWh, resulting in a notable 4.29% increase in total energy production. Variability analysis reveals that increasing maintenance frequency reduces cost fluctuations, while energy loss remains relatively stable. These findings elucidate the interplay among preventive maintenance strategies, cost, and reliability in the realm of wind turbine performance optimization

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A day‐ahead sleeved power purchase agreement model for estimating the profit of wind farms in the Indian energy market

Das

Malakar

2021

Int Trans Electr Energ Syst

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SUMMARY The ever‐increasing demand for green energy sources encourages wind farms to participate in the energy market through long‐term contracts. This paper discusses the existing frameworks of the wind energy market and presents the strategies to integrate wind energy into the grid through the valuable power purchase agreement (PPA). Critical observations reveal that the opportunities of the short‐term energy market remain unexplored for wind farms till date. To promote more investments and to enhance the operating economy, the wind energy sector must adapt to the sporadic nature of generation in the day‐ahead energy market. The selection of accurate PPA for wind farms is a potent area of research and has encouraged the authors to explore this area vividly. In view of this, the main objective of the article is to develop a day‐ahead sleeved PPA model and to evaluate its effectiveness in the Indian energy market. To correlate the proposed model with practicality aspects, seasonal wind speed scenarios are forecasted using the sARIMA model for a practical wind farm. The performance of the employed sARIMA model is evaluated through proper comparative assessment with other models. Thereafter to validate the importance of the developed PPA, a detailed comparative analysis is carried out with the existing long‐term PPA. Further, the potentiality of the proposed sleeved PPA is compared with its other counterparts. Result analysis confirms the usefulness of the proposed PPA model in maximizing the economy of the renewable energy market entities.

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Wind Energy Reliability Analysis based on Monte Carlo Simulation Method

Cited by 8 publications

References 8 publications

Review on the Advancements in Wind Turbine Blade Inspection: Integrating Drone and Deep Learning Technologies for Enhanced Defect Detection

Review on the Advancements in Wind Turbine Blade Inspection: Integrating Drone and Deep Learning Technologies for Enhanced Defect Detection

Maximizing Wind Turbine Efficiency: Monte Carlo Simulation Based on Cost and Energy Loss Analysis for Optimal Preventive Maintenance

A day‐ahead sleeved power purchase agreement model for estimating the profit of wind farms in the Indian energy market

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