This page has only limited features, please log in for full access.

Dr. John D Sørensen
Department of the Built Environment, Aalborg University, 9220 Aalborg, Denmark

Basic Info


Research Keywords & Expertise

0 Probabilistic Design
0 Risk Analysis
0 Wind Turbines
0 Offshore structures
0 structural reliability

Fingerprints

Wind Turbines
Operation and maintenance
Offshore structures
structural reliability
Risk Analysis
Probabilistic Design

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 24 April 2021 in International Journal of Pressure Vessels and Piping
Reads 0
Downloads 0

The progressive loss of integrity of production tubing results from time- and space- variant degradation processes, mainly scale and corrosion. Due to the potential disastrous consequences of failure, it is necessary and required to manage the integrity of production tubing systems by means of inspections, monitoring and maintenance. However, there are substantial uncertainties associated with the degradation processes and it is prudent to account for these in the optimal planning of integrity management. To this end risk informed approaches taking basis in the Bayesian pre-posterior decision analysis and methods of structural reliability, introduced already 3–4 decades ago for integrity management of offshore structures, comprise a strong basis. With such an approach an optimal trade-off between the cost of inspections/maintenance and the benefits due to increased production and reduced risk to personnel and environment can be determined. This paper presents such a framework and demonstrates its implementation on a selected example. The novelty of the framework is the risk informed Bayesian decision framework and the joint consideration of the two common failure modes of the tubing: tubing clogging and leaking due to scale and pitting corrosion respectively.

ACS Style

Akinyemi Olugbenga Akinsanya; Jianjun Qin; Yue Guan; John Dalsgaard Sørensen; Michael Havbro Faber. Risk informed integrity management of sub-surface well production tubings subject to combined scale and corrosion degradations. International Journal of Pressure Vessels and Piping 2021, 192, 104424 .

AMA Style

Akinyemi Olugbenga Akinsanya, Jianjun Qin, Yue Guan, John Dalsgaard Sørensen, Michael Havbro Faber. Risk informed integrity management of sub-surface well production tubings subject to combined scale and corrosion degradations. International Journal of Pressure Vessels and Piping. 2021; 192 ():104424.

Chicago/Turabian Style

Akinyemi Olugbenga Akinsanya; Jianjun Qin; Yue Guan; John Dalsgaard Sørensen; Michael Havbro Faber. 2021. "Risk informed integrity management of sub-surface well production tubings subject to combined scale and corrosion degradations." International Journal of Pressure Vessels and Piping 192, no. : 104424.

Research article
Published: 05 January 2021 in Wind Energy
Reads 0
Downloads 0

The main wind turbine design standard IEC61400‐1 ed. 4 includes an annual target reliability index for structural components of 3.3. Presently, no standards specify specific reliability requirements for existing wind turbines, to be used in relation to verification of structural integrity for life extension or continued operation. For existing structures in general, both economic and sustainability considerations support differentiation in reliability targets, as it is generally more expensive and requires more resources to improve the reliability. ISO2394 “General Principles on Reliability for Structures” includes tables with differentiated reliability targets depending on the consequences of failure and costs of improving reliability, which are derived using risk‐based economic optimization. However, the assumptions behind these tables do not match the specific problem of life extension of wind turbines. In this paper, the risk‐based approach is applied to derive specific target reliability levels for life extension of wind turbines, and a target annual reliability index around 3.1 is proposed.

ACS Style

Jannie S. Nielsen; John D. Sørensen. Risk‐based derivation of target reliability levels for life extension of wind turbine structural components. Wind Energy 2021, 24, 939 -956.

AMA Style

Jannie S. Nielsen, John D. Sørensen. Risk‐based derivation of target reliability levels for life extension of wind turbine structural components. Wind Energy. 2021; 24 (9):939-956.

Chicago/Turabian Style

Jannie S. Nielsen; John D. Sørensen. 2021. "Risk‐based derivation of target reliability levels for life extension of wind turbine structural components." Wind Energy 24, no. 9: 939-956.

Journal article
Published: 29 December 2020 in Renewable Energy
Reads 0
Downloads 0

Floating offshore wind turbine (FOWT) towers are dynamically sensitive to wind and wave excitations. Since the wave tends to be harsher with the increase of wind speed, FOWT towers are likely to experience the most severe vibration operating at the cut-out wind speed. In the present study, the short-term dynamic reliability of a spar-type FOWT is evaluated based on the probability density evolution method (PDEM). For this purpose, an integrated coupled dynamics model for the FOWT is firstly established by incorporating the multibody dynamics with the finite element (FE) method. Next, the conditional joint probability distribution of the significant wave height and peak spectral wave period at the cut-out wind speed is constructed based on the copula model. Then, the stochastic dynamic response and reliability of the FOWT can be analyzed via PDEM. The numerical example of reliability analysis of a 5-MW spar-type FOWT operating at the cut-out wind speed is carried out, in which the long-term met-ocean data at a South China Sea site is utilized. Simulation results show that the reliability of the FOWT for normal operation is less than 0.2 when the acceleration at the tower top is adopted as the failure criterion.

ACS Style

Yupeng Song; Biswajit Basu; Zili Zhang; John Dalsgaard Sørensen; Jie Li; Jianbing Chen. Dynamic reliability analysis of a floating offshore wind turbine under wind-wave joint excitations via probability density evolution method. Renewable Energy 2020, 168, 991 -1014.

AMA Style

Yupeng Song, Biswajit Basu, Zili Zhang, John Dalsgaard Sørensen, Jie Li, Jianbing Chen. Dynamic reliability analysis of a floating offshore wind turbine under wind-wave joint excitations via probability density evolution method. Renewable Energy. 2020; 168 ():991-1014.

Chicago/Turabian Style

Yupeng Song; Biswajit Basu; Zili Zhang; John Dalsgaard Sørensen; Jie Li; Jianbing Chen. 2020. "Dynamic reliability analysis of a floating offshore wind turbine under wind-wave joint excitations via probability density evolution method." Renewable Energy 168, no. : 991-1014.

Journal article
Published: 12 June 2020 in Renewable Energy
Reads 0
Downloads 0

The use of load and structural performance measurement information is vital for efficient structural integrity management and for the cost of energy production with Offshore Wind Turbines (OWTs). OWTs are dynamically sensitive structures subject to an interaction with a control unit exposed to repeated cyclic wind and wave loads causing deterioration and fatigue. This study focuses on the quantification of the value of structural and environmental information on the integrity management of OWT structures, with the focus on fatigue of welded joints. By utilizing decision analysis, structural reliability methods, measurement data, as well as the cost-benefit models, a Value of Information (VoI) analysis can be performed to quantify the most beneficial measurement strategy. The VoI assessment is demonstrated for the integrity management of a butt welded joint of a monopile support structure for a 3 MW OWT with a hub height of approximately 71m. The conditional value of three-year measured oceanographic information and one-year strain monitoring information is quantified posteriori in conjunction with an inspection and repair planning. This paper provides insights on how much benefits can be achieved through structural and environmental information, with practical relevance on reliability-based maintenance of OWT structures.

ACS Style

Lijia Long; Quang Anh Mai; Pablo Gabriel Morato; John Dalsgaard Sørensen; Sebastian Thöns. Information value-based optimization of structural and environmental monitoring for offshore wind turbines support structures. Renewable Energy 2020, 159, 1036 -1046.

AMA Style

Lijia Long, Quang Anh Mai, Pablo Gabriel Morato, John Dalsgaard Sørensen, Sebastian Thöns. Information value-based optimization of structural and environmental monitoring for offshore wind turbines support structures. Renewable Energy. 2020; 159 ():1036-1046.

Chicago/Turabian Style

Lijia Long; Quang Anh Mai; Pablo Gabriel Morato; John Dalsgaard Sørensen; Sebastian Thöns. 2020. "Information value-based optimization of structural and environmental monitoring for offshore wind turbines support structures." Renewable Energy 159, no. : 1036-1046.

Journal article
Published: 25 May 2020 in Reliability Engineering & System Safety
Reads 0
Downloads 0

In this paper, a novel method is proposed to optimize inspection plans for fatigue accounting for the situations where corrosion-free or protected environments are changed to a corrosive environment, denoted as the Transitional Environmental Protection (TEP) process. Probabilistic fracture mechanics and S-N curve approaches are calibrated to simulate crack propagations and planning of inspections and repairs of offshore welds. The method presented is relatively simple and conservative: The transition between protected and corrosive environments is addressed by shifting the S-N curve parameters during the damage calculations, performing crack size recalibration and modifying the crack growth material parameters. These concepts are incorporated in an algorithm, which is applicable to new designs or existing structures where no data from previous inspections is available. An example illustrates the potential of the algorithm.

ACS Style

G.A. Ruiz Muñoz; J.D. Sørensen. Probabilistic inspection planning of offshore welds subject to the transition from protected to corrosive environment. Reliability Engineering & System Safety 2020, 202, 107009 .

AMA Style

G.A. Ruiz Muñoz, J.D. Sørensen. Probabilistic inspection planning of offshore welds subject to the transition from protected to corrosive environment. Reliability Engineering & System Safety. 2020; 202 ():107009.

Chicago/Turabian Style

G.A. Ruiz Muñoz; J.D. Sørensen. 2020. "Probabilistic inspection planning of offshore welds subject to the transition from protected to corrosive environment." Reliability Engineering & System Safety 202, no. : 107009.

Journal article
Published: 14 January 2020 in Applied Sciences
Reads 0
Downloads 0

This paper presents recent contributions to the Marie Skłodowska-Curie Innovative Training Network titled INFRASTAR (Innovation and Networking for Fatigue and Reliability Analysis of Structures-Training for Assessment of Risk) in the field of reliability approaches for decision-making for wind turbines and bridges . Stochastic modeling of uncertainties for fatigue strength parameters is an important step as a basis for reliability analyses. In this paper, the Maximum Likelihood Method (MLM) is used for fitting the statistical parameters in a regression model for the fatigue strength of reinforcement bars. Furthermore, application of the Bootstrapping method is investigated. The results indicate that the latter methodology does not work well in the considered case study because of run-out tests within the test data. Moreover, the use of the Bayesian inference with the Markov Chain Monto Carlo approach is studied. These results indicate that a reduction in the statistical uncertainty can be obtained, and thus, better parameter estimates are obtained. The results are used for stochastic modelling in reliability assessment of a case study with a composite bridge. The reduction in statistical uncertainty shows high impact on the fatigue reliability in a case study on the Swiss viaduct Crêt De l’Anneau.

ACS Style

Sima Rastayesh; Amol Mankar; John Dalsgaard Sørensen; Sajjad Bahrebar. Development of Stochastic Fatigue Model of Reinforcement for Reliability of Concrete Structures. Applied Sciences 2020, 10, 604 .

AMA Style

Sima Rastayesh, Amol Mankar, John Dalsgaard Sørensen, Sajjad Bahrebar. Development of Stochastic Fatigue Model of Reinforcement for Reliability of Concrete Structures. Applied Sciences. 2020; 10 (2):604.

Chicago/Turabian Style

Sima Rastayesh; Amol Mankar; John Dalsgaard Sørensen; Sajjad Bahrebar. 2020. "Development of Stochastic Fatigue Model of Reinforcement for Reliability of Concrete Structures." Applied Sciences 10, no. 2: 604.

Journal article
Published: 20 December 2019 in Sustainability
Reads 0
Downloads 0

This paper uses a system engineering approach based on the Failure Mode and Effect Analysis (FMEA) methodology to do risk analysis of the power conditioner of a Proton Exchange Membrane Fuel Cell (PEMFC). Critical components with high risk, common cause failures and effects are identified for the power conditioner system as one of the crucial parts of the PEMFCs used for backup power applications in the telecommunication industry. The results of this paper indicate that the highest risk corresponds to three failure modes including high leakage current due to the substrate interface of the metal oxide semiconductor field effect transistor (MOSFET), current and electrolytic evaporation of capacitor, and thereby short circuit, loss of gate control, and increased leakage current due to gate oxide of the MOSFET. The MOSFETs, capacitors, chokes, and transformers are critical components of the power stage, which should be carefully considered in the development of the design production and implementation stage. Finally, Bayesian networks (BNs) are used to identify the most critical failure causes in the MOSFET and capacitor as they are classified from the FMEA as key items based on their Risk Priority Numbers (RPNs). As a result of BNs analyses, high temperature and overvoltage are distinguished as the most crucial failure causes. Consequently, it is recommended for designers to pay more attention to the design of MOSFETs’ failure due to high leakage current owing to substrate interface, which is caused by high temperature. The results are emphasizing design improvement in the material in order to be more resistant from high temperature.

ACS Style

Sima Rastayesh; Sajjad Bahrebar; Frede Blaabjerg; Dao Zhou; Huai Wang; John Dalsgaard Sørensen. A System Engineering Approach Using FMEA and Bayesian Network for Risk Analysis—A Case Study. Sustainability 2019, 12, 77 .

AMA Style

Sima Rastayesh, Sajjad Bahrebar, Frede Blaabjerg, Dao Zhou, Huai Wang, John Dalsgaard Sørensen. A System Engineering Approach Using FMEA and Bayesian Network for Risk Analysis—A Case Study. Sustainability. 2019; 12 (1):77.

Chicago/Turabian Style

Sima Rastayesh; Sajjad Bahrebar; Frede Blaabjerg; Dao Zhou; Huai Wang; John Dalsgaard Sørensen. 2019. "A System Engineering Approach Using FMEA and Bayesian Network for Risk Analysis—A Case Study." Sustainability 12, no. 1: 77.

Journal article
Published: 26 November 2019 in Electronics
Reads 0
Downloads 0

This paper presents a methodology based on the failure mode and effect analysis (FMEA) to analyze the failures in the power stage of wind-fuel cell hybrid energy systems. Besides, fault tree analysis (FTA) is applied to describe the probabilistic failures in the vital subcomponents. Finally, the reliability assessment of the system is carried out for a five-year operation that is guaranteed by the manufacturer. So, as the result, the reliability analysis proves that the metal oxide semiconductor field effect transistor (MOSFET) and electrolytic capacitor are the most critical components that introduce damages in the power circuit. Moreover, a comparative study on the reliability assessment by the exponential distribution and the Weibull distribution show that the B1 lifetime obtained by the Weibull distribution is closer to reality.

ACS Style

Sima Rastayesh; Sajjad Bahrebar; Amir Sajjad Bahman; John Dalsgaard Sørensen; Frede Blaabjerg. Lifetime Estimation and Failure Risk Analysis in a Power Stage Used in Wind-Fuel Cell Hybrid Energy Systems. Electronics 2019, 8, 1412 .

AMA Style

Sima Rastayesh, Sajjad Bahrebar, Amir Sajjad Bahman, John Dalsgaard Sørensen, Frede Blaabjerg. Lifetime Estimation and Failure Risk Analysis in a Power Stage Used in Wind-Fuel Cell Hybrid Energy Systems. Electronics. 2019; 8 (12):1412.

Chicago/Turabian Style

Sima Rastayesh; Sajjad Bahrebar; Amir Sajjad Bahman; John Dalsgaard Sørensen; Frede Blaabjerg. 2019. "Lifetime Estimation and Failure Risk Analysis in a Power Stage Used in Wind-Fuel Cell Hybrid Energy Systems." Electronics 8, no. 12: 1412.

Journal article
Published: 12 July 2019 in Structure and Infrastructure Engineering
Reads 0
Downloads 0
ACS Style

Amol Mankar; Sima Rastayesh; John Dalsgaard Sørensen. Fatigue reliability analysis of crêt de l’Anneau viaduct: a case study. Structure and Infrastructure Engineering 2019, 16, 762 -771.

AMA Style

Amol Mankar, Sima Rastayesh, John Dalsgaard Sørensen. Fatigue reliability analysis of crêt de l’Anneau viaduct: a case study. Structure and Infrastructure Engineering. 2019; 16 (4):762-771.

Chicago/Turabian Style

Amol Mankar; Sima Rastayesh; John Dalsgaard Sørensen. 2019. "Fatigue reliability analysis of crêt de l’Anneau viaduct: a case study." Structure and Infrastructure Engineering 16, no. 4: 762-771.

Journal article
Published: 10 July 2019 in Energies
Reads 0
Downloads 0

The paper presents research results from the Marie Skłodowska-Curie Innovative Training Network INFRASTAR in the field of reliability approaches for decision-making for wind turbines and bridges. This paper addresses the application of Bayesian decision analysis for installation of heating systems in wind turbine blades in cases where an ice detection system is already installed in order to allow wind turbines to be placed close to highways. Generally, application of ice detection and heating systems for wind turbines is very relevant in cases where the wind turbines are planned to be placed close to urban areas and highways, where risks need to be considered due to icing events, which may lead to consequences including human fatality, functional disruptions, and/or economic losses. The risk of people being killed in a car passing on highways near a wind turbine due to blades parts or ice pieces being thrown away in cases of over-icing is considered in this paper. The probability of being killed per kilometer and per year is considered for three cases: blade parts thrown away as a result of a partial or total failure of a blade, ice thrown away in two cases, i.e., of stopped wind turbines and of wind turbines in operation. Risks due to blade parts being thrown away cannot be avoided, since low strengths of material, maintenance or manufacturing errors, mechanical or electrical failures may result in failure of a blade or blade part. The blade (parts) thrown away from wind turbines in operation imply possible consequences/fatalities for people near the wind turbines, including in areas close to highways. Similar consequences are relevant for ice being thrown away from wind turbine blades during icing situations. In this paper, we examine the question as to whether it is valuable to put a heating system on the blades in addition to ice detection systems. This is especially interesting in countries with limited space for placing wind turbines; in addition, it is considered if higher power production can be obtained due to less downtime if a heating system is installed.

ACS Style

Sima Rastayesh; Lijia Long; John Dalsgaard Sørensen; Sebastian Thöns; Long. Risk Assessment and Value of Action Analysis for Icing Conditions of Wind Turbines Close to Highways. Energies 2019, 12, 2653 .

AMA Style

Sima Rastayesh, Lijia Long, John Dalsgaard Sørensen, Sebastian Thöns, Long. Risk Assessment and Value of Action Analysis for Icing Conditions of Wind Turbines Close to Highways. Energies. 2019; 12 (14):2653.

Chicago/Turabian Style

Sima Rastayesh; Lijia Long; John Dalsgaard Sørensen; Sebastian Thöns; Long. 2019. "Risk Assessment and Value of Action Analysis for Icing Conditions of Wind Turbines Close to Highways." Energies 12, no. 14: 2653.

Journal article
Published: 20 May 2019 in Marine Structures
Reads 0
Downloads 0

Reassessing the remaining fatigue life of the wind turbine support structures becomes more and more crucial for operation, maintenance, and life extension when they are reaching the end of their design service life. By using measured oceanographic and strain data, each year, remaining fatigue life can be updated to adapt the operation to real loading conditions. Previous works have not put attention to address the complexity of offshore loading combinations and as-constructed state of the structure in estimating structural responses for fatigue behaviour to stochastically predict the remaining fatigue life. The present paper links the oceanographic data to fatigue damage by using measured strain, and uses the Bayesian approach to update the joint distribution of the oceanographic data. Consequently, the failure probability of the support structure can be updated and so the predicted fatigue life. The year-to-year variation of the 10-min mean wind speed, the unrepresentativeness of measured strain, the measurement uncertainty, and corrosion are considered together with uncertainties in Miner′s rule and S–N curves. The present research shows that the real oceanographic data can be used to adjust the predicted remaining fatigue life and eventually give decision support for the wind turbine operation.

ACS Style

Quang A. Mai; Wout Weijtjens; Christof Devriendt; Pablo G. Morato; Philippe Rigo; John D. Sørensen. Prediction of remaining fatigue life of welded joints in wind turbine support structures considering strain measurement and a joint distribution of oceanographic data. Marine Structures 2019, 66, 307 -322.

AMA Style

Quang A. Mai, Wout Weijtjens, Christof Devriendt, Pablo G. Morato, Philippe Rigo, John D. Sørensen. Prediction of remaining fatigue life of welded joints in wind turbine support structures considering strain measurement and a joint distribution of oceanographic data. Marine Structures. 2019; 66 ():307-322.

Chicago/Turabian Style

Quang A. Mai; Wout Weijtjens; Christof Devriendt; Pablo G. Morato; Philippe Rigo; John D. Sørensen. 2019. "Prediction of remaining fatigue life of welded joints in wind turbine support structures considering strain measurement and a joint distribution of oceanographic data." Marine Structures 66, no. : 307-322.

Journal article
Published: 14 March 2019 in Energies
Reads 0
Downloads 0

Due to the considerable increase in clean energy demand, there is a significant trend of increased wind turbine sizes, resulting in much higher loads on the blades. The high loads can cause significant out-of-plane deformations of the blades, especially in the area nearby the maximum chord. This paper briefly presents a discrete Markov chain model as a simplified probabilistic model for damages in wind turbine blades, based on a six-level damage categorization scheme applied by the wind industry, with the aim of providing decision makers with cost-optimal inspection intervals and maintenance strategies for the aforementioned challenges facing wind turbine blades. The in-history inspection information extracted from a database with inspection information was used to calibrate transition probabilities in the discrete Markov chain model. With the calibrated transition probabilities, the damage evolution can be statistically simulated. The classical Bayesian pre-posterior decision theory, as well as condition-based maintenance strategy, was used as a basis for the decision-making. An illustrative example with transverse cracks is presented using a reference wind turbine.

ACS Style

Yi Yang; John Dalsgaard Sørensen. Cost-Optimal Maintenance Planning for Defects on Wind Turbine Blades. Energies 2019, 12, 998 .

AMA Style

Yi Yang, John Dalsgaard Sørensen. Cost-Optimal Maintenance Planning for Defects on Wind Turbine Blades. Energies. 2019; 12 (6):998.

Chicago/Turabian Style

Yi Yang; John Dalsgaard Sørensen. 2019. "Cost-Optimal Maintenance Planning for Defects on Wind Turbine Blades." Energies 12, no. 6: 998.

Journal article
Published: 29 January 2018 in Energies
Reads 0
Downloads 0

Operation and maintenance costs are a major contributor to the Levelized Cost of Energy for electricity produced by offshore wind and can be significantly reduced if existing corrective actions are performed as efficiently as possible and if future corrective actions are avoided by performing sufficient preventive actions. This paper presents an applied and generic diagnostic model for fault detection and condition based maintenance of offshore wind components. The diagnostic model is based on two probabilistic matrices; first, a confidence matrix, representing the probability of detection using each fault detection method, and second, a diagnosis matrix, representing the individual outcome of each fault detection method. Once the confidence and diagnosis matrices of a component are defined, the individual diagnoses of each fault detection method are combined into a final verdict on the fault state of that component. Furthermore, this paper introduces a Bayesian updating model based on observations collected by inspections to decrease the uncertainty of initial confidence matrix. The framework and implementation of the presented diagnostic model are further explained within a case study for a wind turbine component based on vibration, temperature, and oil particle fault detection methods. The last part of the paper will have a discussion of the case study results and present conclusions.

ACS Style

Masoud Asgarpour; John Dalsgaard Sørensen. Bayesian Based Diagnostic Model for Condition Based Maintenance of Offshore Wind Farms. Energies 2018, 11, 300 .

AMA Style

Masoud Asgarpour, John Dalsgaard Sørensen. Bayesian Based Diagnostic Model for Condition Based Maintenance of Offshore Wind Farms. Energies. 2018; 11 (2):300.

Chicago/Turabian Style

Masoud Asgarpour; John Dalsgaard Sørensen. 2018. "Bayesian Based Diagnostic Model for Condition Based Maintenance of Offshore Wind Farms." Energies 11, no. 2: 300.

Article
Published: 10 May 2017 in Energies
Reads 0
Downloads 0

To optimally plan maintenance of wind turbine blades, knowledge of the degradation processes and the remaining useful life is essential. In this paper, a method is proposed for calibration of a Markov deterioration model based on past inspection data for a range of blades, and updating of the model for a specific wind turbine blade, whenever information is available from inspections and/or condition monitoring. Dynamic Bayesian networks are used to obtain probabilities of inspection outcomes for a maximum likelihood estimation of the transition probabilities in the Markov model, and are used again when updating the model for a specific blade using observations. The method is illustrated using indicative data from a database containing data from inspections of wind turbine blades.

ACS Style

Jannie S. Nielsen; John D. Sørensen. Bayesian Estimation of Remaining Useful Life for Wind Turbine Blades. Energies 2017, 10, 664 .

AMA Style

Jannie S. Nielsen, John D. Sørensen. Bayesian Estimation of Remaining Useful Life for Wind Turbine Blades. Energies. 2017; 10 (5):664.

Chicago/Turabian Style

Jannie S. Nielsen; John D. Sørensen. 2017. "Bayesian Estimation of Remaining Useful Life for Wind Turbine Blades." Energies 10, no. 5: 664.

Article
Published: 02 May 2017 in Journal of Marine Science and Engineering
Reads 0
Downloads 0

The offshore wind industry is building and planning new wind farms further offshore due to increasing demand on sustainable energy production and already occupied prime resource locations closer to shore. Costs of operation and maintenance, transport and installation of offshore wind turbines already contribute significantly to the cost of produced electricity and will continue to increase, due to moving further offshore, if the current techniques of predicting offshore wind farm accessibility are to stay the same. The majority of offshore operations are carried out by specialized ships that must be hired for the duration of the operation. Therefore, offshore wind farm accessibility and costs of offshore activities are primarily driven by the expected number of operational hours offshore and waiting times for weather windows, suitable for offshore operations. Having more reliable weather window estimates would result in better wind farm accessibility predictions and, as a consequence, potentially reduce the cost of offshore wind energy. This paper presents an updated methodology of weather window prediction that uses physical offshore vessel and equipment responses to establish the expected probabilities of operation failure, which, in turn, can be compared to maximum allowable probability of failure to obtain weather windows suitable for operation. Two case studies were performed to evaluate the feasibility of the improved methodology, and the results indicated that it produced consistent and improved results. In fact, the updated methodology predicts 57% and 47% more operational hours during the test period when compared to standard alpha-factor and the original methodologies.

ACS Style

Tomas Gintautas; John Dalsgaard Sørensen. Improved Methodology of Weather Window Prediction for Offshore Operations Based on Probabilities of Operation Failure. Journal of Marine Science and Engineering 2017, 5, 20 .

AMA Style

Tomas Gintautas, John Dalsgaard Sørensen. Improved Methodology of Weather Window Prediction for Offshore Operations Based on Probabilities of Operation Failure. Journal of Marine Science and Engineering. 2017; 5 (2):20.

Chicago/Turabian Style

Tomas Gintautas; John Dalsgaard Sørensen. 2017. "Improved Methodology of Weather Window Prediction for Offshore Operations Based on Probabilities of Operation Failure." Journal of Marine Science and Engineering 5, no. 2: 20.

Journal article
Published: 08 April 2017 in Energies
Reads 0
Downloads 0

Inspection and maintenance expenses cover a considerable part of the cost of energy from offshore wind turbines. Risk-based maintenance planning approaches are a powerful tool to optimize maintenance and inspection actions and decrease the total maintenance expenses. Risk-based planning is based on many input parameters, which are in reality often not completely known. This paper will assess the cost impact of this incomplete knowledge based on a case study following risk-based maintenance planning. The sensitivity study focuses on weather forecast uncertainties, incomplete knowledge about the needed repair time on the site as well as uncertainties about the operational range of the boat and helicopter used to access the broken wind turbine. The cost saving potential is estimated by running Crude Monte Carlo simulations. Furthermore, corrective and preventive (scheduled and condition-based) maintenance strategies are implemented. The considered case study focuses on a wind farm consisting of ten 6 MW turbines placed 30 km off the Danish North Sea coast. The results show that the weather forecast is the uncertainty source dominating the maintenance expenses increase when considering risk-based decision-making uncertainties. The overall maintenance expenses increased by 70% to 140% when considering uncertainties directly related with risk-based maintenance planning.

ACS Style

Simon Ambühl; John Dalsgaard Sørensen. Sensitivity of Risk-Based Maintenance Planning of Offshore Wind Turbine Farms. Energies 2017, 10, 505 .

AMA Style

Simon Ambühl, John Dalsgaard Sørensen. Sensitivity of Risk-Based Maintenance Planning of Offshore Wind Turbine Farms. Energies. 2017; 10 (4):505.

Chicago/Turabian Style

Simon Ambühl; John Dalsgaard Sørensen. 2017. "Sensitivity of Risk-Based Maintenance Planning of Offshore Wind Turbine Farms." Energies 10, no. 4: 505.

Journal article
Published: 02 April 2017 in Energies
Reads 0
Downloads 0

The fatigue life of wind turbine cast components, such as the main shaft in a drivetrain, is generally determined by defects from the casting process. These defects may reduce the fatigue life and they are generally distributed randomly in components. The foundries, cutting facilities and test facilities can affect the verification of properties by testing. Hence, it is important to have a tool to identify which foundry, cutting and/or test facility produces components which, based on the relevant uncertainties, have the largest expected fatigue life or, alternatively, have the largest reliability to be used for decision-making if additional cost considerations are added. In this paper, a statistical approach is presented based on statistical hypothesis testing and analysis of covariance (ANCOVA) which can be applied to compare different groups (manufacturers, suppliers, test facilities, etc.) and to quantify the relevant uncertainties using available fatigue tests. Illustrative results are presented as obtained by statistical analysis of a large set of fatigue data for casted test components typically used for wind turbines. Furthermore, the SN curves (fatigue life curves based on applied stress) for fatigue assessment are estimated based on the statistical analyses and by introduction of physical, model and statistical uncertainties used for the illustration of reliability assessment.

ACS Style

Hesam Mirzaei Rafsanjani; John Dalsgaard Sørensen; Søren Fæster; Asger Sturlason. Fatigue Reliability Analysis of Wind Turbine Cast Components. Energies 2017, 10, 466 .

AMA Style

Hesam Mirzaei Rafsanjani, John Dalsgaard Sørensen, Søren Fæster, Asger Sturlason. Fatigue Reliability Analysis of Wind Turbine Cast Components. Energies. 2017; 10 (4):466.

Chicago/Turabian Style

Hesam Mirzaei Rafsanjani; John Dalsgaard Sørensen; Søren Fæster; Asger Sturlason. 2017. "Fatigue Reliability Analysis of Wind Turbine Cast Components." Energies 10, no. 4: 466.

Journal article
Published: 19 February 2016 in Energies
Reads 0
Downloads 0

The levelized cost of energy (LCOE) from wave energy converters (WECs) needs to be decreased in order to be able to become competitive with other renewable electricity sources. Probabilistic reliability methods can be used to optimize the structure of WECs. Optimization is often performed for critical structural components, like welded details, bolts or bearings. This paper considers reliability studies with a focus on plain bearings available from stock for the Wavestar device, which exists at the prototype level. The Wavestar device is a point absorber WEC. The plan is to mount a new power take-off (PTO) system consisting of a discrete displacement cylinder (DDC), which will allow different hydraulic cycles to operate at constant pressure levels. This setup increases the conversion efficiency, as well as decouples the electricity production from the pressure variations within the hydraulic cycle when waves are passing. The new PTO system leads to different load characteristics at the floater itself compared to the actual setup where the turbine/generator is directly coupled to the fluctuating hydraulic pressure within the PTO system. This paper calculates the structural reliability of the different available plain bearings planned to be mounted at the new PTO system based on a probabilistic approach, and the paper gives suggestions for fulfilling the minimal target reliability levels. The considered failure mode in this paper is the brittle fatigue failure of plain bearings. The performed sensitivity analysis shows that parameters defining the initial crack size have a big impact on the resulting reliability of the plain bearing.

ACS Style

Simon Ambühl; Morten Kramer; John Dalsgaard Sørensen. Structural Reliability of Plain Bearings for Wave Energy Converter Applications. Energies 2016, 9, 118 .

AMA Style

Simon Ambühl, Morten Kramer, John Dalsgaard Sørensen. Structural Reliability of Plain Bearings for Wave Energy Converter Applications. Energies. 2016; 9 (2):118.

Chicago/Turabian Style

Simon Ambühl; Morten Kramer; John Dalsgaard Sørensen. 2016. "Structural Reliability of Plain Bearings for Wave Energy Converter Applications." Energies 9, no. 2: 118.

Journal article
Published: 07 September 2015 in Journal of Marine Science and Engineering
Reads 0
Downloads 0

Out of the total wind turbine failure events, blade damage accounts for a substantial part, with some studies estimating it at around 23%. Current operation and maintenance (O&M) practices typically make use of corrective type maintenance as the basic approach, implying high costs for repair and replacement activities as well as large revenue losses, mainly in the case of offshore wind farms. The recent development and evolution of condition monitoring techniques, as well as the fact that an increasing number of installed turbines are equipped with online monitoring systems, offers a large amount of information on the blades structural health to the decision maker. Further, inspections of the blades are often performed in connection with service. In light of the obtained information, a preventive type of maintenance becomes feasible, with the potential of predicting the blades remaining life to support O&M decisions for avoiding major failure events. The present paper presents a fracture mechanics based model for estimating the remaining life of a wind turbine blade, focusing on the crack propagation in the blades adhesive joints. A generic crack propagation model is built in Matlab based on a Paris law approach. The model is used within a risk-based maintenance decision framework to optimize maintenance planning for the blades lifetime.

ACS Style

Mihai Florian; John Dalsgaard Sørensen. Wind Turbine Blade Life-Time Assessment Model for Preventive Planning of Operation and Maintenance. Journal of Marine Science and Engineering 2015, 3, 1027 -1040.

AMA Style

Mihai Florian, John Dalsgaard Sørensen. Wind Turbine Blade Life-Time Assessment Model for Preventive Planning of Operation and Maintenance. Journal of Marine Science and Engineering. 2015; 3 (3):1027-1040.

Chicago/Turabian Style

Mihai Florian; John Dalsgaard Sørensen. 2015. "Wind Turbine Blade Life-Time Assessment Model for Preventive Planning of Operation and Maintenance." Journal of Marine Science and Engineering 3, no. 3: 1027-1040.

Journal article
Published: 15 April 2015 in Energies
Reads 0
Downloads 0

Fatigue failure is one of the main failure modes for wind turbine drivetrain components made of cast iron. The wind turbine drivetrain consists of a variety of heavily loaded components, like the main shaft, the main bearings, the gearbox and the generator. The failure of each component will lead to substantial economic losses such as cost of lost energy production and cost of repairs. During the design lifetime, the drivetrain components are exposed to variable loads from winds and waves and other sources of loads that are uncertain and have to be modeled as stochastic variables. The types of loads are different for offshore and onshore wind turbines. Moreover, uncertainties about the fatigue strength play an important role in modeling and assessment of the reliability of the components. In this paper, a generic stochastic model for fatigue failure of cast iron components based on fatigue test data and a limit state equation for fatigue failure based on the SN-curve approach and Miner’s rule is presented. The statistical analysis of the fatigue data is performed using the Maximum Likelihood Method which also gives an estimate of the statistical uncertainties. Finally, illustrative examples are presented with reliability analyses depending on various stochastic models and partial safety factors.

ACS Style

Hesam Mirzaei Rafsanjani; John Dalsgaard Sørensen. Reliability Analysis of Fatigue Failure of Cast Components for Wind Turbines. Energies 2015, 8, 2908 -2923.

AMA Style

Hesam Mirzaei Rafsanjani, John Dalsgaard Sørensen. Reliability Analysis of Fatigue Failure of Cast Components for Wind Turbines. Energies. 2015; 8 (4):2908-2923.

Chicago/Turabian Style

Hesam Mirzaei Rafsanjani; John Dalsgaard Sørensen. 2015. "Reliability Analysis of Fatigue Failure of Cast Components for Wind Turbines." Energies 8, no. 4: 2908-2923.