scholarly journals Wind Turbine Drivetrain Reliability and Wind Plant Operations and Maintenance Research and Development Opportunities

2021 ◽  
Author(s):  
Jonathan Keller ◽  
Shawn Sheng ◽  
Yi Guo ◽  
Benjamin Gould ◽  
Aaron Greco
2013 ◽  
Author(s):  
Madhur A. Khadabadi ◽  
Karen B. Marais

Wind turbine maintenance is emerging as an unexpectedly high component of turbine operating cost and there is an increasing interest in managing this cost. Here, we present an alternative view of maintenance as a value-driver, and develop an optimization algorithm to maximize the value delivered by maintenance. We model the stochastic deterioration of the turbine in two dimensions: the deterioration rate, and the extent of deterioration, and view maintenance as an operator that moves the turbine to an improved state in which it can generate more power and so earn more revenue. We then use a standard net present value (NPV) approach to calculate the value of the turbine by deducting the costs incurred in the installation, operations and maintenance from the revenue due to the power generation. The application of our model is demonstrated using several scenarios with a focus on blade deterioration. We evaluate the value delivered by implementing blade condition monitoring systems (CMS). A higher fidelity CMS allows the blade state to be determined with higher precision. With this improved state information, an optimal maintenance strategy can be derived. The difference between the value of the turbine with and without CMS can be interpreted as the value of the CMS. The results indicate that a higher fidelity (and more expensive) condition monitoring system (CMS) does not necessarily yield the highest value, and, that there is an optimal level of fidelity that results in maximum value. The contributions of this work are twofold. First, it is a practical approach to wind turbine valuation and operation that takes operating and market conditions into account. This work should therefore be useful to wind farm operators and investors. Second, it shows how the value of a CMS can be explicitly assessed. This work should therefore be useful to CMS manufacturers and wind farm operators.


Author(s):  
Jonathan L. Arendt ◽  
Daniel A. McAdams ◽  
Richard J. Malak

Design is an uncertain human activity involving decisions with uncertain outcomes. Sources of uncertainty in product design include uncertainty in modeling methods, market preferences, and performance levels of subsystem technologies, among many others. The performance of a technology evolves over time, typically exhibiting improving performance. As the performance of a technology in the future is uncertain, quantifying the evolution of these technologies poses a challenge in making long-term design decisions. Here, we focus on how to make decisions using formal models of technology evolution. The scenario of a wind turbine energy company deciding which technology to invest in demonstrates a new technology evolution modeling technique and decision making method. The design of wind turbine arrays is a complex problem involving decisions such as location and turbine model selection. Wind turbines, like many other technologies, are currently evolving as the research and development efforts push the performance limits. In this research, the development of technology performance is modeled as an S-curve; slowly at first, quickly during heavy research and development effort, and slowly again as the performance approaches its limits. The S-curve model typically represents the evolution of just one performance attribute, but designers generally deal with problems involving multiple important attributes. Pareto frontiers representing the set of optimal solutions that the decision maker can select from at any point in time allow for modeling the evolution of technologies with multiple attributes. As the performance of a technology develops, the Pareto frontier shifts to a new location. The assumed S-curve form of technology development allows the designer to apply the uncertainty of technology development directly to the S-curve evolution model rather than applying the uncertainty to the future performance, giving a more focused application of uncertainty in the problem. The multi-attribute technology evolution modeling technique applied in decision-making gives designers greater insight when making long-term decisions involving technologies that evolve.


Author(s):  
Ralph S. Hill

Current American Society of Mechanical Engineers (ASME) nuclear codes and standards rely primarily on deterministic and mechanistic approaches to design. The design code is a separate volume from the code for inservice inspections and both are separate from the standards for operations and maintenance. The ASME code for inservice inspections and code for nuclear plant operations and maintenance have adopted risk-informed methodologies for inservice inspection, preventive maintenance, and repair and replacement decisions. The American Institute of Steel Construction and the American Concrete Institute have incorporated risk-informed probabilistic methodologies into their design codes. It is proposed that the ASME nuclear code should undergo a planned evolution that integrates the various nuclear codes and standards and adopts a risk-informed approach across a facility life-cycle — encompassing design, construction, operation, maintenance and closure.


Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5152
Author(s):  
Conor McKinnon ◽  
James Carroll ◽  
Alasdair McDonald ◽  
Sofia Koukoura ◽  
David Infield ◽  
...  

Anomaly detection for wind turbine condition monitoring is an active area of research within the wind energy operations and maintenance (O & M) community. In this paper three models were compared for multi-megawatt operational wind turbine SCADA data. The models used for comparison were One-Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Elliptical Envelope (EE). Each of these were compared for the same fault, and tested under various different data configurations. IF and EE have not previously been used for fault detection for wind turbines, and OCSVM has not been used for SCADA data. This paper presents a novel method of condition monitoring that only requires two months of data per turbine. These months were separated by a year, the first being healthy and the second unhealthy. The number of anomalies is compared, with a greater number in the unhealthy month being considered correct. It was found that for accuracy IF and OCSVM had similar performances in both training regimes presented. OCSVM performed better for generic training, and IF performed better for specific training. Overall, IF and OCSVM had an average accuracy of 82% for all configurations considered, compared to 77% for EE.


Author(s):  
John M. Jenco ◽  
Donna R. Keck ◽  
Gary L. Johnson

Recent studies have shown that the average annual cost impact of external piping system leakage on commercial nuclear plant operations and maintenance can easily range into the millions of dollars for each reactor unit. Evidence suggests that this significant O&M cost reduction opportunity has largely been overlooked, due to the number of diverse line items and budget areas affected. Results released last year from an EPRI pilot study of more than a dozen reactor units at seven plant sites operated by multiple utilities found that the average annual cost impact was indeed around $1.6 million per year per unit. Subsequent field experience has also demonstrated that an effective fluid leak management program can substantially reduce these costs within the first three years of implementation. This paper presents the general cost impact research results from various studies, outlines key elements of an effective plant fluid leak management program, discusses important implementation issues, and presents results from case studies covering different utility approaches to developing and implementing an effective fluid leak management program. Actual cost data will be included where appropriate.


2020 ◽  
Author(s):  
Eric Mayhaus ◽  
◽  
John Barnett ◽  

For most construction projects in the water/wastewater field, the first time the design can be validated and shown to actually work is after construction and start-up. It’s not too often that there is an opportunity to prove modeling with real-world results BEFORE improvements are constructed. What better way to see design translate from paper to real-life performance than to build a scale model and run multiple flow trials? This paper presents a case study for the design of primary settling tanks (PST) influent baffles at the Nashville Central WWTP (CWWTP by utilizing hydraulic computational fluid dynamics (CFD) modeling and validating results with physical modeling. Physical model results helped gain the plant operations and maintenance (O&M) trust in validating the design and provide the best solution for improvements.


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