A Return on Investment Model for the Implementation of New Technologies on Wind Turbines

Bakhshi, Roozbeh; Sandborn, Peter

doi:10.1109/tste.2017.2729505

Cited by 25 publications

(14 citation statements)

References 26 publications

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“…To determine the economic feasibility of the selected wind turbines, PBP analysis was carried out [58,59]. For this analysis, the following main assumptions were made: (1) the average total cost of small wind turbines is $3000/kW, (2) the average life time of selected wind turbines is 30 years, (3) the variable cost is $0.005/kWh, (4) the nominal variable cost escalation rate is 2% per year, and (5) availability of wind turbines is 98%.…”

Section: Conventional Wind Energy Systemsmentioning

confidence: 99%

Wind Resource Assessment and Economic Viability of Conventional and Unconventional Small Wind Turbines: A Case Study of Maryland

et al. 2020

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Annual mean wind speed distribution models for power generation based on regional wind resource maps are limited by spatial and temporal resolutions. These models, in general, do not consider the impact of local terrain and atmospheric circulations. In this study, long-term five-year wind data at three sites on the North, East, and West of the Baltimore metropolitan area, Maryland, USA are statistically analyzed. The Weibull probability density function was defined based on the observatory data. Despite seasonal and spatial variability in the wind resource, the annual mean wind speed for all sites is around 3 m/s, suggesting the region is not suitable for large-scale power generation. However, it does display a wind power capacity that might allow for non-grid connected small-scale wind turbine applications. Technical and economic performance evaluations of more than 150 conventional small-scale wind turbines showed that an annual capacity factor and electricity production of 11% and 1990 kWh, respectively, are achievable. It results in a payback period of 13 years. Government incentives can improve the economic feasibility and attractiveness of investments in small wind turbines. To reduce the payback period lower than 10 years, modern/unconventional wind harvesting technologies are found to be an appealing option in this region. Key contributions of this work are (1) highlighting the need for studying the urban physics rather than just the regional wind resource maps for wind development projects in the build-environment, (2) illustrating the implementation of this approach in a real case study of Maryland, and (3) utilizing techno-economic data to determine suitable wind harnessing solutions for the studied sites.

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Section: Conventional Wind Energy Systemsmentioning

confidence: 99%

Wind Resource Assessment and Economic Viability of Conventional and Unconventional Small Wind Turbines: A Case Study of Maryland

et al. 2020

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“…The goal of these studies is to investigate how CMS affects the O&M costs. The only previous work that addresses cost modeling of LIDAR, focuses on development of a return on investment (ROI) model, and explains how the model has to be constructed in order to incorporate all the effects that LIDAR has on the operation of a wind farm . The model developed in this paper uses the identical timeline condition modeling concepts developed in Bakhshi and Sandborn, but we incorporate a different financial comparison metric and use the resulting model to optimize the number and circulation of LIDAR within a wind farm (Bakhshi and Sandborn only provide one simple example based on LIDAR).…”

Section: Financial Metricmentioning

confidence: 99%

“…There are several financial metrics that can be used to capture the effects of LIDAR use on the overall finances of a wind farm. ROI is one that was discussed in Bakhshi and Sandborn The problem with ROI is that from an accounting perspective, maximizing ROI does not represent the optimum solution for an organization, ie, a small investment, and a relatively small return can produce a larger ROI than a larger investment with a larger return, so ROI can be misleading.…”

Section: Financial Metricmentioning

confidence: 99%

“…The reason is that the LIDAR has improved the reliability, and the new failure time has to be later than the failure time for the case of no LIDAR. The dependency between the two cases is called “identical timeline conditions.” The construction and analysis of identical timeline conditions has been described in detail in Bakhshi and Sandborn …”

Section: Financial Metricmentioning

confidence: 99%

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Maximizing the returns of LIDAR systems in wind farms for yaw error correction applications

Bakhshi

Sandborn

2020

Wind Energy

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Wind energy is an important source of renewable energy with significant untapped potential around the world. However, the cost of wind energy production is high, and efforts to lower the cost of energy generation will help enable more widespread use of wind energy. Yaw error reduces the efficiency of turbines as well as lowers the reliability of key components in turbines. Light detection and ranging (LIDAR) devices can correct the yaw error; however, they are expensive, and there is a tradeoff between their costs and benefits. In this study, a stochastic discrete-event simula-

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“…The cost of energy (CoE) for offshore wind is currently too high to make it truly competitive with traditional fossil fuel energy generation techniques. 1 Past analysis has shown that costs for offshore wind energy are roughly 40% higher than gas turbine generation and 30% higher than onshore wind. 2 Consequently, industry, research institutes, and academia are working towards lowering the CoE for offshore wind.…”

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confidence: 99%

Wind turbine gearbox failure and remaining useful life prediction using machine learning techniques

et al. 2018

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This research investigates the prediction of failure and remaining useful life (RUL) of gearboxes for modern multi‐megawatt wind turbines. Failure and RUL are predicted through the use of machine learning techniques and large amounts of labelled wind turbine supervisory control and data acquisition (SCADA) and vibration data. The novelty of this work stems from unprecedented access to one of the world's largest wind turbine operational and reliability databases, containing thousands of turbine gearbox failure examples and complete SCADA and vibration data in the build up to those failures. Through access to that data, this paper is unique in having enough failure examples and data to draw the conclusions detailed in the remainder of this abstract. This paper shows that artificial neural networks provide the most accurate failure and RUL prediction out of three machine learning techniques trialled. This work also demonstrates that SCADA data can be used to predict failure up to a month before it occurs, and high frequency vibration data can be used to extend that accurate prediction capability to 5 to 6 months before failure. This paper demonstrates that two class neural networks can correctly predict gearbox failures between 72.5% and 75% of the time depending on the failure mode when trained with SCADA data and 100% of the time when trained with vibration data. Data trends in the build up to failure and weighting of the SCADA data inputs are also provided. Lastly, this work shows how multi‐class neural networks demonstrate more potential in predicting gearbox failure when trained with vibration data as opposed to training with SCADA data.

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A Return on Investment Model for the Implementation of New Technologies on Wind Turbines

Cited by 25 publications

References 26 publications

Wind Resource Assessment and Economic Viability of Conventional and Unconventional Small Wind Turbines: A Case Study of Maryland

Wind Resource Assessment and Economic Viability of Conventional and Unconventional Small Wind Turbines: A Case Study of Maryland

Maximizing the returns of LIDAR systems in wind farms for yaw error correction applications

Wind turbine gearbox failure and remaining useful life prediction using machine learning techniques

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