Engineering topics

Guo, Yi (ORCID:000000026413947X)

Publications and source records attributed to Guo, Yi (ORCID:000000026413947X).

15 records

DOE OSTI2023

Operating Conditions of a Main Bearing Contact in a Commercial Wind Turbine

This presentation described acoustic emissions and temperature characteristics of a commercial main bearing in a wind turbine drivetrain. These characteristics were measured on the outer ring of the bearing by SKF DVST nodes, which are mounted on both upwind and downwind rows of the main bearing at four equally spaced circumferential locations. The measurement started in early 2018 and ends in 2020 and five-month data in 2018 winter was analyzed in this study.

acoustic emission↗

DOE OSTI2023

Nonsteady Load Responses to Mountain-Generated Turbulence Eddies on the DOE 1.5 MW Wind Turbine at the National Wind Technology Center

Field data collected from the NREL/GE 1.5MW wind turbine and met tower at the NREL Wind Technology Center near Boulder, Colorado from June-October 2018 were analyzed to quantify the impacts of turbulence eddies on the load responses measured from sensors on the main shaft, blade and tower. The passage of individual mountain-generated eddies from the met tower to the wind turbine were critically determined by correlating the optimal time shifts in signal between met tower and nacelle anemometers with mean advection time. Loading responses from mountain eddy passage were compared with atmospheric eddies from the north/south, unimpeded by the mountains, and found to be similar. Whereas time variations in torque were highly correlated with time changes in horizontal eddy velocity, the out-of-plane bending moments on the main shaft (directly forcing the main bearing) were uncorrelated with horizontal eddy velocity. This result is consistent with a previous LES study indicating that the main bearing is forced by asymmetrical interactions between the rotor and turbulence eddies, while power fluctuations respond primarily to advective eddy velocity. Surprisingly, the nacelle anemometer produced statistics very similar to the met tower.

ENGINEERING,WIND ENERGY↗

DOE OSTI2023

Wind Turbine Main Bearing Rating Lives as Determined by IEC 61400-1 and ISO 281

This presentation studies the rating lives of wind turbine main bearings, as determined by the IEC 61400-1 and ISO 281 standards. A brief review of relevant bearing life theory and turbine design requirements is provided. This includes a discussion on possible shortcomings, including the existence (or not) of the bearing fatigue load limit and the validity of assuming linear damage accumulation. A detailed study is then undertaken to determine rating lives for two models of main bearing in a 1.5 MW wind turbine. Rating life assessment is carried out under different conditions, including various combinations of main bearing temperature, wind field characteristics, lubricant viscosity and contamination levels. Rating lives are found to be sufficiently above the desired 20 year design life for both bearing models under expected operating conditions. For the larger bearing, operational loads are shown to be below or close to the bearing fatigue load limit a vast majority of the time. Key sensitivities for rating life values are shown to be the temperature and contamination. Overall, the results of this study suggest that rating life assessment does not account for reported rates of main bearing failures in 1 to 3 MW wind turbines. In future work, it is recommended that efforts be undertaken to identify principal root causes of main bearing failures, possibly leading to a new application standard specific to this component. It is also recommended that impacts of partial wake impingement on main bearing rating lives are investigated.

ENGINEERING,WIND ENERGY↗

DOE OSTI2023

The DG03: An Outline of Suggested Changes

The Design Guideline 03 (DG03) published by the National Renewable Energy Laboratory in 2009 is widely used in the wind industry. It allows engineers to get acquainted with design aspects and methodologies to determine pitch and yaw bearing static capacity and fatigue life. The combination of the detailed theoretical descriptions and practical examples makes it a highly application-oriented document. Since its publication, the knowledge about oscillating bearings, slewing bearings, and wind turbines has grown significantly. Some aspects of the DG03 need updates to reflect the current state of the art. This work covers proposed changes for an upcoming revision of DG03.

design guideline↗

DOE OSTI2023

The Wind Turbine Design Guideline DG03: Yaw and Pitch Rolling Bearing Life Revisited - An Outline of Suggested Changes: Preprint

design guideline↗

DOE OSTI2022

Nonsteady Load Responses to Daytime Atmospheric Turbulence Eddies on the DOE 1.5 MW Wind Turbine at NREL

Field data collected from the NREL/GE 1.5MW wind turbine (WT) and met tower (MetT) at the NREL Wind Technology Center near Boulder, CO June-October 2018 were analyzed to quantify the impacts of turbulence eddies on the load responses measured from sensors on the main shaft, blade and tower. The passage of individual mountain-generated eddies from the met tower to the WT were critically determined by correlating the optimal time shifts in signal between MetT and nacelle anemometers with mean advection time. Loading responses from mountain eddy passage were compared with atmospheric eddies from the north/south, unimpeded by the mountains, and found to be similar. Whereas time variations in torque were highly correlated with time changes in horizontal eddy velocity, the out-of-plane bending moments on the main shaft (directly forcing the main bearing) were uncorrelated with horizontal eddy velocity. This result is consistent with a previous LES study indicating that the main bearing is forced by asymmetrical interactions between the WT rotor and turbulence eddies, while power fluctuations respond primarily to advective eddy velocity. Surprisingly, the nacelle anemometer produced statistics very similar to the MetT.

ENGINEERING,WIND ENERGY↗

DOE OSTI2022

Investigation of Multiple Data Streams for Gearbox Bearing Fault Prediction Through Machine-Learning Models

Operations and maintenance (O&M) cost of wind plant accounts up to 30% of total energy cost, which can be reduced through continuous monitoring and successfully detecting incipient wind turbine failures. To accomplish this, condition monitoring and predictive maintenance systems are being implemented in wind industry to support O&M decision making. A wide range of approaches for condition monitoring and fault prediction have been developed. These approaches generally use historical data of wind turbines collected by Supervisory Control and Data Acquisition (SCADA) system to identify patterns that lead to failure. These SCADA data show the overall condition of a wind turbine and can be leveraged to detect when the turbine's performance is degrading and to identify if a fault is developing. However, it becomes challenging to predict the failure of a specific wind turbine gearbox bearing, because the SCADA data are often not directly linked to the component. To bridge the gap, we have investigated features calculated from SCADA data using physics-based models and the gearbox design over the years. The damaged metric we used in the physics domain is frictional energy. Combining these physics domain variables with SCADA data as inputs to various machine learning models for gearbox bearing fault prediction, we have demonstrated the benefits of leveraging both physics and data domain models. It was an attempt to improve frictional-energy-based damage metric by adding data domain inputs, as we had learned that the frictional-energy-based damage metric alone is not sufficient to single out failed bearings from healthy. As condition monitoring data (either vibration or oil debris data) has become available at more and more wind plants, we would like to evaluate whether by adding the condition monitoring data can help further improve the performance of frictional-energy-based damage metric for gearbox bearing fault prediction. Both cases by modeling through various machine learning algorithms are discussed in this study along with some observations.

fault prediction↗

DOE OSTI2021

Numerical Modelling of a Two-Body Point Absorber Featuring Variable Geometry

This work presents a novel wave energy converter (WEC) device concept that incorporates variable geometry modules into a two-body point absorber type WEC. The variable geometry modules consist of air inflatable bags in the surface float and a water inflatable ring in the reaction body. The variable geometry floats are able to provide greater control over the device hydrodynamics; they can be inflated or deflated to emphasize either power absorption or load shedding. The device geometry is controlled in a quasi-static fashion, while the power take-off (PTO) unit is controlled on a wave-to-wave timescale. The surface float is tethered directly to the submerged reaction body through PTO tether lines. A linear time-domain analysis, conducted using open-source Wave Energy Converter (WEC-Sim) software, was used to estimate the absorbed power of the WEC in sea states defined by the Wave Energy Prize. WEC power performance was weighted against the expected capital cost of building the load bearing structure of the device, providing an estimated ACE value. The inclusion of the variable geometry modules was shown to be effective in altering the device geometry to improve power capture with a near proportional increase in expected costs, providing a nearly constant power-to-cost ratio.

cost of energy↗

DOE OSTI2021

Validation Efforts of an Open-Source Aeroacoustics Model for Wind Turbines: Preprint

The open source aeroservoelastic wind turbine solver, OpenFAST, now includes an aeroacoustics model, which is described here and validated against experimental measurements recorded on a GE 1.5-MW wind turbine installed at the Flatirons Campus of the National Renewable Energy Laboratory in Boulder, Colorado. The validation demonstrates satisfactory agreement between numerical predictions and experimental recordings, with discrepancies up to 7 dB in the overall sound pressure levels at low wind speeds and a better agreement around the rated wind speed of the turbine.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

DOE OSTI2021

Investigation of Main Bearing Operating Conditions in a Three-Point Mount Wind Turbine Drivetrain

Wear related failures of spherical roller bearings in the main bearing position of three-point mount wind turbines have been higher than expected and can contribute to higher than anticipated operation-and-maintenance costs. In this paper, the operational conditions of such a main bearing, including measured axial displacement and velocity subject to the estimated axial loads are described for an instrumented, commercial wind turbine. The field measurements suggest a maximum axial speed between the bearing rings that is less than 2 millimeters per second. It is estimated that the axial speed between the ring and rollers is approximately 25% of this value. When compared to a speed of rolling from approximately 200-352 millimeters per second, the measured axial sliding is therefore significantly less than 1% of the speed of rolling. Previous numerical studies of lubricant film formation in rolling contacts have shown that the effect of axial sliding starts to be noticeable only when this ratio exceeds 10%; therefore, the axial velocity represents only a small disturbance to the nominal pure rolling case, and the influence on oil film building can be neglected. A simple analytic model of the main bearing motion was also developed and demonstrated similar displacement and velocity characteristics.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

DOE OSTI2021

Wind Turbine Drivetrain Reliability Assessment and Remaining Useful Life Prediction

This presentation describes a methodology for predicting probability of failure of wind turbine gearbox bearings failed by axial cracking. This methodology was applied to a commercial MW size wind plant and generated results were correlated with actual failures. This work is partnered with WindESCo and funded through DOE's Technology Commercialization Fund.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

DOE OSTI2021

Machine Learning for Gearbox Fault Prediction by Using Both Scada and Modeled Data

This presentation outlines the work in the paper titled "Prognostics of Wind Turbine Gearbox Bearing Failures Using SCADA and Modeled Data" published by the PHM Society and presented at its 2020 annual conference. It is accessible at https://papers.phmsociety.org/index.php/phmconf/article/download/1292/862. The technical work is on machine learning approaches for prognostics for gearbox faults. The methodology combines SCADA time series data and physics domain modeling data, derived from the models developed by the NREL team, as inputs to machine learning models to predict gearbox bearing failures with one month lead time. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score, and AUC (area under receiver operating characteristic curve). Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision, and F1 score, and AUC is about 33%, and 12%, and 6% respectively, based on the best modeling case in this study.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

DOE OSTI2021

Gaining Insights in Loading Events for Wind Turbine Drivetrain Prognostics

Wind energy is one of the largest sources of renewable energy in the world. To further reduce the operations and maintenance (O&M) costs of wind farms, it is essential to be able to accurately pinpoint the root causes of different failure modes of interest. An example of such a failure mode that is not yet fully understood is white etching cracks (WEC). This can cause the bearing lifetime to be reduced to 5–10% of its design value. Multiple hypotheses are available in literature concerning its cause. To be able to validate or disprove these hypotheses, it is essential to have historic high-frequency measurement data (e.g., load and vibration levels) available. In time, this will allow linking to the history of the turbine operating data with failure data. This paper discusses the dynamic loading on the turbine during certain events (e.g., emergency stops, run-ups, and during normal operating conditions). By combining the number of specific events that each turbine has seen with the severity of each event, it becomes possible to assess which turbines are most likely to show signs of damage.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

DOE OSTI2020

Numerical Model Development of a Variable-Geometry Attenuator Wave Energy Converter

Because the wave energy industry is still in its infancy, an optimal design for wave energy converters (WECs) has yet to be established; more work is needed to explore various cost-reduction pathways. The primary cost-reduction pathway considered for this work is the optimization of the geometric profile on an attenuator WEC to maximize power production while, at the same time, minimizing capital expenditures through the use of variable-geometry modules. In this investigation, the variable-geometry modules consist of inflatable bags placed on either side of a base central steel cylinder that would be inflated in low-moderate sea states to maximize power capture and then deflated in moderate-extreme sea states to minimize wave loading. The numerical model and simulation of the attenuator WEC were developed and completed using WEC-Sim, which is an open-source code that is appropriate for use in evaluating the dynamic response of the different WEC models in operational seas. The power production estimates were obtained from the Wave Energy Prize (WEP) sea states, which are representative of U. S. deployment sites, to calculate the average climate capture width that is used in the WEP ACE calculation. Preliminary capital expenditure costs were obtained assuming the base central steel cylinder mass was equal to the fluid displaced mass, minus the mass of the variable-geometry bags. The additional weight required to offset the additional buoyancy from the variable-geometry bags was assumed to come from the addition of seawater ballast. The variable-geometry attenuator model was found to have a similar power capture efficiency as a fixed-body model, but is expected to have a lower characteristic capital expenditure given its more streamlined profile, which demonstrates that variable-geometry modules may provide a realistic cost-reduction pathway to help design a more cost-competitive WEC.

50 EE - Wind and Water Power Program - Water (EE-4↗

DOE OSTI2020

Prognosis of Wind Turbine Gearbox Bearing Failures Using SCADA and Modeled Data

Predictive maintenance and condition monitoring systems for wind turbines have seen increased adoption to minimize downtime, reducing operation and maintenance costs. On today’s wind power plants, the integrated supervisory control and data acquisition (SCADA) system provides low- frequency operational data that can be leveraged to quantify a wind turbine’s health. The aim of this study is to utilize machine-learning techniques to predict axial cracking failures in wind turbine gearbox bearings up to 1 month ahead of time. The failures are assumed to have occurred when the investigated bearing was replaced. While current SCADA systems show the overall condition of a wind turbine, often they do not allow for the investigation of specific gearbox bearings’ health. To enrich bearing fault signatures, additional data are computed through physics-based models using gearbox design information. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score. Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision and F1 score is about 33% and 12% respectively, based on the best modeling case in this study.

49 EE - Wind and Water Power Program - Wind (EE-4W↗