skip to main content

Explore Research Products in the NSF-PAR

Title: Wind Turbine Gearbox Failure Detection Through Cumulative Sum of Multivariate Time Series Data
The wind energy industry is continuously improving their operational and maintenance practice for reducing the levelized costs of energy. Anticipating failures in wind turbines enables early warnings and timely intervention, so that the costly corrective maintenance can be prevented to the largest extent possible. It also avoids production loss owing to prolonged unavailability. One critical element allowing early warning is the ability to accumulate small-magnitude symptoms resulting from the gradual degradation of wind turbine systems. Inspired by the cumulative sum control chart method, this study reports the development of a wind turbine failure detection method with such early warning capability. Specifically, the following key questions are addressed: what fault signals to accumulate, how long to accumulate, what offset to use, and how to set the alarm-triggering control limit. We apply the proposed approach to 2 years’ worth of Supervisory Control and Data Acquisition data recorded from five wind turbines. We focus our analysis on gearbox failure detection, in which the proposed approach demonstrates its ability to anticipate failure events with a good lead time.  more » « less
Award ID(s):
1741173
NSF-PAR ID:
10371712
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Frontiers in Energy Research
Volume:
10
ISSN:
2296-598X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ( , Wind Energy)
    Abstract

    Wind power production is driven by, and varies with, the stochastic yet uncontrollable wind and environmental inputs. To compare a wind turbine's performance, a direct comparison on power outputs is always confounded by the stochastic effect of weather inputs. It is therefore crucial to control for the weather and environmental influence. Toward that objective, our study proposes an energy decomposition approach. We start with comparing the change in the total energy production and refer to the change in total energy as delta energy. On this delta energy, we apply our decomposition method, which is to separate the portion of energy change due to weather effects from that due to the turbine itself. We derive a set of mathematical relationships allowing us to perform this decomposition and examine the credibility and robustness of the proposed decomposition approach through extensive cross‐validation and case studies. We then apply the decomposition approach to Supervisory Control and Data Acquisition data associated with several wind turbines to which leading‐edge protection was carried out. Our study shows that the leading‐edge protection applied on blades may cause a small decline to the power production efficiency in the short term, although we expect the leading‐edge protection to benefit the blade's reliability in the long term.

     
    more » « less
  2. ; ( , High Fidelity Dynamic Modeling and Control of Power Regenerative Hydrostatic Wind Turbine Test Platform)

    Conventional wind turbines are equipped with multi-stage fixed-ratio gearboxes to transmit power from the low speed rotor to the high speed generator. Gearbox failure is a major issue causing high maintenance costs. With a superior power to weight ratio, a hydrostatic transmission (HST) is an ideal candidate for a wind turbine drivetrain. HST, a continuous variable transmission, has the advantage of delivering high power with a fast and accurate response. To evaluate the performance of the HST wind turbine, a power regenerative hydrostatic wind turbine test platform has been developed. A hydraulic power source is used to emulate the dynamics of the turbine rotor. The test platform is an effective tools to validate the control strategies of the HST wind turbine. This paper presents the high fidelity mathematical model of the test platform. The parameters of the dynamic equations are identified by the experiments. The steady state and transient operations results are compared with the experimental data. The detailed control architecture of the start-up and shut-down cycle is described for the test platform.

     
    more » « less
  3. ; ( , Proceedings of the 2017 Workshop on Cyber-Physical Systems Security and PrivaCy - CPS '17)
    Wind energy is one of the major sources of renewable energy. Countries around the world are increasingly deploying large wind farms that can generate a significant amount of clean energy. A wind farm consists of many turbines, often spread across a large geographical area. Modern wind turbines are equipped with meteorological sensors. The wind farm control center monitors the turbine sensors and adjusts the power generation parameters for optimal power production. The turbine sensors are prone to cyberattacks and with the evolving of large wind farms and their share in the power generation, it is crucial to analyze such potential cyber threats. In this paper, we present a formal framework to verify the impact of false data injection attack on the wind farm meteorological sensor measurements. The framework designs this verification as a maximization problem where the adversary's goal is to maximize the wind farm power production loss with its limited attack capability. Moreover, the adversary wants to remain stealthy to the wind farm bad data detection mechanism while it is launching its cyberattack on the turbine sensors. We evaluate the proposed framework for its threat analysis capability as well as its scalability by executing experiments on synthetic test cases. 
    more » « less
  4. ; ( , Proceedings of the National Academy of Sciences)
    A field campaign was carried out to investigate ice accretion features on large turbine blades (50 m in length) and to assess power output losses of utility-scale wind turbines induced by ice accretion. After a 30-h icing incident, a high-resolution digital camera carried by an unmanned aircraft system was used to capture photographs of iced turbine blades. Based on the obtained pictures of the frozen blades, the ice layer thickness accreted along the blades’ leading edges was determined quantitatively. While ice was found to accumulate over whole blade spans, outboard blades had more ice structures, with ice layers reaching up to 0.3 m thick toward the blade tips. With the turbine operating data provided by the turbines’ supervisory control and data acquisition systems, icing-induced power output losses were investigated systematically. Despite the high wind, frozen turbines were discovered to rotate substantially slower and even shut down from time to time, resulting in up to 80% of icing-induced turbine power losses during the icing event. The research presented here is a comprehensive field campaign to characterize ice accretion features on full-scaled turbine blades and systematically analyze detrimental impacts of ice accumulation on the power generation of utility-scale wind turbines. The research findings are very useful in bridging the gaps between fundamental icing physics research carried out in highly idealized laboratory settings and the realistic icing phenomena observed on utility-scale wind turbines operating in harsh natural icing conditions. 
    more » « less
  5. ; ( , Wind Energy)
    Abstract

    Quantification of the performance degradation on the annual energy production (AEP) of a wind farm due to leading‐edge (LE) erosion of wind turbine blades is important to design cost‐effective maintenance plans and timely blade retrofit. In this work, the effects of LE erosion on horizontal axis wind turbines are quantified using infrared (IR) thermographic imaging of turbine blades, as well as meteorological and SCADA data. The average AEP loss of turbines with LE erosion is estimated from SCADA and meteorological data to be between 3% and 8% of the expected power capture. The impact of LE erosion on the average power capture of the turbines is found to be higher at lower hub‐height wind speeds (peak around 50% of the turbine rated wind speed) and at lower turbulence intensity of the incoming wind associated with stable atmospheric conditions. The effect of LE erosion is investigated with IR thermography to identify the laminar to turbulent transition (LTT) position over the airfoils of the turbine blades. Reduction in the laminar flow region of about 85% and 87% on average in the suction and pressure sides, respectively, is observed for the airfoils of the investigated turbines with LE erosion. Using the observed LTT locations over the airfoils and the geometry of the blade, an average AEP loss of about 3.7% is calculated with blade element momentum simulations, which is found to be comparable with the magnitude of AEP loss estimated through the SCADA data.

     
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email