An official website of the United States government
In cold climates, ice formation on wind turbines causes power reduction produced by a wind farm. This paper introduces a framework to predict icing at the farm level based on our recently developed Temporal Convolutional Network prediction model for a single turbine using SCADA data.First, a cross-validation study is carried out to evaluate the extent predictors trained on a single turbine of a wind farm can be used to predict icing on the other turbines of a wind farm. This fusion approach combines multiple turbines, thereby providing predictions at the wind farm level. This study shows that such a fusion approach improves prediction accuracy and decreases fluctuations across different prediction horizons when compared with single-turbine prediction. Two approaches are considered to conduct farm-level icing prediction: decision fusion and feature fusion. In decision fusion, icing prediction decisions from individual turbines are combined in a majority voting manner. In feature fusion, features of individual turbines are averaged first before conducting prediction. The results obtained indicate that both the decision fusion and feature fusion approaches generate farm-level icing prediction accuracies that are 7% higher with lower standard deviations or fluctuations across different prediction horizons when compared with predictions for a single turbine.
more »
« less
Mechanism Analysis of Wind Turbine Var Oscillations
Electromagnetic transient simulation of parallel-connected 4-MW type-3 wind turbines based on original equipment manufacturer's real-code turbine model shows 1.2-Hz turbine–turbine oscillations in reactive power. This letter reveals why such oscillations occur in the individual var measurement while being insignificant in the total var measurement, regardless of the varying grid impedance. We adopt two analysis approaches, i.e., open-loop single-input single-output analysis and network decomposition. These two approaches differ in their treatment of turbine–network interaction. The open-loop analysis shows that the turbine–turbine oscillation mode is due to an open-loop system pole being attracted to an open-loop system zero. Furthermore, we use a network decomposition method to explain why this mode is observable in individual vars, while not observable in the total var. The entire system of n turbines can be viewed as n decoupled circuits. For the two-turbine case, the system has an aggregated mode and a turbine–turbine oscillation mode. The aggregated mode is associated with a circuit associated with the total var, while the turbine–turbine oscillation mode is associated with the var difference and is insensitive to the grid parameters.
more »
« less
- Award ID(s):
- 1807974
- PAR ID:
- 10475621
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Industrial Electronics
- Volume:
- 70
- Issue:
- 10
- ISSN:
- 0278-0046
- Page Range / eLocation ID:
- 10750 to 10754
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Large atmospheric boundary layer fluctuations and smaller turbine-scale vorticity dynamics are separately hypothesized to initiate the wind turbine wake meandering phenomenon, a coherent, dynamic, turbine-scale oscillation of the far wake. Triadic interactions, the mechanism of energy transfers between scales, manifest as triples of wavenumbers or frequencies and can be characterized through bispectral analyses. The bispectrum, which correlates the two frequencies to their sum, is calculated by two recently developed multi-dimensional modal decomposition methods: scale-specific energy transfer method and bispectral mode decomposition. Large-eddy simulation of a utility-scale wind turbine in an atmospheric boundary layer with a broad range of large length-scales is used to acquire instantaneous velocity snapshots. The bispectrum from both methods identifies prominent upwind and wake meandering interactions that create a broad range of energy scales including the wake meandering scale. The coherent kinetic energy associated with the interactions shows strong correlation between upwind scales and wake meandering.more » « less
-
In recent years, the frequency of forced oscillation events due to control system malfunctions or improper parameter settings has increased. Tuning the parameters of exciters and governor models is crucial for maintaining power system stability. Traditional simulation studies typically involve small transient disturbances or step changes to find optimal parameter sets, but existing optimization algorithms often fall short in fine-tuning for forced oscillations. Identifying the sensitive parameters within these control models is essential for ensuring stability during large, sustained disturbances. This study focuses on identifying these critical exciter and governor model parameters by analyzing their influence on sustained forced oscillations. Using Kundur’s two-area system, we analyze common exciter models such as SCRX, ESST1A, and AC7B, along with governor models like GAST, HYGOV, and GGOV1, utilizing PSS®E software version 34. Sustained forced oscillations are injected at generator-1 of area-1, with individual parameter changes dynamically simulated. By considering a local oscillation frequency of 1.4 Hz and an inter-area oscillation mode of 0.25 Hz, we analyze the impact of each parameter change on the magnitude and frequency of forced oscillations as well as on active and reactive power outputs. This novel approach highlights the most influential parameters of each tested model—such as exciter, governor, and turbine gains, as well as time constant parameters—on the impact of forced oscillations. Based on our findings, the sensitive parameters of each tested model are ranked. These would provide valuable insights for industry operators to fine-tune control settings during oscillation events, ultimately enhancing system stability.more » « less
-
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
-
Global instabilities in swirling flows can significantly alter the flame and flow dynamics of swirl-stabilized flames, such as those in modern gas turbine engines. In this study, we characterize the interaction between the precessing vortex core (PVC), which is the consequence of a global hydrodynamic instability, and thermoacoustic instabilities, which are the result of a coupling between combustor acoustics and the unsteady heat release rate. This study is performed using experimental data obtained from a model gas turbine combustor employing two concentric swirling nozzles of air, separated by a ring of fuel injectors, operating at five bar pressure. The flow split between the two streams is systematically varied to observe the impact of flow structure variation on the system dynamics at both non-reacting and reacting conditions. High-speed stereoscopic particle image velocimetry, OH planar laser-induced fluorescence and acetone planar laser-induced fluorescence are used to obtain information about the velocity fields, flame and fuel flow behaviour, respectively. Spectral proper orthogonal decomposition and a complex network analysis are used to identify and characterize the dominant oscillation mechanisms driving the system. In the non-reacting data, a PVC is present in most cases and the amplitude of the oscillation increases with increasing flow through the centre nozzle. In the reacting data, three dominant modes are seen: two thermoacoustic modes and the PVC. Our results show that in the cases where the frequency of the PVC overlaps with either of the thermoacoustic modes, the thermoacoustic modes are suppressed. The complex network analysis coupled with a weakly nonlinear theoretical analysis suggests the mechanisms by which this coupling and suppression of the thermoacoustic mode occur.more » « less
