Quantifying the variability of wind energy

Wind by its very nature is a variable element. Its variation is different on different timescales and spatially its magnitude can change dramatically depending on local climatology and terrain. This has implications in a variety of sectors, not least in the wind energy sector. The accuracy of weather forecasting models has increased significantly in the last few decades and these models are able to give an insight into variability on the hourly and daily timescales. On shorter timescales, predicting chaotic turbulent fluctuations is far more challenging. Similarly, the ability to make seasonal forecasts is extremely limited. General circulation models (GCMs) can give insights into possible future decadal fluctuations, but there are still large uncertainties. Observational data can give useful information concerning variation on a variety of timescales, but data quality and spatial coverage can be variable. An understanding of local scale spatial variations in wind is extremely important in wind farm siting. In the last 40 years, there have been significant advances in predicting these variations using computer models, although there remain significant challenges in understanding the behavior of the wind in certain environments. Both the spatial and temporal variations of wind are important considerations when wind power is integrated into electricity networks, and this will become an ever more important consideration as wind generation makes an increasing contribution to our global energy needs

Effect of power converter on condition monitoring and fault detection for wind turbine

This paper investigates the impact of power electronics converter when attempting wind turbine condition monitoring system and fault diagnosis by the analysis of fault signatures in the electrical output of the turbine. A wind turbine model has been implemented in the MATLAB/Simulink environment. Fault signature analysis for electrical signals is presented. A signal processing algorithm based on a fast fourier transform is then used to potentially identify fault signatures. The results obtained with this model are validated with experimental data measured from a physical test rig. Through comparison between simulation data and experimental data it is concluded that the power converter has significantly reduced fault signatures from the electrical signal though not entirely extinguished them. It may still be possible to extract some fault information after the converter though this is much more challenging than upstream. Further work is needed to see whether it may be possible to modify the power converter particularly the filter design and the switching elements to avoid removing fault signatures from electrical signals without adding significant cost or compromising performance

Challenges in using operational data for reliable wind turbine condition monitoring

Operational data of wind turbines recorded by the Supervisory Control And Data Acquisition (SCADA) system originally intended only for operation and performance monitoring show promise also for assessing the health of the turbines. Using these data for monitoring mechanical components, in particular the drivetrain subassembly with gearbox and bearings, has recently been investigated with multiple techniques. In this paper the advantages and drawbacks of suggested approaches as well as general challenges and limitations are discussed focusing on automated and farm-wide condition monitoring

Assessing the dependence of surface layer atmospheric stability on measurement height at offshore locations

Incorporating atmospheric stability into wind resource assessment modelling is becoming more common. This study investigates some of the challenges associated with calculating stability in the offshore environment. Data are analysed from meteorological masts FINO1 and FINO3 in the German North Sea using measurements at three different heights and results show significant differences in stability assessment depending on which combination of heights are used. All methods show the North Sea to be very unstable for the majority of the time, although by ignoring wind and thermal data from below 50m, the atmosphere appears more stable, indicating the presence of a marine internal boundary layer. Even 80km out to sea, it is suggested FINO3 still feels the effects of land, and it is clear the height of the atmospheric surface layer effects wind speed measurements under certain conditions

Analysis of electrical power data for condition monitoring of a small wind turbine

Certain parts of a wind turbine, for example, the gearbox require significant time and heavy lifting equipment in the event of catastrophic failure necessitating replacement. Continuous condition monitoring has the potential to catch problems early, enable scheduled preventative maintenance and thereby reduce turbine downtime, reduce the number of site visits and prevent secondary damage. Accelerometers applied to mechanical components of the drive train are traditionally used for condition monitoring but require their own data acquisition system and analysis software. In contrast, the electrical current and voltage are continuously measured and could also be used for condition monitoring more cheaply. An experimental data acquisition system has been installed on a small (25kW) onshore turbine in Leicestershire, UK to compare three-phase currents and voltages on the stator windings with six accelerometer signals. Data have been recorded before and after a gearbox failure and replacement. Data were analysed using both Fourier Transform and Morlet Continuous Wavelet Transform methods. Results show that the stator voltages show the same radial and axial mode vibration frequencies as the accelerometers and could therefore be used for condition monitoring. Furthermore, the stator currents show torsional modes of vibration not picked up by the accelerometers

Condition monitoring of wind turbine drive trains by normal behaviour modelling of temperatures

Condition monitoring and early failure detection are needed to reduce operational costs of wind turbines, particularly for offshore farms where accessibility is restricted. Failure detection technologies should be simple and reliable in order to contribute to the overall aim of cost reduction. Operational data from the Supervisory Control And Data Acquisition (SCADA) system are a potential source of information for condition monitoring and have the advantage of being recorded at each turbine without the costs of additional sensors. Detection of drivetrain failures using these ten-minute data has been successfully demonstrated in the last five years. This paper summarises and evaluates different ways of so-called normal behaviour modelling of temperature using SCADA data, i.e. the prediction of a measured temperature under the assumption that the system is behaving normally. After training, the residual of modelled and measured temperature acts as an indicator for possible wear and failures. Multiple approaches are discussed: linear modelling, artificial neural networks in auto-regressive, feedforward and layer recurrent configurations, adaptive neuro-fuzzy inference systems and state estimation techniques. A case study with real data reveals differences of approaches, sensitivity to training data and settings of algorithms. Early failure detection of a gearbox failure is demonstrated, although challenges in achieving reliable monitoring without many false alarms become apparent

Comparison of different modelling approaches of drive train temperature for the purposes of wind turbine failure detection

Effective condition monitoring techniques for wind turbines are needed to improve maintenance processes and reduce operational costs. Normal behaviour modelling of temperatures with information from other sensors can help to detect wear processes in drive trains. In a case study, modelling of bearing and generator temperatures is investigated with operational data from the SCADA systems of more than 100 turbines. The focus is here on automated training and testing on a farm level to enable an on-line system, which will detect failures without human interpretation. Modelling based on linear combinations, artificial neural networks, adaptive neuro-fuzzy inference systems, support vector machines and Gaussian process regression is compared. The selection of suitable modelling inputs is discussed with cross-correlation analyses and a sensitivity study, which reveals that the investigated modelling techniques react in different ways to an increased number of inputs. The case study highlights advantages of modelling with linear combinations and artificial neural networks in a feedforward configuration

Stator winding fault diagnosis in synchronous generators for wind turbine applications

Wind turbine manufacturers have introduced to the market a variety of innovative concepts and configurations for generators to maximize energy capture, reduce costs and improve reliability of wind energy. For the purpose of improving reliability and availability, a number of diagnostic methods have been developed. Stator current signature analysis (SCSA) is potentially an effective technique to diagnose faults in electrical machines, and could be used to detect and diagnose faults in wind turbines. In this study, an investigation was conducted into the application of SCSA to detect stator inter-turn faults in an excited synchronous generator and a permanent magnet synchronous generator. It was found from simulation results that, owing to disruption of magnetic field symmetry and imbalance between the current flowing in the shorted turn and the corresponding diametrically opposite turn in the winding, certain harmonic components in the stator current clearly increased as the number of shorted turns increased. The findings are helpful to detect faults involving only a few turns without ambiguity, in spite of the difference in the configuration of the generators. As expected, because of the different type, configuration and operational condition of the two generators studied, detecting faults through the generator current signature requires a particular approach for each generator type

Advanced algorithms for wind turbine condition monitoring and fault diagnosis

The work undertaken in this research focuses on advanced condition monitoring and fault detection methods for wind turbines (WTs). Fourier Transform (FFT) and Short Time Fourier transform (STFT) algorithms are proposed to effectively extract fault signatures in generator current signals (GCS) for WT fault diagnosis. With this aim, a WT model has been implemented in the MATLAB/Simulink environment to validate the effectiveness of the proposed algorithms. The results obtained with this model are validated with experimental data measured from a physical test rig. The detection of rotor eccentricity is discussed and conclusions drawn on the applicability of frequency tracking algorithms. The newly developed algorithms are compared with a published method to establish their advantages and limitations

Offshore turbine wake power losses: is turbine separation significant?

This paper presents the results of a parametric study of wind turbine wake effects in a hypothetical offshore wind farm with varying turbine separation using a Computational Fluid Dynamics (CFD) model. Results are analyzed from a simulated 40 turbine farm with 60 layout options, 4 wind speeds and 10° directional bins. Results show that increasing turbine separation in one or both directions leads to greater power generation, though this effect diminishes for separations above 8 diameters. Similarly, turbulence intensity is shown to decrease with increases in turbine separation but with little variation beyond 8 diameters. For 3 out of 4 wind speeds when combined with a representative UK offshore wind rose the farm was shown to have an optimal layout orientation along an axis 350°-170°, though the difference in power produced between orientation angles was less than between changes in turbine separation