Patent Description:
It is important to know the temperature of various components of a wind turbine, to ensure they are behaving as expected and prevent damage to any part of the turbine. Usually, multiple temperature sensors are used to track temperature of a given component. Using multiple sensors allows measurements to be compared and verified, and provides redundancy against failure.

However, one or more of these temperature sensors can fail during the lifetime of the turbine, and can be difficult to replace. Without knowledge of the operational temperature of key components, the turbine may have to be taken offline.

There is therefore a need for alternative measurements of the temperature of the components.

<CIT> is a relevant example of prior art.

A first aspect of the invention provides a method of estimating a temperature of a component of a wind turbine, the method comprising:.

In some embodiments, using the model to estimate a temperature of the component of the wind turbine further comprises:
during an operational period:.

The method may further comprise operating the wind turbine in accordance with the estimation of the temperature of the component at the first time.

In some embodiments, the method may further comprise estimating an uncertainty in the temperature of the component at the first time, wherein the uncertainty is based on a statistical uncertainty of the estimate of the temperature at the first time, and on an uncertainty at the time preceding the first time.

In some embodiments, the method may further comprise, during the operational period:.

In some such embodiments, the method may further comprise:.

In some embodiments, using the model to estimate a temperature of the component of the wind turbine may comprise or further comprise calculating a steady state temperature of the component from the model. Such embodiments may comprise comparing the steady state temperature of the component of the wind turbine to corresponding steady state temperatures from one or more other wind turbines. Some embodiments may further comprise identifying an anomaly in the wind turbine based on the comparison. Alternatively or additionally, the method may comprise comparing the steady state temperature to an expected steady state temperature of the wind turbine.

In some embodiments, the component may be at least one of: a generator; a generator winding; a transformer; a transformer winding; a gearbox; gearbox oil; hydraulic oil; a converter; one or more bearings; and a cooling-water system.

In some embodiments, the component may be a generator or generator winding, the one or more operational parameters may further comprise at least one of generator rotor speed; generator voltage; and reactive power.

In some embodiments, the model may relate a temperature of the generator or generator winding at a current time to:.

A second aspect of the invention provides a computer program comprising instructions which, when the program is executed by a computer, causes the computer to perform the method of any of embodiment of the first aspect.

A third aspect of the invention provides a controller for a wind turbine comprising a processor and a memory; wherein the controller is configured to receive measurements of operational parameters from one or more sensors of the wind turbine; and wherein the memory stores instructions which, when executed by the processor, cause the processor to perform the method of any embodiment of the first aspect.

A fourth aspect of the invention provides a wind turbine comprising one or more sensors for measuring operational parameters during operation of the wind turbine; and a controller according any embodiment of the third aspect of the invention.

<FIG> illustrates, in a schematic perspective view, an example of a wind turbine <NUM>. The wind turbine <NUM> includes a tower <NUM>, a nacelle <NUM> at the apex of the tower, and a rotor <NUM> operatively coupled to a generator housed inside the nacelle <NUM>. In addition to the generator, the nacelle houses miscellaneous components required for converting wind energy into electrical energy and various components needed to operate, control, and optimize the performance of the wind turbine <NUM>. The rotor <NUM> of the wind turbine includes a central hub <NUM> and a plurality of blades <NUM> that project outwardly from the central hub <NUM>. In the illustrated embodiment, the rotor <NUM> includes three blades <NUM>, but the number may vary. Moreover, the wind turbine comprises a control system. The control system may be placed inside the nacelle or distributed at a number of locations inside the turbine and communicatively connected.

The wind turbine <NUM> may be included among a collection of other wind turbines belonging to a wind power plant, also referred to as a wind farm or wind park, that serve as a power generating plant connected by transmission lines with a power grid. The power grid generally consists of a network of power stations, transmission circuits, and substations coupled by a network of transmission lines that transmit the power to loads in the form of end users and other customers of electrical utilities.

<FIG> schematically illustrates an embodiment of a control system <NUM> together with elements of a wind turbine. The wind turbine comprises rotor blades <NUM> which are mechanically connected to an electrical generator <NUM> via gearbox <NUM>. In direct drive systems, and other systems, the gearbox <NUM> may not be present. The electrical power generated by the generator <NUM> is injected into a power grid <NUM> via an electrical converter <NUM>. The electrical generator <NUM> and the converter <NUM> may be based on a full scale converter (FSC) architecture or a doubly fed induction generator (DFIG) architecture, but other types may be used.

The control system <NUM> comprises a number of elements, including at least one main controller <NUM> with a processor and a memory, so that the processor is capable of executing computing tasks (such as the methods discussed below) based on instructions stored in the memory. In general, the wind turbine controller ensures that in operation the wind turbine generates a requested power output level. This is obtained by adjusting the pitch angle of the blades <NUM> and/or the power extraction of the converter <NUM>. To this end, the control system comprises a pitch system including a pitch controller <NUM> using a pitch reference <NUM>, and a power system including a power controller <NUM> using a power reference <NUM>. The wind turbine rotor comprises rotor blades that can be pitched by a pitch mechanism. The rotor comprises an individual pitch system which is capable of individual pitching of the rotor blades, and may comprise a common pitch system which adjusts all pitch angles on all rotor blades at the same time. The control system, or elements of the control system, may be placed in a power plant controller (not shown) so that the turbine may be operated based on externally provided instructions.

The control system <NUM> further comprises a number of temperature sensors <NUM> (for clarity only one sensor is represented in <FIG>). These sensors <NUM> are positioned at various locations in the wind turbine <NUM> to measure the temperature of particular components of the turbine, such as the generator windings. The temperature sensors <NUM> provide temperature measurements to the controller <NUM>, which monitors the temperature of the various controllers to ensure correct turbine operation, and may adjust operation of the turbine based on the temperature readings. For example, if a component is getting too hot, the controller <NUM> may control operation of the turbine <NUM> to reduce the temperature of that component (e.g. reducing a physical or electrical load on a component), to prevent permanent damage to the wind turbine <NUM>.

Ideally, the temperature sensors <NUM> will function correctly for the full lifetime of the turbine <NUM>. In practice, however, sensors <NUM> are likely to fail. Repairing or replacing a failed temperature sensor <NUM> may be difficult and costly, for example requiring shutdown of the turbine <NUM>. However continued operation of the wind turbine <NUM> requires knowledge of component temperature, leaving operators with little choice but to repair the sensor <NUM> or risk permanent damage to the turbine <NUM>.

<FIG> illustrates a method <NUM> of estimating the temperature of a component of a wind turbine <NUM>. Using method <NUM>, the temperature can be estimated from current operational parameters of the wind turbine, such as turbine power output. This estimated temperature can be used to in effect replace one or more of the failed temperature sensors, allowing continued safe operation of the turbine <NUM>.

Method <NUM> as illustrated below comprises two distinct phases. In a first, calibration stage, parameters of a model relating component temperature to operational parameters are calculated using data measured on a specific wind turbine <NUM>. Importantly, the model also relates current component temperature to a preceding temperature, to account for thermal inertia. In a second, operational stage, the model is used to estimate component temperature based on current operational parameters for that same turbine <NUM>.

As used herein, temperature may refer to an absolute of a component, or to a relative temperature. For example, the temperature may be relative to a reference temperature, such as a current cooling water temperature.

Method <NUM> begins at step <NUM>, in which measurements of the temperature of the component are measured by one or more temperature sensors <NUM>.

The method then proceeds to step <NUM>, in which measurements of one or more operational parameters of the wind turbine corresponding to the measurements of the temperature are measured. The one or more operational parameters comprise at least measurements of wind speed or power generated by the wind turbine, and may comprise further operational parameters, such as generator speed. Each of these measurements may be performed by appropriate sensors on the wind turbine, such as sensors reporting to a SCADA system, as will be appreciated by those skilled in the art.

The measurements of the operational parameters correspond to the measurements of temperature in that each measurement represents the value of the operational parameter at the time that a corresponding temperature measurement was taken. For example, corresponding measurements may be made at the same or at a similar time, such as within a predetermined time period of each other (e.g. within <NUM> minute, or within <NUM> minutes). For SCADA data, in which typically measurements are performed and averaged over <NUM> minute periods, the temperature and operational parameters may correspond in that they come from the same SCADA data period.

The measurements performed in steps <NUM> and <NUM> may be communicated to the turbine controller <NUM>, which may perform the further steps detailed below. Alternatively the measurements may be communicated to an external system, which may perform the steps below on behalf of the wind turbine <NUM>.

At step <NUM>, coefficients of a model of the temperature of the component are calculated using the measurements of temperature and operational parameters. The model is of the form: <MAT>.

Here, Tn represents the temperature at a current time. Tn-<NUM> represents the temperature at a preceding time - in particular the immediately previous temperature measurement/estimation. x, y, z represent values of operational parameters at the current time, such as power production. Although three operational parameters are represented, any number of parameters may be used. For example, the model may relate the current temperature only to the power/wind speed and preceding temperature. An exemplary model for the case of generator winding temperature is discussed in more detail below.

The coefficients of the model may be found using a regression technique, or any method which is able to learn from training data - i.e. from the measurements performed in steps <NUM> and <NUM>. For example, machine learning or probabilistic modelling techniques may be applied to calculate the coefficients of the model.

The model is segregated into a plurality of bins based on wind speed or power generated by the wind turbine <NUM>. The model comprises respective coefficients for each bin. For example, consider a simple model such as: <MAT> where PWR represents the output power of the turbine <NUM>. Here, ai, bi are coefficients relating, respectively, the square of the power and the preceding temperature to the current temperature.

This model is segregated into a number of bins, based on power generated. A first bin may be used for output powers in the range <NUM>-<NUM> kW; a second bin may be used for output powers in the range <NUM>-<NUM> kW, and so on. Each bin comprises respective coefficients ai, bi for that bin. Thus for the first bin, the model is: <MAT> where a<NUM>, b<NUM> are the respective coefficients for the first bin. Similarly, for the second bin, the model is: <MAT> where a<NUM>, b<NUM> are the respective coefficients for the second bin.

When calculating the coefficients in step <NUM>, each temperature and operational parameter is assigned to a bin based on the power/wind speed at the time that measurement was taken. Only the measurements assigned to a bin are used to calculate the coefficients for that bin, using the regression techniques described above.

Thus the model in effect comprises a number of different temperature models, each tailored to a particular power/wind speed range. This segregation of the model has been found to provide a more accurate temperature estimation than a global model for all power/wind speeds.

In some embodiments, the bins may overlap. For example, the first bin may be for output powers in the range <NUM>-220kW, the second bin for powers in the range <NUM>-320kW, the third bin for powers in the range <NUM>-<NUM> kW and so on. In such embodiments, each temperature and operational parameter measurement may be assigned in step <NUM> to multiple bins, where the wind speed/power output at the time of those measurements is covered by those multiple bins. The measurements are then used to find the coefficients of each bin they are assigned to.

The number of bins used and/or amount of overlap between bins may be selected based on the amount of data available for the generation of the model. For example, if a year or more of data is available, then a large amount of data may be available to allow many overlapping bins. For example, bins may be spaced between 5kW and 50kW apart, e.g. 10kW apart (i.e. bin centres are <NUM> kW apart). In such cases, bin widths may be ±<NUM> kW of the bin centre (or selected from the range ±100kW to ±1000kW). In such cases, the large number of data available in each bin provides robustness to the generated model. When determining the parameters of the model for each bin, a weighting factor such as a Gaussian weighting factor may be applied to the data within the bin, so that data at the centre of the bin affect the resulting parameters more than data at the edges of the bin.

It is to be noted that the model provided in equations (<NUM>)-(<NUM>) is illustrative only. Any number of operational parameters may be used in the model, selected to suit the particular component being modelled, as the skilled person would appreciate. Additionally, any model may include a constant offset coefficient, to be calculated with the coefficients of the operational parameters in step <NUM>.

Once the coefficients of each bin of the model have been determined, the calibration period is complete, and the model is be used to estimate a temperature of the component. In particular, the method <NUM> may move to the operational period, starting at step <NUM>. The method <NUM> may immediately proceed to step <NUM>, or there may be a delay. For example, the method <NUM> may only proceed to step <NUM> when there is a need for temperature estimation, such as when a temperature sensor <NUM> fails.

At step <NUM>, measurements of the operational parameters (i.e. the ones used in the model) are taken at a first time, tx - i.e. a current time. These measurements are taken in the same manner as step <NUM>, discussed above.

At step <NUM>, the measurements taken in step <NUM> are assigned to a bin of the model based on the output power/wind speed at the first time, tx. Where overlapping bins are used, as discussed above, each measurement taken in step <NUM> may be assigned to the bin in which the measurement is closest to the bin centre point.

At step <NUM>, the measurements are input into the model to estimate the temperature, Tx, at the first time, tx, using the values of the operational parameters at the first time, tx, and an estimate of the temperature Tx-<NUM> at a preceding time, tx-<NUM>. For example, for the simple illustrative model shown in equations (<NUM>)-(<NUM>) above, if the output power at tx is in the range for the first bin, the measurements of the operational parameters at tx (in this simple case just output power) and the estimate of the temperature at a preceding time tx-<NUM> are input into equation (<NUM>) to estimate the temperature of the component Tx. The preceding time tx-<NUM> may in particular be the time of the last available operational parameter measurements. For example, SCADA data from a wind turbine is typically measured and averaged into <NUM> minute data periods. The first time may correspond to a most recent <NUM> minute data period, and the preceding time may correspond to the immediately preceding <NUM> minute data period.

The estimate of the temperature Tx-<NUM> at the preceding time tx-<NUM> may have itself been generated using method <NUM>, using measurements of operational parameters at the preceding time tx-<NUM> and an estimate of the temperature Tx-<NUM> at a time tx-<NUM> preceding that. The temperature Tx-<NUM> may for example have been stored in a memory, such as a memory associated with turbine controller <NUM>, and may be retrieved from the memory to estimate the temperature Tx at the first time, tx as part of step <NUM>.

Alternatively, and in particular where no estimate from a previous run of method <NUM> is available, a value may be selected for the temperature Tx-<NUM> at the preceding time tx-<NUM>. For example, a pre-set 'starting' value such as <NUM> may be chosen, or an estimate may be made based on previous observations of typical temperatures in the wind turbine <NUM> or in similar wind turbines. A measurement of the temperature taken by a temperature sensor <NUM> may be used as the preceding temperature Tx-<NUM>, for example the last temperature measurement performed before failure of a sensor <NUM> may be used.

Once the temperature Tx at the first, current time has been estimated, the method <NUM> may end. Alternatively, the steps <NUM>-<NUM> may be continuously run, updating the estimate of the temperature for each new measurement period based on the estimate of the temperature in the previous measurement period. In either case, the estimate of the temperature may be used to inform operation of the wind turbine <NUM>. For example, controller <NUM> may alter operation of the wind turbine <NUM> to reduce a temperature of the component if the temperature is getting too hot.

In some embodiments, an uncertainty value may also be determined for each estimated temperature value. The uncertainty value σx may be based both on the statistical uncertainty Σ from the model for that individual temperature and on the uncertainty σx-<NUM> of the preceding temperature estimate - i.e. the preceding temperature estimate used in the generation of the current temperature estimate. In particular, the uncertainty σx of the estimation of temperature Tx at the first time may be calculated as: <MAT>.

Here, bi is the coefficient relating Tx to Tx-<NUM> of the particular bin of the model used to generate Tx. So, for example in the simple model described above, where the first bin of the model was used calculate the temperature from equation (<NUM>), the coefficient b<NUM> would be used in the uncertainty calculation.

In this way, the thermal inertia inherent to the component is reflected in the uncertainty, providing a more valuable indication of the uncertainty of an individual temperature estimate than the statistical uncertainty alone. This improved uncertainty can then be used to inform decisions on turbine operation.

The temperature (and uncertainty) provided by method <NUM> may be used to validate a temperature measurement from a sensor <NUM>, for example where other temperature sensors <NUM> have failed, leaving no redundancy. In this case, one or more measurements from the (remaining) sensor <NUM> may be received, and may be compared to corresponding estimates of the temperature generated from method <NUM>. If a difference between the real measurements and estimates exceeds a threshold, it may be determined that there is a fault in the sensor <NUM>, or a physical problem in the component (e.g. overheating). The threshold may be a simple predetermined threshold based on the absolute temperature difference between the real and estimated measurements. Alternatively, the uncertainty value discussed above may be used. The threshold may be that the real temperature measurements deviate from the estimated measurements by more than a predetermined multiple of the uncertainty. For example, if the real measurement is more than <NUM>σx or <NUM>σx away from the estimated temperature, it can be determined that there is a problem.

The model generated as part of method <NUM> may also be used to determine a steady state temperature for a given set of fixed operational parameters. In particular, after the coefficients of each bin of the model have been determined in step <NUM> of method <NUM>, the equations of the model may be solved for the case Tn = Tn-<NUM>, for fixed operational parameters to determine the steady state temperature. Calculation of the steady state temperature may be performed without estimating any operational temperatures - in other words where the model is used to determine a steady state temperature, steps <NUM>-<NUM> of method <NUM> may be omitted.

In such embodiments, a single steady state temperature may be calculated, using the coefficients of a single selected bin of the model, and selected operational parameters. Alternatively, multiple steady state temperatures may be calculated for a range of different output power/wind speeds (and hence different bins of the model), and ranges of other operational parameters included in the model, effectively resulting in a map of steady state temperatures.

The steady state temperature/s calculated in this way may be used to compare performance of the turbine <NUM> to other turbines, or to an expected performance from the design of the turbine <NUM>, to determine if the turbine <NUM> is performing correctly. For example, the steady state temperature/s for an individual turbine <NUM> may be compared to the average behaviour of corresponding steady state temperature/s from an ensemble of similar turbines (e.g. the same model, similar location, etc.). As altitude can affect temperature, the ensemble may comprise only turbines at the same or similar altitudes to the individual turbine <NUM>, or an altitude correction factor may be determined from a regression of steady state temperature to altitude for a population of turbines, allowing turbines of different altitudes to be compared. As an alternative approach to building a fleet model, all the data measured on an ensemble of wind turbines may by combined, and used to calculate coefficients of a (binned) collective model, which would then provide average behaviour of the ensemble, and could be compared to results from an individual turbine <NUM> to identify anomalies in the performance of that individual turbine <NUM>.

In any of the embodiments discussed above, the component may be at least one of a generator; a generator winding; a transformer; a transformer winding; a gearbox; gearbox oil; hydraulic oil; a converter; one or more bearings; and a cooling-water system. In general, the component may be any component of a wind turbine that exhibits thermal inertia in its temperature behaviour.

In a particular example, method <NUM> may be applied to estimate the temperature of a generator winding of a wind turbine <NUM>. In such a case, the operational parameters used as part of the model may comprise output power, and at least one of generator rotor speed, generator voltage, and reactive power. For example, the model make take the form: <MAT> where PWR is the turbine output power, RPM is the generator speed, V is the generator voltage, and fi is a constant offset coefficient. In some variants of this model, V may be omitted.

Any of the methods described above may be implemented as a computer program comprising instructions which, when the program is executed by a computer, causes the computer to perform the any of the methods described above. In particular, the computer program may be stored on a turbine controller, such as controller <NUM>, and may cause a processor of the controller to perform any of the methods described above.

Claim 1:
A method of estimating a temperature of a component of a wind turbine, the method comprising:
during a calibration period:
receiving measurements of the temperature of the component measured by a temperature sensor of the wind turbine;
receiving measurements of one or more operational parameters of the wind turbine corresponding to the measurements of the temperature, the one or more operational parameters comprising at least measurements of wind speed or power generated by the wind turbine;
calculating, using the measurements of temperature and of the one or more operational parameters, coefficients of a model of the temperature of the component, wherein:
the model relates a temperature of the component at a current time, Tn, to a value of the one or more operational parameters at the current time and to a temperature of the component at a preceding time, Tn-<NUM>;
the model is segregated into separate bins based on wind speed or power generated by the wind turbine, such that the model comprises, for each bin, respective coefficients relating the temperature of the wind turbine at the current time, Tn, to the value of the one or more operational parameters at the current time and to the temperature of the component at a preceding time, Tn-<NUM>; and
calculating coefficients of the model comprises assigning each measurement of the temperature and the one or more operational parameters to one or more bins of the model, and fitting the coefficients of each bin to the measurements assigned to that bin; and
using the model to estimate a temperature of the component of the wind turbine.