专利汇可以提供SYSTEM AND METHOD FOR DETECTING PERFORMANCE专利检索,专利查询,专利分析的服务。并且A method for monitoring the operation of a wind turbine generator comprising the steps of sampling a physical parameter related to said apparatus to produce an initial data set, conducting a statistical analysis on said initial data set to establish initial statistical values for said parameter; after a predetermined interval, re-sampling the physical parameter to create a re-sampled data set; conducting statistical analysis on the resampled data set to establish subsequent statistical values; comparing said subsequent statistical values with the initial statistical values and; selecting an action based upon said comparison.,下面是SYSTEM AND METHOD FOR DETECTING PERFORMANCE专利的具体信息内容。
The invention relates to the operation of wind turbine generators (WTG) and in particular to data acquisition and analysis for preventive maintenance and active control.
In the cost analysis of large capital intensive machinery, such as wind turbine generators, after the initial capital expenditure the next most important issue is the economic life of the machine over which the capital expenditure may be amortized. To this end, extending the economic life is a critical determinant in the cost efficiency of the system.
It follows that the scheduling of regular maintenance is a key factor in maintaining the economic life of WTG's. This is intended, firstly, as a means of preventative action to stop or limit deterioration. Further, it is intended to detect any potential problems as early as possible and ameliorate these problems as they arise during subsequent maintenance events.
Balanced against the extension of the economic life of the device is the loss of capacity caused by the downtime of the machine during maintenance, not to mention the cost of the maintenance itself. Whilst frequently scheduled maintenance will have an effect on lengthening the economic life, there is a practical limit to this benefit that will be met through the loss of capacity.
It is, therefore, a risk that too infrequent the maintenance events, the greater the likelihood of a problem going undetected until significant damage has been caused by preventable problems through more frequent maintenance events.
An alternative, or complementary, strategy is the monitoring of parameters of the machine, for instance, turbine temperature, base load, unloaded turbine speed, vibration, speed/torque characteristic or cooling water temperature. This is not an exhaustive list and further parameters may be used to continuously or continually monitor these parameters.
This leads to a further problem through having to monitor several parameters and analyze performance based on the collected data. Monitoring such parameters can yield a significant quantity of data which must be stored and analyzed. As with the scheduling of maintenance events, the frequency of data sampling balanced against the ability to store and analyze large quantities of data is one that meets a practical limitation. Whilst several systems exist which do monitor the performance, the problem of data acquisition storage and analysis of large quantities of data is not easily handled.
In a first aspect, the invention provides a method for monitoring the operation of a wind turbine generator comprising the steps of sampling a physical parameter related to said apparatus to produce an initial data set, conducting a statistical analysis on said initial data set to establish initial statistical values for said parameter; after a predetermined interval, re-sampling the physical parameter to create a re-sampled data set; conducting statistical analysis on the re-sampled data set to establish subsequent statistical values; comparing said subsequent statistical values with the initial statistical values and; selecting an action based upon said comparison.
In a second aspect, the invention provides a system for monitoring the operation of a wind turbine generator comprising; a sensor for sensing a physical parameter related to said apparatus; a controller in communication with said sensor for receiving data from the sensor, said controller further arranged to conduct a statistical analysis and output initial statistical values to a database; said database arranged to compare statistical values received from said controller and arranged to initiate an action should subsequent statistical values fall outside acceptable operating limits of the initial statistical values.
Thus the invention limits the need for unscheduled maintenance events by maintaining a variation in monitoring process. It further avoids the collection of large volumes of data by collecting discreet data sets, then conducting statistical analyses on these data sets and comparing them to statistical analyses taken from an initial data sampling.
In a preferred embodiment the physical parameters may include any one or a combination of cooling water temperature, turbine speed, speed/torque characteristic or vibration.
In a further preferred embodiment the statistical values may include mean, any one or a combination of standard deviation, normality, variance, regression, median.
In a further preferred embodiment the predetermined intervals may be any one of one minute, ten minutes, one hour and twelve hours.
In a preferred embodiment, the invention may be applied on a modular level. By examining the data distribution from a large number of components and adaptive control, detection of early failure symptoms in the whole system may permit taking necessary procedures to prevent the failure of the whole system.
It will be convenient to further describe the present invention with respect to the accompanying drawings that illustrate possible arrangements of the invention. Other arrangements of the invention are possible and consequently, the particularity of the accompanying drawing is not to be understood as superseding the generality of the preceding description of the invention.
In the example provided in
By contrast to the representation in
An ideal data distribution, as shown in
In the example given, the machine is a wind turbine generator with the sampled physical parameter being the cooling water exiting from one or more power modules associated with the wind turbine generator.
Subsequent data acquisition after a predetermined interval is taken and an analysis performed on this data so as to produce a mean (μn) standard deviation (σn) and normality (ρn) for the newly collected data. These statistical values are then compared to the initial statistical values taken resulting in one of four permutations:
In the case of (i), (ii) and (iii) some form of corrective action is required and so an unscheduled maintenance event may result. In any event, a warning or alarm is sent by the system to a service center for review. In the case of (ii), an outliner data point in the distribution can be used as the feedback to the controller. This outliner data point increases the σ of the whole distribution. It might indicate the particular module is running at overloaded condition or the module is going to wear out.
Hence, an adaptive control system in the frequency converter controller can detect this signal and share the heavy load on that failing module with the other modules. This will increase the overall degradation tolerance of the module.
However, in the case of (iv), normal operation suggests that no variation to the scheduling of the maintenance events is required until further data collection is scheduled. No alarm should be alerted if the distribution had been shifted (different σ, μ and p values) forward or backward with respect to the reference data but within an acceptable limits and is a normal distribution.
It can, therefore, be seen that by adopting the present invention, maintenance events can be periodically scheduled due to the regular monitoring of specified parameters. Further because of the discreet data sets taken and comparison made based on statistical values only, the volume of data collected is considerably reduced without compromising the regularity of said monitoring.
The system is able to perform pre-processing online supervision of a converter and other parts in a WTG. The result can be used for predictive maintenance and also in active control to prevent unscheduled services.
The invention, therefore, provides significant advantages:
(i) Economy—This is an optimized way to manage the data. Temperature sensors may be eliminated/reduced as only water outlet temperatures are needed in the analysis.
(ii) Functional—Production of the WTG machine may be optimized as any abnormal changes in the machine are detected hence perform maintenance/replacement on the modular converter before it fails. This may reduce unscheduled breakdown of the machine.
(iii) Service—The invention provides the service center with a good insight into the conditions of the converter system and helps them to plan for the next service schedule so as to reduce the risk for breakdown. They can also make a decision on what are the components and special tools to bring during scheduled maintenance events.
(iv) Quality/Life time—The invention may improve the quality of a converter system as unscheduled maintenance had been reduced. The life time of the converter system may be extended as the module is replaced before it fails which might induce other failure in the converter.
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