KR101420846B1 - Fault diagnosis of wind turbine by using active bin - Google Patents

Abstract

The present invention relates to a method for diagnosing a fault of a wind power generator using an active bin. The purpose of the present invention is to provide the method for diagnosing a fault of a wind power generator using an active bin to effectively perform fault diagnosis in operation of the wind power generator by properly setting the active bin in the wind power generator.

Description

{Fault diagnosis of wind turbine by using active bin}

The present invention relates to a fault diagnosis method for a wind turbine using an active bin.

Wind power is a renewable energy source that uses wind turbines to turn wind into electricity. It is relatively cheap and has little carbon emissions. Its use is expanding in many countries. At the end of 2008, more than 1.5% of the world's electricity production has already been produced by wind power generation, and this rate is increasing rapidly. In 2008, 19% of electricity was generated in Denmark, 11% in Spain and Portugal, and 7% in Germany and Ireland, using wind power generation. In May 2009, 80 countries , Which is a commercial power generation system using wind power.

As such, wind power is an attractive source of energy that replaces conventional fossil fuels because it is rich in wind energy, is highly renewable, and does not cause greenhouse effect Be in the spotlight. However, there is a limitation in the implementation of wind power generation in the country or region because it can not create an environment that winds up enough to enable efficient power generation. Also, when building wind farms, there is a feeling of visual rejection, noise pollution, destruction of ecosystems, And environmental adverse effects. Recently, despite the technical difficulties and relatively high costs, offshore wind power generation has become increasingly popular, and various attempts have been made to make wind power generation more convenient and widespread.

BACKGROUND ART [0002] A wind power generation system basically includes a blade that is made windable and rotatable, and a generator that is connected to a rotating shaft to which the wings are connected and generates power through rotation. Generally, since the rotational speed required for power generation in the generator is higher than the rotational speed of the rotating blades of the wind, the gear box is usually further provided which converts the rotational speed to a rotational speed suitable for power generation. Since the wind speed of the wind blowing naturally keeps changing from time to time, the amount of power generated by the wind power generator is not constant, but changes continuously according to the wind speed. In other words, no constant state of control or operation of a wind turbine should be assumed, and such continuous change should be considered.

On the other hand, the International Electrotechnical Commission (IEC) 61400-25-6 (Telecommunications for monitoring and control of wind power plants, TC 88) presents the concept of 'bin'. A bean is a term used in a technique to deal with this data, in the case where a parameter has an ever-changing random pattern rather than converging to a certain value or maintaining a steady state. To briefly describe this technique, a certain range interval in the parameter is predetermined for any physical parameter to be analyzed, and if the measured parameter value is within the range for a predetermined time or more, A statistical processing technique that performs analysis by considering a constant value as a given value (for example, a median value, an average value, etc.), where the term referring to a predetermined range section for the parameter is 'bin'. That is, if some beans are set in advance, and a parameter to be measured and analyzed is included in the bin, control or analysis is performed on the assumption that the bean is operating under a certain condition corresponding to the bin. In this respect, the bean used for control or analysis may also be referred to as an "active bin".

In the case of wind turbines, wind speed is a very randomly changing value from a microscopic point of view, even though it can be predicted from a macroscopic point of view. It is therefore desirable to introduce such an active bean concept when performing any control or analysis on parameters measured in a wind turbine generator. Especially, it is expected that it will be very effective to use this active bean concept for fault diagnosis of wind turbine generators. However, at present, in a method of monitoring for the fault diagnosis of a generator disposed in a wide area such as a wind turbine, a method of monitoring a fault in a power plant using a sensor in a Korean registered patent No. 1152647 ("monitoring method for generating apparatus & This is a method of determining which status is actually defined as a failure because it focuses on a technology that effectively communicates this to a manager after a failure is detected, such as when a failure is detected There is not much research on this.

Various methods have been developed to solve the problem of 'what state should be defined as a fault occurrence', but the fault diagnosis method using the active bean concept can be effectively applied due to the characteristics of the wind turbine, which basically changes operating conditions instantaneously . Therefore, the method of determining the active bean is directly connected to the efficient fault diagnosis, and if the active bean is set well, the fault diagnosis efficiency and accuracy can be greatly improved. On the other hand, if the active bean can not be set well, It is not only costly to maintain, but also increases the risk of accidents. Therefore, a guide to how to determine such an active bean is very urgent for a person skilled in the art.

1. Korea registered patent No. 1152647 ("Monitoring method for power generation device", May 29, 2012)

SUMMARY OF THE INVENTION Accordingly, it is an object of the present invention to provide a wind turbine that can properly and properly set an active bin, and ultimately, The present invention provides a fault diagnosis method for a wind power generator using an active bean.

According to an aspect of the present invention, there is provided a method for diagnosing a fault in a wind turbine using an active bean, the method comprising: a plurality of blades; a rotating shaft that is provided with the blades and rotates by wind; Wherein the reference operating parameter value and the vibration magnitude value are measured by a predetermined time span interval at predetermined time intervals during a predetermined commissioning period, the method comprising the steps of: A calibration graph is generated by matching the reference operating parameter value and the vibration magnitude value collected in the commissioning data collection step, and the number of the reference operating parameter values is counted for each predetermined reference operating parameter range interval to generate a histogram. ; An active bin determination step of determining at least one active bin and an active bin reference value based on the matching graph and the histogram generated in the trial data analysis step; An operating data measuring step in which a reference operating parameter value and a vibration magnitude value are measured while the wind power generator is operated; Comparing the vibration magnitude value measured in the operation data measurement step with an active bin representative value of an active bin corresponding to a reference operation variable value, and determining that a failure has occurred if the vibration magnitude value is exceeded; . ≪ / RTI > At this time, the reference operation variable may be any one selected from among rotation speed, power generation amount, and wind strength.

In the active bin determination step, a cut-in interval is determined as a first major active bin in the histogram, and a range of intervals in which the maximum value of the reference operation variable appears in the histogram is set as a second major active bin And in the histogram, a range of intervals in which the number of counts is maximized between the cut-in and maximum reference operation variables may be determined as the third major active bin.

Determining a first sub-active bin as a first sub-active bin in the histogram, wherein the first sub-active bin has a maximum number of counts between a predetermined minimum reference operation variable and a cut-in period, In the histogram, the second sub-active bin may be determined as a range in which the number of counts is maximum in the remaining range except for the first to third major active bins with respect to the cut-in error.

Also, in the active bin determination step, the active bin reference value may include an average value and a standard deviation value of vibration amplitude values in an active bin range, an average value and a standard deviation value of noise-removed values of a vibration amplitude value in an active bin range, And a maximum value among vibration amplitude values within the empty range.

Also, in the fault diagnosis step, the fault reference value may be determined by using the maximum value of the vibration during the test run period or by the following equation.

Fault diagnosis reference value = average value + constant x standard deviation value

(Where the constant is a positive number)

According to the present invention, fault diagnosis of a wind turbine generator can be performed using an appropriately well set active bean, which ultimately has a great effect of improving accuracy and efficiency of fault diagnosis of a wind turbine generator. More specifically, according to the present invention, it is possible to appropriately set the active bean most optimized for the wind turbine generator for a wind turbine having various characteristics such as a region and a device. As described above, when the failure diagnosis of the wind turbine generator is performed using the active bin values set by the present invention, efficient and accurate data processing can be performed on the wind turbine in which the operating conditions continuously change, Thereby enabling the diagnosis to be performed.

1 is a flowchart of a fault diagnosis method for a wind turbine using an active bean of the present invention;
Figure 2 is a graph of the matching of the rotational speed value and the vibration magnitude value.
3 is a histogram of the rotational speed value;
Figure 4 is an example of a major active bean decision based on the histogram of Figure 3;
Figure 5 is an example of auxiliary active bean determination after major active bean determination.
6 shows an example of displaying an active bin and a failure reference value in the test run collection data.
FIG. 7 is an example of a failure diagnosis in which the active bin and the failure reference value of FIG. 6 are applied to operational data measured during actual wind turbine operation.

Hereinafter, a fault diagnosis method for a wind turbine using the active bean according to the present invention will be described in detail with reference to the accompanying drawings.

In the present invention, a wind turbine generator for performing a fault diagnosis includes a plurality of blades, a blade having the blades, a rotating shaft rotating by wind, and a generator connected to the rotating shaft to generate electric power by rotating the wind turbine . Generally, such a wind turbine generator is provided with a rotational speed measuring sensor for measuring the rotational speed when the rotational shaft rotates by the wind, or a rotational speed can be calculated from the generated power output value. Can be measured. In addition, the most effective parameter to determine the failure of such a wind turbine generator is the vibration value, which is generated when the wind turbine generator is operated in a normal operation in case of occurrence of a failure of important wind turbine generator components such as a blade, a bearing, a gearbox, It is well known that a large vibration occurs suddenly as compared with the vibration which is caused by the vibration. Generally, a general wind turbine generator is provided with about 10 to 20 vibration sensors for measuring such vibrations, and the position of the vibration sensors is various.

Accordingly, in the present invention, one of the operating variables (rotational speed, power generation amount, etc.) of the wind turbine generator is taken as a reference operating parameter, the reference operating parameter and the vibration magnitude value are set as parameters for fault diagnosis analysis, Hereinafter, a method for determining the bin and for diagnosing faults is presented. In addition, the vibration magnitude value may be selected from a plurality of vibration sensors provided in the wind turbine generator. For example, the vibration magnitude value may be provided to the generator (located in the center of the structure of the wind turbine, It is desirable to use the vibration magnitude value measured by the vibration sensor.

FIG. 1 is a flowchart of a fault diagnosis method for a wind turbine using an active bean according to the present invention, and briefly shows the entire steps of the fault diagnosis method of the present invention. As shown in FIG. 1, the fault diagnosis method for a wind turbine using the active bean of the present invention includes a test run data collection step, a test run data analysis step, an active bean determination step, an operation data measurement step, and a failure diagnosis step.

Of these, the commissioning data collection step to the active bean determination step may be said to be performed before the operation of the wind power generator in earnest, that is to say, the actual operation and the failure diagnosis are performed in the subsequent operation data measurement step and the failure diagnosis step do. Each step will be described in more detail below.

In the commissioning data collection step, predetermined wind turbine reference operating variables (rotational speed values as a representative example) and vibration magnitude values are measured for a predetermined time interval interval at predetermined time intervals during a predetermined commissioning period. Since the measured data at this stage is analyzed and used as a criterion for fault diagnosis at the time of operation of the actual wind turbine generator, it is desirable that the trial run period is appropriately set to be long enough to represent annual data. Specifically, the value of the commissioning period may be determined to be between 3 months and 1 year. In addition, since data values to be analyzed are excessively large when data of all times are stored, the time interval value and the time range interval value can be appropriately determined. For example, the time interval value is 10 minutes to 30 minutes, The time range interval value can be determined to be between 30 seconds and 60 seconds. That is, for example, the commissioning data collection step may include calculating the rotational speed value and the vibration magnitude value for 30 seconds every 10 minutes and storing the average value for 3 months, And storing the average value for 6 months].

In the commissioning data analysis step, a matching graph is generated by matching the wind turbine reference operating variables and the vibration magnitude values collected in the commissioning data collecting step, and the number of reference operating variables is counted for each predetermined reference operating parameter range interval, Is generated. FIG. 2 is a graph showing a matching graph in which a rotational speed is selected as a reference wind turbine operating parameter, and a rotational speed value and a vibration magnitude value measured during a trial driving period are matched with each other. In FIG. 2, the abscissa represents the rotational speed (rpm unit in terms of rotational speed) and the ordinate represents the magnitude of vibration (in g unit in terms of acceleration unit), and the vibration magnitude value measured for a certain rotational speed value is represented by each point. 3 shows a histogram of the rotational speed value, and the rotational speed range section can be appropriately determined in consideration of the entire rotational speed range. In FIG. 3, the rotational speed range is 0.5 rpm, but the present invention is not limited thereto. For example, the rotational speed range may be determined to be 1 rpm to 0.5 rpm. That is, the interval may be determined by cutting off by 1 rpm, or the interval may be determined by cutting by 0.25 rpm, or the interval may be determined by cutting by 0.5 rpm, for example. .

In the active bin decision step, an active bin and an active bin reference value are determined based on the matching graph and the histogram generated in the test data analysis step. This is the most essential part of the present invention. The active bin determination step will be described in more detail with reference to the histogram of FIG. 3 as an example.

3, the rotational speed is selected as a reference operating parameter for fault diagnosis and active bean determination. The measured rotational speed values are distributed over 0 to 16 rpm, and the number of times for each rotational speed value is 0 to 800 . In FIG. 3, the histogram is divided by 0.5 rpm. For example, about 750 times in the range of 0 to 0.5 rpm, and about 470 times in the range of 7 to 7.5 rpm (in other words, data collected during the commissioning period As a result, it was found that the rotation rate was about 0 ~ 0.5 rpm, about 750 times, and the rotation speed was about 7 ~ 7.5 rpm about 470 times. Once the histogram for the rotational velocity value is summarized, the active bean and the active bean reference value can now be determined.

FIG. 4 shows an example of a major active bean decision based on the histogram of FIG. 3. FIG. First, the criterion for determining the main mandatory active bin presented in the present invention is as follows.

As can be clearly seen in FIGS. 3 and 4, there is a region in which the number of measured data increases sharply. This is called a cut-in in which the wind turbine starts operating. In fact, This is a frequent operating period. As shown in FIG. 3, the number of counts of the section where the wind is not blowing (the section where the rotational speed is 0 to 0.5 rpm) is considerably large, and the wind turbine is not operating substantially in this section. In FIG. 3, it can be deduced that the wind was not blown sufficiently strong enough to rotate the rotation axis of the wind turbine, considering that it is counted very little before 7 to 7.5 rpm. In fact, the blades and spindles of a wind turbine are fairly huge structures, and little rotation occurs if the wind is not blowing strong enough. The range of 0 to 7 rpm can be considered as a case where the wind turbine does not rotate properly to generate power, that is, the case where the rotating shaft does not rotate almost. On the other hand, from 7 to 7.5 rpm, the amount of data increases sharply, and data amounts having a significant size are measured thereafter. From this, it can be seen that the section of 7 to 7.5 rpm is cut in the histogram of FIG.

In fact, cut-in conditions have been determined from the design of the wind turbine generator and are the most frequently used operating conditions as a reference point in general. This cut-in rotation speed is a very important variable as it is the starting point of the actual operation period as described above, and therefore, the range of the range of the cut-in rotation speed is determined as the first major active bin.

Next, since the condition in which the maximum value of the wind turbine generator reference operating parameter appears is also a very important factor in the operation of the wind turbine generator, the range of the maximum value of the reference operating parameter in the histogram is determined as the second major active bin .

Also, the section where the number of counting times is the largest is also an important part between the cut-in and the condition that the reference operating variable becomes maximum. The fact that the largest number of counts has been reported is that it operates at its rotational speed for the most time when the wind turbine is operated. Therefore, in the histogram, the third major active bin is determined as a range of intervals in which the counting count is maximum between the cut-in and maximum reference operating conditions.

The main active bins determined in this way are shown in Fig. The active bean can be made to match the range of histogram histograms, or it can be determined as a multiple of the range of histogram histograms. In Fig. 4, the range of the histogram is 0.5 rpm when the rotation speed is the reference operation variable, and the active bin is 1 rpm. In the example of FIG. 4, the first major active bin (cut rotation speed) is 7 to 8 rpm, the second major active bin (maximum rotation speed) is 15 to 16 rpm and the third major active bin ~ 9 rpm.

In this way, the main active beans can be determined, and an optional active bin can be further determined for accurate analysis. The auxiliary active bean can be variously determined according to the purpose of analysis, and the present invention further provides the following determination criteria.

As described above, the first active bean in the active bean is the cut active bean. In this case, the wind turbine does not move at all but moves slightly. This means that the wind turbine does not move to such a degree that the wind turbine can operate smoothly, but it operates slightly. This case is not crucial for fault diagnosis because the wind turbine is not actually in full operation, but fault diagnosis can also be performed using data on such cases. Accordingly, in the histogram, the first auxiliary active bin is determined as a range of intervals in which the counting count is maximum between the predetermined minimum reference operating variable and the cut-in, which is determined to be a value smaller than the cut-in. In this case, the reason why the number of counting is less than the maximum number of cuts is because the interval in which the wind turbine is stopped is usually maximized when the standard is set. When the wind turbine is stopped without rotating, it is normal that no vibration occurs. Therefore, precise judgment criteria are not necessary at this time. Therefore, it is desirable to exclude the section including 0, which can be expected to some extent. Of course, the reference operating condition may be appropriately determined to be larger than 0 and smaller than the cut-off value.

Next, it is preferable to consider the range in which the most frequent operating conditions appear after the cut-in. Accordingly, in the histogram, the second sub-active bin is determined as a range in which the number of counting times is maximum in the remaining range except for the first to third major active bins after the cut-in.

FIG. 5 shows an example of further determining the auxiliary active bean after determining the main active bean in this manner. In the example of Fig. 5, the first auxiliary active bin (the maximum rotation speed between the reference rotation speed and the cut-in rotation speed) is 6 to 7 rpm, and the second auxiliary active bin (the second most rotation speed) is 9 to 10 rpm.

The above-described active bean decision criteria in the present invention can be summarized as follows.

■ Major active bin

- First primary active bin: cut-in range interval

- Second major active bin: maximum reference operating variable section

- The third major active bin: the most frequent operating interval between cut-in and maximum reference operating variables

■ Optional active bin

- First auxiliary active bin: the pre-determined reference operating variable The most frequent operating interval between the lower limit and cut-in

- Second assistant active bin: The most frequent driving section among the sections excluding the first to third major active beans in the cut-in and above

When the active bean is determined in this way, a representative value of the active bean is determined. As described above, the active bean is determined based on the histogram as shown in FIG. 3, and the active bin reference value is determined using the reference operation variable and the vibration magnitude matching graph as shown in FIG. The purpose of the present invention is to analyze data for fault diagnosis. In order to establish a criterion for determining whether a vibration is of a certain magnitude under certain conditions, The empty representative value is represented by the vibration magnitude value.

In this case, the active bin reference value can be most easily determined as an average value and a standard deviation value of the amplitude magnitude values in the active bin range. Since it is reasonable to assume that the wind turbine generator is a new product during the commissioning period, it is reasonable to assume that the vibration average value and standard deviation value during the commissioning period are within the stable operating condition range.

However, even during this commissioning period, noise may occur due to sudden changes in the climate, such as when lightning strikes. Therefore, statistical processing may be used to remove the noise, and the average value and the standard deviation value may be determined as the active binarization value. That is, the active bin reference value can also be determined by the average value and the standard deviation value of the noise-removed value of the vibration amplitude value within the active bin range.

Alternatively, for the purpose of simplifying the failure criterion for fault diagnosis, the vibration maximum value and not the vibration average value and the standard deviation value may be used as the reference. That is, in this case, the active bin reference value can be determined as the maximum value among the vibration amplitude values in the active bin range.

Of course, this does not mean that the criterion for determining the active bin reference value is limited. The active bean reference value may be determined as at least one of these examples, and the active bin reference value may be determined in a different manner depending on the degree of failure criteria for failure diagnosis It is possible.

Once the active bean and active bin representations have been determined, the wind turbine is now commissioned and ready for practical operation. During the operation of the wind power generator, an operation data measurement step is performed in which a reference operation parameter and a vibration magnitude value are measured.

In the failure diagnosis step, if the vibration magnitude value measured in the operation data measurement step exceeds a predetermined failure diagnosis reference value using the active bin reference value of the active bin corresponding to the reference operation variable value at that time, . At this time, the failure diagnostic reference value may be determined by a maximum value measured within a test run period or by the following equation.

Failure reference value = average value + constant x standard deviation value

Where the constant is a positive number, for example the constant value may be 1.2 to 1.5. For example, if the measured vibration magnitude value exceeds the failure reference value when the constant value is calculated as 1.2, the warning level and the measured vibration magnitude value when the constant value is calculated as 1.5, If it exceeds, it is determined as an alert level. FIG. 6 shows an example in which the active bin and the failure diagnosis reference value are set to the maximum value during the test run period in the test run collection data, FIG. 7 shows an example of the actual measurement data when the failure diagnosis reference value is determined as an average value and a constant multiple of the standard deviation, An example of an empty decision and failure reference value is shown.

It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. It goes without saying that various modifications can be made.

S1 to S5: Each step of the fault diagnosis method of the wind turbine using the active bean (of the present invention).

Claims (6)

A method of diagnosing a fault in a wind turbine comprising a plurality of blades, a rotating shaft rotated by the wind with the blades, and a generator connected to the rotating shaft to generate electric power,
A commissioning data collection step of measuring a reference operating parameter value and a vibration magnitude value by a predetermined time range interval at predetermined time intervals during a predetermined commissioning period;
A calibration graph is generated by matching the reference operating parameter value and the vibration magnitude value collected in the commissioning data collection step, and the number of the reference operating parameter values is counted for each predetermined reference operating parameter range interval to generate a histogram. ;
An active bin determination step of determining at least one active bin and an active bin reference value based on the matching graph and the histogram generated in the trial data analysis step;
An operating data measuring step in which a reference operating parameter value and a vibration magnitude value are measured while the wind power generator is operated;
Comparing the vibration magnitude value measured in the operation data measurement step with an active bin representative value of an active bin corresponding to a reference operation variable value, and determining that a failure has occurred if the vibration magnitude value is exceeded;
And a fault diagnosis method for the wind turbine using the active bean.
2. The method according to claim 1,
And the wind speed, the rotational speed, the power generation amount, and the wind strength.
2. The method according to claim 1, wherein in the active bin determination step,
In the histogram, a cut-in interval is determined as a first major active bin,
In the histogram, a range in which the maximum value of the reference operation variable is displayed is determined as a second major active bin,
Wherein the third major active bin is determined as a range of intervals in which the number of counting times is maximum between the cut-in and the maximum reference operating variables, as the third major active bin in the histogram.
4. The method of claim 3, wherein in the active bin determination step,
The first auxiliary active bin is determined as a range in which the counted number of counts is maximized between a predetermined minimum reference operation variable and cut-in which is determined as a value smaller than the cut-in rotational speed in the histogram,
Wherein the second auxiliary active bin is determined as a second auxiliary active bin in a range in which the number of counting times is maximum in a remaining range excluding the first to third major active bins with respect to the cut-in error in the histogram. Fault diagnosis method.
2. The method according to claim 1, wherein in the active bin determination step,
The active bin representative value,
The average value and the standard deviation value of the vibration amplitude value in the active bin range,
The average value and the standard deviation value of the noise-removed values of the vibration amplitude value in the active bin range,
Maximum value of vibration amplitude value in active bin range
Wherein the active energy of the at least one active energy source is at least one selected from the group consisting of at least two types of energy sources.
2. The method according to claim 1,
Wherein the failure reference value is determined by a maximum value of a vibration magnitude measured during a trial period for each active bin or by the following equation.
Fault diagnosis reference value = average value + constant x standard deviation value
(Where the constant is a positive number)

Images