CN110907170A - Wind turbine generator gearbox bearing temperature state monitoring and fault diagnosis method - Google Patents
Wind turbine generator gearbox bearing temperature state monitoring and fault diagnosis method Download PDFInfo
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Abstract
A wind turbine generator gearbox bearing temperature state monitoring and fault diagnosis method has important practical value for reducing maintenance cost of a wind power plant and improving operation level. Because the unit operation parameters with similar geographical positions of the wind power plant have special distribution rules and extremely strong correlation, the invention carries out transverse comparison analysis on the bearing temperatures of four 1.5MW unit gear boxes with strong correlation of a certain wind power plant. The method comprises the steps of counting the temperature-power dispersion point distribution characteristics of four strongly-related wind turbine generator gearbox bearings, finding out abnormal changes of the relationship between the monitored wind turbine generator gearbox bearing temperature and the temperatures of other related wind turbine generator gearbox bearings, judging the state of the monitored wind turbine generator gearbox, and sending out fault early warning.
Description
Technical Field
The invention belongs to the technical field of wind turbine generator gearbox bearing fault diagnosis, and particularly relates to a wind turbine generator gearbox bearing temperature state monitoring and fault diagnosis method.
Background
Along with the economic development and the improvement of the living standard of people, the consumption of human beings to energy sources is increased day by day, and the pollution to the environment is more and more serious. In the face of the energy crisis and the environmental crisis, clean and sustainable alternative energy sources must be found. Wind energy is the most promising alternative energy source. In recent years, wind power generation technology is becoming mature, and the power generation cost is gradually reduced and is close to the thermal power cost.
The wind turbine generator has severe operating conditions, such as large external temperature difference change, random wind speed change and the like. The fault rate of the wind turbine generator is high due to uncertain external factors, so that the later operation and maintenance cost of the wind power plant is high.
The gearbox is one of important parts of the wind turbine generator, and the manufacturing technology of the gearbox is mature and has high reliability. Although the failure rate of the gearbox is low, compared with an electric control system and a hydraulic system with the highest failure occurrence frequency, the maintenance process is complex, particularly for offshore wind turbines, special equipment such as ships and cranes and proper weather are needed in the maintenance process, and therefore the downtime and the maintenance cost caused by the failure of the gearbox are the highest among various failures. The wind turbine generator gearbox has the characteristics of speed change and load change during operation. With the change of the wind speed, the rotating speed and the load of each stage of the gearbox change at any time, which brings great challenges for the application of the traditional state monitoring method to the gearbox of the wind turbine generator. The choice of a gearbox condition monitoring method also requires consideration of the cost/performance ratio of the monitoring method. Compared with a thermal power generating unit and a hydroelectric generating unit, the cost of a single fan is lower, and the cost of a gear box monitoring system is a factor which must be considered.
The traditional gearbox bearing fault diagnosis technology, such as vibration analysis, oil analysis and the like, achieves certain results. The wind speed changes randomly, so that the rotating speed and the load of each stage of bearing of the gearbox of the wind turbine generator set change time instead of the stable working condition that the rotating speed is not changed. The current vibration analysis technology is low in fault diagnosis accuracy and high in false alarm and false alarm rate under the time-varying complex working condition of variable rotating speed and variable load of a gearbox bearing. The gear box oil analysis technology is used for collecting a gear box oil sample during the shutdown of the wind turbine generator, analyzing the water content, the number of metal particles and the diameter of lubricating oil in a laboratory to diagnose the state of a gear box bearing, but the oil analysis can only be used for off-line diagnosis, and the on-line real-time monitoring and diagnosis of the gear box bearing cannot be realized. And a multilayer forward neural network is adopted to model and monitor the temperature of the bearing of the gearbox of the wind turbine generator, but the forward neural network has a simple structure and low modeling precision, so that the abnormal change of the temperature of the bearing of the gearbox is difficult to be timely and accurately pre-warned.
The wind turbine generator gearbox fault can cause the long-time halt of the generator, the maintenance cost is high, the halt loss is large, and the wind turbine generator gearbox fault monitoring and diagnosing device has important significance for monitoring and diagnosing the running state of the gearbox.
Disclosure of Invention
In order to solve the problems in the prior art, the invention aims to provide a method for monitoring the temperature state and diagnosing the fault of the bearing of the gearbox of the wind turbine generator. The variation relation and trend between the temperature of the bearing of the gear box of the monitored unit and the temperature of the bearing of the related unit are researched by a control graph method and a gamma curve parameter fitting method, so that the running state of the gear box of the monitored wind turbine generator is monitored and diagnosed on line. The invention does not need to install additional sensors, has simple algorithm, and can realize the state monitoring of the gear box only by the existing operating data of the wind turbine generator.
In order to achieve the purpose, the invention adopts the following technical scheme:
a wind turbine generator gearbox bearing temperature state monitoring and fault diagnosis method comprises the following steps:
wherein:
r-Pearson correlation coefficient;
n is the number of samples;
wi-monitored unit gearbox bearing temperature sample values;
δw-the monitored unit gearbox bearing temperature standard deviation;
zi-other unit gearbox bearing temperature sample values for correlation analysis;
δz-other unit gearbox bearing temperature standard deviations for correlation analysis;
for each of four related wind turbines, the number of bearing temperature dispersion points of the gear box falling in each power interval is counted in sequence and stored in X4×40In an array;
wherein:
Xi,jthe bearing temperature dispersion point number of the gearbox in the jth power interval of the ith unit;
Range(j)＝Max(Xi,j)-Min(Xi,j)
UCL＝M+0.729MRange
LCL＝M-0.729MRange
wherein:
Max(Xi,j) In the jth power interval, the maximum value of the bearing temperature dispersion point number of the gearbox in the four related wind turbines;
Min(Xi,j) In the jth power interval, four phasesTurning off the minimum value of the bearing temperature dispersion points of a gearbox in the wind turbine generator;
range (j) -the range of the bearing temperature dispersion number of the four wind turbine generator gearbox in the jth power interval;
MRange-40 power interval scatter number average pole difference;
UCL-upper limit of scattered point number control;
LCL-lower control limit of scattered point number;
the bearing temperature of the gearbox of the monitored wind turbine generator set has strong correlation with the bearing temperatures of the gearboxes of the other three wind turbine generators; when the gearbox of the monitored wind turbine generator set operates normally, the bearing temperature of the gearbox is very similar to the change trend of the bearing temperature of the gearbox of the other three wind turbine generator sets along with the power; therefore, in each power interval, the temperature range of the bearings of the gear boxes of the four wind turbine generator sets is very small;
when the gearbox of the monitored wind turbine generator system breaks down and the bearing temperature rises abnormally, the bearing temperature variation trend of the monitored wind turbine generator system is greatly different from the bearing temperature variation trend of the gearbox of the other three wind turbine generators which normally operate; the temperature dispersion point number range of the bearings of the gear boxes among the four sets is abnormally increased in certain power intervals;
therefore, when the pole difference of the number of scattered points of the bearing temperature of the gearbox in certain power intervals is found to be abnormally increased, the abnormal operation alarm of the gearbox of the monitored unit is sent out;
an assumption is provided for the distribution situation of the bearing temperature scatter of the four wind turbine generator gearbox, fitting goodness inspection is carried out, and the following formula is adopted for calculation:
Dn＝max|F(x)-Fn(x)|
wherein:
fn (x) -a cumulative distribution function representing the sample observations;
Xi-is the sample observed value;
x is a theoretical value;
I[-∞,x](Xi) An index function, if XiX is equal to 1, otherwise equal to 0;
dn-the maximum deviation between assay amount, F (x) and Fn (x);
f (x) -a cumulative probability distribution function representing the theoretical distribution;
the partial values of the check mass critical value D (n, a) are shown in the following table 1, wherein n is the number of samples;
TABLE 1 partial values of the check quantity threshold D (n, a)
If the sample is from the distribution function F (x), Dn converges to 0; determining a critical value D (n, a) of the Kolmogorov-Smirnov method test according to the confidence level a of 0.05 and the number n of the samples of 40, wherein if Dn < D (n, a), the samples are accepted to be in accordance with the corresponding gamma distribution or Weibull distribution or normal distribution at the significance level of a; after statistical analysis and Kolmogorov-Smirnov method inspection are carried out on the temperature data of the bearing of the gearbox, the temperature data of the bearing of the gearbox conforms to gamma distribution; determining a shape parameter K and a scale parameter theta of the gamma distribution of the bearing temperature scatter points of the gear boxes of the four wind turbine generators by adopting a maximum likelihood estimation method to obtain a gamma distribution curve with the optimal fitting degree of the four wind turbine generators;
when the difference between the fitting parameters of the monitored wind turbine generator and the mean value of the fitting parameters of the three related wind turbine generators exceeds a threshold value, sending an abnormal alarm of the bearing temperature of the gearbox of the monitored wind turbine generator, as follows:
wherein:
Kmfitting shape parameters of the gamma distribution of the bearing temperature scatter points of the monitored wind turbine generator;
θmfitting scale parameters of the gamma distribution of the bearing temperature scatter points of the monitored wind turbine generator;
Δm-the variance between the fitting parameters of the monitored wind turbines and the mean of the fitting parameters of the three related turbines;
ξ -to set a threshold;
when the abnormal temperature alarm of the bearing of the gearbox of the monitored wind turbine generator is sent out simultaneously in the steps 3 and 4, the two abnormal alarms can be mutually verified, the alarm authenticity is strong, and the abnormal operation alarm of the gearbox is sent out to field operators;
when one of the step 3 or the step 4 sends out the abnormal temperature alarm of the bearing of the gearbox of the monitored wind turbine generator, in order to reduce the false alarm rate, the abnormal temperature alarm of the bearing of the gearbox of the monitored wind turbine generator is not sent out, and only the prompt of needing to pay attention to the running state of the gearbox is sent out to field operators.
The method adopts the operation data of the wind turbine generator to analyze, does not need to additionally add a sensor, has low cost and simple principle, and can find the temperature abnormity of the bearing of the gearbox of the wind turbine generator in time and give an alarm.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a distribution control diagram of temperature dispersion of four wind turbine generator gearbox bearings when a monitored unit is normal.
Fig. 3 is a gear box bearing temperature-power dispersion point gamma distribution parameter when the monitored unit is normal.
FIG. 4 is a cross-contrast gamma distribution plot for the associated unit.
Fig. 5 is a timing chart for monitoring a gearbox bearing temperature anomaly of the unit.
FIG. 6 is a gamma distribution curve comparison diagram of a monitored unit with abnormal temperature of a bearing of a gearbox and a related normal unit.
Detailed Description
The invention is described in detail below with reference to the figures and examples. The method comprises the steps of taking gear boxes of 4 1.5MW units in a certain wind power plant as research objects, and selecting 1-minute-level operation data of the units.
As shown in fig. 1, the method for monitoring the temperature state and diagnosing the fault of the bearing of the gearbox of the wind turbine generator set comprises the following steps:
And 2, drawing a gear box bearing temperature-power scatter diagram which takes power as an abscissa and takes gear box bearing temperature as an integrated coordinate for the four wind turbines with strong correlation. Counting the number of temperature scatter points of bearings of the gear box in each power interval divided by 40KW of each unit, and dividing the power interval into P if the lower limit of the power is zero and the upper limit of the power is 1600KWnum40 intervals, and X is stored4×40。
And 3, observing the difference between the bearing temperature scatter points of the gear boxes of the four related wind turbines in different power intervals by using a control chart. And drawing a control chart, and displaying the temperature dispersion point mean distribution condition of the four selected wind turbine generator gearbox bearings as shown in FIG. 2. The temperature dispersion point range of the bearings of the gear boxes of the four units is small in each power interval, the temperature variation trend of the bearings of the gear boxes of the monitored unit is consistent with that of the bearings of the other three relevant units, abnormal variation does not exist, and the running state of the gear boxes of the monitored unit is normal.
And 4, selecting Kolmogorov-Smirnov for carrying out distribution inspection on the bearing temperature-power dispersion points of the gear boxes of the four wind turbine generators.
As shown in fig. 3, the temperature data of the gearbox bearings conforms to gamma distribution, and the distribution parameters are monitored unit parameters K-2.9943 and θ -1.0428. The gamma distribution parameters of the other three related units have K value fluctuating within 3.0000 +/-0.2000 and theta value fluctuating within 1.0000 +/-0.1000. FIG. 4 is a gamma distribution curve diagram comparing with other related units in the transverse direction, and the trend and the parameters are approximately the same, so that the gear box bearing temperature dispersion point distribution among the units with stronger correlation has strong similarity.
Fig. 5 is a control diagram of the monitored wind turbine generator set when the bearing temperature of the gearbox is abnormal, and shows that the high power interval range is far higher than the average range value and fluctuates violently compared with other related units, which indicates that the bearing temperature of the gearbox of the monitored wind turbine generator set is obviously abnormally increased under the high load working condition compared with the other three related units.
Fig. 6 is a fitting curve comparison of the mean gamma distribution parameters of the temperature scatter gamma distribution fitting curve of the monitored wind turbine generator set when the temperature of the gearbox bearing is abnormal and other three related units. In fig. 6, the gamma distribution parameters of the monitored unit are K-5.9824 and θ -0.7256, which are much larger than the mean value of the gamma distribution parameters of the other three related normal units in the same time segment
Therefore, the abnormal temperature of the gearbox bearing of the monitored unit can be seen from the control chart of fig. 5 and the gamma distribution parameter of fig. 6, and therefore an alarm for the abnormal temperature of the gearbox bearing of the monitored unit is sent out.
Claims (1)
1. A wind turbine generator system gear box bearing temperature state monitoring and fault diagnosis method is characterized in that: the method comprises the following steps:
step 1, selecting three wind turbine generators with highest temperature correlation with a bearing of a gearbox of a monitored wind turbine generator, and simultaneously removing shutdown period data, wherein the total number of the three wind turbine generators is four; collecting all monitored wind turbine generator operation data, calculating the correlation between the temperature of a plurality of wind turbine generator gearbox bearings and the temperature of the monitored wind turbine generator gearbox bearings by using a Pearson correlation coefficient formula, and selecting three machines with the highest correlation with the temperature of the monitored wind turbine generator gearbox bearings from the correlation, wherein the Pearson correlation coefficient formula is as follows:
wherein:
r-Pearson correlation coefficient;
n is the number of samples;
wi-monitored unit gearbox bearing temperature sample values;
δw-the monitored unit gearbox bearing temperature standard deviation;
zi-other unit gearbox bearing temperature sample values for correlation analysis;
δz-other unit gearbox bearing temperature standard deviations for correlation analysis;
step 2, picking out points with power greater than 0 from original data of four related wind turbines, and setting an active power interval as [ P ]min,Pmax](ii) a Drawing a bearing temperature-power scatter diagram of the gearbox of the wind turbine generator system by taking the bearing temperature of the gearbox as a vertical coordinate and taking power as a horizontal coordinate; equally dividing the power of the abscissa into P at intervals of 40KWnumEach interval is divided into P if the lower limit of power is zero and the upper limit is 1600KWnum40 intervals;
for each of four related wind turbines, the number of bearing temperature dispersion points of the gear box falling in each power interval is counted in sequence and stored in X4×40In an array;
wherein:
Xi,jthe bearing temperature dispersion point number of the gearbox in the jth power interval of the ith unit;
step 3, observing the difference between the bearing temperature scatter numbers of the gear boxes of the four related wind turbines in different power intervals by using a control chart; the control chart comprises a range (j) of the polar difference of the bearing temperature dispersion points of four wind turbine generator sets in the jth power interval; the average range of the number average pole difference MRange of bearing temperature scatter points of the gearbox in 40 power intervals; the average value M of the number of scattered points of the bearing temperature of the gearbox; the upper control limit UCL of the bearing temperature scatter number of the gearbox; the lower limit LCL is controlled by the number of scattered points of the bearing temperature of the gear box, and the variables are calculated by adopting the following formula:
Range(j)＝Max(Xi,j)-Min(Xi,j)
UCL＝M+0.729MRange
LCL＝M-0.729MRange
wherein:
Max(Xi,j) In the jth power interval, the maximum value of the bearing temperature dispersion point number of the gearbox in the four related wind turbines;
Min(Xi,j) -in the jth power interval,the minimum value of the bearing temperature dispersion points of the gear box in the four related wind turbines;
range (j) -the range of the bearing temperature dispersion number of the four wind turbine generator gearbox in the jth power interval;
MRange-40 power interval scatter number average pole difference;
UCL-upper limit of scattered point number control;
LCL-lower control limit of scattered point number;
the bearing temperature of the gearbox of the monitored wind turbine generator set has strong correlation with the bearing temperatures of the gearboxes of the other three wind turbine generators; when the gearbox of the monitored wind turbine generator set operates normally, the bearing temperature of the gearbox is very similar to the change trend of the bearing temperature of the gearbox of the other three wind turbine generator sets along with the power; therefore, in each power interval, the temperature range of the bearings of the gear boxes of the four wind turbine generator sets is very small;
when the gearbox of the monitored wind turbine generator system breaks down and the bearing temperature rises abnormally, the bearing temperature variation trend of the monitored wind turbine generator system is greatly different from the bearing temperature variation trend of the gearbox of the other three wind turbine generators which normally operate; the temperature dispersion point number range of the bearings of the gear boxes among the four sets is abnormally increased in certain power intervals;
therefore, when the pole difference of the number of scattered points of the bearing temperature of the gearbox in certain power intervals is found to be abnormally increased, the abnormal operation alarm of the gearbox of the monitored unit is sent out;
step 4, a Kolmogorov-Smirnov method is used for detecting the temperature scatter distribution of the gearbox bearing, and gamma distribution is adopted to fit the scatter distribution; finding abnormal operation of the wind turbine generator gearbox to be monitored according to the deviation of the bearing temperature scatter gamma distribution simulation parameter of the wind turbine generator gearbox to be monitored and the average value of the fitting parameters of the other three wind turbine generators;
an assumption is provided for the distribution situation of the bearing temperature scatter of the four wind turbine generator gearbox, fitting goodness inspection is carried out, and the following formula is adopted for calculation:
Dn＝max|F(x)-Fn(x)|
wherein:
fn (x) -a cumulative distribution function representing the sample observations;
Xi-is the sample observed value;
x is a theoretical value;
I[-∞,x](Xi) An index function, if XiX is equal to 1, otherwise equal to 0;
dn-the maximum deviation between assay amount, F (x) and Fn (x);
f (x) -a cumulative probability distribution function representing the theoretical distribution;
the partial values of the check mass critical value D (n, a) are shown in the following table 1, wherein n is the number of samples;
TABLE 1 partial values of the check quantity threshold D (n, a)
If the sample is from the distribution function F (x), Dn converges to 0; determining a critical value D (n, a) of the Kolmogorov-Smirnov method test according to the confidence level a of 0.05 and the number n of the samples of 40, wherein if Dn < D (n, a), the samples are accepted to be in accordance with the corresponding gamma distribution or Weibull distribution or normal distribution at the significance level of a; after statistical analysis and Kolmogorov-Smirnov method inspection are carried out on the temperature data of the bearing of the gearbox, the temperature data of the bearing of the gearbox conforms to gamma distribution; determining a shape parameter K and a scale parameter theta of the gamma distribution of the bearing temperature scatter points of the gear boxes of the four wind turbine generators by adopting a maximum likelihood estimation method to obtain a gamma distribution curve with the optimal fitting degree of the four wind turbine generators;
when the difference between the fitting parameters of the monitored wind turbine generator and the mean value of the fitting parameters of the three related wind turbine generators exceeds a threshold value, sending an abnormal alarm of the bearing temperature of the gearbox of the monitored wind turbine generator, as follows:
wherein:
Kmfitting shape parameters of the gamma distribution of the bearing temperature scatter points of the monitored wind turbine generator;
θmfitting scale parameters of the gamma distribution of the bearing temperature scatter points of the monitored wind turbine generator;
Δm-the variance between the fitting parameters of the monitored wind turbines and the mean of the fitting parameters of the three related turbines;
ξ -to set a threshold;
step 5, synthesizing the abnormal temperature alarm of the monitored wind turbine generator gearbox bearing sent by the step 3 and the step 4 respectively:
when the abnormal temperature alarm of the bearing of the gearbox of the monitored wind turbine generator is sent out simultaneously in the steps 3 and 4, the two abnormal alarms can be mutually verified, the alarm authenticity is strong, and the abnormal operation alarm of the gearbox is sent out to field operators;
when one of the step 3 or the step 4 sends out the abnormal temperature alarm of the bearing of the gearbox of the monitored wind turbine generator, in order to reduce the false alarm rate, the abnormal temperature alarm of the bearing of the gearbox of the monitored wind turbine generator is not sent out, and only the prompt of needing to pay attention to the running state of the gearbox is sent out to field operators.
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