A method for monitoring the operation of carbon brushes in collector rings of wind turbines

Abstract

The present application discloses a method for monitoring the operation of the collector ring carbon brushes of a wind turbine, comprising: establishing an intelligent monitoring system, obtaining monitoring data of the monitoring methods included in the intelligent monitoring system, performing credibility analysis on a variety of monitoring data, and determining characteristic monitoring data of the collector ring carbon brushes; analyzing the characteristic monitoring data to obtain a state monitoring result for each characteristic monitoring data, and generating a state abnormality coefficient of the collector ring carbon brushes from the multiple state monitoring results; determining the operating state of the collector ring carbon brushes according to the state abnormality coefficient, improving the accuracy of the state abnormality coefficient, accurately reflecting the operating state of the collector ring carbon brushes in the current preset time period and the future preset time period, and reducing the failure frequency of the wind turbine.

Description

Technical Field

[0001] The present application relates to the technical field of operation monitoring, and in particular to a method for monitoring the operation of carbon brushes of a wind turbine collector ring. Background Art

[0002] Slip ring carbon brushes are an important component of wind turbines. For a long time, slip ring carbon brush failures have been frequent, which has been the main difficulty in stabilizing the operation of power stations. The traditional operation monitoring method used for slip ring carbon brushes is mainly through manual inspection or single sensor monitoring. The inspection workload is large or the monitoring results of a single sensor are inaccurate, resulting in a decrease in the accuracy of the slip ring carbon brush operation monitoring results, which cannot timely reflect the operating status of the slip ring carbon brush at a certain moment, affecting the normal operation of the wind turbine. Summary of the Invention

[0003] In order to solve the above technical problems, the present application provides a method for monitoring the operation of the collector ring carbon brush of a wind turbine. By constructing an intelligent monitoring system, characteristic monitoring data of multiple monitoring methods are obtained, and the state monitoring results of each characteristic monitoring data are determined. The predicted state monitoring results are generated based on the predicted characteristic monitoring data of the future preset time period, and a state abnormality coefficient is generated based on the state monitoring results. A compensation coefficient is generated based on the predicted state monitoring results and the state abnormality coefficient is corrected to improve the accuracy of the state abnormality coefficient, timely reflect the operating status of the collector ring carbon brush in the current preset time period and the future preset time period, and reduce the failure frequency of the wind turbine.

[0004] In some embodiments of the present application, a method for monitoring the operation of a carbon brush of a collector ring of a wind turbine is provided, comprising:

[0005] Establish an intelligent monitoring system, obtain monitoring data from the monitoring methods included in the intelligent monitoring system, conduct credibility analysis on various monitoring data, and determine the characteristic monitoring data of the collector ring carbon brush;

[0006] Analyze the characteristic monitoring data to obtain the status monitoring results of each characteristic monitoring data, and generate the state abnormality coefficient of the collector ring carbon brush based on multiple status monitoring results;

[0007] The operating state of the slip ring carbon brush is determined according to the abnormal state coefficient.

[0008] In some embodiments of the present application, an intelligent monitoring system is established, including:

[0009] Obtain historical monitoring logs of the slip ring carbon brushes, including multiple monitoring methods and corresponding historical monitoring timeliness, historical monitoring accuracy, and historical monitoring costs;

[0010] Generate historical monitoring evaluation values ​​for corresponding monitoring methods based on historical monitoring timeliness, historical monitoring accuracy, and historical monitoring costs;

[0011] Eliminate the monitoring methods whose historical monitoring evaluation values ​​are less than the preset monitoring evaluation value threshold, and obtain the remaining monitoring methods in the historical monitoring log and the sensor types and number of sensors required for the corresponding monitoring methods;

[0012] Install the corresponding sensors at the preset positions, monitor the operation of the collector ring carbon brushes according to the corresponding monitoring methods, and transmit the monitoring data to the intelligent monitoring terminal to form an intelligent monitoring system.

[0013] In some embodiments of the present application, a credibility analysis is performed on a variety of monitoring data to determine characteristic monitoring data of the slip ring carbon brush, including:

[0014] Determine the sensor type of the sensor corresponding to each type of monitoring data, obtain the data preprocessing method corresponding to the sensor of different sensor types, and perform data preprocessing on the corresponding monitoring data based on the data preprocessing method;

[0015] Preset a preset collection time interval for the monitoring data after data preprocessing, obtain monitoring data of the corresponding monitoring method according to the corresponding preset collection time interval, and generate a monitoring data set within the preset time period of the corresponding monitoring method;

[0016] Generate monitoring data differences based on the monitoring data of corresponding adjacent preset collection time intervals in the monitoring data set, compare the multiple monitoring data differences with the corresponding preset monitoring data differences, and generate a fluctuation evaluation value of the corresponding monitoring data based on the comparison result;

[0017] Set the credibility of the corresponding monitoring data according to the fluctuation evaluation value;

[0018] Pre-set credibility threshold;

[0019] If the credibility is less than the credibility threshold, the corresponding monitoring data is eliminated;

[0020] If the credibility is greater than the credibility threshold, the corresponding monitoring data is set as the characteristic monitoring data of the collector ring carbon brush.

[0021] In some embodiments of the present application, the characteristic monitoring data is analyzed to obtain a status monitoring result for each characteristic monitoring data, including:

[0022] Compare each characteristic monitoring data with a preset standard monitoring data interval. If the characteristic monitoring data is not within the corresponding standard monitoring data interval, calculate the first data difference value between the current characteristic monitoring data and the nearest critical value of the corresponding standard monitoring data interval;

[0023] If the first data difference value is less than the first preset difference value threshold, setting the state monitoring result of the corresponding feature monitoring data to a first-level abnormal state;

[0024] If the first data difference value is greater than the first preset difference value threshold, the state monitoring result of the corresponding feature monitoring data is set to a second-level abnormal state;

[0025] If the characteristic monitoring data is within the corresponding standard monitoring data interval, calculating a second data difference value between the current characteristic monitoring data and the nearest critical value of the corresponding standard monitoring data interval;

[0026] If the second data difference value is less than the second preset difference value threshold, setting the state monitoring result of the corresponding feature monitoring data to a first-level normal state;

[0027] If the second data difference value is greater than the second preset difference value threshold, the state monitoring result of the corresponding characteristic monitoring data is set to the second-level normal state;

[0028] The state abnormality coefficient of the collector ring carbon brush is generated according to the state monitoring results of multiple characteristic monitoring data and the credibility of the corresponding characteristic monitoring data.

[0029] In some embodiments of the present application, the state abnormality coefficient of the slip ring carbon brush is generated according to the state monitoring results of multiple characteristic monitoring data, including:

[0030] The calculation formula of the abnormal state coefficient of the collector ring carbon brush is:

[0031]

[0032] Among them, y is the state abnormality coefficient, a is the state abnormality conversion coefficient, n3 is the number of feature monitoring data of the secondary normal state, n4 is the number of feature monitoring data of the primary normal state, m is the total number of multiple feature monitoring data, k1v is the second data difference value of the vth feature monitoring data of the secondary normal state, uv is the credibility of the vth feature monitoring data of the secondary normal state, p0 is the second preset difference value threshold, k2s is the second data difference value of the sth feature monitoring data of the primary normal state, us is the credibility of the sth feature monitoring data of the primary normal state, n1 is the number of feature monitoring data of the primary abnormal state, n2 is the number of feature monitoring data of the secondary normal state, h0 is the first preset difference value threshold, L1i is the first data difference value of the i-th feature monitoring data of the primary abnormal state, ui is the credibility of the i-th feature monitoring data of the primary abnormal state, L2c is the first data difference value of the c-th feature monitoring data of the secondary abnormal state, and uc is the credibility of the c-th feature monitoring data of the secondary abnormal state.

[0033] In some embodiments of the present application, before determining the operating state of the slip ring carbon brush according to the abnormal state coefficient, the method includes:

[0034] Extract the corresponding sensor type and the historical status monitoring results of the historical feature monitoring data of the corresponding sensor type according to the monitoring data feature library;

[0035] Compare the status monitoring results of the feature monitoring data within a preset period with the historical status monitoring results of the same historical feature monitoring data extracted from the monitoring data feature library, and screen out multiple historical feature monitoring data with the same status monitoring results as each feature monitoring data;

[0036] Construct an actual data change curve based on each feature monitoring data within a preset time period, construct multiple historical data change curves based on the historical monitoring time periods of multiple historical feature monitoring data with the same state monitoring results of each feature monitoring data, and divide the historical data change curves into multiple historical data change curve segments according to the preset time periods;

[0037] Perform similarity analysis on each actual data change curve and the corresponding multiple historical data change curve segments, and set the historical feature monitoring data within an adjacent preset time period after the historical data change curve segment whose similarity is greater than a preset similarity threshold as the predicted feature monitoring data of the feature monitoring data corresponding to the current actual data change curve in the future preset time period;

[0038] Analyze the predicted characteristic monitoring data of each characteristic monitoring data in a preset future period to determine the predicted state monitoring results of the predicted characteristic monitoring data;

[0039] A compensation coefficient is set according to the differences between the predicted state monitoring results of multiple characteristic monitoring data and the current state monitoring results, the state abnormality coefficient is corrected according to the compensation coefficient, and the operating state of the collector ring carbon brush is determined according to the corrected state abnormality coefficient.

[0040] In some embodiments of the present application, performing similarity analysis on each actual data change curve and corresponding multiple historical data change curve segments includes:

[0041] Comparing the characteristic monitoring data of multiple preset collection time intervals in each actual data change curve with the historical characteristic monitoring data of multiple preset collection time intervals in the corresponding multiple historical data change curve segments to obtain the comparison data difference of each preset collection time interval;

[0042] Generate characteristic monitoring data differences based on characteristic monitoring data of adjacent preset collection time intervals in each actual data change curve, and generate historical characteristic monitoring data differences based on historical characteristic monitoring data of adjacent preset collection time intervals in multiple historical data change curve segments corresponding to each actual data change curve;

[0043] Determine the similarity between each actual data change curve and the corresponding historical data change curve segment based on the comparison data difference of each preset collection time interval, the feature monitoring data difference corresponding to adjacent preset collection time intervals, and the historical feature monitoring data difference;

[0044] The calculation formula of the similarity is:

[0045]

[0046] Among them, D is the similarity, j1 is the first weight coefficient, j2 is the second weight coefficient, w is the number of preset collection time intervals in the preset period, △Gr is the comparison data difference of the r-th preset collection time interval, △Q1r is the feature monitoring data difference of the r-th adjacent preset collection time interval, △Q2r is the historical feature monitoring data difference of the r-th adjacent preset collection time interval, and w-1 is the number of adjacent preset collection time intervals.

[0047] In some embodiments of the present application, a compensation coefficient is set based on a plurality of result differences between a predicted state monitoring result and a current state monitoring result of a plurality of feature monitoring data, including:

[0048] Determine the predicted change level of the current state monitoring result according to the difference between the predicted state monitoring result and the current state monitoring result of each characteristic monitoring data;

[0049] The predicted change level includes a predicted increase level and a predicted decrease level, wherein the predicted increase level includes a first preset predicted increase level, a second preset predicted increase level, and a third preset predicted increase level, and the predicted decrease level includes a first preset predicted decrease level, a second preset predicted decrease level, and a third preset predicted decrease level, and each preset predicted increase level and preset predicted decrease level is configured with a corresponding preset compensation coefficient;

[0050] A plurality of preset compensation coefficients are determined according to the predicted change level of the current state monitoring result of each characteristic monitoring data, and the plurality of preset compensation coefficients are averaged to obtain the compensation coefficients.

[0051] In some embodiments of the present application, determining the operating state of the slip ring carbon brush according to the corrected state abnormality coefficient includes:

[0052] Presetting a first preset state abnormal coefficient threshold and a second preset state abnormal coefficient threshold;

[0053] When the corrected state abnormality coefficient is less than the first preset state abnormality coefficient threshold, determining that the operating state of the slip ring carbon brush is normal;

[0054] When the corrected state abnormality coefficient is between the first preset state abnormality coefficient threshold and the second preset state abnormality coefficient threshold, determining that the operating state of the slip ring carbon brush is an abnormal state, and sending an early warning instruction;

[0055] When the corrected state abnormality coefficient is greater than the second preset state abnormality coefficient threshold, it is determined that the operating state of the slip ring carbon brush is a fault state, and an alarm instruction is sent.

[0056] Compared with the prior art, the method for monitoring the operation of carbon brushes of a wind turbine collector ring according to the embodiment of the present application has the following advantages:

[0057] By constructing an intelligent monitoring system, characteristic monitoring data of multiple monitoring methods are obtained, and the state monitoring results of each characteristic monitoring data are determined. The predicted state monitoring results are generated based on the predicted characteristic monitoring data of the future preset time period, and the state abnormality coefficient is generated based on the state monitoring results. The compensation coefficient is generated based on the predicted state monitoring results and the state abnormality coefficient is corrected to improve the accuracy of the state abnormality coefficient, timely reflect the operating status of the collector ring carbon brush in the current preset time period and the future preset time period, and reduce the failure frequency of the wind turbine. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flow chart of a method for monitoring the operation of carbon brushes of a wind turbine collector ring in a preferred embodiment of the present application. DETAILED DESCRIPTION

[0059] The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0060] In the description of this application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application.

[0061] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. Throughout this application, unless otherwise specified, "plurality" means two or more.

[0062] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0063] like Figure 1 As shown, a method for monitoring the operation of carbon brushes of a wind turbine collector ring according to a preferred embodiment of the present application includes:

[0064] Step S101: establishing an intelligent monitoring system, obtaining monitoring data of the monitoring methods included in the intelligent monitoring system, performing credibility analysis on various monitoring data, and determining characteristic monitoring data of the slip ring carbon brushes;

[0065] Step S102: Analyze the characteristic monitoring data to obtain a status monitoring result for each characteristic monitoring data, and generate a state abnormality coefficient of the slip ring carbon brush based on the multiple status monitoring results;

[0066] Step S103: determining the operating state of the slip ring carbon brush according to the abnormal state coefficient.

[0067] In this embodiment, monitoring methods include current monitoring, temperature monitoring, vibration monitoring, and visual monitoring. Current monitoring involves using a current sensor to monitor the output current of the wind turbine. Severe carbon brush wear or poor contact can cause abnormal current flow, and the brush status can be determined by monitoring current changes. Vibration monitoring involves installing a vibration sensor on the rotating components of the wind turbine to monitor the vibration of the carbon brushes. Carbon brush wear or poor contact can cause abnormal vibration, and the brush status can be determined by monitoring vibration. Temperature monitoring involves installing a temperature sensor near the wind turbine's slip rings to monitor the operating temperature of the carbon brushes. When carbon brush wear or poor contact occurs, excessive heat is generated, leading to a temperature increase. Monitoring temperature changes can be used to determine the brush status. Visual monitoring involves installing a camera or fiber optic sensor near the wind turbine's slip rings to monitor the wear and contact status of the carbon brushes in real time. Image processing or optical sensor data analysis can be used to determine the brush status. By analyzing various monitoring methods, an evaluation value is obtained, and the appropriate monitoring method is selected. An intelligent monitoring system is then constructed to accurately monitor and provide early warning of slip ring carbon brush status.

[0068] In some embodiments of the present application, an intelligent monitoring system is established, including:

[0069] Obtain historical monitoring logs of the slip ring carbon brushes, including multiple monitoring methods and corresponding historical monitoring timeliness, historical monitoring accuracy, and historical monitoring costs;

[0070] Generate historical monitoring evaluation values ​​for corresponding monitoring methods based on historical monitoring timeliness, historical monitoring accuracy, and historical monitoring costs;

[0071] Eliminate the monitoring methods whose historical monitoring evaluation values ​​are less than the preset monitoring evaluation value threshold, and obtain the remaining monitoring methods in the historical monitoring log and the sensor types and number of sensors required for the corresponding monitoring methods;

[0072] Install the corresponding sensors at the preset positions, monitor the operation of the collector ring carbon brushes according to the corresponding monitoring methods, and transmit the monitoring data to the intelligent monitoring terminal to form an intelligent monitoring system.

[0073] In this embodiment, by analyzing the monitoring logs of various monitoring methods for monitoring the collector ring carbon brushes, a monitoring evaluation value of each monitoring method is obtained, and a reasonable monitoring method is selected according to the monitoring evaluation value, that is, a monitoring method with high monitoring accuracy, low monitoring degree and timely monitoring, so as to improve the accuracy of subsequent characteristic monitoring data.

[0074] In some embodiments of the present application, a credibility analysis is performed on a variety of monitoring data to determine characteristic monitoring data of the slip ring carbon brush, including:

[0075] Determine the sensor type of the sensor corresponding to each type of monitoring data, obtain the data preprocessing method corresponding to the sensor of different sensor types, and perform data preprocessing on the corresponding monitoring data based on the data preprocessing method;

[0076] Preset a preset collection time interval for the monitoring data after data preprocessing, obtain monitoring data of the corresponding monitoring method according to the corresponding preset collection time interval, and generate a monitoring data set within the preset time period of the corresponding monitoring method;

[0077] Generate monitoring data differences based on the monitoring data of corresponding adjacent preset collection time intervals in the monitoring data set, compare the multiple monitoring data differences with the corresponding preset monitoring data differences, and generate a fluctuation evaluation value of the corresponding monitoring data based on the comparison result;

[0078] Set the credibility of the corresponding monitoring data according to the fluctuation evaluation value;

[0079] Pre-set credibility threshold;

[0080] If the credibility is less than the credibility threshold, the corresponding monitoring data is eliminated;

[0081] If the credibility is greater than the credibility threshold, the corresponding monitoring data is set as the characteristic monitoring data of the collector ring carbon brush.

[0082] In this embodiment, the data preprocessing method includes data cleaning, data standardization and normalization, data feature selection, etc. The data preprocessing method is used to improve the accuracy and reliability of the monitoring data collected by the corresponding sensor, laying the foundation for the subsequent determination of the operating status of the collector ring carbon brush and improving the status accuracy.

[0083] In this embodiment, the fluctuation evaluation value refers to the degree of fluctuation of the monitoring data within a preset time period. A smaller degree of fluctuation indicates that the stability of the monitoring data is higher, that is, the smaller the fluctuation evaluation value, the greater the credibility. A larger degree of fluctuation indicates that the stability of the monitoring data is lower, that is, the larger the fluctuation evaluation value, the lower the credibility.

[0084] In this embodiment, characteristic monitoring data is determined by calculating the accuracy of the monitoring method and the degree of fluctuation of the monitoring data, thereby reducing the amount of data processing and analysis. Based on the analysis results of the characteristic monitoring data, the operating status of the collector ring carbon brush is accurately and quickly judged.

[0085] In some embodiments of the present application, the characteristic monitoring data is analyzed to obtain a status monitoring result for each characteristic monitoring data, including:

[0086] Compare each characteristic monitoring data with a preset standard monitoring data interval. If the characteristic monitoring data is not within the corresponding standard monitoring data interval, calculate the first data difference value between the current characteristic monitoring data and the nearest critical value of the corresponding standard monitoring data interval;

[0087] If the first data difference value is less than the first preset difference value threshold, setting the state monitoring result of the corresponding feature monitoring data to a first-level abnormal state;

[0088] If the first data difference value is greater than the first preset difference value threshold, the state monitoring result of the corresponding feature monitoring data is set to a second-level abnormal state;

[0089] If the characteristic monitoring data is within the corresponding standard monitoring data interval, calculating a second data difference value between the current characteristic monitoring data and the nearest critical value of the corresponding standard monitoring data interval;

[0090] If the second data difference value is less than the second preset difference value threshold, setting the state monitoring result of the corresponding feature monitoring data to a first-level normal state;

[0091] If the second data difference value is greater than the second preset difference value threshold, the state monitoring result of the corresponding characteristic monitoring data is set to the second-level normal state;

[0092] The state abnormality coefficient of the collector ring carbon brush is generated according to the state monitoring results of multiple characteristic monitoring data and the credibility of the corresponding characteristic monitoring data.

[0093] In this embodiment, the abnormality degree of the secondary abnormal state is greater than that of the primary abnormal state, and the normality degree of the primary normal state is less than that of the secondary normal state, that is, the monitoring data of the secondary abnormal state indicates a greater probability of abnormality of the collector ring carbon brush than the monitoring data of the primary abnormal state, and the monitoring data of the secondary normal state indicates a better operating state of the collector ring carbon brush than the monitoring data of the primary normal state.

[0094] In this embodiment, the first data difference value refers to the difference value of the critical data closest to the endpoint of the standard monitoring data interval when the characteristic monitoring data is not in the standard monitoring data interval, and the second data difference value refers to the difference value of the critical data closest to the endpoint of the standard monitoring data interval when the characteristic monitoring data is in the standard monitoring data interval. The first preset difference value threshold is set based on the minimum difference value between the historical monitoring data belonging to the second-level abnormal state and the critical data closest to the standard monitoring data interval, and the second preset difference value threshold is set based on the minimum difference value between the historical monitoring data belonging to the second-level normal state and the critical data closest to the standard monitoring data interval.

[0095] In some embodiments of the present application, the state abnormality coefficient of the slip ring carbon brush is generated according to the state monitoring results of multiple characteristic monitoring data, including:

[0096] The calculation formula of the abnormal state coefficient of the collector ring carbon brush is:

[0097]

[0098] Among them, y is the state abnormality coefficient, a is the state abnormality conversion coefficient, n3 is the number of feature monitoring data of the secondary normal state, n4 is the number of feature monitoring data of the primary normal state, m is the total number of multiple feature monitoring data, k1v is the second data difference value of the vth feature monitoring data of the secondary normal state, uv is the credibility of the vth feature monitoring data of the secondary normal state, p0 is the second preset difference value threshold, k2s is the second data difference value of the sth feature monitoring data of the primary normal state, us is the credibility of the sth feature monitoring data of the primary normal state, n1 is the number of feature monitoring data of the primary abnormal state, n2 is the number of feature monitoring data of the secondary normal state, h0 is the first preset difference value threshold, L1i is the first data difference value of the i-th feature monitoring data of the primary abnormal state, ui is the credibility of the i-th feature monitoring data of the primary abnormal state, L2c is the first data difference value of the c-th feature monitoring data of the secondary abnormal state, and uc is the credibility of the c-th feature monitoring data of the secondary abnormal state.

[0099] In some embodiments of the present application, before determining the operating state of the slip ring carbon brush according to the abnormal state coefficient, the method includes:

[0100] Extract the corresponding sensor type and the historical status monitoring results of the historical feature monitoring data of the corresponding sensor type according to the monitoring data feature library;

[0101] Compare the status monitoring results of the feature monitoring data within a preset period with the historical status monitoring results of the same historical feature monitoring data extracted from the monitoring data feature library, and screen out multiple historical feature monitoring data with the same status monitoring results as each feature monitoring data;

[0102] Construct an actual data change curve based on each feature monitoring data within a preset time period, construct multiple historical data change curves based on the historical monitoring time periods of multiple historical feature monitoring data with the same state monitoring results of each feature monitoring data, and divide the historical data change curves into multiple historical data change curve segments according to the preset time periods;

[0103] Perform similarity analysis on each actual data change curve and the corresponding multiple historical data change curve segments, and set the historical feature monitoring data in the adjacent preset time period after the historical data change curve segment with the greatest similarity as the predicted feature monitoring data of the feature monitoring data corresponding to the current actual data change curve in the future preset time period;

[0104] Analyze the predicted characteristic monitoring data of each characteristic monitoring data in a preset future period to determine the predicted state monitoring results of the predicted characteristic monitoring data;

[0105] A compensation coefficient is set according to the differences between the predicted state monitoring results of multiple characteristic monitoring data and the current state monitoring results, the state abnormality coefficient is corrected according to the compensation coefficient, and the operating state of the collector ring carbon brush is determined according to the corrected state abnormality coefficient.

[0106] In this embodiment, the monitoring data feature library refers to the historical monitoring data collected by the sensor types corresponding to all monitoring methods and the corresponding historical status monitoring results. According to the monitoring data feature library, the historical monitoring data with the same feature monitoring data and the same status monitoring results are screened out, and multiple historical data change curve segments are constructed. According to the similarity between the actual data change curve and the historical data change curve segment, the feature monitoring data is predicted for a preset time period in the future to obtain the predicted feature monitoring data and the predicted status monitoring results.

[0107] In this embodiment, by predicting the characteristic monitoring data of a preset time period in the future, the predicted characteristic monitoring data and the predicted state monitoring results are obtained, the result difference is obtained and a compensation coefficient is set, and the state abnormality coefficient is corrected according to the compensation coefficient to improve the accuracy of the state abnormality coefficient, that is, to improve the accuracy of the diagnosis of the operating status of the collector ring carbon brush.

[0108] In some embodiments of the present application, performing similarity analysis on each actual data change curve and corresponding multiple historical data change curve segments includes:

[0109] Comparing the characteristic monitoring data of multiple preset collection time intervals in each actual data change curve with the historical characteristic monitoring data of multiple preset collection time intervals in the corresponding multiple historical data change curve segments to obtain the comparison data difference of each preset collection time interval;

[0110] Generate characteristic monitoring data differences based on characteristic monitoring data of adjacent preset collection time intervals in each actual data change curve, and generate historical characteristic monitoring data differences based on historical characteristic monitoring data of adjacent preset collection time intervals in multiple historical data change curve segments corresponding to each actual data change curve;

[0111] Determine the similarity between each actual data change curve and the corresponding historical data change curve segment based on the comparison data difference of each preset collection time interval, the feature monitoring data difference corresponding to adjacent preset collection time intervals, and the historical feature monitoring data difference;

[0112] The calculation formula of the similarity is:

[0113]

[0114] Among them, D is the similarity, j1 is the first weight coefficient, j2 is the second weight coefficient, w is the number of preset collection time intervals in the preset period, △Gr is the comparison data difference of the r-th preset collection time interval, △Q1r is the feature monitoring data difference of the r-th adjacent preset collection time interval, △Q2r is the historical feature monitoring data difference of the r-th adjacent preset collection time interval, and w-1 is the number of adjacent preset collection time intervals.

[0115] In this embodiment, the comparative data difference refers to the data difference between the feature monitoring data and the historical feature monitoring data at the same preset collection time interval. The difference similarity is determined based on multiple comparative data differences, and the fluctuation degree similarity of adjacent preset collection time intervals is obtained based on multiple feature monitoring data differences and historical feature monitoring data differences, thereby screening out the historical data change curve segment with the greatest similarity to the actual data change curve, and setting the historical feature monitoring data of the preset time period adjacent to the historical data change curve segment with the greatest similarity as the predicted feature detection data of the current feature monitoring data.

[0116] In some embodiments of the present application, a compensation coefficient is set based on a plurality of result differences between a predicted state monitoring result and a current state monitoring result of a plurality of feature monitoring data, including:

[0117] Determine the predicted change level of the current state monitoring result according to the difference between the predicted state monitoring result and the current state monitoring result of each characteristic monitoring data;

[0118] The predicted change level includes a predicted increase level and a predicted decrease level, wherein the predicted increase level includes a first preset predicted increase level, a second preset predicted increase level, and a third preset predicted increase level, and the predicted decrease level includes a first preset predicted decrease level, a second preset predicted decrease level, and a third preset predicted decrease level, and each preset predicted increase level and preset predicted decrease level is configured with a corresponding preset compensation coefficient;

[0119] A plurality of preset compensation coefficients are determined according to the predicted change level of the current state monitoring result of each characteristic monitoring data, and the plurality of preset compensation coefficients are averaged to obtain the compensation coefficients.

[0120] In this embodiment, the predicted rising level means that the predicted state monitoring result is better than the state in the current state monitoring result. For example, if the current state monitoring result is a level one abnormal state and the predicted state monitoring result is a level two normal state, then the predicted rising level is the second predicted rising level; or if the current state monitoring result is a level one normal state and the predicted state monitoring result is a level two abnormal state, then the predicted falling level is the second predicted falling level.

[0121] In this embodiment, the values ​​of the preset compensation coefficient and the compensation coefficient are both (0.65, 1.35). When the predicted decline level is greater, the preset compensation coefficient is smaller, and when the predicted rise level is greater, the preset compensation coefficient is larger.

[0122] In this embodiment, by predicting the characteristic monitoring data of the future preset time period, the predicted state monitoring results of each characteristic monitoring data in the future preset time period are obtained, and compared with the current state monitoring results, so as to determine the compensation coefficient to correct the state abnormality coefficient, improve the monitoring accuracy of the operating status of the collector ring carbon brush in the current preset time period and the future preset time period, and formulate adjustment strategies in time to reduce the probability of failure.

[0123] In some embodiments of the present application, determining the operating state of the slip ring carbon brush according to the corrected state abnormality coefficient includes:

[0124] Presetting a first preset state abnormal coefficient threshold and a second preset state abnormal coefficient threshold;

[0125] When the corrected state abnormality coefficient is less than the first preset state abnormality coefficient threshold, determining that the operating state of the slip ring carbon brush is normal;

[0126] When the corrected state abnormality coefficient is between the first preset state abnormality coefficient threshold and the second preset state abnormality coefficient threshold, determining that the operating state of the slip ring carbon brush is an abnormal state, and sending an early warning instruction;

[0127] When the corrected state abnormality coefficient is greater than the second preset state abnormality coefficient threshold, it is determined that the operating state of the slip ring carbon brush is a fault state, and an alarm instruction is sent.

[0128] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and replacements can be made without departing from the technical principles of the present application. These improvements and replacements should also be regarded as the scope of protection of the present application.

Claims

1. A method for monitoring the operation of a wind turbine collector ring carbon brush, characterized in that: include: Establish an intelligent monitoring system, obtain monitoring data from the monitoring methods included in the intelligent monitoring system, conduct credibility analysis on various monitoring data, and determine the characteristic monitoring data of the collector ring carbon brush; Analyze the characteristic monitoring data to obtain the status monitoring results of each characteristic monitoring data, and generate the state abnormality coefficient of the collector ring carbon brush based on multiple status monitoring results; Determining the operating state of the slip ring carbon brush according to the abnormal state coefficient; Establish an intelligent monitoring system, including: Obtain historical monitoring logs of the slip ring carbon brushes, including multiple monitoring methods and corresponding historical monitoring timeliness, historical monitoring accuracy, and historical monitoring costs; Generate historical monitoring evaluation values ​​for corresponding monitoring methods based on historical monitoring timeliness, historical monitoring accuracy, and historical monitoring costs; Eliminate the monitoring methods whose historical monitoring evaluation values ​​are less than the preset monitoring evaluation value threshold, and obtain the remaining monitoring methods in the historical monitoring log and the sensor types and number of sensors required for the corresponding monitoring methods; Install the corresponding sensors at the preset positions, monitor the operation of the collector ring carbon brushes according to the corresponding monitoring methods, and transmit the monitoring data to the intelligent monitoring terminal to form an intelligent monitoring system; Conduct credibility analysis on various monitoring data to determine the characteristic monitoring data of the collector ring carbon brush, including: Determine the sensor type of the sensor corresponding to each type of monitoring data, obtain the data preprocessing method corresponding to the sensor of different sensor types, and perform data preprocessing on the corresponding monitoring data based on the data preprocessing method; Preset a preset collection time interval for the monitoring data after data preprocessing, obtain monitoring data of the corresponding monitoring method according to the corresponding preset collection time interval, and generate a monitoring data set within the preset time period of the corresponding monitoring method; Generate monitoring data differences based on the monitoring data of corresponding adjacent preset collection time intervals in the monitoring data set, compare the multiple monitoring data differences with the corresponding preset monitoring data differences, and generate a fluctuation evaluation value of the corresponding monitoring data based on the comparison result; Set the credibility of the corresponding monitoring data according to the fluctuation evaluation value; Pre-set credibility threshold; If the credibility is less than the credibility threshold, the corresponding monitoring data is eliminated; If the credibility is greater than the credibility threshold, the corresponding monitoring data is set as the characteristic monitoring data of the collector ring carbon brush.

2. The method for monitoring the operation of the carbon brush of the collector ring of a wind turbine according to claim 1, wherein: Analyze the characteristic monitoring data to obtain the status monitoring results of each characteristic monitoring data, including: Compare each characteristic monitoring data with a preset standard monitoring data interval. If the characteristic monitoring data is not within the corresponding standard monitoring data interval, calculate the first data difference value between the current characteristic monitoring data and the nearest critical value of the corresponding standard monitoring data interval; If the first data difference value is less than the first preset difference value threshold, setting the state monitoring result of the corresponding feature monitoring data to a first-level abnormal state; If the first data difference value is greater than the first preset difference value threshold, the state monitoring result of the corresponding feature monitoring data is set to a second-level abnormal state; If the characteristic monitoring data is within the corresponding standard monitoring data interval, calculating a second data difference value between the current characteristic monitoring data and the nearest critical value of the corresponding standard monitoring data interval; If the second data difference value is less than the second preset difference value threshold, setting the state monitoring result of the corresponding feature monitoring data to a first-level normal state; If the second data difference value is greater than the second preset difference value threshold, the state monitoring result of the corresponding characteristic monitoring data is set to the second-level normal state; The state abnormality coefficient of the collector ring carbon brush is generated according to the state monitoring results of multiple characteristic monitoring data and the credibility of the corresponding characteristic monitoring data.

3. The method for monitoring the operation of the carbon brush of the collector ring of a wind turbine according to claim 2, wherein: The status abnormality coefficient of the collector ring carbon brush is generated based on the status monitoring results of various characteristic monitoring data, including: The calculation formula of the abnormal state coefficient of the collector ring carbon brush is: ； Among them, y is the state abnormality coefficient, a is the state abnormality conversion coefficient, n3 is the number of feature monitoring data of the secondary normal state, n4 is the number of feature monitoring data of the primary normal state, m is the total number of multiple feature monitoring data, k1v is the second data difference value of the vth feature monitoring data of the secondary normal state, uv is the credibility of the vth feature monitoring data of the secondary normal state, p0 is the second preset difference value threshold, k2s is the second data difference value of the sth feature monitoring data of the primary normal state, us is the credibility of the sth feature monitoring data of the primary normal state, n1 is the number of feature monitoring data of the primary abnormal state, n2 is the number of feature monitoring data of the secondary normal state, h0 is the first preset difference value threshold, L1i is the first data difference value of the i-th feature monitoring data of the primary abnormal state, ui is the credibility of the i-th feature monitoring data of the primary abnormal state, L2c is the first data difference value of the c-th feature monitoring data of the secondary abnormal state, and uc is the credibility of the c-th feature monitoring data of the secondary abnormal state.

4. The method for monitoring the operation of the carbon brush of the collector ring of a wind turbine according to claim 3, wherein: Before determining the operating state of the slip ring carbon brush according to the abnormal state coefficient, the method includes: Extract the corresponding sensor type and the historical status monitoring results of the historical feature monitoring data of the corresponding sensor type according to the monitoring data feature library; Compare the status monitoring results of the feature monitoring data within a preset period with the historical status monitoring results of the same historical feature monitoring data extracted from the monitoring data feature library, and screen out multiple historical feature monitoring data with the same status monitoring results as each feature monitoring data; Construct an actual data change curve based on each feature monitoring data within a preset time period, construct multiple historical data change curves based on the historical monitoring time periods of multiple historical feature monitoring data with the same state monitoring results of each feature monitoring data, and divide the historical data change curves into multiple historical data change curve segments according to the preset time periods; Perform similarity analysis on each actual data change curve and the corresponding multiple historical data change curve segments, and set the historical feature monitoring data within an adjacent preset time period after the historical data change curve segment whose similarity is greater than a preset similarity threshold as the predicted feature monitoring data of the feature monitoring data corresponding to the current actual data change curve in the future preset time period; Analyze the predicted characteristic monitoring data of each characteristic monitoring data in a preset future period to determine the predicted state monitoring results of the predicted characteristic monitoring data; A compensation coefficient is set according to the differences between the predicted state monitoring results of multiple characteristic monitoring data and the current state monitoring results, the state abnormality coefficient is corrected according to the compensation coefficient, and the operating state of the collector ring carbon brush is determined according to the corrected state abnormality coefficient.

5. The method for monitoring the operation of the carbon brush of the collector ring of a wind turbine according to claim 4, characterized in that: Perform similarity analysis on each actual data change curve and the corresponding multiple historical data change curve segments, including: Comparing the characteristic monitoring data of multiple preset collection time intervals in each actual data change curve with the historical characteristic monitoring data of multiple preset collection time intervals in the corresponding multiple historical data change curve segments to obtain the comparison data difference of each preset collection time interval; Generate characteristic monitoring data differences based on characteristic monitoring data of adjacent preset collection time intervals in each actual data change curve, and generate historical characteristic monitoring data differences based on historical characteristic monitoring data of adjacent preset collection time intervals in multiple historical data change curve segments corresponding to each actual data change curve; Determine the similarity between each actual data change curve and the corresponding historical data change curve segment based on the comparison data difference of each preset collection time interval, the feature monitoring data difference corresponding to adjacent preset collection time intervals, and the historical feature monitoring data difference; The calculation formula of the similarity is: Where D is the similarity, j1 is the first weight coefficient, j2 is the second weight coefficient, and w is the number of preset collection time intervals within the preset period. is the comparison data difference of the rth preset collection time interval, is the difference in characteristic monitoring data between the rth adjacent preset collection time intervals, is the difference in historical characteristic monitoring data between the rth adjacent preset collection time intervals, and w-1 is the number of adjacent preset collection time intervals.

6. The method for monitoring the operation of the carbon brush of the collector ring of a wind turbine according to claim 5, characterized in that: The compensation coefficient is set based on the differences between the predicted state monitoring results and the current state monitoring results of various characteristic monitoring data, including: Determine the predicted change level of the current state monitoring result according to the difference between the predicted state monitoring result and the current state monitoring result of each characteristic monitoring data; The predicted change level includes a predicted increase level and a predicted decrease level, wherein the predicted increase level includes a first preset predicted increase level, a second preset predicted increase level, and a third preset predicted increase level, and the predicted decrease level includes a first preset predicted decrease level, a second preset predicted decrease level, and a third preset predicted decrease level, and each preset predicted increase level and preset predicted decrease level is configured with a corresponding preset compensation coefficient; A plurality of preset compensation coefficients are determined according to the predicted change level of the current state monitoring result of each characteristic monitoring data, and the plurality of preset compensation coefficients are averaged to obtain the compensation coefficients.

7. The method for monitoring the operation of the carbon brush of the collector ring of a wind turbine according to claim 6, characterized in that: Determine the operating status of the collector ring carbon brush based on the corrected abnormal status coefficient, including: Presetting a first preset state abnormal coefficient threshold and a second preset state abnormal coefficient threshold; When the corrected state abnormality coefficient is less than the first preset state abnormality coefficient threshold, determining that the operating state of the slip ring carbon brush is normal; When the corrected state abnormality coefficient is between the first preset state abnormality coefficient threshold and the second preset state abnormality coefficient threshold, determining that the operating state of the slip ring carbon brush is an abnormal state, and sending an early warning instruction; When the corrected state abnormality coefficient is greater than the second preset state abnormality coefficient threshold, it is determined that the operating state of the slip ring carbon brush is a fault state, and an alarm instruction is sent.

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