METHOD FOR MONITORING ONE OR MORE ELECTRIC DRIVES OF AN ELECTROMECHANICAL SYSTEM

Abstract

A method monitors one or more electric drives of an electromechanical installation, particularly a wind orientation control of a wind turbine. The drive or drives work on a movable machine element of the installation, e.g., on a bearing ring of an azimuth bearing. A plurality of currents, e.g., phase currents of a plurality of phases, and/or a plurality of drives are measured during the operation of the drives at a predetermined sampling rate and are stored as series of measurement values with a predetermined quantity m of measurement values. Statistical characteristic values are calculated from one or more series of measurement values, and one or more pieces of state information and/or one or more state prognoses are/is generated for one or more drives through analysis of the time evolution of the characteristic values and/or through analysis of a relationship of the characteristic values of different motor currents.

Claims

1. A method for monitoring one or more electric drives (M1, M2, M3, M4) of an electromechanical installation, wherein the drive or drives (M1, M2, M3, M4) work on a movable machine element of the installation, comprising the steps of: measuring one or more motor currents of one or more of the drives (M1, M2, M3, M4) during the operation of the drives at a predetermined sampling rate and storing the measured one or more currents as a series of measurement values which is associated with a respective one of the drives and has a predetermined quantity (m) of measurement values in each instance, wherein statistical characteristic values of each respective drive (M1, M2, M3, M4) are calculated from one or more series of measurement values, wherein one or more pieces of state information and/or one or more state prognoses are/is generated for one or more of the drives through analysis of the time evolution of the characteristic values and/or through analysis of a relationship of the characteristic values of different motor currents.

2. The method according to claim 1, wherein the sampling rate is more than 0.2 Hz.

3. The method according to claim 1, claim 1, wherein each series of measurement values has 100 to 1000 measurement values.

4. The method according to claim 1, wherein the installation has one or more single-phase or polyphase AC drives as the drives (M1, M2, M3, M4).

5. The method according to claim 1, wherein the installation has one or more DC drives as the drives.

6. The method according to claim 1, wherein a plurality of the drives (M1, M2, M3, M4) are provided, all of which work on the same movable machine element, and wherein these drives are each formed as polyphase drives, each with a plurality of phases (L1, L2, L3).

7. The method according to claim 1, wherein the electromechanical installation is formed as a wind orientation control of a wind turbine, and wherein the movable machine element is formed by a bearing ring of an azimuth bearing.

8. The method according to claim 1, wherein measurement distribution functions or distribution density functions are determined from the series of measurement values and are possibly stored, and/or wherein the characteristic values are determined from the measurement distribution functions or distribution density functions, and the distribution functions or distribution density functions are recorded in characteristic values, respectively.

9. The method according to claim 1, wherein one or more of the characteristic values are calculated from the following groups and possibly intermediately stored as statistical characteristic values from individual series of measurement values for the respective motor current or the respective phase of the respective drive: mean values of individual currents/phases or of all currents/phases, mean value of all of the currents/phases of a drive, minimum, maximum, standard deviation, variance, first sigma, second sigma, third sigma, median, RMS (effective value), sum of the squares, measurement value density, value of the highest measurement value density.

10. The method according to claim 1, wherein one or more of the characteristic values are calculated from the following group and possibly intermediately stored as statistical characteristic values from a plurality of series of measurement values of different phases and/or different drives: covariance, correlation coefficient.

11. The method according to claim 1, wherein a time evolution of the characteristic values of a current or phase, respectively, or of a drive is analyzed by carrying out an ordering based on previously determined and stored characteristic values, wherein, characteristic values are first determined as reference values in a learning phase and are stored, and/or wherein temporally consecutively determined characteristic values are correlated.

12. The method according to claim 1, wherein the relationship or the time evolution of the relationship of characteristic values of different currents or phases, respectively, or drives is analyzed, by correlating characteristic values or the temporal progression of characteristic values.

13. The method according to claim 1, wherein statistical characteristic values which represent a relationship of a series of measurement values of different currents or phases, respectively, or drives, the statistical characteristic values being in the form of covariances and/or correlation coefficients, are analyzed by correlating with stored reference values or limiting values or analyzing their temporal progression.

14. The method according to claim 1, wherein the measurement values are recorded in the installation and possibly intermediately stored, and wherein the determined measurement values are transmitted to an externally arranged evaluation device and are stored and evaluated in the evaluation device as series of measurement values.

15. The method according to claim 1, wherein a feedback is generated from the determined state information and fed back to the installation, and wherein the operation of the installation is adapted depending on the feedback by interrupting or modifying the operation of the installation depending on the state information or feedback, respectively.

16. An electromechanical installation, having at least one moveable machine element, and having one or more electric drives (M, M2, M3, M4) which work on the moveable machine element, wherein the installation is adapted to implement a method according to claim 1.

17. The installation according to claim 16, wherein the drives are outfitted with, or connected to, one or more measuring devices (5) for measuring the motor currents.

18. The installation according to claim 16, wherein the installation is outfitted with a local control device (4), wherein the measurement values are measured with the control device (4) and are locally intermediately stored in the control device, and wherein the measurement values are transmitted to an externally arranged evaluation device (7) which, e.g., has a data server and/or evaluation server with an evaluation program.

19. The installation according to claim 16, wherein the external evaluation device has an interface via which the state information is transmittable to one or more terminals or is pollable by one or more terminals (8, 9, 10).

Description

[0040] The invention will be described in the following referring to drawings showing only one embodiment example. The drawings show:

[0041] FIG. 1 a highly simplified schematic depiction of an electromechanical installation embodied as wind orientation control with a state monitoring arrangement according to the invention;

[0042] FIG. 2 an enlarged detail from the installation in FIG. 1;

[0043] FIGS. 3a, b statistical characteristic values (covariances) for properly functioning (new) drives on the one hand and defective drives with increased slip on the other hand;

[0044] FIGS. 4a, b statistical characteristic values (correlation coefficients) for the drives according to FIGS. 3a, b;

[0045] FIGS. 5a, 5b simplified histograms and distribution density functions.

[0046] FIGS. 1 and 2 show a highly simplified schematic depiction of an electromechanical installation embodied as wind orientation control, specifically with a state monitoring arrangement for the electric drives of the wind orientation control.

[0047] The wind orientation control serves to adjust the nacelle of a wind turbine in direction of the prevailing wind. The nacelle is rotatably supported at the tower head and can be adjusted by a plurality of electric drives. FIG. 1 shows a simplified detail of a tower head bearing 1 with a bearing ring 2 on which a plurality of electric drives M1, M2, M3, M4 operate. An exemplary arrangement with internal toothing of the azimuth bearing and with internal drives and external azimuth brake 3 is shown. The electric drives are connected to a control device 4 which is arranged in the wind turbine, e.g., in the tower head or in the nacelle. The drives are formed as three-phase AC motors. In the embodiment example, there are four drives M1, M2, M3, M4, all of which act on the same machine element, namely, the same bearing ring 2. The control device 4 activates the drives, as needed, in order to adjust the nacelle to a change in wind direction. According to the invention, the drives are outfitted with, or connected to, measuring devices 5 with which the phase currents of all three phases L1, L2, L3 of each individual drive M1, M2, M3, M4 are measured. Further, conventional protective devices, e.g., a motor breaker switch 11 and a relay switch 12, which can be provided in a conventional manner are indicated in FIG. 2. All of the phase currents of the drives are detected by the measuring devices 5 with a high sampling rate, e.g., 0.5 Hz to 5 Hz, e.g., approximately 1 Hz, i.e., every second. Startup spikes and stop spikes are already factored out of the installation by the measuring devices 5 or by the control device 4. During the operation of the motors, which generally only lasts a few seconds within the framework of a wind orientation control, the measurement values are (temporarily) buffered in the control device 4. The measurement values are conveyed (e.g., via TCP/IP) to an evaluation device or storage/evaluation device 7 (e.g., after measurement is concluded or after the wind orientation control is halted) via an A/D converter 13 and a microcontroller MC by means of an interface or communications device 6. For example, the storage/evaluation device 7 is formed as, or outfitted with, a database server for storing large amounts of data, and evaluation software is stored in the evaluation device 7. The measurement values are evaluated in the storage/evaluation device and state information for the drives M1, M2, M3, M4 is generated therefrom. This state information can be polled and visualized via various terminals, e.g., a PC 8, a tablet 9 or a smartphone 10. The terminals can communicate with the evaluation device 7 in a wired or wireless manner.

[0048] In the depicted embodiment example, all three phase currents L1, L2, L3 are accordingly measured for all of the motors M1, M2, M3 and M4, that is, with the high sampling rate of, e.g., 1 Hz as was described above. Series of measurement values with a predetermined quantity m of measurement values are stored in each instance, 600 measurement values per series of measurement values in the embodiment example, i.e., the measurement values are stored in clusters of 600 measurement points, namely, for every individual phase of every motor so that, in the embodiment example, twelve series of measurement values are generated and stored. At a sampling rate of 1 Hz and 600 measurements per series of measurement values, one series of measurement values represents a time period of 10 minutes. This time period does not represent one continuous measurement but rather covers the total operation of the respective drive as a result of a plurality of temporally consecutive wind orientations. Consequently, complete series of measurement values need not be stored in the control device 6 which is located in the area of the drives; rather, only the individual measurements are temporarily stored during the operation of the drives and transmitted to the storage/evaluation device 7. The measurement values are collected in the latter as series of measurement values so that they can undergo an evaluation subsequently.

[0049] It is particularly important that a statistical/stochastic evaluation and analysis of the measuring value series is carried out according to the invention, that is, statistical characteristic values are generated from the series of measurement values, namely, e.g., characteristic values for a correlation between a plurality of phases or a plurality of drives on the one hand and statistical characteristic values for individual phases of the respective drive on the other hand. State information for one drive or for all of the drives can be generated individually or through suitable combinations from these statistical characteristic values, namely, with the evaluation software stored in the storage/evaluation device 7 or with an evaluation algorithm which generates the state information representing the respective state of the individual drives.

[0050] Accordingly, for example, one or more of the following statistical characteristic values can be formed from the final m measurements, e.g., 600 measurements, and therefore from each individual series of measurement values (via the measurement distribution densities) for each drive and each phase: [0051] mean value of each individual phase, mean value of the total L1/L2/L3, i.e., (sum L1/600+sum L2/600+sum L3/600)/3, mean value of the individual phase measurements (L1+L2+L3)/3, RMS, measurement value density, value of the highest measurement value density or maximum of the measurement value distribution, minimum of the series of measurement values, maximum of the series of measurement values, standard deviation, variance, first sigma, second sigma, third sigma, median.
These statistical characteristic values contain statistical information about the series of measurement values without taking into account relationships between individual series of measurement values.

[0052] Additionally, it is particularly advantageous to calculate statistical characteristic values for a correlation between a plurality of series of measurement values, i.e., series of measurement values of a plurality of phases and a plurality of drives. This has to do chiefly with covariances of the measurement value curves and/or the correlation coefficients. Accordingly, for example, all of the covariances and correlation coefficients for all possible combinations of series of measurement values (M (1-N) Li to M (1-n) Lj can be evaluated. All of the statistical characteristic values are provided with a timestamp and stored in the evaluation/storage device. An evaluation of the stored statistical characteristic values is carried out with the stored algorithm in that the statistical values are ordered and analyzed, and the state of the installation or drives is deduced therefrom.

[0053] In this regard, reference is made, for example, to FIGS. 3a and 3b and to FIGS. 4a and 4b.

[0054] The covariances for a plurality of series of measurement values for a new or properly working drive or plurality of drives are plotted in FIG. 3a. Four consecutively recorded and evaluated measurement phases are shown, each with 600 measurement points and, consequently, over a time period of ten minutes. The individual evaluations are designated by A1, A2, A3 and A4. The covariances are plotted for the various combinations M.sub.nL.sub.i to M.sub.nL.sub.j. The letters on the x axis are provided only for some exemplary combinations. It will be seen that all of the covariances are relatively low so that it can be concluded that the drive or drives is/are working properly.

[0055] On the other hand, FIG. 3b shows a situation in which the covariances increase slightly whenever drive M4 is involved so that it can be concluded that drive M4 has a defect, e.g., increased slip. This is particularly evident in covariance M4L1 to M4L3, i.e., with a covariance in which the various phases of the same drive M4 are involved.

[0056] Similar information can be obtained through evaluation of the correlation coefficients according to FIGS. 4a and 4b. It will be seen again that the correlation coefficient is in a high range for all of the measurement phases (see FIG. 3a), while it drops to low values in case of a defective drive (compare FIG. 3b). It will be seen that this happens in correlation coefficients whenever drive M4 is involved so that a fault can be determined in the area of a drive in a particularly reliable manner via the correlation coefficient. Accordingly, the malfunctioning motors can be verified based on the comparison or based on relationships of the individual correlation coefficients, and this can be done in a timely manner before damage occurs. It is particularly important that the time evolution of these covariances or correlation coefficients can be analyzed within the framework of a time series analysis or trend value analysis.

[0057] FIGS. 3a, 3b and 4a, 4b show only an exemplary evaluation and analysis of determined statistical characteristic values. In a particularly preferable manner, further statistical characteristic values are evaluated and monitored so that different types of error or types of wear can be detected.

[0058] FIGS. 5a and 5b show exemplary histograms and distribution density functions of series of measurement values for a drive in new condition (FIG. 5a) and a defective drive (FIG. 5b). It will be seen that the measurement value distribution for a drive in new condition is approximately normal. The frequency of the phase currents indicated on the x axis is plotted. On the other hand, FIG. 5b shows a distribution for an already existing installation having a defect. These measurement distribution densities can be stored in statistical characteristic numbers, or statistic characteristic numbers, e.g., the spread, the first, second and third sigmas, the modal value, the variance, the minimum, the maximum, the standard deviation and/or the median, can be determined from the distribution densities. By taking into account the correlation with stored reference values or, in particular, by means of an analysis of the time evolution of these statistical characteristic values, an individual drive or an individual motor current can be analyzed or monitored, and wear can be reliably detected or prognosticated without having to monitor a reference system. Only the data of the drive which were previously stored are needed as reference in that the time evolution of the distribution density function or characteristic numbers thereof is analyzed. Accordingly, for example, the three sigma values are utilized for verification of temporary measurement in order to reliably detect acute occurrence of damage. For example, when measurement values accumulate outside of the sigma values, this is an indication of an error, and the farther the measurement value is from the modal value, the clearer and more acute the error event. By monitoring or analyzing an individual drive or an individual phase in this way (without taking into account a reference system), correlations of the series of measurement values recorded over time and the characteristic values determined therefrom can also be analyzed, and correlation coefficients can be determined and evaluated.

[0059] It is further advantageous that the storage of the very extensive data and the evaluation are not carried out in the local control 4 in the wind turbine, but rather only the measurement and transmission of the measurement values is carried out in the latter (possibly after temporary buffering). The storage/evaluation device 7 is generally far away from the wind turbine so that, e.g., a plurality of wind turbines can be monitored via a central monitoring device.

[0060] Further, visualization software can be additionally stored in the storage/evaluation device 7. This visualization software visually displays the collected and derived values from the database for the user and serves as communications interface between the evaluation algorithm and the user. A corresponding reaction to the error event, e.g., a warning email or on-demand stoppage of the wind orientation control, can also be carried out.

[0061] Further, the storage/evaluation device 7 is shown in a highly simplified manner in the drawings. It can comprise a database server in particular. The software for data evaluation, anomaly detection and error reporting can also be stored on the same server. Alternatively, however, an additional computer/server can be provided for implementing the evaluation, anomaly detection and error reporting.