Current-sensing-based online detection method for motor faults of wind turbine generators

Abstract

The application belongs to the technical field of wind power generation, and discloses a wind driven generator motor fault online detection method based on current sensing. The method acquires motor current data in real time based on a pre-built current acquisition system. A cloud server in the current acquisition system first receives current data of corresponding motors uploaded by each current transformer, cleans the current data of each motor, compares the motor current data filtered by starting current of the motor with a threshold, compares horizontal currents of motors of the same type based on a KNN clustering algorithm, compares longitudinal historical current data of a single motor, and judges whether the motor running state is normal in stages. The wind driven generator motor fault online detection method solves the problem that the vibration monitoring method can only analyze mechanical faults of the motor, and multi-dimensional early warning greatly improves the accuracy of motor fault judgment.

Description

Technical Field

[0001] The present invention belongs to the field of wind power generation and relates to an online detection method for wind generator motor faults based on current sensing. Background Art

[0002] At present, there are two main types of motor failures in wind turbines. The first is yaw motor failure. Wind turbines are mostly installed in coastal and hilly areas, where the turbulence intensity is relatively large. The unit transmission system needs to withstand more fatigue loads, and the transmission function of the wind turbine unit yaw system mainly relies on the yaw motor and yaw reducer transmission. The unit yaw braking function relies on the yaw motor electromagnetic brake braking. The yaw motor failure seriously affects the yaw direction of the motor and restricts the wind power generation efficiency; the second is the heat dissipation motor failure. There are many types of ventilation and heat dissipation motors in the heat dissipation system inside the wind turbine cabin. The failure of the heat dissipation motor affects the mechanical system and electrical system of the entire wind turbine. The failure of any heat dissipation motor will affect the normal operation of the wind turbine and even seriously affect the service life of the wind turbine system.

[0003] At present, the main problems that cause the yaw motor and heat dissipation motor to fail are motor bearing damage and jamming, and short circuit between phases or turns of the motor internal winding. At present, the fault monitoring of the yaw motor and heat dissipation motor is mainly carried out through vibration monitoring methods, such as Figure 2 As shown in the figure, this method installs vibration sensors in the horizontal and vertical directions of the yaw motor and the heat dissipation motor to measure their vibration peak value, effective value, frequency and other parameters. The operating status and condition of the motor can be judged by the vibration frequency and other data during motor operation, thus providing data reference for motor fault monitoring. However, vibration monitoring has the following problems:

[0004] First, vibration monitoring can only determine whether there are mechanical problems with the motor, not whether there are electrical issues. The core monitoring goal of vibration monitoring is to analyze mechanical problems with motor bearings, gears, and other components through vibration. However, mechanical problems are only one manifestation of motor failure, and vibration monitoring methods cannot analyze electrical problems, thus having certain limitations. Second, vibration monitoring has accuracy issues. Vibration sensors are affected by the on-site installation environment and vibration interference from other equipment, which can easily lead to misjudgments of the motor's operating status. Furthermore, the frequency changes in the early stages of a motor failure are slight, making them difficult for vibration sensors to detect. Third, on-site construction and deployment are difficult. The yaw motor and cooling motor have fixed positions in the wind turbine, and most motors have limited installation space. The small cabin space also makes wiring and routing very difficult, making vibration sensor installation difficult or impossible, and the construction difficulty and cost are relatively high.

[0005] Based on this, there is an urgent need for a wind turbine motor fault detection solution to ensure the stable operation of the wind turbine equipment. Summary of the Invention

[0006] The purpose of the present invention is to propose an online detection method for wind turbine motor faults based on current sensing. The method is based on a pre-built current acquisition system, which remotely collects and obtains the current data of each motor in the wind turbine in real time, cleans the current data of each motor, and then performs threshold comparison, lateral current comparison of multiple motors of the same type, and longitudinal historical current data comparison of a single motor to judge whether the motor operating status is normal in a graded manner.

[0007] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions:

[0008] An online fault detection method for wind turbine motors based on current sensing is implemented using a pre-built current acquisition system that includes a cloud server and multiple current transformers.

[0009] Each current transformer collects the current data of one phase of the three-phase current of any motor in the wind turbine. The collected current data is uploaded to the cloud server via the intelligent gateway;

[0010] The wind turbine generator motor fault online detection method based on current sensing comprises the following steps:

[0011] Step 1. The cloud server receives the current data of the corresponding motor uploaded by each current transformer in the current acquisition system, and cleans the current data to filter out the starting current of the corresponding motor;

[0012] Step 2. Perform a rough threshold judgment on the motor current data after the data cleaning in step 1;

[0013] When the motor current value collected by the current transformer exceeds the preset threshold, it is directly determined that the motor has a fault; when the motor current value collected by the current transformer does not exceed the preset threshold, go to step 3;

[0014] Step 3. Based on KNN clustering, compare the horizontal currents of motors of the same type and multiple motors that do not exceed the threshold;

[0015] For motor currents with relatively small data differences, proceed to step 4 to perform longitudinal historical current data comparison. For motor currents with relatively large data differences, compare them with the motor rated current to determine whether the motor current is within the preset safety range.

[0016] If the motor current value is within the preset safety range, the motor current is further transferred to step 4 for comparison with longitudinal historical current data. If it is not within the preset safety range, it is directly determined that the motor is abnormal.

[0017] Step 4. Further compare the motor current value with its own historical normal motor current value;

[0018] The average historical normal value of the motor current is the average of the historical current data under normal operation of the motor;

[0019] If the difference between the motor current value and the average of its own historical normal motor current values ​​is within the preset range, it is determined that the motor is operating normally; otherwise, the development trend of the difference between the motor current value and its various historical normal values ​​is further determined;

[0020] If the difference trend increases gradually over time, it indicates that the circuit or internal mechanical problem is gradually worsening. If the difference trend tends to be stable over time, it is judged that the circuit or internal mechanical problem is caused by dust or circuit aging.

[0021] In addition, based on the current sensing-based online detection method for wind turbine motor faults, the present invention also proposes a corresponding current sensing-based online detection system for wind turbine motor faults, the technical solution of which is as follows:

[0022] A wind turbine motor fault online detection system based on current sensing, comprising:

[0023] The data cleaning module is used to clean the current data of the corresponding motor uploaded by each current transformer in the current acquisition system received by the cloud server to filter out the starting current of the motor;

[0024] The threshold judgment module is used to perform a rough threshold judgment on the motor current data after data cleaning;

[0025] When the motor current value collected by the current transformer exceeds the preset threshold, it is determined that the motor has failed; when the motor current value collected by the current transformer does not exceed the preset threshold, it switches to the horizontal current comparison module for multiple motors of the same type;

[0026] The module for comparing the transverse currents of multiple motors of the same type is used to compare the transverse currents of motors of the same type and multiple motors based on the KNN clustering algorithm for motor currents that do not exceed the threshold.

[0027] The motor current with relatively small data differences is transferred to the longitudinal historical current data comparison module for judgment; the motor current with relatively large data differences is compared with the rated current value to determine whether it is within the preset safety range;

[0028] If the motor current is within the preset safety range, the module will be transferred to the longitudinal historical current data comparison module for further judgment. If the motor current is not within the preset safety range, it will be directly judged as a motor abnormality.

[0029] and a longitudinal historical current data comparison module for comparing the motor current value with its own historical normal motor current value;

[0030] The average historical normal value of the motor current is the average of the historical current data under normal operation of the motor;

[0031] If the difference between the motor current value and the average of its own historical normal motor current values ​​is within the preset range, it is determined that the motor is operating normally; otherwise, the development trend of the difference between the motor current value and its various historical normal values ​​is further determined;

[0032] The specific situation of motor abnormality can be further distinguished based on whether the difference development trend changes smoothly over time.

[0033] In addition, based on the above-mentioned current sensing-based online detection method for wind turbine motor faults, the present invention also proposes a computer device, which includes a memory and one or more processors; the memory stores executable code, and when the processor executes the executable code, it is used to implement the steps of the above-mentioned current sensing-based online detection method for wind turbine motor faults.

[0034] In addition, based on the above-mentioned current sensing-based online detection method for wind turbine motor faults, the present invention also proposes a computer-readable storage medium on which a program is stored; when the program is executed by a processor, it is used to implement the steps of the above-mentioned current sensing-based online detection method for wind turbine motor faults.

[0035] The present invention has the following advantages:

[0036] As described above, the present invention describes an online detection method for wind turbine motor faults based on current sensing. This method solves the drawback that traditional vibration monitoring methods can only analyze mechanical faults of motors. Through the motor current data, it can quickly locate whether it is a motor circuit problem or a motor mechanical fault problem. For example, when the A, B, and C three-phase current data of the motor operation exceed the preset threshold value, it is confirmed that the motor has a major mechanical fault resulting in an overall increase in the motor current; when the current value of one phase of the A, B, and C three-phase current of the motor is 0 or the current value of one phase increases, it is judged that there is a problem with the circuit of the motor. When the current of one phase is 0, the corresponding current phase is missing. When the current value of one phase increases, the corresponding circuit terminal is loose, resulting in an increase in resistance. After the motor current value is compared with the horizontal current of the same type and multiple motors and the longitudinal historical current data of a single motor, the development and changes of the motor's operating health status can be finely judged. For example, if the current of a certain motor does not exceed the preset threshold, it means that the motor is operating normally at the current stage. The motor current enters the horizontal current comparison stage of the same type and multiple motors. If the motor current value deviates from the current of the same type motor for horizontal comparison, it is judged that a small mechanical fault has occurred inside the motor or a phase wire is loose. The change in current is more intuitive than the change in vibration. The method of the present invention adopts multi-dimensional early warning. After the current data of each motor is cleaned and the starting current of the motor is filtered out, the threshold comparison, the horizontal current comparison of multiple motors of the same type and the longitudinal historical current data of a single motor are performed. The hierarchical judgment method combining rough judgment and fine judgment greatly improves the accuracy of motor fault judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Schematic diagram of a method for online detection of wind turbine motor faults based on current sensing in an embodiment of the present invention;

[0038] Figure 2 is a schematic diagram of a vibration detection method;

[0039] Figure 3 is a schematic diagram of a current acquisition system according to an embodiment of the present invention;

[0040] Figure 4 Schematic diagram of a current acquisition main control box and a current transformer in a current acquisition system in an embodiment of the present invention;

[0041] Figure 5 Schematic diagram of a current acquisition main control box in an embodiment of the present invention;

[0042] Figure 6 Schematic diagram of the motor starting current in an embodiment of the present invention;

[0043] Figure 7 This is a flow chart of data cleaning in an embodiment of the present invention;

[0044] Figure 8 This is a flowchart of a rough threshold determination in an embodiment of the present invention;

[0045] Figure 9 This is a flow chart of obtaining critical values ​​of motor rated current and motor fault current using a binary method in an embodiment of the present invention;

[0046] Figure 10 This is a flow chart showing the comparison of transverse currents of multiple motors of the same type in an embodiment of the present invention;

[0047] Figure 11 This is a flow chart of setting the K value in an embodiment of the present invention;

[0048] Figure 12 This is a flow chart of longitudinal historical current data comparison in an embodiment of the present invention;

[0049] Figure 13 This is a flow chart of performing a horizontal comparative cluster analysis of multiple groups of motors to determine motor circuit problems and potential motor failures in an embodiment of the present invention;

[0050] Figure 14 This is a flow chart for determining motor abnormality by comparing longitudinal historical current data in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0052] Example 1

[0053] Before introducing the online detection method for wind turbine motor fault based on current sensing in the present invention, it is necessary to build a Figure 3 The current acquisition system shown in the figure includes the underlying sensing layer equipment and the cloud server.

[0054] Specifically, the underlying sensing layer equipment includes a current transmitter and multiple current transformers.

[0055] There are two types of motors in a wind turbine: yaw motors and cooling motors. There are multiple yaw motors and cooling motors, and each current transformer is used to collect current data for one phase of the three currents of a corresponding motor in the wind turbine.

[0056] For example, a motor has three-phase currents A, B, and C, and each motor corresponds to three current transformers, wherein each current transformer collects the A-phase, B-phase, and C-phase currents of the motor respectively.

[0057] Similarly, assuming there are M yaw motors and cooling motors, 3M current transformers are needed to collect current data. Here, M is a natural number greater than 0 and is related to the actual number of yaw motors and cooling motors.

[0058] Each current transformer converts the collected current data from analog data to digital data through a current transmitter, and uploads the collected motor current data to the cloud server via wireless transmission.

[0059] Each wind turbine corresponds to an intelligent gateway and a current transmitter. A current transmitter is connected to multiple current transformers. The current data collected by each current transformer is transmitted to the same cloud server through the corresponding intelligent gateway.

[0060] The cloud server is used to analyze the uploaded current data. It determines whether the motor is operating normally by comparing the current threshold of each motor, comparing the currents of multiple motors of the same type horizontally, and comparing the historical current data of a single motor vertically.

[0061] The deployment of the current acquisition main control box and current transformer in the current acquisition system is as follows: Figure 4 shown.

[0062] The current transformer is installed at the fan distribution cabinet, and the current acquisition main control box is installed near the fan distribution cabinet. The current acquisition main control box is responsible for collecting data from the current transformer. The leads of the current transformer are led out from the fan distribution cabinet and connected to the current acquisition main control box to complete data collection. The collected data will be converted from analog to digital through the current transformer.

[0063] Specifically, the current acquisition main control box structure in this embodiment is as follows: Figure 5 The current acquisition main control box includes an air switch, a 12V power supply, an intelligent gateway, a current transmitter, and a terminal block.

[0064] The power cable from the current acquisition main control box is connected to a 12V power supply, which powers the smart gateway and current transmitter. The analog value collected by the current transformer is transmitted to the current transmitter via the terminal block.

[0065] The current transmitter converts the analog current into standard modbus485 data and sends it to the smart gateway. The smart gateway uploads the motor current data to the cloud server via wireless transmission.

[0066] In this embodiment, the current transformer preferably adopts the AKH-0.66W-7 series current transformer, which has the advantages of small size, high precision, and strong load capacity, and can output small current signals such as 5mA-20mA.

[0067] In addition to its high data acquisition accuracy, the current transformer is also easy to install on site. The fan distribution cabinet supplies power to each motor, and the current transformer is installed in the fan distribution cabinet where the motor is connected.

[0068] The current transformers are centrally installed in the fan power distribution cabinet, which has high space utilization, is easy to fix and simple to deploy. This avoids the problems of difficult installation and wiring of vibration sensors and insufficient installation space, reduces the difficulty of installation work and improves installation efficiency.

[0069] like Figure 1 As shown, after clearly introducing the pre-built current acquisition system, the online detection method for wind turbine motor faults based on current sensing in this embodiment is described in detail below.

[0070] The online detection method for wind turbine motor faults based on current sensing in this embodiment includes the following steps:

[0071] Step 1. The cloud server receives the current data of the corresponding motor uploaded by each current transformer in the current acquisition system, and performs data cleaning on the current data to filter out the starting current of the motor.

[0072] The current data uploaded by the smart gateway must first be cleaned by the cloud server to filter out the motor's starting current. This ensures the authenticity and reliability of the current data.

[0073] like Figure 6 As shown, the horizontal axis t represents time, the vertical axis I represents current value, I e Indicates the rated current. Since the motor is stationary at the moment power is applied, the rotor in the motor is stationary. At this time, the rotor speed is 0, and the synchronous rotating magnetic field cuts the rotor winding at the maximum cutting speed, causing the rotor winding to induce and reach the highest electromotive force, generating a large current in the rotor winding. This current will offset the magnetic flux of the stator magnetic field in the motor. To maintain the original magnetic flux that is compatible with the power supply voltage, the stator winding will automatically increase the current. Because the rotor current is very large at this time, the stator current in the motor will also increase significantly, up to 5 to 7 times the rated current. Therefore, it is necessary to clean and filter the motor starting current data to ensure that the motor current data is normally operating. That is, after the data is cleaned, the motor starting current is filtered out.

[0074] Since the starting current of the motor is maintained for a very short time, usually not exceeding 0.5s, the current data is filtered by the online detection method for wind turbine motor faults based on current sensing in this embodiment, as shown in FIG. Figure 7 shown.

[0075] The motor current data collected by the current acquisition system is judged. When it is identified that the motor current starts to change from 0A to non-0A, the data within 2s of the current motor current is defined as the starting current. This part of the data, i.e. the defined starting current data, is filtered, and the data after 2s of the current motor current is used for the rough threshold judgment in step 2.

[0076] Step 2. Perform a rough threshold judgment on the motor current data after the data cleaning in step 1.

[0077] When the motor current value collected by the current transformer exceeds the preset threshold, it is directly determined that the motor has a fault; when the motor current value collected by the current transformer does not exceed the preset threshold, go to step 3.

[0078] Threshold detection is the most direct method. When a motor experiences bearing damage, jamming, or internal winding failure, the motor's resistance increases, causing a change in current. Using threshold detection can effectively and intuitively identify faulty motors. Specifically, when a motor experiences bearing damage, jamming, or internal winding failure, the motor's resistance increases, causing a change in current. At this point, the current values ​​for phases A, B, and C all increase. Alternatively, if a motor experiences a phase loss, resulting in a zero current in one phase but increased currents in the other two phases, the motor is determined to have a phase loss problem.

[0079] The process of threshold rough judgment is as follows Figure 8 shown.

[0080] First, the threshold parameters of the motor current data are preset. The motor current data collected by the current acquisition system is compared with the preset threshold parameters after data cleaning. When the motor current data exceeds this threshold, an early warning will be directly triggered to push a prompt indicating a motor failure.

[0081] Whether the threshold rough judgment can play an early warning role depends on the setting of the early warning threshold, such as Figure 9 shown.

[0082] The method of the present invention combines the rated current of the motor itself and the historical data of the motor current collected by the cloud server, extracts the current value of the time period when the equipment fails, that is, the faulty motor current, and uses the dichotomy method to obtain the critical value of the motor rated current and the motor fault current, and sets the critical value as the current warning threshold, that is, the preset threshold in the threshold rough judgment.

[0083] The specific process of obtaining the critical values ​​of the motor rated current and the motor fault current using the dichotomy method is as follows:

[0084] Based on the current value collected from the current faulty motor as the upper limit of the current warning threshold, the rated current is used as the reference lower limit for setting the current warning threshold, and the middle value between the upper limit and the rated current is determined by binary search. The middle value is used as the upper limit of the current warning threshold. The experiment determines whether the current value is a fault current, that is, whether the motor is faulty when the current middle value is used as the current value. If the current middle value is a fault current, the middle value between the current value and the rated current is continued to be taken, and the new middle value is used to verify the motor fault until the current value obtained by binary search is the upper limit of the current value for normal motor operation. The current value of this critical value is used as the current warning threshold of the motor.

[0085] Step 3. For motor currents that do not exceed the threshold, further compare the horizontal currents of the same type and multiple motors based on the KNN clustering algorithm. The motor currents with relatively small data differences are further transferred to step 4 for judgment.

[0086] The motor current with relatively large data difference is compared with the rated current value.

[0087] Determine whether the motor current is within the preset safety range. If the motor current is within the preset safety range, go to step 4 for further judgment. If the motor current is not within the preset safety range, directly determine that the motor is abnormal.

[0088] Specifically, we first calculate the current mean of multiple motors of the same type, and then further calculate the distance between the current value of each motor of the same type and the obtained current mean of that type:

[0089]

[0090] Where n is the number of times the current data is obtained, and the current data includes the time and current value of the current comparison; x n Indicates the time of obtaining the current comparison for the nth time, represents the average time of obtaining current comparisons for n times, and 0.2 is the weight coefficient of the time of obtaining current comparisons, which is used to reduce the error of overall distance judgment caused by the time difference of uploading from different smart gateways; n Indicates the current value obtained for the nth time, represents the average of the current values ​​obtained n times, and 0.8 is the weight coefficient of the current value.

[0091] Sort the calculated distance values ​​in ascending order.

[0092] The K distance values ​​that are at the front are taken as the set K values ​​of the motors participating in the horizontal current comparison.

[0093] The first K motor currents obtained by arranging in ascending order are further compared with the longitudinal historical current data using step 4. The remaining motor currents after excluding the first K motor currents after arranging in ascending order are judged as follows:

[0094] Calculate whether the difference between the current value of the remaining motors and the rated current value is within the preset safety range;

[0095] For motor currents whose difference is within the safe range, the process proceeds to step 4 to continue comparing longitudinal historical current data. For motor currents whose difference is outside the safe range, the process directly determines that the motor is abnormal.

[0096] It should be noted that a motor failure corresponds to a major mechanical fault or wiring fault in the motor, which directly affects the use of the wind turbine system. A motor abnormality corresponds to a minor fault or wiring problem in the motor, which has little impact on current use. However, it is important to monitor the development trend of the motor abnormality and replace or repair the motor promptly to prevent it from affecting subsequent use.

[0097] The process of horizontal comparative cluster analysis is as follows Figure 10 shown.

[0098] The motor current data of the same model and type of motors in the same area of ​​the in-service wind turbine generator sets are compared. When all motors are operating normally, the current operating curves of their motors tend to a certain average value.

[0099] Using the KNN clustering algorithm, the distance between the motor current value and the current mean is calculated, and the motor current data participating in the comparison are arranged in ascending order according to the distance difference, and then the K value is set.

[0100] For example, if there are 10 motors involved in the comparison, the K value can be set to 7. Then the current values ​​of the first 7 motors closest to the mean current (i.e., the first 7 motors arranged in ascending order of distance value) are all regarded as normal motor currents.

[0101] The currents of the remaining three motors will be compared with the rated currents of the motors for difference analysis.

[0102] If the difference is within the preset safety range, it will continue to be used for subsequent longitudinal historical current data comparison. If the difference exceeds the safety range, it will be judged as a motor failure and an alarm message will be triggered to indicate motor abnormality.

[0103] The key to the accuracy of KNN clustering algorithm in motor fault analysis and judgment lies in the setting of K value.

[0104] If the value of K is too small, the presence of noise will significantly affect motor fault diagnosis. For example, if K is 1, if the nearest point is noise, a large deviation will occur. Reducing K means that the overall model becomes more complex and prone to overfitting. If K is too large, it is equivalent to using training examples from a larger neighborhood, which will increase the learning approximation error. In this case, examples far from the input target point will also affect motor fault analysis and judgment, causing errors in motor fault diagnosis. Therefore, the setting of K requires continuous data feeding and optimization.

[0105] The specific process of setting K value is as follows Figure 11 As shown, the specific process is as follows:

[0106] First, a K value is provisionally determined based on the N motor currents that participate in the same type, multi-motor transverse current comparison; the first K motor currents are analyzed to determine whether there are abnormal current values ​​in the first K motor currents. If there are abnormal current values, the K value is reduced until no abnormal current values ​​are present in the first K motor currents; if the first K motor currents are all normal current values, the remaining NK motor currents are analyzed again. If the normal current values ​​account for no less than 30% of the remaining NK motor currents, the K value is increased until the normal current values ​​account for less than 30% of the remaining NK current values.

[0107] By comparing and clustering multiple groups of motors, we can analyze the motor circuit problems and potential motor failures. We can also analyze and determine the potential failures of motors that appear to be operating normally, such as loose wire connections and aging motor circuits. The specific process is as follows: Figure 13 As shown, the motor that has passed the rough threshold judgment indicates that the current motor is still operating normally; further horizontal comparison and cluster analysis of multiple groups of motors are used to analyze the motor circuit problems and potential motor failures. If the current value of a motor is discrete from the current values ​​of other motors during the horizontal comparison of motor current values ​​(specifically, the distance between the current value of each motor of the same type and the current mean of the type is calculated and sorted in ascending order according to each distance value, and the current of the remaining motors after excluding the first K motor currents) it indicates that the motor current may be abnormal. Further analysis of the three-phase currents of the motor A, B, and C is performed. If the three-phase currents are all higher than the normal values ​​of the other motor currents, it indicates that there is a minor mechanical fault problem in the internal mechanical structure of the motor; if the current value of one or two phases of the motor current is abnormal, it indicates that there is a loose wire or circuit aging problem in the circuit of the motor.

[0108] Step 4. Compare the motor current that enters the longitudinal historical current data comparison with its own motor current historical normal value. The average of its own motor current historical normal value is the average of the historical current data under normal operation of the motor.

[0109] The historical normal motor current value is the historical current data under normal operation of the motor, and the longitudinal historical current data comparison process is shown in Figure 12 If the difference between the motor current value and the average of the historical normal motor current value is within the preset range, it is determined that the motor is operating normally; if the difference between the motor current value and the average of the historical normal motor current value is outside the preset range, it is determined that the motor is abnormal; by further analyzing the trend of the difference between the motor current value and each historical normal motor current value, whether the difference development trend changes smoothly over time is used to determine the specific situation of the motor abnormality.

[0110] For example, if the trend is smooth, it may be caused by dust and line aging; if the trend is rising or falling, it means that the problem circuit or internal mechanical problem is increasingly serious. The longitudinal comparison analysis of the current compares the motor operating current with the historical normal value to exclude the motor failure caused by the motor's own properties.

[0111] As shown in Figure 14 The average of the historical current data under normal operation of the motor is set as the average of the historical normal motor current value, and the current motor current value is compared with the average of the historical normal motor current value of the motor (the average is the average of the historical current data under normal operation of the motor, which is a fixed value after determination).

[0112] First, the average of the historical normal motor current value of the motor is determined; then the fluctuation range of the motor current is confirmed, assuming that the fluctuation range of the normal current is 0.5 A, in order to prevent interference, the threshold of the fluctuation range is set to 0.8 A, and it is determined that the fluctuation of the average of the historical normal motor current value is not more than 0.8 A for the normal operation of the motor, if the difference between the motor current value and the average of the historical normal motor current value is greater than 0.8 A, it is determined that the motor is abnormal.

[0113] For the motor with abnormal motor current, the difference analysis of the current is performed, and the difference of the normal historical motor current data of the motor before operation is saved, and the difference historical data before the abnormal situation occurs is also analyzed. The motor current value is compared with the historical normal motor current value one by one, if the difference data gradually increases over time, it means that the problem circuit or internal mechanical problem is increasingly serious, if the difference is always smooth, it may be caused by dust and line aging, and the wind turbine needs to be cleaned and maintained in time.

[0114] The method of the present invention uses a current acquisition system to remotely acquire motor current data in real time, and performs real-time analysis and processing of the motor current data via a cloud server. The current acquisition system is easy to install and wire on-site, making it highly feasible. The present invention establishes a diversified motor fault warning method that can analyze and determine motor operating faults and abnormal conditions based on current data. The method compares the motor current data after filtering out the motor's starting current with a threshold value, and then uses a KNN clustering algorithm to perform horizontal current comparisons of multiple motors of the same type, as well as longitudinal historical current data comparisons of a single motor, to determine whether the motor's operating status is normal.

[0115] This method is designed to use three levels of judgment: threshold comparison, horizontal current comparison of multiple motors of the same type, and vertical current comparison of a single motor. First, using the threshold rough judgment can directly determine the motor with a large mechanical failure or a line problem. This motor has a serious impact on the system and must be replaced in time. Next, the motors entering the horizontal comparison are all within the threshold, indicating that the motor is currently operating normally and has little impact on the current system operation. Horizontal comparison of multiple motors of the same type and in the same area can be more detailed to analyze and discover potential problems and failures in the operation of a certain motor. This type of motor can be replaced in time but requires continuous attention. For the final vertical comparison, in addition to being able to see the healthy development and changes of the motor itself, it can also filter out false alarms caused by the motor's own characteristics in the horizontal comparison. If the current data of a motor itself is different from others, through the vertical comparison of the motor itself, it is found that it has been in this state, but the motor status is normal, indicating that the motor is a special case and should be filtered out during the horizontal comparison to ensure the reliability and stability of the three-level warning.

[0116] For example, in extreme cases, if the current data of a motor during normal operation is higher than that of other motors, and the difference between its current value and the rated current is greater than the set safety range, then the motor's current will always be judged to be abnormal during horizontal comparison. Therefore, it is necessary to make special marking processing on it so that the motor no longer participates in the horizontal comparison of the motors selected in the area, and directly participates in the final vertical comparison link, thereby reducing the data interference caused by special data to the horizontal comparison analysis and improving the accuracy of the horizontal comparison analysis.

[0117] Example 2

[0118] This embodiment 2 describes an online detection system for wind turbine motor faults based on current sensing. This system is based on the same inventive concept as the online detection method for wind turbine motor faults based on current sensing in embodiment 1.

[0119] Specifically, the wind turbine motor fault online detection system based on current sensing includes the following modules:

[0120] The data cleaning module is used to clean the current data of the corresponding motor uploaded by each current transformer in the current acquisition system received by the cloud server to filter out the starting current of the motor;

[0121] The threshold judgment module performs a rough threshold judgment on the motor current data after data cleaning;

[0122] When the motor current value collected by the current transformer exceeds the preset threshold, it is determined that the motor has failed; when the motor current value collected by the current transformer does not exceed the preset threshold, it switches to the horizontal current comparison module for multiple motors of the same type;

[0123] The module for comparing the transverse currents of multiple motors of the same type is used to compare the transverse currents of motors of the same type and multiple motors based on the KNN clustering algorithm for motor currents that do not exceed the threshold.

[0124] The motor current with relatively small data differences is transferred to the longitudinal historical current data comparison module for judgment; the motor current with relatively large data differences is compared with the rated current value to determine whether it is within the preset safety range;

[0125] If the motor current is within the preset safety range, the module will be transferred to the longitudinal historical current data comparison module for further judgment. If the motor current is not within the preset safety range, it will be directly judged as a motor abnormality.

[0126] And a longitudinal historical current data comparison module is used to compare the motor current value with its own motor current historical normal value; the motor current historical normal value is the historical current data under normal operation of the motor;

[0127] If the difference between the motor current value and the average of its own historical normal motor current values ​​is within the preset range, it is determined that the motor is operating normally; otherwise, the development trend of the difference between the motor current value and its various historical normal values ​​is further determined;

[0128] The specific situation of motor abnormality can be further distinguished based on whether the difference development trend changes smoothly over time.

[0129] It should be noted that, in the current sensing-based online detection system for wind turbine motor faults, the implementation process of the functions and effects of each functional module is detailed in the implementation process of the corresponding steps in the method in Example 1, which will not be repeated here.

[0130] Example 3

[0131] This embodiment 3 describes a computer device, which includes a memory and one or more processors.

[0132] The memory stores executable codes, which, when executed by the processor, are used to implement the steps of the method for online detection of wind turbine motor faults based on current sensing in the first embodiment.

[0133] In this embodiment, the computer device is any device or apparatus with data processing capability, which will not be described in detail here.

[0134] Example 4

[0135] This embodiment 4 describes a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, the program performs the steps of a method for online detection of motor faults in a wind turbine generator based on current sensing.

[0136] The computer-readable storage medium can be an internal storage unit of any device or apparatus with data processing capabilities, such as a hard disk or memory, or an external storage device of any device with data processing capabilities, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc. equipped on the device.

[0137] Of course, the above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above-mentioned embodiments. It should be noted that all equivalent substitutions and obvious deformation forms made by any technician familiar with this field under the guidance of this specification fall within the substantive scope of this specification and should be protected by the present invention.

Claims

1. A wind turbine motor fault online detection method based on current sensing is implemented based on a pre-built current acquisition system; it is characterized by: The current acquisition system includes a cloud server and multiple current transformers; Each current transformer collects the current data of one phase of the three-phase current of any motor in the wind turbine. The collected current data is uploaded to the cloud server via the intelligent gateway; The wind turbine generator motor fault online detection method based on current sensing comprises the following steps: Step 1. The cloud server receives the current data of the corresponding motor uploaded by each current transformer in the current acquisition system, and cleans the current data to filter out the starting current of the corresponding motor; Step 2. Perform a rough threshold judgment on the motor current data after the data cleaning in step 1; When the motor current value collected by the current transformer exceeds the preset threshold, it is directly determined that the motor has a fault; when the motor current value collected by the current transformer does not exceed the preset threshold, go to step 3; Step 3. Based on KNN clustering, compare the horizontal currents of motors of the same type and multiple motors that do not exceed the threshold; For motor currents with relatively small data differences, go to step 4 to compare longitudinal historical current data. The motor current with relatively large data differences is compared with the motor rated current to determine whether the motor current is within the preset safety range; If the motor current value is within the preset safety range, the process proceeds to step 4 to compare the motor current with historical current data. If it is not within the preset safety range, the motor is determined to be abnormal. Step 4. Further compare the motor current value with its own historical normal motor current value, wherein the average of its own historical normal motor current value is the average of the historical current data of the motor under normal operation; If the difference between the motor current value and the average of its own historical normal motor current values ​​is within the preset range, it is determined that the motor is operating normally; otherwise, the development trend of the difference between the motor current value and its various historical normal values ​​is further determined; The specific situation of motor abnormality can be further distinguished based on whether the difference development trend changes smoothly over time.

2. The method for online detection of wind turbine motor faults based on current sensing according to claim 1, characterized in that: The current acquisition system includes a bottom-layer sensing layer device and a cloud server; The underlying sensing layer equipment includes a current transmitter and multiple current transformers. Each current transformer is used to collect single-phase current data of a motor in a wind turbine. The motor current data is converted from analog data to digital data by the current transmitter, and the collected motor current data is uploaded to the cloud server via wireless transmission.

3. The method for online detection of wind turbine motor faults based on current sensing according to claim 1, characterized in that: In the step 1, the motor current data collected by the current acquisition system is judged. When it is identified that the motor current changes from 0 A to non-0 A, the data of the current motor current within 2 seconds is defined as the starting current; The starting current data is filtered, and the current motor current data after 2 seconds is used for the rough threshold judgment in step 2.

4. The method for online detection of wind turbine motor faults based on current sensing according to claim 1, characterized in that: In step 2, the method for determining the preset threshold is: Combined with the rated current of the motor itself and the historical data of the motor current collected by the intelligent gateway, the current value during the motor fault period, i.e., the motor fault current, is taken out. The critical value of the motor's rated current and the motor fault current is obtained using the dichotomy method, and the critical value is set as the current warning threshold, i.e., the preset threshold in the threshold rough judgment.

5. The method for online detection of wind turbine motor faults based on current sensing according to claim 1, characterized in that: The step 3 is specifically as follows: First, calculate the current mean of multiple motors of the same type, and further calculate the distance between the current value of each motor of the same type and the obtained current mean of the type, and sort the calculated distance values ​​in ascending order; Take the K values ​​that are at the front of each distance value as the set K value of the motor participating in the horizontal current comparison; The first K motor currents obtained by arranging in ascending order are further compared with the longitudinal historical current data using step 4. The remaining motor currents after excluding the first K motor currents after arranging in ascending order are judged as follows: Calculate whether the difference between the current value of the remaining motors and the rated current value is within the preset safety range; For motor currents whose difference is within the safe range, the process proceeds to step 4 to continue comparing longitudinal historical current data. For motor currents whose difference is outside the safe range, the process directly determines that the motor is abnormal.

6. The method for online detection of wind turbine motor faults based on current sensing according to claim 5, characterized in that: In step 3, the formula for calculating the distance between the current value of each motor of the same type and the obtained current mean value of the type is as follows: Where n is the number of times the current data is obtained, and the current data includes the time and current value of the current comparison; x n Indicates the time of obtaining the current comparison for the nth time, represents the average time of obtaining current comparisons for n times, and 0.2 is the weight coefficient of the time of obtaining current comparisons, which is used to reduce the error of overall distance judgment caused by the time difference of uploading from different smart gateways; n Indicates the current value obtained for the nth time, represents the average of the current values ​​obtained n times, and 0.8 is the weight coefficient of the current value.

7. The method for online detection of wind turbine motor faults based on current sensing according to claim 5, characterized in that: In step 3, the process of adjusting the K value is as follows: First, a K value is tentatively determined based on the currents of N motors participating in the horizontal current comparison of multiple motors of the same type; Analyze the first K motor currents to determine whether there are abnormal current values ​​in the first K motor currents. If there are abnormal current values, reduce the K value until there are no abnormal current values ​​in the first K motor currents. If the first K motor currents are all normal current values, analyze the remaining NK motor currents. If the normal current values ​​account for no less than 30% of the remaining NK motor currents, increase the K value until the normal current values ​​account for less than 30% of the remaining NK current values.

8. The online detection system for wind turbine motor faults based on current sensing is characterized by: include: The data cleaning module is used to clean the current data of the corresponding motor uploaded by each current transformer in the current acquisition system received by the cloud server to filter out the starting current of the motor; The threshold judgment module is used to perform a rough threshold judgment on the motor current data after data cleaning; When the motor current value collected by the current transformer exceeds the preset threshold, it is determined that the motor has failed; when the motor current value collected by the current transformer does not exceed the preset threshold, it switches to the horizontal current comparison module for multiple motors of the same type; The module for comparing the transverse currents of multiple motors of the same type is used to compare the transverse currents of motors of the same type and multiple motors based on the KNN clustering algorithm for motor currents that do not exceed the threshold. The motor current with relatively small data differences is transferred to the longitudinal historical current data comparison module for judgment; the motor current with relatively large data differences is compared with the rated current value to determine whether it is within the preset safety range; If the motor current is within the preset safety range, the module will be transferred to the longitudinal historical current data comparison module for further judgment. If the motor current is not within the preset safety range, it will be directly judged as a motor abnormality. And a longitudinal historical current data comparison module is used to compare the motor current value with its own motor current historical normal value; the average of its own motor current historical normal value is the average of the historical current data under normal operation of the motor; If the difference between the motor current value and the average of its own historical normal motor current values ​​is within the preset range, it is determined that the motor is operating normally; otherwise, the development trend of the difference between the motor current value and its various historical normal values ​​is further determined; The specific situation of motor abnormality can be further distinguished based on whether the difference development trend changes smoothly over time.

9. A computer device comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that: When the processor executes the executable code, the steps of the method for online detection of wind turbine motor faults based on current sensing according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by the processor, the steps of the online detection method for wind turbine motor faults based on current sensing as described in any one of claims 1 to 7 are implemented.

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