Remote monitoring system for wind driven generator

Abstract

The invention discloses a remote monitoring system for a wind driven generator, relates to the technical field of wind driven generators, and solves the problems that an original monitoring mode for the wind driven generator is not comprehensive, and the numerical value accuracy in the monitoring process cannot be fully guaranteed. When correlation analysis is carried out on the front transmission assembly of the wind driven generator, whether the ratio of the number of turns of the front transmission assembly and the number of turns of the front transmission assembly is consistent with the speed-up ratio or not is determined, so that the abnormal parameters are determined, whether the occurrence frequency of the abnormal parameters has a rule or not is judged according to the occurrence time of the corresponding abnormal parameters, and comprehensive judgment is carried out. And the judgment result is displayed, such a judgment mode is more comprehensive, the determined judgment accuracy is higher, the cooling maintenance state of the generator is considered, and a better monitoring effect is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of wind turbines, and in particular to a remote monitoring system for wind turbines. Background Art

[0002] The working principle of a wind turbine involves converting wind energy into mechanical energy and then into electrical energy, among which the process of cutting magnetic flux lines is the core of the power generation process; the basic components of a wind turbine include blades, a nacelle, a tower, etc., among which the blades capture wind energy, convert it into mechanical energy, and drive the rotor in the nacelle to rotate. When the rotor rotates, it cuts the magnetic flux lines, generating an induced electromotive force in the rotor conductor, thereby generating current.

[0003] The application with publication number CN108252858B discloses a remote monitoring system for a wind turbine, which solves the problem that on-duty personnel who are not at the wind turbine site cannot timely understand the operating status of the wind turbine when a fault occurs. The key points of its technical solution are: it includes a wind turbine, a control unit connected to the wind turbine, and a remote monitoring terminal connected to the control unit for the on-duty personnel to remotely monitor the operating status of the generator in real time; the control unit includes an abnormal operation sensing module that detects the operating status of the wind turbine and outputs a sensing signal T1-n, an abnormal operation braking module that is connected to the abnormal operation sensing module and is controlled by the sensing signal T1-n to perform braking protection on the wind turbine, and an operation data transmission module that transmits abnormal operation data of the wind turbine to the remote monitoring terminal, so that the on-duty personnel who are not at the wind turbine site can understand the operating status of the wind turbine when a fault occurs at the first time.

[0004] During the remote monitoring of wind turbines, the wind turbines are generally identified based on their power generation status to determine whether they have power generation problems. However, this original monitoring method, firstly, does not analyze the actual operating status of the wind turbine. Secondly, it does not analyze the abnormal power generation caused by the abnormal rotor speed changes due to the self-cooling protection measures inside the wind turbine. This is because the original monitoring method of wind turbines is not comprehensive and cannot fully guarantee the accuracy of the numerical values ​​during the monitoring process. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a remote monitoring system for a wind turbine, which solves the problem that the original monitoring method for wind turbines is not comprehensive and cannot fully guarantee the numerical accuracy during the monitoring process.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A remote monitoring system for a wind turbine generator, comprising:

[0007] The cycle value monitoring terminal monitors the cycle data generated by the specified wind turbine within a preset cycle, where the cycle data includes the total power generation and the total number of blade revolutions;

[0008] The cycle data processing end performs correlation analysis on the cycle data monitored in the preset cycle to determine the power generation corresponding to a single circle, and then performs numerical analysis on the single circle power generation determined in the preset cycle and the single circle power generation in the past cycle to assess whether the wind turbine is generating power normally in the preset cycle. Based on the assessment results, the preset cycle is calibrated as an abnormal cycle or a standard cycle. The specific method is as follows:

[0009] Based on the cycle data monitored in this preset cycle, the total power generation F and the total number of blade revolutions Z are confirmed, and the single-circle power generation D in this preset cycle is confirmed by using F÷Z=D;

[0010] Based on this preset cycle, five groups of standard cycles are identified from the past preset cycles. Compared with other standard cycles, the five groups of standard cycles are closest to the timeline of the preset cycle. The single-cycle power generation of the five groups of standard cycles is determined in the same way as the single-cycle power generation of the preset cycle, and calibrated as D k , where k represents different standard cycles, where the length of the standard cycle is consistent with the length of the preset cycle;

[0011] The single-circle power generation D of this preset cycle is compared with the single-circle power generation D of the five sets of standard cycles. k Perform variance processing, confirm the pending variance F, and compare the pending variance F with the preset value Y1. If F>Y1, then mark this preset period as an abnormal period; if F≤Y1, then mark this preset period as a standard period, and transmit the marked abnormal period to the front-end data analysis terminal for period data analysis;

[0012] The associated data monitoring terminal monitors the number of blade revolutions, the number of rotor revolutions and the real-time power generation data of the wind turbine in real time, and transmits the relevant data of the real-time monitoring to the front-end data analysis terminal;

[0013] The front data analysis end, based on the determined abnormal period, extracts the number of blade revolutions and the number of rotor revolutions belonging to the abnormal period, and numerically combines and analyzes the extracted number of blade revolutions and the number of rotor revolutions to determine whether the front transmission component of the wind turbine is abnormal. The specific method is as follows:

[0014] Based on the number of blade revolutions confirmed in this abnormal cycle, the relevant time period of the first group of complete revolutions is identified from the number of blade revolutions. The relevant time period is the initial position of the blade at the beginning of this cycle. The duration of reaching this initial position again after one revolution is identified from the number of rotor revolutions in this relevant time period and calibrated as ZQ1. Based on the number of blade revolutions, the relevant time period of the second group of revolutions is identified, and the number of rotor revolutions ZQ2 in the corresponding relevant time period is identified. This is repeated until the last group of complete revolutions of the blade revolutions in this abnormal cycle is determined and the corresponding number of rotor revolutions ZQ is determined. i , where i represents different relevant time periods;

[0015] Based on the acceleration ratio B set by the planetary gear of the wind turbine generator, where B is a preset value, several ZQ i Compare with the acceleration ratio B and set ZQ i =B are calibrated as standard parameters, and ZQ i The relevant parameters that are ≠B are marked as abnormal parameters;

[0016] The abnormal parameters that appear in the row are marked as the same group of abnormal parameters, and a group of abnormal parameters is also recorded as one abnormal parameter, and the specific number of abnormal parameters G is identified:

[0017] If G ≥ 3, then based on the time relationship, identify whether the intervals between adjacent abnormal parameters are consistent:

[0018] If they are consistent, a normal front transmission signal is generated and transmitted to the display end, and then executed at the rear data analysis end;

[0019] If not all are consistent, a front transmission abnormality signal is generated and transmitted to the display terminal, and then executed at the rear data analysis terminal;

[0020] If G < 3, a front transmission fluctuation signal is generated and transmitted to the display terminal, and then executed at the rear data analysis terminal;

[0021] The post-data analysis end, based on the determined abnormal period, extracts the number of rotor revolutions and power generation data belonging to the abnormal period, and numerically combines and analyzes the extracted number of rotor revolutions and power generation data to determine whether the post-transmission component of the wind turbine is abnormal. The specific method is as follows:

[0022] Based on the confirmed number of rotor revolutions in this abnormal cycle, the initial position of the rotor at the initial moment of this abnormal cycle is recorded. When the rotor reaches this position again after rotating B revolutions, a set of calibration periods is recorded, and the calibration power belonging to this calibration period is confirmed from the power generation data. Then, the subsequent calibration periods and calibration power are confirmed one by one, where B is the acceleration ratio and B is an integer.

[0023] The confirmed calibration period is calibrated as JZq, and the corresponding calibration power is calibrated as JDq, where q represents different calibration periods;

[0024] Confirm the calibration quantity of each calibration period: JDq÷JZq=DDq, where DDq is the calibration quantity;

[0025] The minimum speed and maximum speed belonging to the abnormal cycle are identified from the number of rotor revolutions, and the minimum power generation and the maximum power generation are determined based on the preset power generation factor, where the minimum power generation = minimum speed × power generation factor, where the maximum power generation = maximum speed × power generation factor, and the standard power generation range is determined based on the minimum power generation and the maximum power generation, where the power generation factor is the preset factor. The confirmed several calibration single electric quantities DDq are compared with the standard power generation range:

[0026] When DDq∈standard power generation interval, no calibration is performed;

[0027] when The standard power generation interval is marked as abnormal power generation, and the calibration period associated with the abnormal power generation is marked as the abnormal period;

[0028] Identify whether the abnormal period is consistent with the period in which the abnormal parameters appear. If they are consistent, no processing signal is generated. If they are not consistent, an abnormal power generation signal is generated and displayed.

[0029] The present invention provides a remote monitoring system for wind turbines. Compared with the prior art, it has the following advantages:

[0030] Beneficial effects:

[0031] The present invention monitors and analyzes the periodic power generation of the wind turbine generator, determines whether the power generation of the generator in the current cycle is abnormal based on the numerical characteristics of the power generation in the most recent cycle, thereby locking the abnormal cycle, determining the power generation characteristics of such wind turbine generators based on the most recent cycle data, and then identifying whether the wind turbine generator is generating power normally based on the determined power generation characteristics, and the relevant accuracy of the identification can be fully guaranteed;

[0032] When performing correlation analysis on the front transmission components of the wind turbine, the ratio of the number of revolutions of the two is identified to determine whether it is consistent with the acceleration ratio, thereby determining the abnormal parameters. Then, according to the time when the corresponding abnormal parameters appear, it is determined whether there is a regularity in the frequency of occurrence of the abnormal parameters, so as to make a comprehensive judgment and display the judgment results. This type of judgment method is more comprehensive, the judgment accuracy is higher, and the cooling maintenance status of the generator is taken into account to achieve a better monitoring effect;

[0033] When performing correlation analysis on the rear transmission component of the wind turbine, the abnormal power generation is confirmed by the corresponding number of rotor revolutions and the related power generation. Then, whether the duration of the abnormal power generation is consistent with the time of the abnormal parameter is analyzed to assess whether there is any abnormal power generation in the rear transmission component of the wind turbine. The assessment result is more accurate and the overall monitoring effect is better. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] See also Figure 1 , the present application provides a remote monitoring system for a wind turbine, including a periodic value monitoring terminal, a periodic data processing terminal, a front-end data analysis terminal, a related item data monitoring terminal, a rear-end data analysis terminal and a display terminal;

[0037] The periodic value monitoring terminal is electrically connected to the periodic data processing terminal input node, and the periodic data processing terminal and the associated item data monitoring terminal are electrically connected to the front-end data analysis terminal input node respectively, wherein the front-end data analysis terminal is electrically connected to the display terminal and the rear-end data analysis terminal input node respectively, and the rear-end data analysis terminal is electrically connected to the display terminal input node;

[0038] Among them, the cycle value monitoring end monitors the cycle data generated by the specified wind turbine within the preset cycle, wherein the cycle data includes the total power generation and the total number of blade rotations, and transmits the cycle data monitored within the preset cycle to the cycle data processing end. Specifically, the corresponding cycle is the preset cycle, generally 24h or 12h, and its specific setting value is prepared in advance by the operator based on experience, wherein the specific process of the corresponding wind turbine in the relevant power generation is: the external wind drives the blades to rotate, and the rotating shaft inside the blades meshes with the planetary gears inside the generator, driving the planetary gears to rotate so that the internal rotor to be determined rotates at a constant ratio, and the internal rotor performs the relevant cutting magnetic flux line movement to perform relevant power generation;

[0039] Among them, the cycle data processing end performs correlation analysis on the cycle data monitored within the preset cycle to determine the power generation corresponding to a single circle, and then performs numerical analysis on the single circle power generation determined in the preset cycle and the single circle power generation in the past cycle to assess whether the wind turbine is generating power normally within the preset cycle, and calibrates the preset cycle as an abnormal cycle or a standard cycle based on the assessment result. Specifically, the reason for comparing the relevant data of the preset cycle with the relevant data of the past cycle is that during the power generation process of the corresponding wind turbine, since the internal working components are all gear parts meshing with the corresponding transmission, such working components will have some wear during long-term use, which will lead to a decrease in the corresponding power generation. Therefore, it is necessary to determine the power generation characteristics of such wind turbines from the most recent cycle data, and then identify whether the wind turbine is generating power normally based on the determined power generation characteristics, and the relevant accuracy of the identification can be fully guaranteed. Among them, the specific method of assessment is as follows:

[0040] Based on the cycle data monitored in this preset cycle, the total power generation F and the total number of blade revolutions Z are confirmed, and the single-circle power generation D in this preset cycle is confirmed by using F÷Z=D;

[0041] Taking this preset cycle as a benchmark, five groups of standard cycles (not abnormal cycles) are identified from the past preset cycles. The five groups of standard cycles are closest to the timeline of the preset cycle compared to other standard cycles (that is, after the preset cycle is confirmed, it is necessary to conduct past analysis to identify several groups of standard cycles that are closest to the preset cycle. Several groups of preset cycles just after the wind turbine generator set is put into use belong to the standard cycle. The corresponding operator can also perform numerical monitoring to determine the standard cycle. The relevant cycle judgment is carried out from the sixth preset cycle after the wind turbine generator set is put into use). The single-cycle power generation of the five groups of standard cycles is determined in the same confirmation method as the single-cycle power generation of the preset cycle, and is calibrated as D k , where k represents different standard cycles, where the length of the standard cycle is consistent with the length of the preset cycle. After the preset cycle is confirmed, it will be evaluated as a standard cycle or an abnormal cycle. The single-circle power generation of the corresponding cycle can be confirmed based on the total power generation of the relevant cycle in the past five groups of cycles and the total number of blade revolutions;

[0042] The single-circle power generation D of this preset cycle is compared with the single-circle power generation D of the five sets of standard cycles. kVariance processing is performed to confirm the pending variance F, and the pending variance F is compared with the preset value Y1, wherein the specific value of Y1 is determined by the operator based on experience. If F>Y1, the preset period is calibrated as an abnormal period; if F≤Y1, the preset period is calibrated as a standard period, and the calibrated abnormal period is transmitted to the front-end data analysis terminal for period data analysis. Specifically, the specific numerical performance of the period value can be identified and compared based on the data of the most recent standard period to assess whether the period is an abnormal period or a standard period, and the assessment result is more standardized.

[0043] Among them, the related data monitoring end monitors the number of blade revolutions, the number of rotor revolutions and the real-time power generation data of the wind turbine in real time, and transmits the relevant data of the real-time monitoring to the front-end data analysis end;

[0044] Among them, the front data analysis end, based on the determined abnormal cycle, extracts the number of blade revolutions and the number of rotor revolutions belonging to this abnormal cycle, and performs numerical merging analysis on the extracted number of blade revolutions and the number of rotor revolutions to determine whether the front transmission component of the wind turbine is abnormal. Specifically, during the specific analysis and processing of the wind turbine, the blades drive the planetary gears to rotate, and the planetary gears drive the rotor to accelerate. In this transmission process, if the planetary gears fall off or other conditions occur, numerical errors will occur in the accelerated motion of the rotor, which will cause the occurrence of abnormal cycles. Therefore, the specific method for determining whether the front transmission component of the wind turbine is abnormal is as follows:

[0045] Based on the number of blade revolutions confirmed in this abnormal cycle, the relevant time period of the first group of complete revolutions is identified from the number of blade revolutions. The relevant time period is the initial position of the blade at the beginning of this cycle. The duration of reaching this initial position again after one revolution is identified from the number of rotor revolutions in this relevant time period and calibrated as ZQ1. Based on the number of blade revolutions, the relevant time period of the second group of revolutions is identified, and the number of rotor revolutions ZQ2 in the corresponding relevant time period is identified. This is repeated until the last group of complete revolutions of the blade revolutions in this abnormal cycle is determined and the corresponding number of rotor revolutions ZQ is determined. i , where i represents different relevant time periods (for example, in this abnormal period, the blades rotated 20.5 times in total, so the blades rotated 20 times in a complete rotation process, and 20 complete rotations generated 20 standard relevant time periods, so that the rotation data ZQ2 of the corresponding rotor in the corresponding relevant time period can be confirmed);

[0046] Based on the acceleration ratio B set by the planetary gear of the wind turbine, where B is a preset value, obtained by the operator through the relevant adjustment of the planetary gear in advance, and is a set of fixed values, and the acceleration ratio is that the rotor rotates B circles for every one rotation of the blade, several ZQ i Compare with the acceleration ratio B and set ZQ i =B are calibrated as standard parameters, and ZQ i The relevant parameters that are ≠B are marked as abnormal parameters;

[0047] The abnormal parameters that appear in the row are marked as the same group of abnormal parameters, and a group of abnormal parameters are also recorded as one abnormal parameter (for the convenience of subsequent analysis), and the specific number of abnormal parameters G is identified:

[0048] If G≥3 (including abnormal parameters in a row and abnormal parameters that occur individually), then based on the time relationship, identify whether the intervals between adjacent abnormal parameters are consistent:

[0049] If they are consistent, it means that the wind turbine is in a cooling state, and its power generation is too large, causing the generator to overheat. In this case, the planetary gears can only be regularly adjusted down through the relevant control components to ensure that the power generation is not too large. A normal front transmission signal is generated and transmitted to the display end, and then executed at the post-data analysis end;

[0050] If they are not all consistent, it means that there is no regularity in the changes between the transmission components inside the wind turbine, and there may be related abnormalities in its transmission process, so a front transmission abnormality signal is generated and transmitted to the display end, and then executed by the post-data analysis end;

[0051] If G<3, it means that such abnormal parameters may occur once, and it is very likely that the transmission jamming relationship caused by the lubrication transmission or other tiny dropped objects between the transmission components causes relevant fluctuations in its value. In this case, a front transmission fluctuation signal is generated and transmitted to the display end, and then executed on the post-data analysis end. When external personnel receive such front transmission fluctuation signals, they can decide whether to conduct relevant inspections. Generally, based on the numerical monitoring of other subsequent cycles, if such abnormal signals continue to appear, relevant inspections are required to ensure that the wind turbine can operate normally.

[0052] Among them, the post-data analysis end, based on the determined abnormal period, extracts the number of rotor revolutions and power generation data belonging to the abnormal period, and numerically combines and analyzes the extracted number of rotor revolutions and power generation data to determine whether the post-transmission component of the wind turbine is abnormal. Specifically, during the power generation process of the wind turbine, the corresponding rotor is generally rotated to cut the magnetic flux lines, thereby completing the corresponding power generation process;

[0053] The specific method for determining whether the rear transmission component of the wind turbine is abnormal is as follows:

[0054] Based on the confirmed number of rotor revolutions in this abnormal cycle, the initial position of the rotor at the initial moment of this abnormal cycle is recorded. When the rotor reaches this position again after rotating B revolutions, a set of calibration periods is recorded, and the calibration power belonging to this calibration period is confirmed from the power generation data. Then, the subsequent calibration periods and calibration power are confirmed one by one, where B is the acceleration ratio and B is an integer.

[0055] The confirmed calibration period is calibrated as JZq, and the corresponding calibration power is calibrated as JDq, where q represents different calibration periods, and the specific duration of each different calibration period may be different or the same, because the corresponding rotor has a fast-slow relationship in the rotation process;

[0056] Confirm the calibration quantity of each calibration period: JDq÷JZq=DDq, where DDq is the calibration quantity;

[0057] The minimum speed and maximum speed belonging to the abnormal cycle are identified from the number of rotor revolutions, and the minimum power generation and the maximum power generation are determined based on the preset power generation factor, where the minimum power generation = minimum speed × power generation factor, and the maximum power generation = maximum speed × power generation factor. The standard power generation range is determined based on the minimum power generation and the maximum power generation, where the power generation factor is a preset factor, and its specific value is determined by the operator based on experience. The confirmed several calibration single electricity DDq are compared with the standard power generation range:

[0058] When DDq∈standard power generation interval, no calibration is performed;

[0059] when The standard power generation interval is marked as abnormal power generation, and the calibration period associated with the abnormal power generation is marked as the abnormal period;

[0060] Identify whether the abnormal time period is consistent with the time period in which the abnormal parameters appear. If they are consistent, no processing signal is generated. If they are not completely consistent, an abnormal power generation signal is generated and displayed. Specifically, the abnormal parameters are the relevant parameters confirmed in the front transmission analysis process. Such parameters are slower than other speeds due to transmission abnormalities, which will synchronously affect the power generation and cause a synchronous decrease in power generation. When the time period in which the confirmed abnormal power generation (that is, the decreased power generation) appears is inconsistent with the time period in the front-end processing process, the abnormal power generation may not be caused by the front-end transmission, so it is necessary to generate a corresponding abnormal signal for relevant display. If it is completely consistent with the time period of the front-end abnormal parameters, it is affected by the front-end transmission, resulting in a decrease in the power generation of the rear-end power generation component.

[0061] This monitoring and processing method can fully guarantee the comprehensiveness of wind turbines during power generation monitoring and achieve better remote monitoring and processing effects.

[0062] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0063] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A remote monitoring system for a wind turbine, characterized in that: include: The cycle value monitoring terminal monitors the cycle data generated by the specified wind turbine within a preset cycle, where the cycle data includes the total power generation and the total number of blade revolutions; The cycle data processing end performs correlation analysis on the cycle data monitored in the preset cycle to determine the power generation corresponding to a single circle, and then performs numerical analysis on the single circle power generation determined in the preset cycle and the single circle power generation in the past cycle to assess whether the wind turbine generates power normally in the preset cycle, and calibrates the preset cycle as an abnormal cycle or a standard cycle based on the assessment result; The associated data monitoring terminal monitors the number of blade revolutions, the number of rotor revolutions and the real-time power generation data of the wind turbine in real time, and transmits the relevant data of the real-time monitoring to the front-end data analysis terminal; The front data analysis end extracts the number of blade revolutions and the number of rotor revolutions belonging to the abnormal period based on the determined abnormal period, and numerically combines and analyzes the extracted number of blade revolutions and the number of rotor revolutions to determine whether the front transmission component of the wind turbine is abnormal; The post-data analysis end, based on the determined abnormal period, extracts the number of rotor revolutions and power generation data belonging to this abnormal period, and numerically combines and analyzes the extracted number of rotor revolutions and power generation data to determine whether the post-transmission component of the wind turbine is abnormal.

2. A remote monitoring system for a wind turbine according to claim 1, characterized in that: The specific method for the periodic data processing end to assess whether the wind turbine generator is generating electricity normally within the preset period is: Based on the cycle data monitored in this preset cycle, the total power generation F and the total number of blade revolutions Z are confirmed, and the single-circle power generation D in this preset cycle is confirmed by using F÷Z=D; Based on this preset cycle, five groups of standard cycles are identified from the past preset cycles. Compared with other standard cycles, the five groups of standard cycles are closest to the timeline of the preset cycle. The single-cycle power generation of the five groups of standard cycles is determined in the same way as the single-cycle power generation of the preset cycle, and calibrated as D k , where k represents different standard cycles, where the length of the standard cycle is consistent with the length of the preset cycle; The single-circle power generation D of this preset cycle is compared with the single-circle power generation D of the five sets of standard cycles. k Perform variance processing, confirm the pending variance F, and compare the pending variance F with the preset value Y1. If F>Y1, mark this preset period as an abnormal period.

3. A remote monitoring system for wind turbines according to claim 2, characterized in that: When the to-be-determined variance F≤Y1, the preset period is calibrated as the standard period, and the calibrated abnormal period is transmitted to the front-end data analysis terminal for period data analysis.

4. A remote monitoring system for wind turbines according to claim 2, characterized in that: The specific method for the front data analysis end to determine whether the front transmission component of the wind turbine is abnormal is: Based on the number of blade revolutions confirmed in this abnormal cycle, the relevant time period of the first group of complete revolutions is identified from the number of blade revolutions. The relevant time period is the initial position of the blade at the beginning of this cycle. The duration of reaching this initial position again after one revolution is identified from the number of rotor revolutions in this relevant time period and calibrated as ZQ1. Based on the number of blade revolutions, the relevant time period of the second group of revolutions is identified, and the number of rotor revolutions ZQ2 in the corresponding relevant time period is identified. This is repeated until the last group of complete revolutions of the blade revolutions in this abnormal cycle is determined and the corresponding number of rotor revolutions ZQ is determined. i , where i represents different relevant time periods; Based on the acceleration ratio B set by the planetary gear of the wind turbine generator, where B is a preset value, several ZQ i Compare with the acceleration ratio B and ZQ i =B related parameters are calibrated as standard parameters, and ZQ i The relevant parameters that are ≠B are marked as abnormal parameters; The abnormal parameters that appear in the row are marked as the same group of abnormal parameters, and a group of abnormal parameters is also recorded as one abnormal parameter, and the specific number of abnormal parameters G is identified: If G ≥ 3, then based on the time relationship, identify whether the intervals between adjacent abnormal parameters are consistent: If they are consistent, a normal front transmission signal is generated and transmitted to the display end, and then executed at the rear data analysis end; If they are not all consistent, a front transmission abnormality signal is generated and transmitted to the display end, and then executed at the post-data analysis end.

5. A remote monitoring system for wind turbines according to claim 4, characterized in that: When the specific number G of the abnormal parameters is less than 3, a front transmission fluctuation signal is generated and transmitted to the display end, and then executed by the rear data analysis end.

6. A remote monitoring system for wind turbines according to claim 4, characterized in that: The specific method for the post-data analysis end to determine whether the post-transmission component of the wind turbine is abnormal is as follows: Based on the confirmed number of rotor revolutions in this abnormal cycle, the initial position of the rotor at the initial moment of this abnormal cycle is recorded. When the rotor reaches this position again after rotating B revolutions, a set of calibration periods is recorded, and the calibration power belonging to this calibration period is confirmed from the power generation data. Then, the subsequent calibration periods and calibration power are confirmed one by one, where B is the acceleration ratio and B is an integer. The confirmed calibration period is calibrated as JZq, and the corresponding calibration power is calibrated as JDq, where q represents different calibration periods; Confirm the calibration single electricity quantity for each calibration period: JDq÷JZq=DDq, where DDq is the calibration single electricity quantity; The minimum speed and maximum speed belonging to the abnormal cycle are identified from the number of rotor revolutions, and the minimum power generation and the maximum power generation are determined based on the preset power generation factor, where the minimum power generation = minimum speed × power generation factor, where the maximum power generation = maximum speed × power generation factor, and the standard power generation range is determined based on the minimum power generation and the maximum power generation, where the power generation factor is the preset factor. The confirmed several calibration single electric quantities DDq are compared with the standard power generation range: when The standard power generation interval is marked as abnormal power generation, and the calibration period associated with the abnormal power generation is marked as the abnormal period; Identify whether the abnormal period is consistent with the period in which the abnormal parameters appear. If they are consistent, no processing signal is generated. If they are not consistent, an abnormal power generation signal is generated and displayed.

7. A remote monitoring system for a wind turbine generator according to claim 6, characterized in that: When the calibration single quantity DDq∈ standard power generation interval, no calibration is performed.

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