State monitoring device, nacelle monitoring device, monitoring system and monitoring method

Abstract

To detect a lightning strike while reducing a manufacturing cost.SOLUTION: A state monitoring device 200 acquires a vibration value of a first blade 101, a vibration value of a second blade 102, and a vibration value of a third blade 103 from a first vibration sensor 111, a second vibration sensor 112, and a third vibration sensor 113, respectively. The state monitoring device 200 detects a lightning strike to a wind power generation device when all the absolute values of the vibration value of the first blade 101, the vibration value of the second blade 102, and the vibration value of the third blade 103 exceed a first threshold value.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to a condition monitoring device, a nacelle monitoring device, a monitoring system, and a monitoring method.

Background Art

[0002] For example, Japanese Patent Application Laid-Open No. 2005-62080 (Patent Document 1) discloses a wind power generation device. This wind power generation device has a plurality of blades, and the plurality of blades are located at high positions. Therefore, lightning strikes on the plurality of blades may occur. When a lightning strike occurs, the blades of the wind power generation device may be damaged. Thus, the wind power generation device has a lightning strike detection device for detecting lightning strikes. The lightning strike detection device has a Rogowski coil and a detection unit that detects the lightning current flowing through the Rogowski coil during a lightning strike.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the invention described in Patent Document 1 above, since a detection device (lightning strike detection device) for detecting abnormalities such as lightning strikes is required, there may be a problem that the manufacturing cost of the wind power generation device increases.

[0005] The present disclosure has been made to solve the above problems, and an object thereof is to detect an abnormality of a wind power generation device while suppressing the manufacturing cost.

Means for Solving the Problems

[0006] The state monitoring device of the present disclosure monitors the state of a wind power generation device having a plurality of blades. The state monitoring device includes an interface and a control device. The interface acquires the vibration values of the plurality of blades from a plurality of vibration sensors that each detect the vibration values of the plurality of blades. The control device detects a lightning strike on the wind power generation device when all of the absolute values of the vibration values of the plurality of blades exceed a first threshold value.

[0007] The state monitoring method of the present disclosure is a method for monitoring the state of a wind power generation device having a plurality of blades. The state monitoring method includes acquiring the vibration values of the plurality of blades. Further, the state monitoring method includes detecting that there has been a lightning strike on the wind power generation device when the absolute values of the vibration values of the plurality of blades exceed a first threshold value.

Advantages of the Invention

[0008] According to the present disclosure, it is possible to detect an abnormality in a wind power generation device while suppressing manufacturing costs.

Brief Description of the Drawings

[0009]

Figure 1

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Figure 12

Embodiments for Carrying Out the Invention

[0010] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the embodiments described below, when referring to the number, quantity, etc., unless otherwise specified, the scope of the present disclosure is not necessarily limited to such number, quantity, etc. For the same parts and corresponding parts, the same reference numerals may be given, and repeated descriptions may not be repeated. It is initially planned to appropriately combine and use the configurations in the embodiments.

[0011] [Configuration of Wind Power Generation System] FIG. 1 is a diagram showing a configuration example of a wind power generation device 100 and a state monitoring system 500 of the present disclosure. In FIG. 1, a front view of the wind power generation device 100 is shown.

[0012] The wind power generation device 100 has M (M is an integer of 2 or more) blades. The M blades correspond to the "plurality of blades" of the present disclosure. In the present embodiment, it is assumed that M = 3. That is, the wind power generation device 100 has three blades, a first blade 101, a second blade 102, and a third blade 103. As a modification, M may be other values. For example, M = 2 may be used, or M = 4 may be used.

[0013] The wind power generation device 100 further includes a hub 104, a nacelle 105, a tower 180, a first down conductor 161, a second down conductor 162, and a third down conductor 163. The first blade 101, the second blade 102, and the third blade 103 are joined to the hub 104. The nacelle 105 is installed behind the hub 104. The tower 180 holds the first blade 101, the second blade 102, the third blade 103, the hub 104, and the nacelle 105.

[0014] The condition monitoring system 500 includes a condition monitoring device 200 and M (in this embodiment, M = 3) vibration sensors. In the example of FIG. 1, the condition monitoring device 200 is installed inside the hub 104. The condition monitoring device 200 is a device that monitors the condition of the wind power generation device 100, and more typically, a device that monitors the conditions of a plurality of blades.

[0015] In FIG. 1, the first vibration sensor 111, the second vibration sensor 112, and the third vibration sensor 113 are disclosed as three vibration sensors. The first vibration sensor 111, the second vibration sensor 112, and the third vibration sensor 113 are installed on the first blade 101, the second blade 102, and the third blade 103, respectively. The first vibration sensor 111, the second vibration sensor 112, and the third vibration sensor 113 detect the vibration values of the first blade 101, the second blade 102, and the third blade 103, respectively. In the present embodiment, it is assumed that the first vibration sensor 111, the second vibration sensor 112, and the third vibration sensor 113 are acceleration sensors. That is, the first vibration sensor 111, the second vibration sensor 112, and the third vibration sensor 113 detect the acceleration of the first blade 101, the acceleration of the second blade 102, and the acceleration of the third blade 103 as vibration values, respectively. Note that as a modification, the vibration sensor may be other sensors, for example, a displacement sensor or a velocity sensor.

[0016] The first down-conductor 161, the second down-conductor 162, and the third down-conductor 163 are respectively installed inside the first blade 101, the second blade 102, and the third blade 103. The first down-conductor 161, the second down-conductor 162, and the third down-conductor 163 are collectively also simply referred to as the "down-conductor". When lightning strikes the wind power generation device 100, the down-conductor discharges the lightning current based on the lightning strike safely to the ground. That is, when lightning strikes the wind power generation device 100, the lightning current flows through the down-conductor.

[0017] Figure 2 is a diagram showing the internal structure of the hub 104 and the nacelle 105, etc. As described above, a state monitoring device 200 is installed inside the hub 104. Also, in the example of Figure 2, vibration sensors are installed inside each blade. In the example of Figure 2, a first vibration sensor 111 is installed inside the first blade 101, and a third vibration sensor 113 is installed inside the third blade 103. Although not particularly shown, a second vibration sensor 112 is installed inside the second blade 102. The vibration values (accelerations) detected by the first vibration sensor 111, the second vibration sensor 112, and the third vibration sensor 113 are transmitted to the state monitoring device 200 as time-series data.

[0018] Also, in the example of Figure 2, a part of the first down-conductor 161 inside the first blade 101 is shown. Also, since the vibration sensor and the down-conductor are installed inside the blade, the wiring 141 from the vibration sensor (first vibration sensor 111) and the first down-conductor 161 are arranged close to each other (see location α in Figure 2). Thus, in each blade, the wiring 141 from the vibration sensor and the down-conductor are installed close to each other.

[0019] The condition monitoring device 200 executes a lightning strike pre - anomaly detection process, a lightning strike detection process, and a blade anomaly determination process based on the vibration values detected by the first vibration sensor 111, the second vibration sensor 112, and the third vibration sensor 113. The lightning strike pre - anomaly detection process is a process for detecting anomalies in the first blade 101, the second blade 102, and the third blade 103. In the lightning strike pre - anomaly detection process, the condition monitoring device 200 determines, for example, that the blade on which the vibration sensor that output the vibration value is abnormal when the vibration value belongs to an abnormal range. Details of the lightning strike detection process and the blade anomaly determination process will be described later. In the present embodiment, the first vibration sensor 111, the second vibration sensor 112, and the third vibration sensor 113 are used in common for the lightning strike pre - anomaly detection process, the lightning strike detection process, and the blade anomaly determination process.

[0020] The main shaft 121 holds the hub 104 and the nacelle 105 such that the hub 104 is rotatably held by the nacelle 105. Further, inside the nacelle 105, a main bearing 122, a speed increaser 123, a generator 124, and a nacelle monitoring device 300 are installed.

[0021] The main bearing 122 rotatably holds the main shaft 121. The speed increaser 123 amplifies the rotation of the hub 104 to the number of rotations required for power generation. The generator 124 converts the rotational force of the hub 104 (the rotational force amplified by the speed increaser 123) into electricity. The main shaft 121, the main bearing 122, the speed increaser 123, and the generator 124 correspond to the "mechanism installed in the nacelle" of the present disclosure.

[0022] The nacelle 105 is further provided with a vibration sensor 125, a rotation sensor 126, a vibration sensor 127, and a vibration sensor 128. The vibration sensor 125, the rotation sensor 126, the vibration sensor 127, and the vibration sensor 128 respectively detect the vibration value of the main bearing 122, the rotational speed (rotational position) of the main shaft 121, the vibration value of the speed increaser 123, and the vibration value of the generator 124. The vibration sensor 125, the rotation sensor 126, the vibration sensor 127, and the vibration sensor 128 correspond to the "nacelle sensors" of the present disclosure. The vibration value of the main bearing 122, the rotational speed (rotational position) of the main shaft 121, the vibration value of the speed increaser 123, and the vibration value of the generator 124 correspond to the "parameters" of the present disclosure. The vibration sensor 125, the rotation sensor 126, the vibration sensor 127, and the vibration sensor 128 transmit the detected values to the nacelle monitoring device 300 as time-series data.

[0023] The nacelle monitoring device 300 includes a determination device 310. The determination device 310 determines the presence or absence of an abnormality in the mechanisms installed in the nacelle 105 (that is, the main shaft 121, the main bearing 122, the speed increaser 123, and the generator 124) based on the time-series data from the vibration sensor 125, the rotation sensor 126, the vibration sensor 127, and the vibration sensor 128. This determination process is also referred to as a nacelle abnormality determination process (see step S114 in FIG. 12).

[0024] In the following description, the first blade 101, the second blade 102, and the third blade 103 may be collectively referred to as "three blades". Also, the first vibration sensor 111, the second vibration sensor 112, and the third vibration sensor 113 may be collectively referred to as "three vibration sensors".

[0025] [Hardware Configuration of State Monitoring Device and Nacelle Monitoring Device] FIG. 3 is a diagram showing an example of the hardware configuration of the state monitoring device 200 and the nacelle monitoring device 300. The state monitoring device 200 includes a CPU (Central Processing Unit) 201, a memory 202, and a communication interface 203. In FIG. 3, the communication interface is described as "I / F". The communication interface 203 corresponds to the "signal input unit" and the "external interface" of the present disclosure. The nacelle monitoring device 300 includes a CPU 301, a memory 302, and a communication interface 303. The CPU 201 and the CPU 301 execute various processes.

[0026] The memories 202 and 302 include a ROM (Read Only Memory), a RAM (Random Access Memory), and the like. The ROM is a non-rewritable non-volatile memory, and the RAM is a volatile memory. The communication interfaces 203 and 303 communicate with external devices. For example, the state monitoring device 200 transmits a subsequent lightning strike signal to the communication interface 303 via the communication interface 203 of the state monitoring device 200. The lightning strike signal corresponds to the "detection signal" of the present disclosure.

[0027] Further, a control device 240 is configured by the CPU 201 and at least a part of the storage area of the memory 202. The control device 240 may also be referred to as a "control circuit" or "at least one controller". Further, a determination device 310 (see FIG. 2) is configured by the CPU 301 and at least a part of the storage area of the memory 302.

[0028] [Waveform of vibration value] FIG. 4 is a diagram showing waveforms of time-series data of vibration values measured by the state monitoring device 200 from the first vibration sensor 111, the second vibration sensor 112, and the third vibration sensor 113. Hereinafter, the vibration value measured by the first vibration sensor 111, the vibration value measured by the second vibration sensor 112, and the vibration value measured by the third vibration sensor 113 are also referred to as the first vibration value, the second vibration value, and the third vibration value, respectively. Further, the time-series data of the first vibration value, the second vibration value, and the third vibration value are also referred to as the first time-series data, the second time-series data, and the third time-series data, respectively.

[0029] In FIG. 4 and FIGS. 5 to 8 described later, the vertical axis represents the vibration value (acceleration), and the horizontal axis represents time. Further, in FIG. 4, the first vibration value, the second vibration value, and the third vibration value are indicated by a solid line, a broken line, and a one-dot chain line, respectively.

[0030] The first vibration value, the second vibration value, and the third vibration value are values reflecting the gravitational acceleration associated with the rotation of the first blade 101, the second blade 102, and the third blade 103, respectively. Therefore, the waveforms of the time-series data of the first vibration value, the second vibration value, and the third vibration value are sine curves. Further, the first blade 101, the second blade 102, and the third blade 103 are installed at intervals of 120 degrees in the rotation direction of the hub 104. Therefore, the phases of the first vibration value, the second vibration value, and the third vibration value at the same time are shifted by 120 degrees each. When the rotational speed of the hub 104 is, for example, 20 min -1 In the case of, the first vibration value, the second vibration value, and the third vibration value increase and decrease at a period of 20 / 60 ≈ 0.33 Hz.

[0031] FIG. 5 is a diagram showing the relationship between the rotational position (rotation angle) of the first blade 101 and the first vibration value. In the example of FIG. 5, the rotation angle of the first blade 101 when the first blade 101 is in the uppermost position is set to 0 degrees. In the example of FIG. 5, when the rotation angle of the first blade 101 is 0 degrees, the first vibration value is 0, and when the rotation angle of the first blade 101 is 90 degrees, the first vibration value is 9.8 m / s 2This results in. Also, when the rotation angle of the first blade 101 is 180 degrees, the first vibration value becomes 0, and when the rotation angle of the first blade 101 is 270 degrees, the first vibration value is -9.8 m / s 2 This results in.

[0032] The gravitational acceleration g acting on the vibration sensor (first vibration sensor 111) is 9.8 m / s 2 Therefore, as shown in FIG. 5, the maximum value of the first vibration value is 9.8 m / s 2 and the minimum value of the first vibration value is -9.8 m / s 2 This results in. However, in reality, the phase of the vibration value data advances due to the influence of the electronic circuit of the condition monitoring device 200, etc., so a deviation may occur from the actual change in the gravitational acceleration component. Also, due to the mounting attitude and frequency characteristics of the vibration sensor, etc., the maximum value of the vibration value is slightly smaller than 9.8 m / s 2 and the minimum value of the vibration value may be slightly larger than -9.8 m / s 2 In addition, when noise (such as high-frequency noise) is mixed in the time-series data output by the vibration sensor, the condition monitoring device 200 will obtain a vibration value larger than 9.8 m / s 2 or a vibration value smaller than -9.8 m / s 2 as the actual vibration value. This noise is generated by, for example, the rubbing of the hub 104 or the three blades.

[0033] [Lightning Strike Detection Process] Next, the lightning strike detection process by the condition monitoring device 200 will be described. As described with reference to FIG. 2, when a lightning strike occurs on the wind power generation device 100, a lightning current flows through the down conductor. Also, as described with reference to FIG. 2, for example, inside the hub 104, the wiring 141 from the vibration sensor is arranged in the vicinity of the down conductor (see location α in FIG. 2). Therefore, an induced lightning surge based on the lightning strike enters the wiring 141 of the vibration sensor through the down conductor.

[0034] FIG. 6 is a diagram showing waveforms of a first vibration value, a second vibration value, and a third vibration value when lightning strikes the wind power generation device 100. In the waveform of the vibration value, the induced lightning surge that has invaded the wiring 141 from the vibration sensor appears as noise in the form of an impulse waveform (see part A in FIG. 6).

[0035] FIG. 7 is an enlarged view of a portion where the vibration value (acceleration) of the waveform in FIG. 6 is 0. Further, in FIG. 7, waveforms of the absolute values of the first vibration value, the second vibration value, and the third vibration value are shown. As shown in FIG. 7, when lightning strikes the wind power generation device 100, all of the absolute values of the first vibration value, the second vibration value, and the third vibration value are 9.8 m / s, which is the first threshold value. 2 exceed it.

[0036] Therefore, the condition monitoring device 200 of the present embodiment acquires the first vibration value, the second vibration value, and the third vibration value (first time series data, second time series data, and third time series data) in a predetermined first collection period, and all of the absolute values of the first vibration value, the second vibration value, and the third vibration value are 9.8 m / s, which is the first threshold value. 2 When it exceeds, it is detected that lightning has struck the wind power generation device 100 (see "lightning strike detection" in FIG. 7). Note that when all of the absolute values of the first vibration value, the second vibration value, and the third vibration value are the first threshold value, it may be detected that lightning has struck the wind power generation device 100, or it may be detected that lightning has not struck the wind power generation device 100.

[0037] For example, as shown in FIG. 2, inside the hub 104, when the wiring 141 of any first vibration sensor 111 is close to the first down conductor 161, the absolute values of the first vibration value, the second vibration value, and the third vibration value are 9.8 m / s due to noise associated with the induced lightning surge. 2 The timing of exceeding is the same timing. Therefore, the condition monitoring device 200 detects that lightning has struck the wind power generation device 100 when all of the absolute values of the first vibration value, the second vibration value, and the third vibration value exceed 9.8 m / s, which is the first threshold value, at the same timing. 2 exceed it.

[0038] Regarding the same timing in this embodiment, the timing when all of the absolute values of the first vibration value, the second vibration value, and the third vibration value exceed the first threshold value is not necessarily exactly the same, and may be regarded as approximately the same. Regarding the approximately same timing, as long as it is possible to detect a lightning strike on the wind power generation device 100, the timing when the absolute value of the first vibration value exceeds the first threshold value, the timing when the absolute value of the second vibration value exceeds the first threshold value, and the timing when the absolute value of the third vibration value exceeds the first threshold value, there may be a time difference (for example, 0.1 seconds) between at least two of these timings.

[0039] Note that the above-mentioned first collection period in this embodiment is, for example, 20 seconds. However, the first collection period is appropriately determined according to specifications such as the filter processing described later and the data capacity of the state monitoring device 200.

[0040] In addition, when only two vibration values exceed the first threshold value, or when only one vibration value exceeds the first threshold value in the state monitoring device 200 of this embodiment, it is determined that no lightning strike has occurred on the wind power generation device 100. This is because when only two vibration values exceed the first threshold value, or when only one vibration value exceeds the first threshold value, it is highly likely that the vibration value exceeds the first threshold value due to factors other than a lightning strike.

[0041] [Blade abnormality determination process] When a lightning strike occurs on the wind power generation device 100, at least one abnormality (for example, damage) may occur in at least one of the three blades (the first blade 101, the second blade 102, and the third blade 103). Therefore, when the state monitoring device 200 detects a lightning strike on the wind power generation device 100, it executes a blade abnormality determination process for detecting the presence or absence of abnormalities in the three blades.

[0042] FIG. 8 is a diagram showing an example of waveforms of a first vibration value, a second vibration value, and a third vibration value acquired by the state monitoring device 200 during a predetermined second collection period. FIGS. 8(A), 8(B), and 8(C) show the waveforms of the first vibration value, the second vibration value, and the third vibration value, respectively.

[0043] The state monitoring device 200 extracts time-series data for a predetermined number of rotations L of the three blades from the time-series data for the first collection period output by each of the three vibration sensors. L is an integer of 1 or more, and in the present embodiment, L = 1, that is, the predetermined number of rotations is one rotation.

[0044] FIG. 9 is a diagram showing three vibration data (the above-extracted data) for one rotation of the three blades when no abnormality occurs in the three blades. The vertical axis in FIG. 9 indicates the vibration value, and the horizontal axis indicates the rotation angle of the blade. FIGS. 9(A), 9(B), and 9(C) show the waveforms of the first vibration value, the second vibration value, and the third vibration value, respectively. In FIGS. 9(A), 9(B), and 9(C), the rotation angles are shown aligned.

[0045] After lightning strike detection, when no abnormality occurs in any of the first blade 101, the second blade 102, and the third blade 103, the correlation of the time series of the vibration values tends to be strong as shown in FIG. 9. On the other hand, when an abnormality occurs in any of the first blade 101, the second blade 102, and the third blade 103, the correlation between the vibration value of the blade in which the abnormality has occurred and the vibration values of the other blades tends to be weak.

[0046] Therefore, in view of the above tendency, the state monitoring device 200 of the present embodiment detects the presence or absence of an abnormality in the three blades using a correlation parameter indicating the correlation of the time-series data of the first vibration value, the second vibration value, and the third vibration value. In the present embodiment, the correlation parameter is a correlation coefficient.

[0047] FIG. 10 is a diagram for explaining an application example of the correlation coefficient. As shown in FIG. 10, the state monitoring device 200 calculates a correlation coefficient indicating the correlation between two time-series data out of all combinations of the time-series data of the three blades that can be obtained. Hereinafter, the two time-series data are also referred to as "one time-series data" and "the other time-series data".

[0048] Specifically, the state monitoring device 200 extracts N (N is an integer of 2 or more) vibration values at the same rotational angle from each of the three time-series data in a predetermined second collection period. Then, the correlation coefficient between one time-series data and the other time-series data is calculated. That is, the state monitoring device 200 calculates the correlation coefficient σ12, the correlation coefficient σ23, and the correlation coefficient σ31.

[0049] Also, all the above combinations are "first time-series data and second time-series data", "second time-series data and third time-series data", and "third time-series data and first time-series data". That is, the correlation coefficient σ12, the correlation coefficient σ23, and the correlation coefficient σ31 are the correlation coefficient between the first time-series data and the second time-series data, the correlation coefficient between the second time-series data and the third time-series data, and the correlation coefficient between the third time-series data and the first time-series data, respectively. The state monitoring device 200 calculates the correlation coefficient by, for example, the following formula (1).

[0050] σ = (A1) / (B1×B2) (1) Here, A1 in formula (1) indicates the covariance between "one time-series data" and "the other time-series data". Also, B1 indicates the standard deviation of the N vibration values in one time-series data, and B2 indicates the standard deviation of the N vibration values in the other time-series data.

[0051] In FIG. 10(A), an example is shown where the correlation coefficient σ12, the correlation coefficient σ23, and the correlation coefficient σ31 are "0.73", "0.80", and "0.23", respectively. In FIG. 10(B), an example is shown where the correlation coefficient σ12, the correlation coefficient σ23, and the correlation coefficient σ31 are "0.70", "0.48", and "0.35", respectively. In FIG. 10(C), an example is shown where the correlation coefficient σ12, the correlation coefficient σ23, and the correlation coefficient σ31 are "0.51", "0.34", and "0.28", respectively.

[0052] The state monitoring device 200 determines whether each of the three calculated correlation coefficients (correlation coefficient σ12, correlation coefficient σ23, and correlation coefficient σ31) is less than the second threshold value. In the present embodiment, the second threshold value is 0.60.

[0053] Although not particularly shown, if all three correlation coefficients are greater than the second threshold value, the state monitoring device 200 determines that all three blades are normal. Also, as shown in FIG. 10(A), when one of the three correlation coefficients is less than the second threshold value and two correlation coefficients are greater than or equal to the second threshold value, the state monitoring device 200 determines that all three blades are normal.

[0054] Next, as shown in FIG. 10(B), when two of the three correlation coefficients are less than the second threshold value and one correlation coefficient is greater than or equal to the second threshold value, it is highly likely that an abnormality has occurred in one of the three blades. In the case of FIG. 10(B), it is highly likely that an abnormality has occurred in the third blade 103. Therefore, in the case of FIG. 10(B), the state monitoring device 200 determines that an abnormality has occurred in one blade.

[0055] Next, as shown in FIG. 10(C), when all three correlation coefficients are less than the second threshold value, it is highly likely that an abnormality has occurred in at least two of the three blades. Therefore, in the case of FIG. 10(C), the state monitoring device 200 determines that an abnormality has occurred in two or three blades.

[0056] As described above, when the number of correlation coefficients less than the second threshold among the correlation coefficients of the two time-series data in all the above combinations is two or more (that is, in the cases of FIGS. 10(B) and 10(C)), the state monitoring device 200 detects that an abnormality has occurred in any one of the three blades.

[0057] As a modification, the above provisions of "less than the second threshold" and "equal to or greater than the second threshold" may be replaced with the provisions of "less than or equal to the second threshold" and "greater than the second threshold", respectively.

[0058] [Functional block diagram of state monitoring device] FIG. 11 is a functional block diagram of the state monitoring device 200. The state monitoring device 200 includes a signal input unit 250, a first LPF (Low Pass Filter) 251, a second LPF 252, a third LPF 253, a first absolute value unit 261, a second absolute value unit 262, a third absolute value unit 263, a comparison unit 266, a correlation unit 268, a determination unit 270, a generation unit 272, a communication unit 274, a first storage unit 281, and a second storage unit 282. The first storage unit 281 stores a first threshold (in this embodiment, 9.8 m / s 2 ). Further, the second storage unit 282 stores a second threshold (in this embodiment, 0.6).

[0059] The control device 240 in FIG. 3 has the functions of the signal input unit 250, the first LPF (Low Pass Filter) 251, the second LPF 252, the third LPF 253, the first absolute value unit 261, the second absolute value unit 262, the third absolute value unit 263, the comparison unit 266, the correlation unit 268, the determination unit 270, the generation unit 272, and the communication unit 274. The memory 202 has the functions of the first storage unit 281 and the second storage unit 282.

[0060] The signal input unit 250 acquires the first time-series data (first vibration value) transmitted from the first vibration sensor 111, the second time-series data (second vibration value) transmitted from the second vibration sensor 112, and the third time-series data (third vibration value) transmitted from the third vibration sensor 113 over the above-mentioned first collection period.

[0061] Next, the first LPF 251, the second LPF 252, and the third LPF 253 each perform a filtering process for suppressing noise in the first time-series data, the second time-series data, and the third time-series data. The cut-off frequencies of the first LPF 251, the second LPF 252, and the third LPF 253 in the present embodiment are 3 Hz.

[0062] Next, the first absolute value unit 261, the second absolute value unit 262, and the third absolute value unit 263 each calculate the absolute value of the output value from the first LPF 251, the output value from the second LPF 252, and the output value from the third LPF 253. Waveform data as shown in FIG. 7 is generated by the processing of the first absolute value unit 261, the second absolute value unit 262, and the third absolute value unit 263.

[0063] Next, the comparison unit 266 compares the output values (absolute values of vibration values) from the first absolute value unit 261 to the third absolute value unit 263 with the first threshold value (9.8 m / s 2 ). Then, when all of the output values (absolute values of vibration values) from the first absolute value unit 261 to the third absolute value unit 263 exceed the first threshold value, the comparison unit 266 detects a lightning strike on the wind power generation device 100. Further, when at least one of the output values from the first absolute value unit 261 to the third absolute value unit 263 is equal to or less than the first threshold value, the comparison unit 266 determines that no lightning strike has occurred on the wind power generation device 100.

[0064] When the comparison unit 266 detects a lightning strike on the wind power generation device 100, it transmits a lightning strike signal to the correlation unit 268 and the communication unit 274. The lightning strike signal is a signal indicating that a lightning strike on the wind power generation device 100 has been detected. The communication unit 274 transmits the lightning strike signal to the nacelle monitoring device 300. When the nacelle monitoring device 300 receives the lightning strike signal, the determination device 310 of the nacelle monitoring device 300 executes the above-described nacelle abnormality determination process.

[0065] Also, when the correlation unit 268 receives the lightning strike signal, it calculates the correlation coefficient σ12, the correlation coefficient σ23, and the correlation coefficient σ31 using the first time series data, the second time series data, and the third time series data from the signal input unit 250 (see the description of the above formula (1) and FIG. 10). Then, the determination unit 270 determines whether the correlation coefficient σ12, the correlation coefficient σ23, and the correlation coefficient σ31 exceed the second threshold value. When the number of correlation coefficients less than the second threshold value among the correlation coefficient σ12, the correlation coefficient σ23, and the correlation coefficient σ31 is 2 or more (in the case of FIG. 10(B) or FIG. 10(C)), an abnormality of the three blades is detected (it is determined that at least one of the three blades is abnormal). On the other hand, when the number of correlation coefficients less than the second threshold value among the correlation coefficient σ12, the correlation coefficient σ23, and the correlation coefficient σ31 is less than 1 (in the case of FIG. 10(A)), the normality of the three blades is detected (it is determined that the three blades are normal).

[0066] When the determination unit 270 determines that the three blades are abnormal, the generation unit 272 generates an abnormal signal. Then, the communication unit 274 outputs it to the external device 400. The external device 400 is, for example, a management device of the administrator of the wind power generation device 100 and an owner device of the owner of the wind power generation device 100. Also, when the determination unit 270 determines that the three blades are normal, the generation unit 272 generates a normal signal and outputs it to the external device 400. When the external device 400 receives the abnormal signal, it notifies the owner or the administrator that the three blades are abnormal. When the external device 400 receives the normal signal, it notifies the owner or the administrator that the three blades are normal. The external device 400 and the nacelle monitoring device 300 correspond to the "external devices" of the present disclosure.

[0067] [Flowchart] FIG. 12 is a flowchart showing the main processing flow of the state monitoring device 200 and the like. The state monitoring device 200 executes the processes of step S102, step S104, and step S106 in parallel. Step S102 is a process in which the state monitoring device 200 acquires the first time-series data in the above-described first collection period from the first vibration sensor 111. Step S104 is a process in which the state monitoring device 200 acquires the second time-series data in the above-described first collection period from the second vibration sensor 112. Step S106 is a process in which the state monitoring device 200 acquires the third time-series data in the above-described first collection period from the third vibration sensor 113.

[0068] Next, in step S108, the state monitoring device 200 executes the above-described lightning strike detection process based on the first time-series data, the second time-series data, and the third time-series data. Next, in step S110, the state monitoring device 200 determines whether or not a lightning strike on the wind power generation device 100 has been detected in the lightning strike detection process of step S108.

[0069] In step S110, if lightning strike is not detected (NO in step S110), the process in FIG. 12 ends. On the other hand, in step S110, if lightning strike is detected (YES in step S110), the process proceeds to step S111.

[0070] In step S111, the state monitoring device 200 outputs a lightning strike signal to an external device (for example, the nacelle monitoring device 300). Then, the process proceeds to step S112 and step S114. The process of step S112 is executed by transmitting the lightning strike signal in FIG. 11 to the correlation unit 268, and the process of step S114 is executed by transmitting the lightning strike signal to the nacelle monitoring device 300.

[0071] In step S112, the state monitoring device 200 executes the above-described blade abnormality determination process. Also, in step S114, the nacelle monitoring device 300 executes the above-described nacelle abnormality determination process. The nacelle monitoring device 300 transmits the result of the nacelle abnormality determination process to the above-described external device 400.

[0072] Next, in step S116, the state monitoring device 200 determines whether an abnormality has been detected in the blade abnormality determination process of step S112 and the nacelle abnormality determination process of step S114.

[0073] If an abnormality is detected (YES in step S116), in step S118, the state monitoring device 200 outputs an abnormality signal to the above-described external device 400. The abnormality signal includes information indicating the location where the abnormality has occurred (the blade and the mechanism within the nacelle 105). If no abnormality is detected (NO in step S116), in step S120, the state monitoring device 200 outputs a normal signal to the above-described external device 400. As a modification, in step S120, the state monitoring device 200 may not output a normal signal.

[0074] [Parentheses] (1) As described above, when all of the absolute values of the vibration values of the M blades exceed the first threshold value, the state monitoring device 200 detects a lightning strike on the wind power generation device 100. Therefore, the state monitoring device 200 can detect a lightning strike on the wind power generation device 100 without requiring a special device such as the above-described lightning strike detection device. Thus, the state monitoring device 200 of the present embodiment can detect a lightning strike while suppressing the manufacturing cost.

[0075] (2) Also, as described with reference to FIG. 11, the first LPF 251, the second LPF 252, and the third LPF 253 each execute a filtering process for suppressing noise in the first time series data, the second time series data, and the third time series data, respectively. Therefore, even if noise is mixed in the time series data output from the three vibration sensors, the noise can be suppressed. Thus, the state monitoring device 200 of the present embodiment can improve the accuracy of lightning strike detection.

[0076] (3) Also, as described with reference to FIG. 7 and the like, the first threshold value is 9.8 m / s 2 . According to such a configuration, the manufacturer of the state monitoring device 200 does not need to determine the first threshold value by experiments or the like, and thus the burden of determining the first threshold value can be reduced.

[0077] (4) Also, as shown in FIG. 11, the first absolute value units 261 to 263 calculate the absolute values of the first time series data to the third time series data, respectively. Then, when all of the absolute values exceed the first threshold value, the state monitoring device 200 detects a lightning strike on the wind power generation device.

[0078] When lightning strikes the wind power generation device 100, the vibration values detected by the three vibration sensors may become excessively large negative values. Assuming this case, in order to detect lightning strikes, it is conceivable that the condition monitoring device 200 is configured to hold both a first threshold value with a positive value and a first threshold value with a negative value. However, with such a configuration, the storage capacity used for storing the first threshold value may increase. On the other hand, by calculating the absolute value, the condition monitoring device 200 can make the vibration value a positive value. Therefore, the condition monitoring device 200 can be made unnecessary to hold the first threshold value with a negative value.

[0079] (5) Further, when the absolute values of the vibration values of the three blades exceed the first threshold value at the same timing, the condition monitoring device 200 detects a lightning strike on the wind power generation device 100. When a lightning strike occurs on the wind power generation device 100, the absolute values of the vibration values of the three blades exceed the first threshold value at the same timing. Therefore, according to such a configuration, the condition monitoring device 200 can accurately detect a lightning strike on the wind power generation device 100.

[0080] (6) Further, when the condition monitoring device 200 detects a lightning strike on the wind power generation device 100, it calculates three correlation parameters (correlation coefficient σ12, correlation coefficient σ23, and correlation coefficient σ31). Then, when the number of correlation parameters less than the second threshold value among the three correlation parameters is two or more, the condition monitoring device 200 detects an abnormality in the three blades.

[0081] For example, when a lightning strike on the wind power generation device 100 is detected, it is conceivable to execute a confirmation process for confirming the presence or absence of abnormalities (such as damage) in the three blades due to this lightning strike. In such a confirmation process, a configuration for comparing data before the lightning strike detection (for example, blade vibration data) with data after the lightning strike detection is conceivable. However, in the case of such a configuration, the control device needs to store the data before the lightning strike detection. On the other hand, according to the above-described configuration, the abnormalities of the three blades are detected using the correlation of the vibration values of the three blades. Therefore, the state monitoring device 200 can detect the abnormalities of the three blades without storing the data before the lightning strike detection.

[0082] (7) Further, the correlation parameter of the present embodiment is a correlation coefficient. Therefore, using a known correlation coefficient, abnormalities of a plurality of blades can be detected.

[0083] (8) Further, when the nacelle monitoring device 300 receives a lightning strike signal from the state monitoring device 200, it executes the above-described nacelle abnormality determination process. When a lightning strike occurs in the wind power generation device 100, the mechanism installed in the nacelle 105 may be damaged. Therefore, the state monitoring device 200 can execute the abnormality determination process not only for the blades but also for the mechanism installed in the nacelle 105 in cooperation with the nacelle monitoring device 300.

[0084] [Modification Example] (A) In the above example, a configuration in which the first threshold value is 9.8 m / s 2 has been described. However, the first threshold value may be other values. For example, time-series data in a state where there is no impulse waveform due to an induced lightning surge may be collected in advance, and the first threshold value may be determined from the collected time-series data. For example, filter processing by an LPF may be executed on the time-series data collected in advance, and the maximum value after the filter processing may be set as the first threshold value.

[0085] (B) In the above example, the configuration in which the second collection period is a predetermined period was described. However, the state monitoring device 200 may change the second collection period according to, for example, the rotation period of the hub 104. For example, the minimum value of the second collection period may be determined by the following formula (2).

[0086] Minimum value of the second collection period = rotation period × (1 + ((M - 1) / M)) (2) Since the number of blades in the present embodiment is 3, the minimum value of the second collection period is the rotation period × (5 / 3). Also, the maximum value of the second collection period is appropriately determined according to specifications such as the data capacity of the state monitoring device 200.

[0087] Next, an example of a method for calculating the rotation period of formula (2) will be described. For example, the state monitoring device 200 performs a fast Fourier transform (FFT) on the output values of the first LPF 251 to the third LPF 253 in FIG. 11. Then, the state monitoring device 200 identifies the peak value obtained by the fast Fourier transform as the frequency corresponding to the rotation period. The state monitoring device 200 sets the reciprocal of the identified frequency as the rotation period.

[0088] (C) The above three correlation coefficients may be calculated as follows. For example, the state monitoring device 200 may use a frequency filter for the three time series data to obtain the correlation coefficient of each blade in a specific frequency band. Also, the state monitoring device 200 may obtain the correlation coefficient of each blade in a plurality of frequency bands. Further, when such a configuration is adopted, the state monitoring device 200 may perform an abnormality determination using a second threshold value for each of the plurality of frequency bands.

[0089] (D) The state monitoring device 200 may store the calculated correlation coefficient as statistical data. The storage location may be the state monitoring device 200 or the above-described management device. Also, the second threshold value may be determined from the stored statistical data. For example, the second threshold value may be set to 1 / 3 of the median absolute deviation of a plurality of correlation coefficients indicated by the statistical data. Further, the second threshold value may be set from a statistic such as the average value or standard deviation of the plurality of correlation coefficients, or may be set as an absolute evaluation value from past performance. Also, although the configuration in which the second threshold values of the above-described correlation coefficients σ12, correlation coefficient σ23, and correlation coefficient σ31 are common values (0.6) has been described, the second threshold values of each of the correlation coefficients σ12, correlation coefficient σ23, and correlation coefficient σ31 may be set individually.

[0090] (E) In the description of FIG. 11, the configuration of the state monitoring device 200 having the first absolute value units 261 to 263 has been described. However, the first absolute value units 261 to 263 may not be provided (it is not necessary to calculate the absolute values of the vibration values of the blades). Even in such a configuration, the state monitoring device 200 detects a lightning strike on the wind power generation device 100 when the absolute values of the vibration values of all of the plurality of blades exceed the first threshold value.

[0091] (F) The above-described correlation parameter may be other parameters instead of the correlation coefficient. For example, the correlation parameter may be the degree of waveform similarity in FIG. 9.

[0092] (G) In the above-described embodiment, the abnormality of the wind power generation device 100 has been described as a lightning strike on the wind power generation device. However, the abnormality of the wind power generation device 100 may be other abnormalities, for example, it may be regarded as an abnormality due to a gust of wind on the wind power generation device 100. The abnormality due to a gust of wind on the wind power generation device 100 is, for example, damage to the blade due to the gust.

[0093] [Appendix] Those skilled in the art will understand that the above-described exemplary embodiments are specific examples of the following aspects.

[0094] (Item 1) The condition monitoring device of the present disclosure monitors the condition of a wind power generation device having a plurality of blades. The condition monitoring device includes a signal input unit, a control device, and an external interface. The signal input unit acquires the vibration values of the plurality of blades from a plurality of vibration sensors that detect the vibration values of the plurality of blades. The control device detects an abnormality in the wind power generation device when all of the absolute values of the vibration values of the plurality of blades exceed a first threshold value. The external interface transmits a detection signal indicating that an abnormality in the wind power generation device has been detected to an external device.

[0095] According to such a configuration, it is possible to detect an abnormality in the wind power generation device without requiring a special device such as the above-described detection device. Therefore, any condition monitoring device can detect an abnormality in the wind power generation device while suppressing the manufacturing cost.

[0096] (Item 2) The condition monitoring device according to Item 1, wherein the plurality of vibration sensors output time-series data of the vibration values detected by the vibration sensors. The control device performs a filtering process for suppressing noise on the time-series data.

[0097] According to such a configuration, even if noise is mixed in the time-series data output by the vibration sensors, the noise can be suppressed.

[0098] (Item 3) The condition monitoring device according to Item 2, wherein the plurality of vibration sensors are acceleration sensors. The first threshold value is 9.8 m / s 2 and is.

[0099] According to such a configuration, the manufacturer of the condition monitoring device does not need to determine the first threshold value by experiments or the like, so the burden of determining the first threshold value can be reduced.

[0100] (Item 4) The condition monitoring device according to any one of Items 1 to 3, wherein the control device calculates the absolute values of the vibration values of the plurality of blades. Then, the control device detects a lightning strike on the wind power generation device when all of the absolute values of the vibration values of the plurality of blades exceed the first threshold value.

[0101] When lightning strikes a wind power generation device, the vibration value detected by the vibration sensor may become an excessively large negative value. Assuming this case, in order to detect lightning strikes, the control device may be configured to hold both a first threshold value of a positive value and a first threshold value of a negative value. However, with such a configuration, the storage capacity used for storing the first threshold value may increase. On the other hand, according to the above configuration, the control device can calculate the absolute value of the vibration value so that the vibration value becomes a positive value. Therefore, the control device can be made not to need to hold the first threshold value of a negative value.

[0102] (Item 5) The state monitoring device according to any one of Items 1 to 4, wherein the control device detects a lightning strike on the wind power generation device when the absolute values of the vibration values of a plurality of blades exceed the first threshold value at the same timing.

[0103] When a lightning strike occurs on the wind power generation device, the absolute values of the vibration values of a plurality of blades exceed the first threshold value at the same timing. Therefore, according to such a configuration, the state monitoring device can accurately detect a lightning strike on the wind power generation device.

[0104] (Item 6) The state monitoring device according to any one of Items 1 to 5, wherein the plurality of blades are three or more blades. The plurality of vibration sensors are three or more vibration sensors. The three or more vibration sensors output time-series data of vibration values detected by the vibration sensors. The control device extracts time-series data for a predetermined number of rotations of the plurality of blades from the time-series data output by three or more vibration sensors. When the control device detects a lightning strike on the wind power generation device, the control device calculates a correlation parameter indicating the correlation between two time-series data in all combinations of two possible time-series data among the extracted time-series data of three or more blades. When the number of correlation parameters less than the second threshold value among the correlation parameters of the two time-series data in all combinations is two or more, the control device detects an abnormality in any one of the plurality of blades. The external interface transmits an abnormality signal indicating that the abnormality has been detected to an external device.

[0105] For example, when a lightning strike on the wind power generation device is detected, it is conceivable to execute a confirmation process for confirming the presence or absence of an abnormality (such as damage) in a plurality of blades due to this lightning strike. In such a confirmation process, a configuration in which data before the lightning strike detection (for example, blade vibration data) is compared with data after the lightning strike detection is conceivable. However, in the case of such a configuration, the control device needs to store the data before the lightning strike detection. On the other hand, according to the above configuration, an abnormality in a plurality of blades is detected by using the correlation of vibration values of three or more blades. Therefore, the state monitoring device having the above configuration can detect an abnormality in a plurality of blades without storing the data before the lightning strike detection.

[0106] (Item 7) The state monitoring device according to Item 6, wherein the correlation parameter is a correlation coefficient.

[0107] According to such a configuration, an abnormality in a plurality of blades can be detected by using a known correlation coefficient.

[0108] (Item 8) The state monitoring device according to any one of Items 1 to 7, wherein the abnormality of the wind power generation device is a lightning strike on the wind power generation device.

[0109] According to such a configuration, a lightning strike on the wind power generation device can be detected.

[0110] (Item 9) The wind power generation device further includes a nacelle, a mechanism installed in the nacelle, and a nacelle sensor that detects a parameter for determining the presence or absence of an abnormality in the mechanism. The nacelle monitoring device includes an interface that communicates with the state monitoring device according to any one of Items 1 to 8. The nacelle monitoring device includes a determination device that determines the presence or absence of an abnormality in the mechanism based on the parameter when receiving a detection signal from the state monitoring device.

[0111] When a lightning strike on the wind power generation device is detected, the mechanism installed in the nacelle may be damaged. Therefore, according to the above configuration, when the state monitoring device detects a lightning strike, the presence or absence of an abnormality in the mechanism can be determined.

[0112] (Item 10) The state monitoring system includes the state monitoring device according to any one of Items 1 to 8 and a plurality of vibration sensors.

[0113] (Item 11) The state monitoring method of the present disclosure is a method for monitoring the state of a wind power generation device having a plurality of blades. The state monitoring method includes obtaining vibration values of the plurality of blades. The state monitoring method also includes detecting that there has been a lightning strike on the wind power generation device when the absolute value of the vibration values of the plurality of blades exceeds a first threshold value.

[0114] The embodiments disclosed this time should be considered as illustrative in all respects and not restrictive. The scope of the present disclosure is shown not by the description of the above embodiments but by the claims, and it is intended that all modifications within the meaning and scope equivalent to the claims are included.

Explanation of Reference Numerals

[0115] 100 Wind power generation device, 101 First blade, 102 Second blade, 103 Third blade, 104 Hub, 105 Nacelle, 111 First vibration sensor, 112 Second vibration sensor, 113 Third vibration sensor, 121 Main shaft, 122 Main bearing, 123 Speed increaser, 124 Generator, 126 Rotation sensor, 161 First downconductor, 162 Second downconductor, 163 Third downconductor, 180 Tower, 200 Condition monitoring device, 240 Control device, 250 Acquisition unit, 261 First absolute value unit, 262 Second absolute value unit, 263 Third absolute value unit, 266 Comparison unit, 268 Correlation unit, 270 Judgment unit, 272 Generation unit, 281 First storage unit, 282 Second storage unit, 300 Nacelle monitoring device, 310 Determination device, 500 Condition monitoring system.

Claims

1. A condition monitoring device for monitoring the condition of a wind power generation device having a plurality of blades, comprising: a signal input unit that acquires vibration values of the plurality of blades from a plurality of vibration sensors that detect vibration values of the plurality of blades; a control device that detects an abnormality of the wind power generation device when all of the absolute values of the vibration values of the plurality of blades exceed a first threshold; and an external interface that transmits a detection signal indicating that an abnormality of the wind power generation device has been detected to an external device.

2. The plurality of vibration sensors output time-series data of the vibration values detected by the vibration sensors, and the control device performs a filtering process for suppressing noise on the time-series data. The condition monitoring device according to claim 1.

3. The plurality of vibration sensors are acceleration sensors. The first threshold value is 9.8 m / s ２ The state monitoring device according to claim 2, which is such.

4. The control device: calculates the absolute values of the vibration values of the plurality of blades; and detects lightning strikes on the wind power generation device when all of the absolute values of the vibration values of the plurality of blades exceed the first threshold. The condition monitoring device according to any one of claims 1 to 3.

5. The control device detects a lightning strike on the wind power generation device when the absolute values of the vibration values of the plurality of blades exceed the first threshold at the same timing. The condition monitoring device according to any one of claims 1 to 3.

6. The plurality of blades are three or more blades, the plurality of vibration sensors are three or more vibration sensors, the three or more vibration sensors output time-series data of the vibration values detected by the vibration sensors, the control device: extracts time-series data for a predetermined number of rotations of the plurality of blades from the time-series data output by the three or more vibration sensors; when detecting a lightning strike on the wind power generation device, calculates a correlation parameter indicating the correlation between any two sets of the extracted time-series data of the three or more blades in all combinations of the two sets of time-series data that can be obtained; and detects an abnormality of any one of the plurality of blades when the number of correlation parameters less than a second threshold among the correlation parameters of the two sets of time-series data in all combinations is two or more; and the external interface transmits an abnormality signal indicating that an abnormality of the blade has been detected to an external device. The condition monitoring device according to any one of claims 1 to 3.

7. The state monitoring device according to claim 6, wherein the correlation parameter is a correlation coefficient.

8. The state monitoring device according to any one of claims 1 to 3, wherein the abnormality of the wind power generation device is a lightning strike on the wind power generation device.

9. The wind power generation device further includes a nacelle, a mechanism installed in the nacelle, a nacelle sensor that detects a parameter for determining the presence or absence of an abnormality in the mechanism, an interface that communicates with the state monitoring device according to any one of claims 1 to 3, a determination device that determines the presence or absence of an abnormality in the mechanism based on the parameter when receiving the detection signal from the state monitoring device. A nacelle monitoring device.

10. A state monitoring system comprising the state monitoring device according to any one of claims 1 to 3 and the plurality of vibration sensors.

11. A state monitoring method for monitoring the state of a wind power generation device having a plurality of blades, comprising: the state monitoring method includes acquiring vibration values of the plurality of blades, detecting that there is an abnormality in the wind power generation device when the absolute value of the vibration value of the plurality of blades exceeds a first threshold, and transmitting a detection signal indicating that the abnormality of the wind power generation device has been detected to an external device. A state monitoring method.

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