Status monitoring devices, cabin monitoring devices, monitoring systems and monitoring methods

Abstract

The condition monitoring device (200) of the present invention acquires the vibration values ​​of the first blade (101), the second blade (102), and the third blade (103) from the first vibration sensor (111), the second vibration sensor (112), and the third vibration sensor (113), respectively. When the absolute values ​​of the vibration values ​​of the first blade (101), the second blade (102), and the third blade (103) all exceed a first threshold, the condition monitoring device (200) detects a lightning strike on the wind power generation device.

Description

Technical Field

[0001] This disclosure relates to a status monitoring device, a cabin monitoring device, a monitoring system, and a monitoring method. Background Technology

[0002] For example, Japanese Patent Application Publication No. 2005-62080 (Patent Document 1) discloses a wind power generation device. This wind power generation device has multiple blades located at a high position. Therefore, lightning strikes sometimes occur on these blades. When a lightning strike occurs, the blades of the wind power generation device may be damaged. Therefore, the aforementioned wind power generation device includes a lightning strike detection device for detecting lightning strikes. The lightning strike detection device includes a Rogowski coil and a detection unit that detects the lightning current flowing through the Rogowski coil during a lightning strike. Existing technical documents Patent documents

[0003] Patent Document 1: Japanese Patent Application Publication No. 2005-62080 Summary of the Invention

[0004] However, in the invention described in the aforementioned Patent Document 1, the need for a detection device (lightning strike detection device) to detect anomalies such as lightning strikes may lead to a problem of increased manufacturing costs for wind power generation devices.

[0005] This disclosure was made to address the aforementioned problems and aims to detect anomalies in wind power generation devices while reducing manufacturing costs. Technical solutions to solve technical problems

[0006] The condition monitoring device disclosed herein monitors the condition of a wind power generation unit having multiple blades. The condition monitoring device includes an interface and a control unit. The interface acquires vibration values ​​of multiple blades from multiple vibration sensors that respectively detect the vibration values ​​of the multiple blades. When the absolute value of the vibration values ​​of all multiple blades exceeds a first threshold, the control unit detects a lightning strike on the wind power generation unit.

[0007] The condition monitoring method disclosed herein is a method for monitoring the condition of a wind power generation device having multiple blades. The condition monitoring method includes acquiring vibration values ​​of the multiple blades. Furthermore, the condition monitoring method includes detecting a lightning strike on the wind power generation device when the absolute value of the vibration values ​​of the multiple blades exceeds a first threshold. Invention Effects

[0008] According to this disclosure, it is possible to detect anomalies in wind power generation devices while suppressing manufacturing costs. Attached Figure Description

[0009] Figure 1 This is a diagram illustrating the status monitoring system, etc., disclosed herein. Figure 2 It is a diagram showing the wheel hub and the internal structure of the engine compartment, etc. Figure 3 This is a diagram illustrating an example of the hardware structure of a status monitoring device and a cabin monitoring device. Figure 4 It is a graph showing the waveform of the time series data of the vibration values. Figure 5 This is a graph showing the relationship between the rotational position of the first blade and the first vibration value. Figure 6 This is a graph showing the waveform of the vibration values ​​when a lightning strike occurs on a wind power generation device. Figure 7 It is Figure 6 A magnified image of the portion of the waveform where the vibration value is 0. Figure 8 This is a diagram showing an example of a waveform displaying vibration values. Figure 9 This is a graph showing the vibration data of the three blades rotating one revolution. Figure 10 This is a graph showing specific examples of correlation coefficients. Figure 11 This is a functional block diagram of the status monitoring device. Figure 12 This is a flowchart illustrating the main processing flow of the status monitoring device, etc. Detailed Implementation

[0010] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In the embodiments described below, when the number, quantity, etc., are mentioned, the scope of the present disclosure is not necessarily limited to that number, quantity, etc., unless specifically stated otherwise. The same reference numerals are used to denote identical or equivalent components, and repeated descriptions are sometimes omitted. The structures in the embodiments can be appropriately combined and used as initially planned.

[0011] [Structure of a wind power generation system] Figure 1 This is a diagram illustrating a structural example of the wind power generation device 100 and the condition monitoring system 500 of this disclosure. Figure 1 The image shows a front view of the wind power generation device 100.

[0012] The wind power generation device 100 has M blades (M is an integer greater than or equal to 2). M blades correspond to the "multiple blades" of this disclosure. In this embodiment, M is set to 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. Alternatively, as a variation, M can be set to other values, such as M=2 or M=4.

[0013] The wind power generation unit 100 also includes a hub 104, a nacelle 105, a tower 180, a first downconductor 161, a second downconductor 162, and a third downconductor 163. First blade 101, second blade 102, and third blade 103 are engaged with the hub 104. The nacelle 105 is located behind the hub 104. The tower 180 holds the first blade 101, second blade 102, third blade 103, hub 104, and nacelle 105.

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

[0015] exist Figure 1 The present invention discloses three vibration sensors: a first vibration sensor 111, a second vibration sensor 112, and a third vibration sensor 113. These three sensors are respectively disposed on a first blade 101, a second blade 102, and a third blade 103. 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 this embodiment, 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 second blade 102, and the third blade 103 as vibration values. Alternatively, as a variation, the vibration sensors can be other sensors, such as displacement sensors or velocity sensors.

[0016] The first down conductor 161, the second down conductor 162, and the third down conductor 163 are respectively disposed 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 referred to as "down conductors". When the wind power generation device 100 is struck by lightning, the down conductors safely release the lightning current to the ground. That is, when the wind power generation device 100 is struck by lightning, the lightning current flows through the down conductors.

[0017] Figure 2This diagram shows the internal structure of the wheel hub 104 and the engine compartment 105. As described above, a status monitoring device 200 is installed inside the wheel hub 104. Furthermore, in... Figure 2 In the example, vibration sensors are installed inside each blade. Figure 2 In the example, a first vibration sensor 111 is disposed inside the first blade 101, and a third vibration sensor 113 is disposed inside the third blade 103. Additionally, although not specifically illustrated, a second vibration sensor 112 is disposed inside the second blade 102. The vibration values ​​(acceleration) detected by the first vibration sensor 111, the second vibration sensor 112, and the third vibration sensor 113 are transmitted as time-series data to the condition monitoring device 200.

[0018] In addition, Figure 2 In the example, a portion of the first downlead 161 inside the first blade 101 is shown. Furthermore, since the vibration sensor and the downlead are located inside the blade, the wiring 141 from the vibration sensor (first vibration sensor 111) is configured close to the first downlead 161 (see reference). Figure 2 (Location α). Therefore, in each blade, the wiring 141 from the vibration sensor is positioned close to the downlead wire.

[0019] The status monitoring device 200 performs pre-strike anomaly detection processing, lightning strike detection processing, and blade anomaly determination processing based on the vibration values ​​detected by the first vibration sensor 111, the second vibration sensor 112, and the third vibration sensor 113. Pre-strike anomaly detection processing detects anomalies in the first blade 101, the second blade 102, and the third blade 103. For example, in pre-strike anomaly detection processing, when the vibration value falls within an abnormal range, the status monitoring device 200 determines that the blade equipped with the vibration sensor that outputs the vibration value is abnormal. Details of the lightning strike detection processing and the blade anomaly determination processing will be described later. In this embodiment, the first vibration sensor 111, the second vibration sensor 112, and the third vibration sensor 113 are used concurrently in pre-strike anomaly detection processing, lightning strike detection processing, and blade anomaly determination processing.

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

[0021] The main bearing 122 rotatably holds the main shaft 121. The speed increaser 123 amplifies the rotation of the hub 104 to the speed required for power generation. The generator 124 converts the rotational force of the hub 104 (amplified by the speed increaser 123) into electrical energy. The main shaft 121, main bearing 122, speed increaser 123, and generator 124 correspond to the "mechanism disposed within the nacelle" of this disclosure.

[0022] Vibration sensors 125, 126, 127, and 128 are also installed in the engine room 105. Vibration sensors 125, 126, 127, and 128 respectively detect the vibration values ​​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. Vibration sensors 125, 126, 127, and 128 correspond to the "engine room sensors" of this disclosure. The vibration values ​​of the main bearing 122, the rotational speed (rotational position) of the main shaft 121, the speed increaser 123, and the generator 124 correspond to the "parameters" of this disclosure. Vibration sensors 125, 126, 127, and 128 transmit the detected values ​​as time-series data to the engine room monitoring device 300.

[0023] The nacelle monitoring device 300 includes a determination device 310. Based on time-series data from vibration sensors 125, rotation sensors 126, vibration sensors 127, and vibration sensors 128, the determination device 310 determines whether there are any abnormalities in the mechanisms installed in the nacelle 105 (i.e., main shaft 121, main bearing 122, speed increaser 123, and generator 124). This determination process is also referred to as the nacelle abnormality determination process (see [reference]). Figure 12 Step S114).

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

[0025] [Hardware structure of status monitoring devices and cabin monitoring devices] Figure 3 This diagram illustrates an example of the hardware structure of the status monitoring device 200 and the cabin monitoring device 300. The status monitoring device 200 includes a CPU (Central Processing Unit) 201, a memory 202, and a communication interface 203. Furthermore, in... Figure 3In this document, the communication interface is referred to as "I / F". Communication interface 203 corresponds to the "signal input unit" and "external interface" of this disclosure. The cabin monitoring device 300 includes a CPU 301, a memory 302, and a communication interface 303. CPU 201 and CPU 301 perform various processes.

[0026] Memory 202 and memory 302 include ROM (Read Only Memory) and RAM (Random Access Memory), etc. ROM is non-volatile memory that cannot be rewritten, while RAM is volatile memory. Communication interface 203 and communication interface 303 communicate with external devices. For example, status monitoring device 200 sends a lightning strike signal (described later) to communication interface 303 via communication interface 203 of status monitoring device 200. The lightning strike signal corresponds to the "detection signal" of this disclosure.

[0027] Furthermore, at least a portion of the storage area of ​​the CPU 201 and the memory 202 constitutes the control device 240. The control device 240 may also be referred to as a "control circuit" or "at least one controller." Additionally, at least a portion of the storage area of ​​the CPU 301 and the memory 302 constitutes the determination device 310 (see reference). Figure 2 ).

[0028] [Waveform of vibration value] Figure 4 This is a graph showing the 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 values ​​measured by the first vibration sensor 111, the second vibration sensor 112, and the third vibration sensor 113 will be referred to as the first vibration value, the second vibration value, and the third vibration value, respectively. Furthermore, the time-series data of the first vibration value, the second vibration value, and the third vibration value will be referred to as first time-series data, second time-series data, and third time-series data, respectively.

[0029] exist Figure 4 And the following Figures 5-8 In the graph, the vertical axis represents the vibration value (acceleration), and the horizontal axis represents time. Furthermore, in... Figure 4 In the diagram, the first vibration value, the second vibration value, and the third vibration value are represented by solid lines, dashed lines, and dotted lines, respectively.

[0030] The first, second, and third vibration values ​​reflect the gravitational acceleration values ​​accompanying the rotation of the first blade 101, second blade 102, and third blade 103, respectively. Therefore, the waveforms of the time-series data for the first, second, and third vibration values ​​are sinusoidal curves. Furthermore, the first blade 101, second blade 102, and third blade 103 are spaced 120 degrees apart in the rotation direction of the hub 104. Therefore, the phases of the first, second, and third vibration values ​​at the same moment are each shifted by 120 degrees. When the rotational speed of the hub 104 is, for example, 20 min... -1 At that time, the first vibration value, the second vibration value, and the third vibration value increase or decrease with a period of 20 / 60≈0.33Hz.

[0031] Figure 5 This is a graph showing the relationship between the rotational position (rotation angle) of the first blade 101 and the first vibration value. Figure 5 In the example, when the first blade 101 is in its uppermost position, the rotation angle of the first blade 101 is set to 0 degrees. Figure 5 In the example, when the rotation angle of the first blade 101 is 0 degrees, the first vibration value is 0; when the rotation angle of the first blade 101 is 90 degrees, the first vibration value is 9.8 m / s². 2 Furthermore, when the rotation angle of the first blade 101 is 180 degrees, the first vibration value is 0; when the rotation angle of the first blade 101 is 270 degrees, the first vibration value is -9.8 m / s². 2 .

[0032] The gravitational force g applied to the vibration sensor (first vibration sensor 111) is 9.8 m / s². 2 Therefore, as Figure 5 As shown, the maximum value of the first vibration is 9.8 m / s². 2 The minimum value of the first vibration is -9.8 m / s². 2 However, in reality, the vibration data is affected by the electronic circuitry of the condition monitoring device 200, causing a phase advance, which may lead to a deviation from the actual change in the gravitational acceleration component. Furthermore, due to factors such as the installation orientation or frequency characteristics of the vibration sensor, the maximum vibration value is sometimes slightly less than 9.8 m / s². 2 The minimum vibration value is slightly greater than -9.8 m / s. 2 Additionally, when the time-series data output by the vibration sensor contains noise (such as high-frequency noise), the condition monitoring device 200 obtains a reading greater than 9.8 m / s. 2 The vibration value is less than -9.8 m / s. 2 The vibration value is taken as the actual vibration value. The noise is generated by the hub 104 or the creaking of the three blades, etc.

[0033] [Lightning Strike Detection and Handling] Next, the lightning strike detection process of the status monitoring device 200 will be explained. For example... Figure 2 As explained, when the wind power generation device 100 is struck by lightning, the lightning current flows through the down conductor. Furthermore, as... Figure 2 As illustrated, for example inside the hub 104, the wiring 141 from the vibration sensor is positioned near the downlead (see reference). Figure 2 (Location α). Therefore, the lightning surge induced by the lightning strike enters the wiring 141 of the vibration sensor via the down conductor.

[0034] Figure 6 This is a diagram showing the waveforms of the first, second, and third vibration values ​​when the wind power generation unit 100 is struck by lightning. In the vibration value waveforms, the induced lightning surge intruding into the wiring 141 from the vibration sensor manifests as noise resembling a pulse waveform (see reference). Figure 6 Part A).

[0035] Figure 7 It is Figure 6 A magnified image of the portion of the waveform where the vibration value (acceleration) is 0. Furthermore, in Figure 7 The waveforms of the absolute values ​​of the first, second, and third vibration values ​​are shown in the image. Figure 7 As shown, when a lightning strike occurs on the wind power generation device 100, the absolute values ​​of the first vibration value, the second vibration value, and the third vibration value all exceed the first threshold of 9.8 m / s. 2 .

[0036] Therefore, the state monitoring device 200 of this embodiment acquires a first vibration value, a second vibration value, and a third vibration value (first time series data, second time series data, and third time series data) within a predetermined first collection period. When the absolute values ​​of all three vibration values ​​exceed 9.8 m / s², which is a first threshold, the device monitors the vibration value. 2 At that time, a lightning strike occurred on the wind power generation unit 100 (refer to...). Figure 7 (Lightning strike detection). Alternatively, it can be configured such that a lightning strike to the wind power generation device 100 is detected when the absolute values ​​of the first vibration value, the second vibration value, and the third vibration value are all equal to the first threshold, or it can be configured such that no lightning strike to the wind power generation device 100 is detected.

[0037] For example, such as Figure 2 As shown, inside the hub 104, when the first lead wire 161 is close to the wiring 141 of any of the first vibration sensors 111, the absolute values ​​of the first vibration value, the second vibration value, and the third vibration value exceed 9.8 m / s due to the noise accompanying the induced lightning surge.2 The timing is the same. Therefore, when the absolute values ​​of the first, second, and third vibration values ​​all exceed the first threshold of 9.8 m / s at the same time, 2 In the event of a lightning strike, the status monitoring device 200 detects a lightning strike on the wind power generation device 100.

[0038] Regarding the same timing in this embodiment, it is not limited to the timing when the absolute values ​​of the first vibration value, the second vibration value, and the third vibration value all exceed the first threshold being exactly the same; they can also be approximately the same. For approximately the same timing, as long as a lightning strike to the wind power generation device 100 can be detected, there can be a time difference (e.g., 0.1 seconds) between at least two of the timings when the absolute value of the first vibration value exceeds the first threshold, the timing when the absolute value of the second vibration value exceeds the first threshold, and the timing when the absolute value of the third vibration value exceeds the first threshold.

[0039] Furthermore, the first collection period described in this embodiment is, for example, 20 seconds. However, the first collection period may be appropriately determined based on specifications such as the filtering process described later and the data capacity of the status monitoring device 200.

[0040] Furthermore, if only two vibration values ​​exceed the first threshold, or if only one vibration value exceeds the first threshold, the status monitoring device 200 of this embodiment determines that no lightning strike has occurred on the wind power generation device 100. This is because, if only two vibration values ​​exceed the first threshold, or if only one vibration value exceeds the first threshold, it is more likely that the vibration value exceeded the first threshold due to other factors rather than a lightning strike.

[0041] [Leaf Anomaly Identification and Handling] When a lightning strike occurs on the wind power generation unit 100, at least one of the three blades (first blade 101, second blade 102, and third blade 103) may sometimes experience an anomaly (e.g., damage). Therefore, when the condition monitoring device 200 detects a lightning strike on the wind power generation unit 100, it performs a blade anomaly determination process to detect whether there is an anomaly in the three blades.

[0042] Figure 8 This is a diagram illustrating an example of the waveforms of the first vibration value, the second vibration value, and the third vibration value acquired by the status monitoring device 200 during a predetermined second collection period. Figure 8 (A) Figure 8 (B) and Figure 8 (C) shows the waveforms of the first vibration value, the second vibration value, and the third vibration value, respectively.

[0043] The status monitoring device 200 extracts the time series data corresponding to the specified rotational speed L of the three blades from the time series data corresponding to the first collection period output by the three vibration sensors. L is an integer greater than or equal to 1. In this embodiment, L=1, that is, the specified rotational speed is 1 revolution.

[0044] Figure 9 This is a graph showing the three vibration data (the data extracted above) corresponding to one rotation of the three blades when no abnormality occurs. Figure 9 The vertical axis represents the vibration value, and the horizontal axis represents the rotation angle of the blade. Figure 9 (A) Figure 9 (B) and Figure 9 (C) shows the waveforms of the first vibration value, the second vibration value, and the third vibration value, respectively. Figure 9 (A) Figure 9 (B) and Figure 9 In (C), the rotation angle is shown in alignment.

[0045] If, after lightning strike detection, no abnormalities are found in the first blade 101, the second blade 102, and the third blade 103, then... Figure 9 As shown, the temporal correlation of vibration values ​​tends to become stronger. On the other hand, when any one of the first blade 101, the second blade 102, and the third blade 103 experiences an anomaly, the correlation between the vibration value of the blade that experienced the anomaly and the vibration values ​​of the other blades tends to become weaker.

[0046] Therefore, in view of the above trend, the condition monitoring device 200 of this embodiment uses a correlation parameter representing the correlation between time series data of the first vibration value, the second vibration value, and the third vibration value to detect whether there is any abnormality in the three blades. In this embodiment, the correlation parameter is the correlation coefficient.

[0047] Figure 10 This is a graph used to illustrate an application example of the correlation coefficient. For example... Figure 10 As shown, the condition monitoring device 200 calculates the correlation coefficient, representing the correlation between two available time series data points, for all combinations of the time series data points from the three blades. Hereinafter, the two time series data points are also referred to as "one time series data point" and "the other time series data point".

[0048] Specifically, the state monitoring device 200 extracts N (N is an integer greater than 2) vibration values ​​with the same rotation angle from each of three time series data within a predetermined second collection period. Then, it calculates the correlation coefficient between one time series data and another. That is, the state monitoring device 200 calculates correlation coefficients σ12, σ23, and σ31.

[0049] Furthermore, 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 coefficients σ12, σ23, and σ31 are the correlation coefficients between the first time series data and the second time series data, the second time series data and the third time series data, and the third time series data and the first time series data, respectively. The status monitoring device 200 calculates the correlation coefficients, for example, by the following formula (1).

[0050] σ=(A1) / (B1×B2) (1) Here, A1 in equation (1) represents the covariance between "one time series data" and "another time series data". In addition, B1 represents the standard deviation of N vibration values ​​in one time series data, and B2 represents the standard deviation of N vibration values ​​in another time series data.

[0051] Figure 10 (A) shows examples of correlation coefficients σ12, σ23 and σ31 with “0.73”, “0.80” and “0.23”, respectively. Figure 10 (B) shows examples of correlation coefficients σ12, σ23 and σ31 with “0.70”, “0.48” and “0.35”, respectively. Figure 10 (C) shows examples of correlation coefficients σ12, σ23 and σ31 with “0.51”, “0.34” and “0.28”, respectively.

[0052] The status 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 a second threshold. In this embodiment, the second threshold is 0.60.

[0053] Although not specifically illustrated, if all three correlation coefficients are greater than the second threshold, the status monitoring device 200 determines that all three blades are normal. Furthermore, as... Figure 10 As shown in (A), when one of the three correlation coefficients is less than the second threshold and the other two correlation coefficients are above the second threshold, the status monitoring device 200 determines that all three blades are normal.

[0054] Next, as Figure 10 As shown in (B), when two of the three correlation coefficients are less than the second threshold and one correlation coefficient is above the second threshold, the probability of an abnormality in one of the three leaves is high. Figure 10In case (B), the third blade 103 is more likely to malfunction. Therefore, in Figure 10 In case (B), the status monitoring device 200 determines that an abnormality has occurred in a blade.

[0055] Next, as Figure 10 As shown in (C), when all three correlation coefficients are less than the second threshold, there is a high probability that at least two of the three leaves are abnormal. Therefore, in Figure 10 In case (C), the status monitoring device 200 determines that two or three blades have malfunctioned.

[0056] As mentioned above, if the number of correlation coefficients less than the second threshold among the two time series data in all the above combinations is 2 or more (i.e., Figure 10 (B) and Figure 10 (C) In the case of an anomaly, the status monitoring device 200 detects that any one of the three blades has an anomaly.

[0057] In addition, as a variation, the above provisions of "less than the second threshold" and "above the second threshold" can be replaced by provisions of "below the second threshold" and "greater than the second threshold", respectively.

[0058] [Functional block diagram of the status monitoring device] Figure 11 This is a functional block diagram of the status monitoring device 200. The status 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 judgment 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 (9.8 m / s in this embodiment). 2 Furthermore, the second storage unit 282 stores a second threshold (0.6 in this embodiment).

[0059] Figure 3 The control device 240 has the functions of 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 judgment unit 270, a generation unit 272, and a communication unit 274. The memory 202 has the functions of a first storage unit 281 and a second storage unit 282.

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

[0061] Next, the first LPF251, the second LPF252, and the third LPF253 respectively perform filtering processes to suppress noise in the first time series data, the second time series data, and the third time series data. In this embodiment, the cutoff frequency of the first LPF251, the second LPF252, and the third LPF253 is 3Hz.

[0062] Next, the first absolute value unit 261, the second absolute value unit 262, and the third absolute value unit 263 calculate the absolute values ​​of the output values ​​from the first LPF 251, the second LPF 252, and the third LPF 253, respectively. Through the processing by the first absolute value unit 261, the second absolute value unit 262, and the third absolute value unit 263, the following is generated: Figure 7 The waveform data shown.

[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 (9.8 m / s²). 2 The comparison unit 266 then compares the output values ​​(absolute values ​​of vibration values) from the first absolute value unit 261 to the third absolute value unit 263. Then, when all 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, the comparison unit 266 detects a lightning strike on the wind power generation device 100. Furthermore, when at least one of the output values ​​from the first absolute value unit 261 to the third absolute value unit 263 is below the first threshold, the comparison unit 266 determines that a lightning strike on the wind power generation device 100 has not occurred.

[0064] When the comparison unit 266 detects a lightning strike on the wind power generation unit 100, it sends a lightning strike signal to the related unit 268 and the communication unit 274. The lightning strike signal indicates that a lightning strike on the wind power generation unit 100 has been detected. The communication unit 274 then sends the lightning strike signal to the nacelle monitoring device 300. When the nacelle monitoring device 300 receives the lightning strike signal, its determination device 310 performs the aforementioned nacelle anomaly determination process.

[0065] Furthermore, when the relevant unit 268 receives a lightning strike signal, it uses the first time series data, the second time series data, and the third time series data from the signal input unit 250 to calculate the correlation coefficients σ12, σ23, and σ31 (refer to the above formula (1) and Figure 10(Explanation). Then, the judgment unit 270 determines whether the correlation coefficients σ12, σ23, and σ31 exceed the second threshold. If the number of correlation coefficients σ12, σ23, and σ31 that are less than the second threshold is 2 or more ( Figure 10 (B) or Figure 10 (C) case), detect anomalies in all three leaves (determine that at least one of the three leaves is abnormal). On the other hand, if the number of correlation coefficients less than the second threshold among correlation coefficients σ12, σ23, and σ31 is less than 1 ( Figure 10 (A) case), the three leaves were tested and found to be normal (judged as normal).

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

[0067] [flow chart] Figure 12 This is a flowchart illustrating the main processing flow of the status monitoring device 200, etc. The status monitoring device 200 executes steps S102, S104, and S106 in parallel. Step S102 is the process by which the status monitoring device 200 acquires first time-series data during the first collection period from the first vibration sensor 111. Step S104 is the process by which the status monitoring device 200 acquires second time-series data during the first collection period from the second vibration sensor 112. Step S106 is the process by which the status monitoring device 200 acquires third time-series data during the first collection period from the third vibration sensor 113.

[0068] Next, in step S108, the status monitoring device 200 performs the aforementioned 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 status monitoring device 200 determines whether a lightning strike to the wind power generation device 100 was detected during the lightning strike detection process in step S108.

[0069] In step S110, if no lightning strike is detected (the value in step S110 is "No"), Figure 12 The processing ends. On the other hand, in step S110, when a lightning strike is detected ("Yes" in step S110), the processing proceeds to step S111.

[0070] In step S111, the status monitoring device 200 outputs a lightning strike signal to an external device (e.g., the cabin monitoring device 300). Then, the process proceeds to steps S112 and S114. By... Figure 11 The lightning strike signal is sent to the relevant unit 268, thereby executing the processing step S112. The lightning strike signal is sent to the cabin monitoring device 300, thereby executing the processing step S114.

[0071] In step S112, the status monitoring device 200 performs the aforementioned blade anomaly determination process. Furthermore, in step S114, the nacelle monitoring device 300 performs the aforementioned nacelle anomaly determination process. The nacelle monitoring device 300 sends the result of the nacelle anomaly determination process to the aforementioned external device 400.

[0072] Next, in step S116, the status monitoring device 200 determines whether any abnormality was detected in the blade abnormality determination process in step S112 and the nacelle abnormality determination process in step S114.

[0073] When an anomaly is detected ("Yes" in step S116), in step S118, the condition monitoring device 200 outputs an anomaly signal to the external device 400. This anomaly signal contains information indicating the location of the anomaly (blades and mechanisms within the nacelle 105). When no anomaly is detected ("No" in step S116), in step S120, the condition monitoring device 200 outputs a normal signal to the external device 400. Alternatively, as a variation, in step S120, the condition monitoring device 200 may not output a normal signal.

[0074] [Summarize] (1) As described above, when the absolute values ​​of the vibration values ​​of all M blades exceed the first threshold, the condition monitoring device 200 detects a lightning strike on the wind power generation device 100. Therefore, the condition monitoring device 200 can detect a lightning strike on the wind power generation device 100 without the need for a special device such as the lightning strike detection device described above. Thus, if the condition monitoring device 200 of this embodiment is used, lightning strikes can be detected while suppressing manufacturing costs.

[0075] (2) In addition, such as Figure 11As explained, the first LPF251, the second LPF252, and the third LPF253 respectively perform filtering processes to suppress noise in the first time series data, the second time series data, and the third time series data. Therefore, even if noise is mixed in the time series data output by the three vibration sensors, the noise can be suppressed. As a result, the accuracy of lightning strike detection can be improved in the condition monitoring device 200 of this embodiment.

[0076] (3) In addition, such as Figure 7 As explained, the first threshold is 9.8 m / s. 2 According to this structure, since the manufacturer of the status monitoring device 200 does not need to determine the first threshold through experiments, the burden of determining the first threshold can be reduced.

[0077] (4) In addition, such as Figure 11 As shown, the first absolute value unit 261 to the third absolute value unit 263 calculate the absolute values ​​of the first time series data to the third time series data, respectively. Then, when all the absolute values ​​exceed the first threshold, the status monitoring device 200 detects a lightning strike on the wind power generation device.

[0078] When the wind power generation unit 100 is struck by lightning, the vibration values ​​detected by the three vibration sensors sometimes become excessively negative. Considering this, to detect lightning strikes, a structure could be considered where the condition monitoring device 200 maintains both a first threshold value for positive values ​​and a first threshold value for negative values. However, if such a structure were used, the storage capacity for storing the first threshold value might increase. To address this, by having the condition monitoring device 200 calculate the absolute value, the vibration value can be set to a positive value. Therefore, the condition monitoring device 200 does not need to maintain a first threshold value for negative values.

[0079] (5) Furthermore, if the absolute values ​​of the vibration values ​​of the three blades exceed the first threshold at the same time, 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 will exceed the first threshold at the same time. Therefore, according to this structure, the condition monitoring device 200 can detect lightning strikes on the wind power generation device 100 with high accuracy.

[0080] (6) In addition, when a lightning strike is detected on the wind power generation device 100, the condition monitoring device 200 calculates three relevant parameters (correlation coefficient σ12, correlation coefficient σ23, and correlation coefficient σ31). Then, when the number of relevant parameters less than the second threshold among the three relevant parameters is more than 2, the condition monitoring device 200 detects an anomaly in the three blades.

[0081] For example, when a lightning strike is detected on the wind power generation unit 100, a confirmation process can be considered to verify whether the lightning strike caused any abnormalities (damage, etc.) in the three blades. In this confirmation process, a structure could be considered that compares data before the lightning strike detection (e.g., blade vibration data) with data after the lightning strike detection. However, in this structure, the control device needs to store the data before the lightning strike detection. In contrast, according to the above structure, the correlation of the vibration values ​​of the three blades is used to detect abnormalities in the three blades. Therefore, the condition monitoring device 200 can detect abnormalities in the three blades without storing the data before the lightning strike detection.

[0082] (7) Furthermore, the relevant parameter in this embodiment is the correlation coefficient. Therefore, it is possible to detect anomalies in multiple blades using a known correlation coefficient.

[0083] (8) Furthermore, upon receiving a lightning strike signal from the condition monitoring device 200, the nacelle monitoring device 300 performs the aforementioned nacelle anomaly determination process. When the wind power generation unit 100 is struck by lightning, the mechanisms installed within the nacelle 105 may sometimes be damaged. Therefore, the condition monitoring device 200 can cooperate with the nacelle monitoring device 300 to perform anomaly determination processes not only on the blades but also on the mechanisms installed within the nacelle 105.

[0084] [Variation Example] (A) In the example above, the first threshold is described as 9.8 m / s. 2 The structure is as follows. However, the first threshold can also be set to other values. For example, time series data under conditions where there are no pulse waveforms caused by induced lightning surges can be collected in advance, and the first threshold can be determined based on the collected time series data. For example, LPF filtering can be performed on the pre-collected time series data, and the maximum value after filtering can be set as the first threshold.

[0085] (B) In the example above, a structure was described where the second collection period is a predetermined period. However, the status monitoring device 200 may also change the second collection period, for example, based on the rotation cycle of the hub 104. For example, the minimum value of the second collection period may also be determined by the following formula (2).

[0086] Minimum value during the second collection period = rotation period × (1 + ((M-1) / M)) (2) Since the number of blades in this embodiment is 3, the minimum value during the second collection period is the rotation period × (5 / 3). Furthermore, the maximum value during the second collection period is appropriately determined based on specifications such as the data capacity of the status monitoring device 200.

[0087] Next, an example of the method for calculating the rotation period of equation (2) will be provided. For example, the state monitoring device 200... Figure 11 The output values ​​of the first LPF251 to the third LPF253 are subjected to a Fast Fourier Transform (FFT). Then, the state monitoring device 200 determines the peak value obtained by the Fast Fourier Transform as the frequency corresponding to the rotation period. The reciprocal of this determined frequency is set as the rotation period.

[0088] (C) The three correlation coefficients mentioned above can also be calculated as follows. For example, the condition monitoring device 200 can also use a frequency filter to calculate the correlation coefficient of each blade in a specific frequency band for the three time series data. In addition, the condition monitoring device 200 can also calculate the correlation coefficient of each blade in multiple frequency bands. Furthermore, when using this structure, the condition monitoring device 200 can also use a second threshold to determine anomalies for each of the multiple frequency bands.

[0089] (D) The status monitoring device 200 can also store the calculated correlation coefficients as statistical data. This storage location can be the status monitoring device 200 itself or the aforementioned management device. Furthermore, the second threshold can be determined based on the stored statistical data. For example, the second threshold can be 1 / 3 of the median absolute deviation of the multiple correlation coefficients shown in the statistical data. Additionally, the second threshold can be set based on statistical measures such as the mean or standard deviation of the multiple correlation coefficients, or it can be set based on past performance as an absolute evaluation value. Furthermore, although it has been stated that the second thresholds for the correlation coefficients σ12, σ23, and σ31 are of a common value (0.6), the second thresholds for each of the correlation coefficients σ12, σ23, and σ31 can be set individually.

[0090] (E) In Figure 11 The description states that the condition monitoring device 200 has a structure with a first absolute value section 261 to a third absolute value section 263. However, it may also lack the first absolute value section 261 to the third absolute value section 263 (or the absolute value of the blade vibration may not be calculated). Even with this structure, the condition monitoring device 200 detects a lightning strike to the wind power generation device 100 when the absolute values ​​of the vibration values ​​of multiple blades all exceed the first threshold.

[0091] (F) The aforementioned relevant parameters may not be correlation coefficients but other parameters. For example, the relevant parameters may also be... Figure 9 The similarity of the waveforms.

[0092] (G) In the above embodiment, it was described that the malfunction of the wind power generation device 100 is a structure caused by a lightning strike. However, the malfunction of the wind power generation device 100 can also be other malfunctions, such as malfunctions caused by gusts of wind. An malfunction caused by gusts of wind is, for example, blade damage caused by gusts.

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

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

[0095] Based on this structure, malfunctions in wind power generation systems can be detected without the need for specialized devices like the aforementioned detection equipment. Therefore, if it were a condition monitoring device, it would be possible to detect malfunctions in wind power generation systems while minimizing manufacturing costs.

[0096] (Item 2) In the condition monitoring device described in Item 1, multiple vibration sensors output time-series data of the vibration values ​​detected by the vibration sensors. The control device performs filtering processing on the time-series data to suppress noise.

[0097] Based on this structure, noise can be suppressed even if the time-series data output by the vibration sensor contains noise.

[0098] (Item 3) In the condition monitoring device described in Item 2, multiple vibration sensors are acceleration sensors. The first threshold is 9.8 m / s². 2 .

[0099] According to this structure, since the manufacturer of the condition monitoring device does not need to determine the first threshold through experiments, the burden of determining the first threshold can be reduced.

[0100] (Item 4) In the condition monitoring device described in any one of the first to third methods, the control device calculates the absolute values ​​of the vibration values ​​of multiple blades. Then, when the absolute values ​​of the vibration values ​​of all multiple blades exceed a first threshold, the control device detects a lightning strike on the wind power generation device.

[0101] When a wind turbine is struck by lightning, the vibration value detected by the vibration sensor can sometimes be excessively negative. Considering this, a structure could be considered where the control device maintains both a first threshold value for positive and a first threshold value for negative values ​​to detect lightning strikes. However, such a structure might increase the storage capacity required to store the first threshold value. Therefore, according to the structure described above, the vibration value can be set to positive by having the control device calculate the absolute value of the vibration value. Thus, the control device does not need to maintain a first threshold value for negative values.

[0102] (Item 5) In any one of the status monitoring devices in items 1 to 4, when the absolute value of the vibration values ​​of multiple blades exceeds a first threshold at the same time, the control device detects a lightning strike on the wind power generation device.

[0103] When a lightning strike occurs on a wind turbine, the absolute values ​​of the vibrations of multiple blades exceed a first threshold at the same time. Therefore, based on this structure, the condition monitoring device can detect lightning strikes to wind turbines with high precision.

[0104] (Item 6) In the condition monitoring device described in any one of items 1 to 5, the plurality of blades consists of three or more blades. The plurality of vibration sensors consists of 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 corresponding to a specified rotational speed of the plurality of blades from the time-series data output by the three or more vibration sensors. When a lightning strike on the wind power generation device is detected, the control device calculates a correlation parameter representing the correlation between two available time-series data from the extracted time-series data of the three or more blades. When the number of correlation parameters less than a second threshold in all combinations of the correlation parameters of the two time-series data is two or more, the control device detects an anomaly in any one of the plurality of blades. An external interface sends an anomaly signal indicating that the anomaly has been detected to an external device.

[0105] For example, when a lightning strike on a wind turbine is detected, a confirmation process can be implemented to determine whether the strike caused anomalies (damage, etc.) in multiple blades. This confirmation process could involve comparing data before the lightning strike detection (e.g., blade vibration data) with data after the strike. However, in this case, the control device needs to store the data before the strike. In contrast, the above-described structure uses the correlation of vibration values ​​from three or more blades to detect anomalies in multiple blades. Therefore, a condition monitoring device with this structure can detect anomalies in multiple blades without storing data before the strike.

[0106] (Item 7) In the status monitoring device described in Item 6, the relevant parameter is the correlation coefficient.

[0107] Based on this structure, well-known correlation coefficients can be used to detect anomalies in multiple blades.

[0108] (Item 8) In any of the status monitoring devices in items 1 to 7, the anomaly of the wind power generation device is a lightning strike on the wind power generation device.

[0109] This structure enables the detection of lightning strikes to wind power generation devices. (Item 9) The wind power generation device further includes: a nacelle; a mechanism disposed within the nacelle; and nacelle sensors for detecting parameters used to determine whether the mechanism is abnormal. The nacelle monitoring device includes an interface for communicating with the condition monitoring device described in any one of items 1 to 8. The nacelle monitoring device includes a determination device that, upon receiving a detection signal from the condition monitoring device, determines whether the mechanism is abnormal based on parameters.

[0110] When a lightning strike is detected on a wind turbine, the mechanisms housed within the nacelle can sometimes be damaged. Therefore, based on the aforementioned structure, when the condition monitoring device detects a lightning strike, it can determine whether there is any abnormality in the mechanisms.

[0111] (Item 10) The condition monitoring system includes a condition monitoring device as described in any one of items 1 to 8 and a plurality of vibration sensors.

[0112] (Item 11) The condition monitoring method of this disclosure is a method for monitoring the condition of a wind power generation device having multiple blades. The condition monitoring method includes acquiring vibration values ​​of the multiple blades. Furthermore, the condition monitoring method includes detecting a lightning strike on the wind power generation device when the absolute value of the vibration values ​​of the multiple blades exceeds a first threshold.

[0113] All descriptions of the embodiments disclosed herein should be considered illustrative rather than limiting. The scope of this disclosure is not defined by the above description of the embodiments, but by the claims, including all modifications within the same meaning and scope as the claims. Label Explanation

[0114] 100 Wind power generation unit, 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 down conductor, 162 Second down conductor, 163 Third down conductor, 180 Tower, 200 Status 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 Judgment device, 500 Status monitoring system.

Claims

1. A condition monitoring device for monitoring the condition of a wind power generation device having multiple blades, characterized in that it comprises: A 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; A control device that detects an anomaly in the wind power generation device when the absolute values ​​of the vibration values ​​of all of the multiple blades exceed a first threshold. as well as An external interface that sends a detection signal to an external device indicating that an anomaly has been detected in the wind power generation device.

2. The status monitoring device as described in claim 1, characterized in that, The multiple vibration sensors output time-series data of the vibration values ​​detected by the sensors. The control device performs filtering on the time series data to suppress noise.

3. The status monitoring device as described in claim 2, characterized in that, The multiple vibration sensors are accelerometers. The first threshold is 9.8 m / s 2 .

4. The status monitoring device as described in any one of claims 1 to 3, characterized in that, The control device calculates the absolute value of the vibration values ​​of the plurality of blades. When the absolute value of the vibration values ​​of all of the plurality of blades exceeds the first threshold, a lightning strike on the wind power generation device is detected.

5. The status monitoring device as described in any one of claims 1 to 3, characterized in that, When the absolute value of the vibration values ​​of the plurality of blades exceeds the first threshold at the same time, the control device detects a lightning strike on the wind power generation device.

6. The status monitoring device as described in any one of claims 1 to 3, characterized in that, The plurality of blades refers to three or more blades. The multiple vibration sensors refer to 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 corresponding to the specified rotational speeds of the multiple blades from the time-series data output by the three or more vibration sensors. When a lightning strike on the wind power generation device is detected, the correlation parameter representing the correlation between two available time series data points from the extracted time series data of the three or more blades is calculated. If, in all combinations of the two time-series data, the number of correlation parameters less than the second threshold is two or more, an anomaly is detected in any one of the plurality of leaves. The external interface sends an abnormal signal to an external device indicating that an abnormality has been detected in the blade.

7. The status monitoring device as described in claim 6, characterized in that, The relevant parameter is the correlation coefficient.

8. The status monitoring device as described in any one of claims 1 to 3, characterized in that, The malfunction of the wind power generation device was caused by a lightning strike.

9. A cabin monitoring device, characterized in that, The wind power generation device also includes: cabin; The mechanisms located within the cabin; and Cabin sensors that detect parameters used to determine whether the mechanism is abnormal. The cabin surveillance system includes: An interface that communicates with the status monitoring device as described in any one of claims 1 to 3; and A determination device that, upon receiving the detection signal from the status monitoring device, determines whether the mechanism is abnormal based on the parameters.

10. A status monitoring system, characterized in that, include: The status monitoring device as described in any one of claims 1 to 3; as well as The multiple vibration sensors.

11. A condition monitoring method for monitoring the condition of a wind power generation device with multiple blades, characterized in that, The status monitoring method includes: Obtain the vibration values ​​of the plurality of blades; When the absolute value of the vibration values ​​of the plurality of blades exceeds a first threshold, an abnormality is detected in the wind power generation device; and Send a detection signal to external devices indicating that an anomaly has been detected in the wind power generation device.

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