Fault detection device and fault detection method

Abstract

A fault detection device according to one embodiment of the present invention flies around an electric power facility and detects faults in the electric power facility, the fault detection device comprising: a measurement unit that measures the distance to the electric power facility and the shape of the electric power facility; a flight control unit that, on the basis of the results from the measurements made by the measurement unit, controls a flight path so that a constant distance from the electric power facility is maintained; a determination unit that determines noise data around the electric power facility while the flight control unit controls the flight path so that a constant distance from the electric power facility is maintained; and an assessment unit that assesses whether there is a fault in the electric power facility by comparing the noise data determined by the determination unit with noise data determined in advance for individual failure sites and noise data determined in advance for individual failure patterns.

Description

Abnormal Detection Device and Abnormal Detection Method

[0001] The present invention relates to an abnormal detection device and an abnormal detection method.

[0002] Power facilities such as wind power generation facilities are required to be inspected regularly. For example, the inspection items of wind power generation facilities include meteorological conditions, visual inspection of the appearance of the wind turbine, inspection inside the nacelle, etc.

[0003] Also, in order to efficiently detect abnormalities in the nacelle of a wind power generation facility, a technique is known in which a camera is installed inside the nacelle and images are acquired regularly to constantly monitor the inside of the nacelle (see, for example, Non-Patent Document 1).

[0004] "Contributing to the efficiency improvement of the visual inspection of the inside of the nacelle during the tower ascent by introducing Wind AI", [online], Arithmer Co., Ltd. Website, [searched on September 11, 2024], Internet <URL: https: / / www.arithmer.co.jp / post / 20230925>

[0005] However, conventionally, since it is an inspection using a camera, there is a problem that only the external state (physical state) can be inspected. Also, in the inspection using a camera, there were blind spots.

[0006] The present invention has been made in view of the above problems, and an object thereof is to provide an abnormal detection device and an abnormal detection method capable of efficiently determining the presence or absence of abnormalities in internal equipment of a power facility to be inspected.

[0007] An abnormality detection device according to one embodiment of the present invention is an abnormality detection device that flies around power equipment and detects abnormalities in the power equipment, and is characterized by comprising: a measuring unit that measures the distance to the power equipment and the shape of the power equipment; a flight control unit that controls the flight path to maintain a constant distance from the power equipment based on the results measured by the measuring unit; a measuring unit that measures noise data around the power equipment while the flight control unit controls the flight path to maintain a constant distance from the power equipment; and a determination unit that determines whether or not there is an abnormality in the power equipment by comparing noise data measured in advance for each fault location and noise data measured in advance for each fault pattern with the noise data measured by the measuring unit.

[0008] Furthermore, an abnormality detection method according to one embodiment of the present invention is an abnormality detection method for detecting an abnormality in power equipment by flying around the power equipment, and is characterized by including: a measurement step of measuring the distance to the power equipment and the shape of the power equipment; a flight control step of controlling the flight path to maintain a constant distance from the power equipment based on the results measured in the measurement step; a measurement step of measuring noise data around the power equipment while the flight path is being controlled to maintain a constant distance from the power equipment by the flight control step; and a determination step of determining whether or not there is an abnormality in the power equipment by comparing noise data measured in advance for each fault location and noise data measured in advance for each fault pattern with the noise data measured in the measurement step.

[0009] According to the present invention, it is possible to efficiently determine whether or not there are any abnormalities in the internal equipment of the power equipment being inspected.

[0010] This is a schematic overhead view illustrating the outline of the abnormality detection method performed by an abnormality detection device according to one embodiment. This is a functional block diagram illustrating the functions of an abnormality detection device according to one embodiment. This is a schematic overhead view illustrating the flight path control performed by the flight control unit. (a) is a diagram illustrating the degree of failure determined by the determination unit for fault location A of the nacelle. (b) is a diagram illustrating the degree of failure determined by the determination unit for fault location B of the nacelle. (c) is a diagram illustrating the degree of failure determined by the determination unit for fault locations A and B of the nacelle. (a) is a diagram illustrating the degree of failure determined by the determination unit for a nacelle failure pattern (temperature rise). (b) is a diagram illustrating the degree of failure determined by the determination unit for a nacelle failure pattern (cable condition: partial contact failure). (c) is a diagram illustrating the degree of failure determined by the determination unit for a nacelle failure pattern (cable condition: on the verge of disconnection). This flowchart shows an example of how an anomaly detection device works.

[0011] The following describes an abnormality detection device and method according to one embodiment with reference to the drawings. Figure 1 is a schematic overhead view illustrating the outline of the abnormality detection method performed by the abnormality detection device according to one embodiment. In the abnormality detection method according to one embodiment, an abnormality detection device 2, which has the function of a small unmanned aircraft such as a drone flying around the nacelle 100 of the wind power generation equipment 1 that is under inspection, acquires noise (radio noise) data to determine (detect) whether or not there is an internal equipment abnormality in the nacelle 100.

[0012] The nacelle 100 is located in the center of the wind power generation facility 1 and is a power facility that houses a generator (not shown) that generates electricity from the wind received by the blades 10. For example, the nacelle 100 is said to have a length X [m] and a width Y [m].

[0013] Figure 2 is a functional block diagram illustrating the functions of an anomaly detection device 2 according to one embodiment. The anomaly detection device 2 according to one embodiment includes, for example, a control unit 20, a storage unit 21, a flight control unit 22, a flight function unit 23, a measurement unit 24, a measurement unit 25, and a determination unit 26, and is equipped with the functions of a small unmanned aircraft such as a drone.

[0014] The control unit 20 includes, for example, a CPU 200 and controls each part that constitutes the abnormality detection device 2. Specifically, the control unit 20 controls the operation of the abnormality detection device 2 as a small unmanned aircraft flying around power equipment such as the nacelle 100, and controls the operation of detecting abnormalities in power equipment such as the nacelle 100.

[0015] The memory unit 21 is a database that stores data necessary for processing performed by the anomaly detection device 2. For example, the memory unit 21 pre-stores information such as video data, design drawings (shape information), and location information (GPS coordinates of the center of the nacelle 100: longitude and latitude) related to the nacelle 100.

[0016] Furthermore, the memory unit 21 pre-stores noise data measured for each fault location of the nacelle 100, and noise data measured for each fault pattern of the nacelle 100 (waveform, level value, frequency components, time variation of the levels of each frequency component, etc.) as reference data for reference. This reference data for faults is pre-measured (received) by the anomaly detection device 2.

[0017] Furthermore, the storage unit 21 stores the processing steps performed by the anomaly detection device 2, or the resulting output data, so that it can be added to the database.

[0018] The flight control unit 22 uses information indicating, for example, the GPS coordinates (longitude and latitude) of the anomaly detection device 2 itself to control the flight function unit 23, which is equipped with the functions for the anomaly detection device 2 to fly as a small unmanned aircraft. Specifically, the flight control unit 22 determines and controls the flight path to maintain a constant distance between the anomaly detection device 2 and the nacelle 100, based on the results measured by the measurement unit 24 (described later) and the information stored in the storage unit 21.

[0019] Figure 3 is a schematic overhead view showing the flight path control performed by the flight control unit 22. As shown in Figure 3, the flight control unit 22 controls the aircraft to maintain a distance A [m] from the nacelle 100, for example, when an anomaly detection device 2 is detected. At this time, the flight control unit 22 controls the aircraft to avoid areas where the blades 10, wind vane, anemometer, lightning rod, etc. of the wind turbine 1 are located, designating these areas as no-fly zones.

[0020] The measurement unit 24 (Figure 2) is equipped with, for example, a LiDER (Light Detection and Ranging) (not shown) to measure the distance to the nacelle 100 to be judged and the shape of the nacelle 100, and outputs the measurement results to the control unit 20.

[0021] The measurement unit 25 measures noise data around the nacelle 100 while the flight function unit 23 controls the flight path to maintain a constant distance between the anomaly detection device 2 and the nacelle 100, and outputs the measurement results to the control unit 20. For example, the measurement unit 25 measures the waveform, level value, and time variation of frequency components of the radio waves emitted by the nacelle 100.

[0022] The measurement unit 25 measures noise generated from internal equipment (e.g., generators, motors, etc.) of the nacelle 100 at multiple locations while the anomaly detection device 2 flies around the nacelle 100, maintaining a constant distance from it. For example, the measurement unit 25 measures noise during periodic inspections of the wind power generation equipment 1.

[0023] The noise generated by the internal equipment of the nacelle 100 has a large MF and HF band component, resulting in a long near-field distance and significant power attenuation with distance. For example, the near-field distance at a frequency of 3 MHz is 16 m. Therefore, the anomaly detection device 2 approaches the nacelle 100 in the air to measure the noise.

[0024] The determination unit 26 compares noise data measured in advance for each fault location and noise data measured in advance for each fault pattern with the noise data measured by the measurement unit 25 to determine whether or not there is an abnormality in the nacelle 100, and outputs the determination result to the control unit 20. The determination unit 26 may also perform determination using AI (artificial intelligence).

[0025] Specifically, the determination unit 26 determines whether or not there is a malfunction in the nacelle 100, the location of the malfunction, and the degree of the malfunction based on the time changes of the radio wave waveform, level value, and frequency components that have been measured in advance for each malfunction location and malfunction pattern in the nacelle 100.

[0026] The determination unit 26 then compares the noise data measured in advance for each fault location and each fault pattern with the noise data measured by the measurement unit 25 to classify the abnormality into three levels, such as red, yellow, and blue. For example, the red level indicates a condition where operation must be stopped immediately and repairs are necessary. The yellow level indicates a condition where operation does not need to be stopped immediately, but planned repairs are necessary. The blue level indicates no abnormality.

[0027] Figure 4 is a graph illustrating the results of the determination unit 26's determination for each fault location of the nacelle 100. Figure 4(a) is a diagram illustrating the degree of failure determined by the determination unit 26 for fault location A of the nacelle 100. Figure 4(b) is a diagram illustrating the degree of failure determined by the determination unit 26 for fault location B of the nacelle 100. Figure 4(c) is a diagram illustrating the degree of failure determined by the determination unit 26 for fault locations A and B of the nacelle 100.

[0028] As shown in Figure 4, the determination unit 26 performs a process to compare the strength of radio waves at each predetermined frequency for each fault location, and determines the degree of the fault.

[0029] Figure 5 is a graph illustrating the results of the determination unit 26's determination for each failure pattern of the nacelle 100. Figure 5(a) is a diagram illustrating the degree of failure determined by the determination unit 26 for a failure pattern (temperature rise) of the nacelle 100. Figure 5(b) is a diagram illustrating the degree of failure determined by the determination unit 26 for a failure pattern (cable condition: partial contact failure) of the nacelle 100. Figure 5(c) is a diagram illustrating the degree of failure determined by the determination unit 26 for a failure pattern (cable condition: on the verge of disconnection) of the nacelle 100.

[0030] As shown in Figure 5, the determination unit 26 performs a process to compare the strength of radio waves at each predetermined frequency for each failure pattern, and determines the degree of the failure.

[0031] Next, an example of the operation of the anomaly detection device 2 will be described. Figure 6 is a flowchart showing an example of the operation of the anomaly detection device 2. As shown in Figure 6, in step 100 (S100), the anomaly detection device 2 first has the control unit 20 acquire the position information and shape information of the nacelle 100 from the storage unit 21.

[0032] In step 102 (S102), the anomaly detection device 2 moves itself to the target position by the flight control unit 22 controlling the flight function unit 23. At this time, the flight control unit 22, under the control of the control unit 20, roughly determines the target position to which the anomaly detection device 2 should move using the GPS coordinates (longitude and latitude) of the anomaly detection device 2 itself and the position information (GPS coordinates: longitude and latitude) of the nacelle 100, and moves the anomaly detection device 2 to that target position.

[0033] In step 104 (S104), the flight control unit 22, under the control of the control unit 20, determines and controls the flight path to maintain a constant distance between the anomaly detection device 2 and the nacelle 100, based on the results measured by the measurement unit 24 and the information stored in the storage unit 21.

[0034] In step 106 (S106), the determination unit 26 reads the reference data stored in the storage unit 21 under the control of the control unit 20.

[0035] In step 108 (S108), the determination unit 26 acquires measurement data measured by the measurement unit 25 (for example, noise data measured during periodic inspection) under the control of the control unit 20.

[0036] In step 110 (S110), the determination unit 26 compares the noise data measured by the measurement unit 25 with the reference data read (reference data for faults in three levels: red, yellow, and blue).

[0037] In step 112 (S112), the determination unit 26 determines whether or not there is an abnormality in the nacelle 100 based on the comparison results from the processing in S110, for example, by dividing it into three levels: red, yellow, and blue, and outputs the determination result to the control unit 20.

[0038] In step 114 (S114), the memory unit 21 stores the determination result of the determination unit 26 (data in three levels: red, yellow, and blue).

[0039] In this way, the anomaly detection device 2 flies while maintaining a constant distance from power equipment such as the nacelle 100, measures noise data, and compares it with reference data to determine whether or not there is an anomaly in the power equipment. Therefore, it is possible to efficiently determine whether or not there is an anomaly in the internal equipment of the power equipment being inspected.

[0040] Furthermore, each function of the anomaly detection device 2 may be configured in part or in whole using hardware such as a PLD (Programmable Logic Device) or FPGA (Field Programmable Gate Array), or it may be configured as a program executed by a processor such as a CPU.

[0041] For example, the anomaly detection device 2 can be implemented using a computer and a program, and the program can be recorded on a storage medium or provided via a network.

[0042] The functions realized by the components described herein may be implemented in a circuitry or processing circuitry, including general-purpose processors, application-specific processors, integrated circuits, ASICs (Application Specific Integrated Circuits), CPUs (a Central Processing Unit), conventional circuits, and / or combinations thereof, programmed to realize the functions described herein.

[0043] A processor includes transistors and other circuits and is considered circuitry or processing circuitry. A processor may also be a programmed processor that executes programs stored in memory.

[0044] In this specification, circuitry, units, and means are hardware programmed to implement the described functions or hardware that executes them. The hardware may be any hardware disclosed in this specification or any hardware known to be programmed or to execute to implement the described functions.

[0045] When the hardware is a processor regarded as being of the circuitry type, the circuitry, means, or unit is a combination of hardware and software used to configure the hardware and / or the processor.

[0046] 1... Wind power generation equipment, 2... Abnormality detection device, 10... Blade, 20... Control unit, 21... Storage unit, 22... Flight control unit, 23... Flight function unit, 24... Measurement unit, 25... Measuring unit, 26... Determination unit, 100... Nacelle, 200... CPU

Claims

1. An anomaly detection device that flies around power equipment to detect abnormalities in the power equipment, comprising: a measuring unit that measures the distance to the power equipment and the shape of the power equipment; a flight control unit that controls the flight path to maintain a constant distance from the power equipment based on the results measured by the measuring unit; a measuring unit that measures noise data around the power equipment while the flight control unit controls the flight path to maintain a constant distance from the power equipment; and a determination unit that determines whether or not there is an abnormality in the power equipment by comparing noise data measured in advance for each fault location and noise data measured in advance for each fault pattern with the noise data measured by the measuring unit.

2. The abnormality detection device according to claim 1, characterized in that the measuring unit measures the time changes in the waveform, level value, and frequency components of the radio waves emitted by the power equipment, and the determination unit determines the location and degree of the fault based on the time changes in the waveform, level value, and frequency components of the radio waves that have been measured in advance for each fault location and fault pattern.

3. An anomaly detection method for detecting an anomaly in power equipment by flying around the power equipment, comprising: a measurement step of measuring the distance to the power equipment and the shape of the power equipment; a flight control step of controlling the flight path to maintain a constant distance from the power equipment based on the results measured in the measurement step; a measurement step of measuring noise data around the power equipment while the flight path is being controlled to maintain a constant distance from the power equipment by the flight control step; and a determination step of determining whether or not there is an anomaly in the power equipment by comparing noise data measured in advance for each fault location and noise data measured in advance for each fault pattern with the noise data measured in the measurement step.

4. The abnormality detection method according to claim 3, characterized in that the measurement step measures the time changes in the waveform, level value, and frequency components of the radio waves emitted by the power equipment, and the determination step determines the location and degree of the failure based on the time changes in the waveform, level value, and frequency components of the radio waves that have been measured in advance for each failure location and each failure pattern.

Images