JP-2026075436-A - Wind power generation equipment monitoring device, wind power generation equipment monitoring method, and program

JP2026075436AJP 2026075436 AJP2026075436 AJP 2026075436AJP-2026075436-A

Abstract

[Problem] To provide a wind power generation equipment monitoring device that can ensure the accuracy of abnormality detection of equipment even when it is not possible to continuously acquire operating data of the equipment. [Solution] A wind power generation equipment monitoring device according to one embodiment includes: a feature extraction unit that extracts features from an acoustic signal indicating the detection result of sound generated by at least one piece of equipment installed in the power generation facility; a storage unit that stores machine learning models that have been pre-classified according to the operating state of the equipment using sound; an equipment state estimation unit that identifies the operating state of the equipment based on the acoustic signal and estimates whether or not there is an abnormality in the equipment using a machine learning model corresponding to the identified operating state; and a notification unit that outputs an alert signal indicating an abnormality in the equipment. [Selection Diagram] Figure 1

Inventors

吉水謙司
伊東亮
上田隆司
谷山賀浩
鹿仁島康裕
平野俊夫
池田和徳
永田寿一
高橋則雄
渡邉和

Assignees

株式会社東芝
東芝エネルギーシステムズ株式会社

Dates

Publication Date: 20260508
Application Date: 20241022

Claims (11)

A feature extraction unit that extracts feature quantities from an acoustic signal showing the detection result of sound generated by at least one device installed in the power generation facility, A storage unit that stores machine learning models pre-classified according to the operating state of the equipment using the aforementioned sound, A device state estimation unit identifies the operating state of the device based on the aforementioned acoustic signal and estimates whether or not there is an abnormality in the device using a machine learning model corresponding to the identified operating state. A notification unit that outputs an alert signal indicating an abnormality in the aforementioned equipment, A wind power generation equipment monitoring device equipped with the following features.
The system further comprises a model creation unit for creating the aforementioned machine learning model, The wind power generation equipment monitoring device according to claim 1, wherein the model creation unit collects operating data of the equipment and the characteristic quantities of the acoustic signal for a certain period of time, and creates the machine learning model by classifying the operating state based on the correlation between the collected operating data and the characteristic quantities using machine learning.
The aforementioned device is a rotating device, The wind power generation equipment monitoring device according to claim 1 or 2, wherein the feature extraction unit further performs frequency analysis on the acoustic signal while shifting the results of frequency analysis within a predetermined time window by a predetermined time to extract fluctuating components that depend on the rotational speed of the rotating equipment, and identifies the acoustic frequencies corresponding to the fluctuating components.
The aforementioned acoustic signal indicates the detection result of wind noise from the blades installed on the wind turbine. The wind power generation equipment monitoring device according to claim 1 or 2, wherein the equipment status estimation unit determines the operating state based on the interval of the wind noise indicated in the acoustic signal.
The wind power generation equipment monitoring device according to claim 1 or 2, wherein the equipment status estimation unit estimates that the equipment is abnormal when the feature quantity deviates from the separation boundary surface shown in the machine learning model.
Multiple devices are arranged in the power generation facility. An acoustic sensor that detects the sound from the aforementioned multiple devices is installed on the mobile device. Map data showing the arrangement of the multiple devices is stored in the storage unit. The wind power generation equipment monitoring device according to claim 1 or 2, wherein the equipment status estimation unit identifies the equipment currently in operation from the plurality of equipment using the location data from which the sound was detected and the map data.
Multiple devices are arranged in the power generation facility. Multiple acoustic sensors, which detect the sounds of the aforementioned multiple devices, are fixed in locations that are far apart from each other. The wind power generation equipment monitoring device according to claim 1 or 2, wherein the equipment status estimation unit identifies the sound source equipment from the plurality of equipment based on the time difference in which the sound was detected among the plurality of acoustic sensors and the speed of sound.
The aforementioned device is a rotating device to which a reflective member is attached, The reflected light from the reflective member is received by the light receiving sensor. The wind power generation equipment monitoring device according to claim 1 or 2, wherein the equipment status estimation unit determines the operating state of the rotating equipment based on the interval at which the light receiving sensor receives the reflected light.
The temperature of the aforementioned device is measured by a thermographic camera. The wind power generation equipment monitoring device according to claim 1 or 2, wherein the equipment status estimation unit identifies the operating status of the equipment based on the temperature change measured by the thermographic camera.
Using the sound generated by at least one device installed in the power generation facility, a machine learning model is created that is pre-classified according to the operating state of the device. Feature quantities are extracted from the acoustic signal showing the detection results of the aforementioned sound, Based on the aforementioned acoustic signal, the operating state of the equipment is identified. A machine learning model corresponding to the identified operating conditions is used to estimate whether or not there is an abnormality in the equipment. The device outputs an alert signal indicating an abnormality. Method for monitoring wind power generation equipment.
A process to create a machine learning model that is pre-classified according to the operating state of at least one device installed in a power generation facility, using the sound generated by the device. A process for extracting feature quantities from the acoustic signal showing the detection results of the aforementioned acoustics, A process to identify the operating state of the equipment based on the aforementioned acoustic signal, A process to estimate whether or not there is an abnormality in the equipment using a machine learning model corresponding to the identified operating state, A process that outputs an alert signal indicating an abnormality in the aforementioned equipment, A program that causes a computer to execute something.

Description

Embodiments of the present invention relate to a wind power generation equipment monitoring device, a wind power generation equipment monitoring method, and a program. In power generation facilities that are difficult to access, such as wind and hydroelectric power plants, the cost of personnel accessing the equipment in the event of a malfunction increases maintenance costs. Therefore, there is a need for simple and accurate equipment malfunction detection. One way to ensure the accuracy of malfunction detection is to continuously acquire operating data from equipment installed within the power generation facility. However, if the equipment in the power generation facility is manufactured by another company or owned by the operator, it is not always possible to continuously acquire operating data from that equipment. Patent No. 6216242 This is a block diagram showing the configuration of a wind power generation equipment monitoring device according to the first embodiment.(A) is a schematic diagram showing an example of a fixed-bottom offshore wind turbine, and (B) is a schematic diagram showing an example of a floating offshore wind turbine.This is a schematic diagram illustrating the interior of a wind turbine nacelle.(A) is a diagram showing the waveform of the acoustic signal output from the acoustic sensor, (B) is a diagram showing the result of processing the acoustic signal with a Fast Fourier Transform, and (C) is a diagram showing an example in which the frequency band of the acoustic signal is divided into 1/3 octave bands.This is a data distribution diagram showing an example of wind turbine operating conditions.This is a correlation map diagram showing an example of the correlation between the rotational speed of the main engine and acoustic frequency.This diagram shows an example of the operation classification of the main engine and auxiliary engine.A flowchart illustrating the method for monitoring wind turbines is shown.This figure shows an example of the relationship between the rotational speed component of a rotating machine and its acoustic frequency.This figure shows an example of the waveform of the acoustic signal from an acoustic sensor that detected wind noise from the blade.This is a block diagram showing the configuration of a yaw control device according to the fourth embodiment.This is a block diagram showing an example configuration of a mobile device.This is a block diagram illustrating a wind power generation equipment monitoring method according to the fifth embodiment.This is a block diagram illustrating a wind power generation equipment monitoring method according to the sixth embodiment.This is a schematic diagram of the main engine and auxiliary engine according to the sixth embodiment.This is a block diagram illustrating a wind power generation equipment monitoring method according to the seventh embodiment. The embodiments of the present invention will be described below with reference to the drawings. The embodiments described below are not intended to limit the present invention. (First Embodiment) Figure 1 is a block diagram showing the configuration of a wind power generation equipment monitoring device according to the first embodiment. The wind power generation equipment monitoring device 10 according to this embodiment is a device that monitors the main engine (blades, gearbox, generator, bearings, etc.) and auxiliary equipment installed in a wind turbine for wind power generation. Note that the equipment monitored by the wind power generation equipment monitoring device 10 is not limited to the main engine and auxiliary equipment, but may also include, for example, rotating equipment such as turbines that rotate using water power installed in hydroelectric power generation, or equipment that needs to be operated unattended. Figure 2(A) is a schematic diagram showing an example of a fixed-bottom offshore wind turbine. Figure 2(B) is a schematic diagram showing an example of a floating offshore wind turbine. Figure 3 is a schematic diagram showing the interior of the nacelle of the wind turbine 20 shown in Figures 2(A) and 2(B). Note that the wind turbine 20 is not limited to offshore installation; it may also be a ground-based wind turbine. The wind turbine 20 shown in Figures 2(A) and 2(B) comprises multiple blades 21, a hub 22, a nacelle 23, and a tower 24. Each blade 21 is arranged radially so as to be connected to the rotor shaft 25 (see Figure 2) by the hub 22. The pitch angle of each blade 21 is adjusted relative to the wind inflow direction to efficiently convert the wind's flow energy into rotational energy. A drive mechanism (not shown), including a motor, brakes, and emergency power supply, is provided within the hub 22 to adjust this pitch angle. The tower 24 is constructed vertically on the upper surface of a foundation 26 built on the seabed and exposed above sea level. The nacelle 23 is mounted at the top of the tower 24 via a yaw control device 4