CN109763944B - Non-contact monitoring system and monitoring method for blade faults of offshore wind turbine - Google Patents
Non-contact monitoring system and monitoring method for blade faults of offshore wind turbine Download PDFInfo
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Abstract
本发明公开了一种海上风机叶片故障非接触式监测系统及监测方法，所述的系统包括布置在海上风机基础、塔基或单独平台上的气象信息采集系统，布置在海上风机附近海域的水文信息采集系统，布置在海上风机基础或塔架上的气动声学采集系统，气象信息采集系统、水文信息采集系统和气动声学采集系统所采集的数据实时传输至采集节点，再由采集节点通过无线终端传输至岸上数据中心，数据中心，监测系统和监控设备之间通过工业以太网连接通信。本发明所公开的监测系统，不需要破坏风机叶片的原有结构，同时也方便检测设备的维护。既能满足传统监控室监测要求，同时还有远程电脑和手持设备可以方便现场维护。
The invention discloses a non-contact monitoring system and a monitoring method for the fault of an offshore wind turbine blade. The system includes a meteorological information acquisition system arranged on the offshore wind turbine foundation, a tower base or a separate platform, and a hydrological data collection system arranged in the sea area near the offshore wind turbine. Information acquisition system, the aeroacoustic acquisition system arranged on the foundation or tower of the offshore wind turbine, the data collected by the meteorological information acquisition system, the hydrological information acquisition system and the aeroacoustic acquisition system are transmitted to the acquisition node in real time, and then by the acquisition node through the wireless terminal It is transmitted to the onshore data center, and the communication between the data center, the monitoring system and the monitoring equipment is connected through the industrial Ethernet. The monitoring system disclosed in the invention does not need to destroy the original structure of the fan blade, and also facilitates the maintenance of the detection equipment. It can not only meet the monitoring requirements of traditional monitoring rooms, but also have remote computers and handheld devices to facilitate on-site maintenance.
Description
Technical Field
The invention belongs to the field of instruments and meters, and particularly relates to a non-contact monitoring system and a non-contact monitoring method for blade faults of an offshore wind turbine based on pneumatic acoustic signal characteristics in the field.
Background
The traditional energy can not meet the sustainable development requirements of resources and environment, the final source of wind energy resources is solar energy which is inexhaustible and inexhaustible, and people pay more and more attention to and pay attention to the final source, and the offshore wind power becomes an important direction for the development of the wind power technology due to the progress and maturity of the wind power technology.
However, since the offshore wind farm is generally built in an intertidal zone or an offshore area, the construction cost is high, and the complex marine environment increases the difficulty in operating and maintaining the offshore wind turbine. The wind turbine blade is used as a wind energy acquisition device, the reliability of the wind turbine blade directly affects the power generation quality and efficiency of an offshore wind turbine generator, and in addition, the offshore wind turbine blade is larger in size and higher in manufacturing cost than an onshore wind turbine blade, and accordingly, after serious faults occur, the destructiveness is larger, and the economic loss is higher. However, the current home and abroad wind turbine blade fault detection and diagnosis methods still stay at a relatively low level. Therefore, the development of a real-time online monitoring system and a monitoring method which can timely detect the fault and accurately identify the fault type at the initial stage of the fault of the fan blade and provide support and basis for fault elimination has important value.
On one hand, the sensor used in the existing online detection mode of the fan blade fault needs to be arranged in the fan blade in advance, which can generate adverse effect on the structure of the fan blade, reduce the strength of the blade and increase the manufacturing difficulty of the blade; on the other hand, once the prefabricated sensor fails, the maintenance cost is high.
Disclosure of Invention
The invention aims to solve the technical problem of providing a non-contact monitoring system and a non-contact monitoring method for blade faults of an offshore wind turbine based on pneumatic acoustic signal characteristics.
The invention adopts the following technical scheme:
the non-contact monitoring system for the blade fault of the offshore wind turbine has the improvement that: the system comprises a meteorological information acquisition system arranged on an offshore wind turbine foundation, a tower foundation or an independent platform, a hydrological information acquisition system arranged in an offshore area near the offshore wind turbine, and a pneumatic acoustic acquisition system arranged on the offshore wind turbine foundation or the tower, wherein data acquired by the meteorological information acquisition system, the hydrological information acquisition system and the pneumatic acoustic acquisition system are transmitted to an acquisition node in real time and then transmitted to an onshore data center through a wireless terminal by the acquisition node, and the data center, the monitoring system and the monitoring equipment are connected and communicated through an industrial Ethernet.
Furthermore, the number of the offshore wind turbines is more than two, and each offshore wind turbine is provided with a meteorological information acquisition system, a hydrological information acquisition system, a pneumatic acoustic acquisition system, an acquisition node and a wireless terminal.
Furthermore, the meteorological information acquisition system comprises a temperature sensor, a humidity sensor, a wind sensor, a rainfall sensor and a lightning sensor.
Further, the hydrological information acquisition system comprises a wave sensor, an ocean current sensor and a tide sensor.
Furthermore, the pneumatic acoustic acquisition system comprises an acoustic sensor and an acoustic signal processing unit, the measurement range of the acoustic sensor is 20 Hz-20 kHz, the sampling frequency of a digital-to-analog converter of the acoustic signal processing unit is not lower than 200kHz, the acquisition bit number is not lower than 16 bits, and the acquisition channel is not less than 4 channels.
Further, the wireless terminal transmits data to an onshore data center by means of microwave communication.
Furthermore, the monitoring equipment comprises a central monitoring room computer monitoring platform, a remote computer and handheld equipment.
The non-contact monitoring method for the blade fault of the offshore wind turbine uses the monitoring system, and the improvement is that the non-contact monitoring method comprises the following steps:
(1) acquiring meteorological information through a meteorological information acquisition system, and correcting noise interference of wind noise, extreme storms, thunder and rain; acquiring hydrological information through a hydrological information acquisition system, and correcting noise interference of sea waves, currents and tides; acquiring pneumatic acoustic information of the fan blade through a pneumatic acoustic acquisition system; the acquisition process is controlled by the acquisition node and the acquired data is transmitted to the onshore data center through the wireless terminal;
(2) carrying out decision-level information fusion on meteorological information, hydrological information and aerodynamic acoustic information of the fan blade, correcting interference of environmental noise on the acquisition of the aerodynamic acoustic information, calculating multi-scale sample entropy of the aerodynamic acoustic information based on variational modal decomposition, namely extracting sample entropy of time series on different scales, so as to construct a state feature vector set of the fan blade, analyzing and extracting time-frequency features of the constructed aerodynamic acoustic information through a time-frequency analysis tool, inputting the multi-scale sample entropy feature vector set constructed by test samples into a neural network for learning and training, continuously updating network weight and structure, and finally converging to obtain a neural network model for identifying and classifying fan blade faults;
(3) downloading the neural network model into a monitoring system, calculating the time-frequency characteristics of newly acquired pneumatic acoustic information by the monitoring system, inputting the time-frequency characteristics into the neural network model for identification and classification, and updating the weight and the neural network structure in time according to the new characteristics by the neural network model to enhance the generalization capability of the neural network;
(4) the monitoring system provides the state and the fault information of each offshore wind turbine blade in real time, sends alarm information to the monitoring equipment when a fault occurs, and meanwhile, the state of each offshore wind turbine blade can be checked on the monitoring equipment in real time and is processed when the fault occurs.
Further, the pneumatic acoustic acquisition system removes the interference of the background noise of the marine environment by using a self-adaptive filtering method according to the time-frequency characteristics of the signals to obtain pure pneumatic acoustic information of the fan blade.
The invention has the beneficial effects that:
the monitoring system disclosed by the invention does not need to damage the original structure of the fan blade, and is convenient for maintaining the detection equipment. The monitoring requirement of the traditional monitoring room can be met, and meanwhile, a remote computer and handheld equipment can be convenient for field maintenance.
The monitoring method disclosed by the invention comprehensively and accurately describes the pneumatic noise signal of the fan and the environmental background noise through a multi-sensor information fusion technology, and corrects the interference of the environmental noise on the pneumatic acoustic information acquisition. An offshore wind turbine blade aerodynamic acoustic feature vector set is established through a self-adaptive signal processing method, and then state monitoring and fault identification of the offshore wind turbine blade are achieved through a machine learning method.
Drawings
FIG. 1 is a schematic diagram of the monitoring system disclosed in embodiment 1 of the present invention;
fig. 2 is a schematic flow chart of the monitoring method disclosed in embodiment 1 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In this embodiment, the number of the offshore wind turbines is two or more, and each offshore wind turbine is provided with a meteorological information acquisition system, a hydrological information acquisition system, a pneumatic acoustic acquisition system, an acquisition node and a wireless terminal, so that a shore data center can obtain data of each offshore wind turbine, and a monitoring system can read various information of each offshore wind turbine of an offshore wind farm at any time through the data center. The meteorological information acquisition system comprises a temperature sensor, a humidity sensor, a wind sensor, a rainfall sensor and a lightning sensor. The hydrological information acquisition system comprises a wave sensor, an ocean current sensor and a tide sensor. The pneumatic acoustic acquisition system comprises an acoustic sensor and an acoustic signal processing unit, the measuring range of the acoustic sensor is 20 Hz-20 kHz, the sampling frequency of a digital-to-analog converter of the acoustic signal processing unit is not lower than 200kHz, the acquisition number is not lower than 16, and in order to ensure the accuracy of acquiring acoustic signals, the acquisition channel is not less than 4 channels. The wireless terminal transmits data to an onshore data center by means of microwave communication. The monitoring equipment comprises a central monitoring room computer monitoring platform, a remote computer and handheld equipment, wherein the handheld equipment is intelligent mobile phone equipment, and various monitoring equipment can realize functions of checking fan blade state data, monitoring faults, processing alarms and the like.
As shown in fig. 2, the embodiment further discloses a non-contact monitoring method for offshore wind turbine blade fault, and the monitoring system includes the following steps:
(1) acquiring meteorological information through a meteorological information acquisition system, and correcting noise interference of wind noise, extreme storms, thunder and rain; acquiring hydrological information through a hydrological information acquisition system, and correcting noise interference of sea waves, currents and tides; acquiring aerodynamic acoustic information of the fan blade through an aerodynamic acoustic acquisition system, specifically, in the embodiment, the aerodynamic acoustic acquisition system performs variation modal decomposition on a signal, and by comparing the correlation with the rotation period of the fan blade and the time-frequency characteristics of sub-bands, the sub-bands interfered by background noise of the marine environment are removed, so that pure aerodynamic acoustic information of the fan blade is obtained; the acquisition process is controlled by the acquisition node and the acquired data is transmitted to the onshore data center through the wireless terminal;
(2) the method comprises the steps of carrying out decision-level information fusion on meteorological information, hydrological information and aerodynamic acoustic information of the fan blade, correcting interference of environmental noise on the acquisition of the aerodynamic acoustic information, measuring the complexity of signals on the whole by calculating multi-scale sample entropies of the aerodynamic acoustic information based on variation modal decomposition, namely extracting the sample entropies of time series on different scales, mining deep-level detail characteristics of the signals on different scales to construct a state characteristic vector set of the fan blade, analyzing and extracting time-frequency characteristics of the aerodynamic acoustic information by a time-frequency analysis tool when the fan blade fails, inputting the multi-scale sample entropy characteristic vector set established by a test sample into a neural network for learning and training, and continuously updating network weight and structure, finally converging to obtain a neural network model for identifying and classifying the faults of the fan blades;
(3) downloading the neural network model into a monitoring system, calculating the time-frequency characteristics of newly acquired pneumatic acoustic information by the monitoring system, inputting the time-frequency characteristics into the neural network model for identification and classification, and updating the weight and the neural network structure in time according to the new characteristics by the neural network model to enhance the generalization capability of the neural network;
(4) the monitoring system provides the state and the fault information of each offshore wind turbine blade in real time, sends alarm information to the monitoring equipment when a fault occurs, and meanwhile, the state of each offshore wind turbine blade can be checked on the monitoring equipment in real time and is processed when the fault occurs.
In this embodiment, the aeroacoustic acquisition system removes the interference of the background noise of the marine environment by using an adaptive filtering method according to the time-frequency characteristics of the signals, so as to obtain pure aeroacoustic information of the fan blade.
Claims (1)
1. A non-contact monitoring method for blade faults of an offshore wind turbine uses a non-contact monitoring system for blade faults of the offshore wind turbine, wherein the system comprises a meteorological information acquisition system arranged on an offshore wind turbine foundation, a tower foundation or an independent platform, a hydrological information acquisition system arranged in a sea area near the offshore wind turbine, and a pneumatic acoustic acquisition system arranged on the offshore wind turbine foundation or a tower, wherein data acquired by the meteorological information acquisition system, the hydrological information acquisition system and the pneumatic acoustic acquisition system are transmitted to an acquisition node in real time and then transmitted to an onshore data center through a wireless terminal by the acquisition node, and the data center, the monitoring system and monitoring equipment are connected and communicated through an industrial Ethernet; the number of the offshore wind turbines is more than two, and each offshore wind turbine is provided with a meteorological information acquisition system, a hydrological information acquisition system, a pneumatic acoustic acquisition system, an acquisition node and a wireless terminal; the meteorological information acquisition system comprises a temperature sensor, a humidity sensor, a wind sensor, a rainfall sensor and a lightning sensor; the hydrological information acquisition system comprises a wave sensor, an ocean current sensor and a tide sensor; the pneumatic acoustic acquisition system comprises an acoustic sensor and an acoustic signal processing unit, wherein the measuring range of the acoustic sensor is 20 Hz-20 kHz, the sampling frequency of a digital-to-analog converter of the acoustic signal processing unit is not lower than 200kHz, the acquisition bit number is not lower than 16 bits, and the acquisition channel is not less than 4 channels; the wireless terminal transmits data to an onshore data center in a microwave communication mode; the monitoring equipment comprises a central monitoring room computer monitoring platform, a remote computer and handheld equipment, and is characterized by comprising the following steps:
(1) acquiring meteorological information through a meteorological information acquisition system, and correcting noise interference of wind noise, extreme storms, thunder and rain; acquiring hydrological information through a hydrological information acquisition system, and correcting noise interference of sea waves, currents and tides; acquiring pneumatic acoustic information of the fan blade through a pneumatic acoustic acquisition system; the acquisition process is controlled by the acquisition node and the acquired data is transmitted to the onshore data center through the wireless terminal;
(2) carrying out decision-level information fusion on meteorological information, hydrological information and aerodynamic acoustic information of the fan blade, correcting interference of environmental noise on the acquisition of the aerodynamic acoustic information, calculating multi-scale sample entropy of the aerodynamic acoustic information based on variational modal decomposition, namely extracting sample entropy of time series on different scales, so as to construct a state feature vector set of the fan blade, analyzing and extracting time-frequency features of the constructed aerodynamic acoustic information through a time-frequency analysis tool, inputting the multi-scale sample entropy feature vector set constructed by test samples into a neural network for learning and training, continuously updating network weight and structure, and finally converging to obtain a neural network model for identifying and classifying fan blade faults;
(3) downloading the neural network model into a monitoring system, calculating the time-frequency characteristics of newly acquired pneumatic acoustic information by the monitoring system, inputting the time-frequency characteristics into the neural network model for identification and classification, and updating the weight and the neural network structure in time according to the new characteristics by the neural network model to enhance the generalization capability of the neural network;
(4) the monitoring system provides the state and the fault information of each offshore wind turbine blade in real time, sends alarm information to the monitoring equipment when a fault occurs, and meanwhile, the state of each offshore wind turbine blade can be checked on the monitoring equipment in real time and is processed when the fault occurs.
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| CN112067701B (en) * | 2020-09-07 | 2024-02-02 | 国电电力新疆新能源开发有限公司 | Fan blade remote auscultation method based on acoustic diagnosis |
| CN112539142B (en) * | 2020-11-12 | 2022-09-13 | 中国电建集团华东勘测设计研究院有限公司 | Analysis and identification method for noise of monitoring data of offshore wind power structure state |
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| CN114412729B (en) * | 2022-01-30 | 2024-08-06 | 西安翔迅科技有限责任公司 | Fan blade damage monitoring device and monitoring method thereof |
| CN114738205B (en) * | 2022-04-28 | 2022-12-27 | 北京千尧新能源科技开发有限公司 | Method, device, equipment and medium for monitoring state of floating fan foundation |
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