CN107061183A - A kind of automation method for diagnosing faults of offshore wind farm unit - Google Patents

Abstract

The invention discloses an automatic fault diagnosis method for an offshore wind power unit. The method includes the following steps: installing a sensor for a monitoring item on the offshore wind power structure to be diagnosed; performing preprocessing on the signal extracted by the sensor, and removing the extracted signal useless or interfering signals contained in the signal; carry out specific data analysis on the signal after the preprocessing, remove the false and save the true; compare and analyze the amplitude obtained after the data analysis of the signal with the fault threshold set by the data analysis, and then determine Whether the structure of the wind turbine is in failure; according to the situation of the failure, it is decided to take corresponding countermeasures. The invention can perform real-time alarm, fault analysis and potential fault prediction and alarm for the operating state of the offshore wind turbine, and is of great value in reducing sudden failures of the wind turbine and reducing unnecessary losses.

Description

An automatic fault diagnosis method for offshore wind turbines

technical field

The invention relates to a fault diagnosis method, in particular to an automatic fault diagnosis method for an offshore wind turbine.

Background technique

Faults during the operation of wind turbines are one of the keys that affect the benefits of wind power operating companies. Generally speaking, the operation failure of wind turbines is the key factor that determines whether the wind turbine can operate stably for a long time, and its mechanism is complex and involves many factors. At present, the operation and maintenance of wind farms mainly use the monitoring of the main control system of the unit and regular inspections to understand the operating status of the unit. Due to the large amount of information that constitutes the operating status of the accessories, the calculation and processing process is complex, especially for the processing of massive data obtained at high sampling frequency, which requires a certain period of time; and the subsequent fault diagnosis is often not enough to restore the damage caused by the fault to the wind turbine. Therefore, building an online monitoring, real-time alarm and fault diagnosis system for wind turbines is essential for monitoring operating conditions, taking appropriate measures in a timely manner, preventing and reducing the occurrence of unit failures, and performing early warning of failures through in-depth data analysis to achieve predictive maintenance. very important.

Contents of the invention

The purpose of the present invention is to overcome the defects of the prior art, and provide an automatic fault diagnosis method for offshore wind turbines, using wireless sensor data communication network, combined with advanced computer information technology, to solve the real-time fault alarm of the prior art and system Untimely problems, realize trend analysis and fault warning of offshore wind turbine operation status.

In order to achieve the above object, the present invention adopts the following technical solutions:

An automatic fault diagnosis method for offshore wind turbines, characterized in that the diagnosis method comprises the following steps:

a. Install sensors for the monitoring items on the offshore wind turbines to be diagnosed;

b. Preprocess the signal extracted by the sensor to remove useless or interference signals contained in the extracted signal;

c. Carry out specific data analysis on the signal after the preprocessing, remove the false and preserve the true;

d. Compare and analyze the value obtained after the data analysis of the signal with the failure threshold set by the data analysis, and then determine whether the wind turbine is in failure;

e. If the obtained analysis data fails, that is, it has exceeded the set failure threshold, the system will give an alarm, and then determine the mechanism and characteristics of the failure of the wind turbine, and then determine the location of the failure, and finally make a decision, and decide to stop for maintenance Or continue to monitor; if there is no fault, the system will not alarm, and you can decide to continue the detection; if some data in the obtained analysis data shows that there may be a slight fault or wear of the wind turbine of the machine, you can according to the fault location and Relevant components carry out early warning or early protection.

The sensors in the step a include a low-frequency piezoelectric acceleration sensor, which is mainly used to monitor the main shaft of the generator set and the input bearing of the gearbox; a general-purpose piezoelectric acceleration sensor, which is mainly used to monitor the planetary gear train, output shaft and power generation of the gearbox. The vibration signal of the machine bearing; the speed sensor is mainly used to measure the speed of the fan main shaft and the speed of the generator rotor, the strain sensor and the displacement sensor are used for the displacement and overturning of the blade and the tower respectively.

An ad hoc network communication module is added to the existing digital interface and communication protocol of the sensor.

The wireless data acquisition system adopted by the diagnostic method includes a data acquisition device and a short-distance wireless receiving and transmitting device. The data acquisition device has a built-in ZigBee module conforming to the IEEE 802.15.4 wireless protocol, which can preprocess the collected data and eliminate A large amount of useless or interference data, and transmit the processing data to the short-distance wireless receiving and transmitting device. The wireless transmitting and receiving device and the ad hoc network communication module installed in the sensor are configured one by one through the data acquisition device to form a one-to-multipoint wireless distributed communication network. The wireless distributed communication network system can be scheduled or at any time Realize the automatic collection of real-time data of the measuring point. The data collection device has the functions of dormancy and background wake-up. It can be configured on-site or the data management terminal originally loads the interface protocol to execute background collection and send instructions in real time. The wireless transmitting and receiving device receives The data sent by the data acquisition device is transmitted to the user data analysis platform through WCDMA.

The data analysis and processing system in the diagnosis step c is a data analysis platform based on LabVIEW.

The data analysis platform method includes: vibration time domain parameter analysis includes amplitude domain analysis of signal and time domain analysis of signal; frequency domain analysis of vibration signal includes FFT spectrum analysis of signal, cepstrum analysis of signal and power spectrum analysis of signal ; Signal order spectrum analysis; Signal wavelet analysis.

The automatic fault diagnosis method for wind turbines provided by the present invention can monitor the operating status of wind turbines in real time online, and can give an alarm immediately when a fault occurs, avoiding serious consequences caused by untimely fault alarms; at the same time, through the data analysis Predict the operating status of wind turbines with the fault diagnosis module, diagnose existing or upcoming wind turbine operation or component failures, and promptly remind relevant units to carry out maintenance, improve the operating life of wind turbines, and reduce the failure rate of wind turbines , to reduce the economic loss caused by wind turbine failure.

Description of drawings

The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

Fig. 1 is the main schematic diagram of the automatic fault diagnosis method of the offshore wind turbine of the present invention;

Fig. 2 is a structural schematic diagram of an automatic fault diagnosis method for an offshore wind turbine according to the present invention.

detailed description

An automatic fault diagnosis method for offshore wind turbines disclosed by the present invention uses a wireless sensor data communication network and combines advanced computer information technology to solve the problem that existing technologies and systems do not provide timely alarms for real-time faults, and realizes fault diagnosis at sea. Wind turbine operating status trend analysis and fault warning.

As shown in Figure 1, an automatic fault diagnosis method for offshore wind turbines disclosed by the present invention, the main steps of the method include:

a. Install sensors for the monitoring items on the offshore wind turbines to be diagnosed;

b. Preprocess the signal extracted by the sensor to remove useless or interference signals contained in the extracted signal;

c. Carry out specific data analysis on the signal after the preprocessing, remove the false and preserve the true;

d. Compare and analyze the amplitude obtained after the data analysis of the signal with the failure threshold set by the data analysis, and then determine whether the wind turbine is in failure;

e. If the obtained analysis data fails, that is, it has exceeded the set failure threshold, the system will give an alarm, and then determine the mechanism and characteristics of the failure of the wind turbine, and then determine the location of the failure, and finally make a decision, and decide to stop for maintenance Or continue to monitor; if there is no fault, the system will not alarm, and you can decide to continue the detection; if some data in the obtained analysis data shows that there may be a slight fault or wear of the wind turbine of the machine, you can according to the fault location and Relevant components carry out early warning or early protection.

As shown in Figure 2, a specific embodiment of an automatic fault diagnosis method for an offshore wind turbine disclosed by the present invention firstly installs corresponding sensors on the components to be monitored, such as installing a low-frequency piezoelectric acceleration sensor on the generator set At the input bearing of the main shaft and the gearbox, install the universal piezoelectric acceleration sensor at the planetary gear train of the gearbox, the output shaft and the bearing of the generator, install the speed sensor at the main shaft of the fan and the rotor of the generator, and install the strain sensor with the At the tip, middle and root of the blade, displacement sensors are installed in the vertical and horizontal directions of the tower respectively; an ad hoc network communication module is added to the existing digital interface and communication protocol of the above sensors.

The sensor transmits the monitored data to the wireless data acquisition system.

The wireless data acquisition system preprocesses the received data, eliminates a large amount of useless or interference data, and transmits the data to the short-distance wireless receiving and transmitting device.

The above-mentioned wireless data acquisition system includes a data acquisition device and a short-distance wireless receiving and transmitting device. The data acquisition device has a built-in ZigBee module conforming to the IEEE 802.15.4 wireless protocol, and the ad hoc network communication module installed in the wireless transmitting and receiving device and the sensor Through the configuration of data acquisition devices one by one, a one-to-multipoint wireless distributed communication network is formed. The wireless distributed communication network system can realize automatic collection of real-time data of measuring points at regular intervals or at any time. The data acquisition device has sleep and background wake-up functions, which can Through the on-site configuration or the data management terminal originally loaded the interface protocol, real-time execution of background collection and sending instructions, the wireless transmitting and receiving device receives the data sent by the data collection device and transmits it to the user data analysis platform remotely through WCDMA.

The above-mentioned user data analysis platform is a data analysis platform based on LabVIEW, which can store, query and analyze the received data. After analyzing the data, the LabVIEW data analysis system includes time domain analysis, frequency domain analysis and other analysis methods to obtain The required number is compared and analyzed with the fault threshold of the wind turbine set by the system itself, and then it is determined whether the wind turbine is faulty.

When there is a fault, start the fault alarm system, and the staff can know the characteristics of the wind turbine emission fault and its specific cause and location according to the data of the alarm system provided by the system, and decide to take corresponding countermeasures to repair the fault; When there is no fault, the staff can also judge whether the wind turbine has slight wear or abnormality according to the data of the analysis system, and then take corresponding early protection and countermeasures; when there is no fault and all data are within the normal range, you can take The decision to let the system continue to monitor.

The technical content and technical characteristics of the present invention have been disclosed above, and those skilled in the art may still make replacements and modifications that do not deviate from the essence of the present invention based on the teachings of the present invention. Therefore, the protection scope of the present invention is not limited to those disclosed in the embodiments. The content also includes various substitutions and modifications that do not depart from the essence of the present invention.

Claims (6)

1. a kind of automation method for diagnosing faults of offshore wind farm unit, it is characterised in that the diagnostic method includes following step Suddenly：

A, the sensor for wanting in the offshore wind farm units' installation to be diagnosed monitoring project；

B, the signal extracted to sensor are pre-processed, and remove the useless or interference signal extracted and contained in signal；

C, specific data analysis is carried out to the pretreated later signal of described progress, eliminated the false and retained the true；

D, the fault threshold for setting institute's value and data analysis after signal progress data analysis are analyzed, then really Whether Wind turbines are determined in failure；

If e, income analysis data break down exceedes set fault threshold, system is alarmed, it is then determined that wind Mechanism and the fault signature of appearance that motor breaks down, and then determine location of fault occur, decision-making is finally carried out, decision-making is entered Row maintenance down is to continue with monitoring；If do not broken down, system is not alarmed, then can proceed inspection with decision-making Survey；If having partial data to show in income analysis data, there is slight failure or mill in possible machine Wind turbines Damage, then can be given warning in advance or protected in advance according to trouble location and associated components.

2. automation method for diagnosing faults according to claim 1, it is characterised in that：Sensor bag in the step a Low frequency piezoelectric acceleration sensor is included, is mainly used in monitoring the main shaft of generating set and the input shaft bearing of gear-box；Universal pressure Electric acceleration transducer, is mainly used in monitoring the vibration signal of gearbox planetary train, output shaft and dynamo bearing；Rotating speed is passed Sensor, is mainly used in monitoring the rotating speed of blower fan main shaft and the rotating speed of generator amature；Strain transducer and displacement transducer, respectively Displacement for blade and tower, topple.

3. automation method for diagnosing faults according to claim 2, it is characterised in that：In the existing number of described sensor Word interface and communication protocol install self-organized network communication module additional.

4. automation method for diagnosing faults according to claim 1, it is characterised in that：It is wireless that the diagnostic method is used Data collecting system includes data acquisition device and short-distance wireless receiving and transmitting unit, meets built in the data acquisition device The data gathered can be pre-processed by the ZigBee module of IEEE 802.15.4 wireless protocols, be rejected substantial amounts of useless Or interference data, and processing data is transferred to short-distance wireless receiving and transmitting unit；Described wireless transmitter receiver dress Put and configured one by one by data acquisition device with the self-organized network communication module installed additional in sensor, form wireless point of a pair of multiple spots Cloth communication network, described wireless distributed network system can timing or realize at any time the automatic of measuring point real time data is adopted Collection, described data acquisition device has dormancy and backstage arousal function, can be original by situ configuration or data management end Loading interface agreement, performs background acquisition and sends instruction, wireless transmitting and receiving device receives data acquisition device and sent in real time Data after Users'Data Analysis platform is transferred to by WCDMA.

5. automation method for diagnosing faults according to claim 1, it is characterised in that：Data in the diagnosis algorithm c Analysis process system is the Data Analysis Platform based on LabVIEW.

6. method for diagnosing faults is automated according to claim 1 or 5, it is characterised in that：Described Data Analysis Platform Method includes：Vibration time domain parameter analysis includes the time-domain analysis of the amplitude domain analysis and signal of signal；Vibration signal frequency-domain analysis The power spectrumanalysis of FFT spectrum analysis, the cepstrum analysis of signal and signal including signal；Signal rank spectrum analysis；Signal Wavelet analysis.