Fan cabin detection method and system

Abstract

The invention provides a fan cabin detection method and system. The fan cabin detection method comprises the following steps: acquiring satellite signals, meteorological data and inertia measurement dynamic data; according to the satellite signal, real-time dynamic differential coordinate data are obtained through a real-time dynamic differential algorithm, and meteorological error calibration data are obtained according to the meteorological data; and according to the real-time dynamic differential coordinate data, the meteorological error calibration data and the inertia measurement dynamic data, obtaining position and attitude data of the fan cabin. According to the invention, accurate real-time monitoring can be realized while the number of sensors is reduced.

Description

TECHNICAL FIELD

[0001] The application relates to the field of fans, in particular to a fan nacelle detection method and system. BACKGROUND

[0002] The fan nacelle is one of the core components of a wind turbine generator set, which is usually a cabin structure installed on the top of the tower, mainly used for containing and protecting the key equipment of the wind turbine generator set, supporting the impeller and converting mechanical energy into electrical energy for transmission to the power grid. Due to factors such as extreme weather or long operation time, the fan nacelle may be damaged. In order to comprehensively and timely monitor the operation state and safety of the fan, the fan safety monitoring system in the prior art usually integrates a large number of sensors including temperature sensors, vibration sensors, accelerometers, wind speed and direction sensors and the like. The signal interference or data redundancy between a large number of sensors affects the real-time monitoring accuracy, and the position, posture and other information of the fan nacelle cannot be accurately and timely monitored.

[0003] It can be seen that how to accurately and timely monitor the fan nacelle while reducing the number of sensors has become a technical problem that technicians in the field need to solve. SUMMARY

[0004] In order to overcome the defects of the prior art, the application provides a fan nacelle detection method, which comprises the following steps: acquiring satellite signals, meteorological data and inertial measurement dynamic data; obtaining real-time dynamic difference coordinate data by a real-time dynamic difference algorithm according to the satellite signals, and obtaining meteorological error calibration data according to the meteorological data; obtaining fan nacelle position and posture data according to the real-time dynamic difference coordinate data, the meteorological error calibration data and the inertial measurement dynamic data.

[0005] The application also provides a fan nacelle detection system, which comprises the following: a fan monitoring station comprising a satellite monitoring station receiver, a meteorological sensor, an inertial sensor and a real-time dynamic difference algorithm edge solution system, the satellite monitoring station receiver being used to acquire satellite signals, the meteorological sensor being used to acquire meteorological data, and the inertial sensor being used to acquire inertial measurement dynamic data; a field station reference station comprising a satellite reference station receiver and a real-time dynamic difference algorithm edge solution system, the satellite reference station receiver being used to receive satellite signals; The real-time dynamic difference algorithm edge solution systems of the fan monitoring station and the field station reference station are used to solve the satellite signals to obtain real-time dynamic difference coordinate data by a real-time dynamic difference algorithm, and obtain fan nacelle position and posture data in combination with the meteorological error calibration data and the inertial measurement dynamic data.

[0006] The fan nacelle detection method provided by the application obtains satellite signals, meteorological data and inertial measurement dynamic data, obtains real-time dynamic difference coordinate data by using a real-time dynamic difference algorithm according to the satellite signals, obtains meteorological error calibration data according to the meteorological data, and obtains fan nacelle position and attitude data according to the real-time dynamic difference coordinate data, the meteorological error calibration data and the inertial measurement dynamic data. In this way, the satellite signals are used, and a small amount of sensor data is obtained, so that accurate real-time monitoring can be realized while reducing the number of sensors. In addition, the complexity and cost of the system are reduced. BRIEF DESCRIPTION OF DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0008] Figure 1 A flowchart of the fan nacelle detection method provided by the application; Figure 2 A schematic diagram of the fan nacelle detection system provided by the application. DETAILED DESCRIPTION

[0009] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0010] The present application will be described in detail below with reference to the drawings. Please refer to Figure 1 , Figure 1 A flowchart of the fan nacelle detection method provided by the application.

[0011] As shown in Figure 1 , in step S1, satellite signals, meteorological data and inertial measurement dynamic data are obtained.

[0012] The satellite signals include carrier signals, and the frequency and initial phase of the carrier signals are known. In some embodiments, the satellite is a Beidou satellite of China.

[0013] The meteorological data is the meteorological data of the location of the satellite signal receiver, and in some embodiments, the meteorological data includes temperature, humidity, and air pressure. The temperature, humidity, and air pressure are the main factors affecting signal propagation in the troposphere.

[0014] The inertial measurement dynamic data is the dynamic data of the fan cabin, and in some embodiments, can include acceleration and angular velocity data.

[0015] In step S2, real-time dynamic difference coordinate data is obtained from the satellite signal by a real-time dynamic difference algorithm, and meteorological error calibration data is obtained from the meteorological data.

[0016] The real-time dynamic difference data is the position data of the fan cabin obtained by processing the satellite signal by a real-time dynamic difference algorithm. The real-time dynamic difference algorithm is an RTK algorithm that includes positioning calculation. Specifically, after the fan cabin receives the satellite signal, it performs differential elimination of the carrier in the satellite signal with a reference station that also receives the satellite signal and has a known and determined position to eliminate common errors and determine the distance between the satellite and the receiver. The common errors include satellite orbit errors, satellite clock errors, and atmospheric delays. The specific formula is Φ = f × Δt + N × 2π, where Φ is the carrier phase observation, f is the carrier frequency, Δt is the signal propagation time difference, and N is the integer ambiguity, which represents the carrier integer number that cannot be directly observed. Since the wavelength of the carrier is short (about 19 cm), the RTK algorithm positioning calculation can calculate the precise difference of the carrier phase, enabling centimeter-level positioning of the fan cabin, and the position refers to three-dimensional coordinates. In some embodiments, the positioning calculation can be a least squares method. In other embodiments, the positioning calculation can be a Kalman filter method. In some embodiments, the field station reference station can calculate the error between the fan monitoring station and the fan monitoring station, and send the difference data to the fan monitoring station. After receiving the difference data, the fan monitoring station calculates the positioning data using the RTK algorithm.

[0017] Although the real-time dynamic difference algorithm can eliminate the common errors between the reference station and the fan cabin, the temperature, humidity, and air pressure of the troposphere above the reference station and the fan cabin can be different due to the large regional differences in the troposphere. Therefore, in order to further improve the accuracy of the monitoring data, meteorological data is introduced to obtain meteorological error calibration data for error calibration. The specific formula is δtrop = f(meteorological parameters, satellite elevation angle, geographic location, etc.), where δtrop is the position error calibration data.

[0018] In step S3, fan cabin position and attitude data is obtained from the real-time dynamic difference coordinate data, meteorological error calibration data, and inertial measurement dynamic data.

[0019] The three data are fused to realize accurate monitoring of the running state of the fan.

[0020] In some embodiments, the step S3 obtains the fan nacelle position and attitude data according to the real-time dynamic differential coordinate data, the meteorological error calibration data and the inertial measurement dynamic data, and includes: In step S31, different weights are respectively given to the real-time dynamic differential coordinate data, the meteorological error calibration data and the inertial measurement dynamic data. In step S32, the initial nacelle data is obtained by fusing the weights. In step S33, the fan nacelle position and attitude data are obtained by smoothing the initial nacelle data through a filtering algorithm.

[0021] The fan nacelle detection method provided by the application obtains satellite signals, meteorological data and inertial measurement dynamic data, obtains real-time dynamic differential coordinate data by using a real-time dynamic differential algorithm according to the satellite signals, obtains meteorological error calibration data according to the meteorological data, and obtains fan nacelle position and attitude data according to the real-time dynamic differential coordinate data, the meteorological error calibration data and the inertial measurement dynamic data. In this way, the satellite signals are used, and a small amount of sensor data is obtained, so that accurate real-time monitoring can be realized while reducing the number of sensors. In addition, the complexity and cost of the system are also reduced.

[0022] In some embodiments, the fan nacelle detection method provided by the application further includes: In step S4, it is judged whether the fan nacelle position and attitude data are abnormal values, if yes, step S5 is executed, and if no, step S1 is executed.

[0023] In some embodiments, the judgment of whether the fan nacelle position and attitude data are abnormal values includes: It is judged whether the fan nacelle position and attitude data exceed a preset threshold.

[0024] In other embodiments, the judgment of whether the fan nacelle position and attitude data are abnormal values includes: The fan nacelle position and attitude data are input into a pre-trained deep learning model to judge whether they are abnormal data.

[0025] In step S5, a warning mechanism is triggered.

[0026] Specifically, it can be an alarm, a notification or a log record, so that the operation and maintenance personnel can discover and handle the fault in time.

[0027] The application further provides a fan nacelle detection system, which includes: The wind turbine monitoring station comprises a satellite monitoring station receiver, a meteorological sensor, an inertial sensor and a real-time dynamic difference algorithm edge solution system. The station reference station comprises a satellite reference station receiver and a real-time dynamic difference algorithm edge solution system. The real-time dynamic difference algorithm edge solution system of the wind turbine monitoring station and the real-time dynamic difference algorithm edge solution system of the station reference station are used to obtain real-time dynamic difference coordinate data by solving the satellite signals through a real-time dynamic difference algorithm, and obtain wind turbine cabin position and attitude data by combining the meteorological error calibration data and the inertial measurement dynamic data.

[0028] In some embodiments, the satellite monitoring station receiver is a dual-antenna receiver.

[0029] In some embodiments, the real-time dynamic difference algorithm edge solution system is built into the satellite reference station receiver.

[0030] The wind turbine cabin detection system provided by the application comprises a wind turbine monitoring station and a station reference station, and obtains satellite signals, meteorological data and inertial measurement dynamic data, obtains real-time dynamic difference coordinate data by using a real-time dynamic difference algorithm according to the satellite signals, obtains meteorological error calibration data according to the meteorological data, and obtains wind turbine cabin position and attitude data according to the real-time dynamic difference coordinate data, the meteorological error calibration data and the inertial measurement dynamic data. In this way, satellite signals are used, and a small amount of sensor data is obtained, so that accurate and real-time monitoring can be realized while reducing the number of sensors. In addition, the complexity and cost of the system are reduced.

[0031] In order to better communicate, in some embodiments, the wind turbine cabin detection system provided by the application further comprises a dual-measuring antenna for attitude and direction finding, and a communication antenna; and the station reference station further comprises a 3D choke high-precision positioning antenna and a communication antenna.

[0032] In some embodiments, the wind turbine cabin detection system provided by the application further comprises a judgment and alarm module for judging whether the wind turbine cabin position and attitude data are abnormal values, and triggering a pre-warning mechanism when the wind turbine cabin position and attitude data are abnormal values.

Claims

1. A method of detecting a fan engine, characterized by, The method comprises the following steps: acquiring satellite signals, meteorological data, and inertial measurement dynamic data; obtaining real-time dynamic difference coordinate data from the satellite signals by using a real-time dynamic difference algorithm, and obtaining meteorological error calibration data from the meteorological data; obtaining wind turbine nacelle position and attitude data from the real-time dynamic difference coordinate data, the meteorological error calibration data, and the inertial measurement dynamic data.

2. The fan nacelle detection method of claim 1, wherein, The method of obtaining wind turbine nacelle position and attitude data from the real-time dynamic difference coordinate data, the meteorological error calibration data, and the inertial measurement dynamic data comprises the following steps: assigning different weights to the real-time dynamic difference coordinate data, the meteorological error calibration data, and the inertial measurement dynamic data, respectively; combining the weights to obtain initial nacelle data; smoothing the initial nacelle data by using a filtering algorithm to obtain wind turbine nacelle position and attitude data.

3. The fan nacelle detection method of claim 1, wherein, The inertial measurement dynamic data comprises acceleration and angular acceleration.

4. The fan nacelle detection method of any of claims 1-3, wherein, The method further comprises the following steps: judging whether the wind turbine nacelle position and attitude data is an abnormal value; triggering a warning mechanism when the wind turbine nacelle position and attitude data is an abnormal value.

5. The fan nacelle detection method of claim 4, wherein, The method of judging whether the wind turbine nacelle position and attitude data is an abnormal value comprises the following steps: inputting the wind turbine nacelle position and attitude data into a pre-trained deep learning model to judge whether it is abnormal data.

6. The fan nacelle detection method of any of claims 1-3, wherein, The satellite is a Beidou satellite.

7. A fan nacelle detection system, characterized in that, The method comprises the following steps: a wind turbine monitoring station comprising a satellite monitoring station receiver, a meteorological sensor, an inertial sensor, and a real-time dynamic difference algorithm edge calculation system, the satellite monitoring station receiver being used to acquire satellite signals, the meteorological sensor being used to acquire meteorological data, and the inertial sensor being used to acquire inertial measurement dynamic data; a field station reference station comprising a satellite reference station receiver and a real-time dynamic difference algorithm edge calculation system, the satellite reference station receiver being used to receive satellite signals; the real-time dynamic difference algorithm edge calculation systems of the wind turbine monitoring station and the field station reference station are used to calculate real-time dynamic difference coordinate data from the satellite signals by using a real-time dynamic difference algorithm, and to obtain wind turbine nacelle position and attitude data in combination with the meteorological error calibration data and the inertial measurement dynamic data.

8. The fan nacelle detection system of claim 7, wherein, The satellite monitoring station receiver is a dual-antenna receiver.

9. The fan nacelle detection system of claim 8, wherein, The wind turbine monitoring station further comprises a dual-measurement antenna for attitude and direction finding, and a communication antenna. The field station reference station further comprises a 3D choke high-precision positioning antenna and a communication antenna.

10. The fan nacelle detection system of claim 9, wherein, The method further comprises the following steps: a judging alarm module for judging whether the wind turbine nacelle position and attitude data is an abnormal value; a warning mechanism is triggered when the wind turbine nacelle position and attitude data is an abnormal value.

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