WO2020007375A1

WO2020007375A1 - Continuous monitoring-based method and device for identifying 1p signal of wind turbine, terminal, and computer readable storage medium

Info

Publication number: WO2020007375A1
Application number: PCT/CN2019/098094
Authority: WO
Inventors: 胡卫华; 滕军; 张笑
Original assignee: 哈尔滨工业大学（深圳）
Priority date: 2018-07-06
Filing date: 2019-07-29
Publication date: 2020-01-09
Also published as: CN109100103B; CN109100103A

Abstract

A continuous monitoring-based method for identifying a 1p signal of a wind turbine, comprising: acquiring the structural dynamics response of a wind turbine (A); decomposing the structural dynamics response of the wind turbine into several intrinsic mode functions (B); transforming each intrinsic mode function so as to obtain the instantaneous frequency corresponding to said function, and calculating the instantaneous frequency average for each intrinsic mode function (C); treating intrinsic mode functions of which the instantaneous frequency average is in a low frequency segment as valid intrinsic mode functions, and using the sum of said valid intrinsic mode functions as the target intrinsic mode function, said low frequency segment being a frequency range below 1Hz (D); identifying the frequency corresponding to the target intrinsic mode function and establishing a stability graph, and extracting the 1p frequency on the basis of the stability graph (E). Also disclosed is a continuous monitoring-based device for identifying a 1p signal of a wind turbine, comprising an acquisition module (110), a decomposition module (120), a transformation module (130), a screening module (140), and an identification module (150). Also disclosed are a continuous monitoring-based terminal and computer readable storage medium for identifying a 1p signal of a wind turbine. The present5 invention can effectively identify a 1p signal and extract the 1p frequency, thereby providing effective and reliable automated means for wind turbine structure monitoring.

Description

Method, device, terminal and computer-readable storage medium for fan 1p signal recognition based on continuous monitoring

Cross-reference to related applications

This application requires a Chinese patent application filed with the Chinese Patent Office on July 06, 2018 under the application number CN201810738808.8 and titled "Continuous Monitoring-based Fan 1p Signal Identification Method, Device, Terminal, and Computer-readable Storage Medium" Priority, the entire contents of which are incorporated herein by reference.

Technical field

The present application belongs to the technical field of fan modal analysis, and specifically relates to a method, a device, a terminal, and a computer-readable storage medium for fan 1p signal recognition based on continuous monitoring.

Background technique

With the increase of people's awareness of environmental protection, the demand for clean energy is increasing, which brings good development opportunities to the wind power industry. The wind turbines are mostly built in unmanned areas, where the wind speed is high and the environment is harsh, making it difficult to reach the site for maintenance. Especially for offshore wind turbines, the environment is more severe and harder to reach, making measurement and maintenance face huge challenges.

In order to achieve continuous monitoring of wind turbines, some online monitoring systems have emerged. At present, the online monitoring system is mostly used to monitor the 3p (three-speed excitation frequency) and 6p (six-speed excitation frequency) and other signals. defect.

Summary of the invention

In order to overcome the shortcomings of the prior art, this application provides a method, device, terminal, and computer-readable storage medium for fan 1p signal recognition based on continuous monitoring, which can effectively identify 1p signals and extract 1p frequency, providing effective for wind turbine structure monitoring. And reliable means of automation.

The purpose of this application is achieved by the following technical solutions:

A method for identifying a 1p signal of a fan based on continuous monitoring includes:

Obtain the structural dynamic response of the fan;

Decomposing the structural dynamic response of the wind turbine into several eigenmode functions;

Transform each eigenmode function to obtain the instantaneous frequency corresponding to it, and calculate the mean instantaneous frequency value of each eigenmode function;

The eigenmode function whose average instantaneous frequency is located in the low frequency section is taken as the effective eigenmode function, and the sum of the effective eigenmode functions is used as the target eigenmode function, the low frequency section is Frequency range below 1Hz;

The frequency corresponding to the target eigenmode function is identified and a steady state map is established, and a 1p frequency is extracted according to the steady state map.

As an improvement of the above technical solution, the "identifying the frequency corresponding to the target eigenmode function and establishing a steady state map" includes:

Establish discrete state space equations;

Calculating the fan frequency, damping ratio and modal shape according to the discrete state space equation and the structural dynamic response of the fan;

A steady state diagram is established according to the frequency and modal shape of the fan.

As a further improvement of the above technical solution, the transform is a Hilbert transform.

As a further improvement of the above technical solution, the structural dynamic response of the wind turbine is an acceleration, speed or displacement response.

As a further improvement of the above technical solution, "obtaining the structural dynamic response of the wind turbine" includes:

Determine the time constant period of the fan;

Obtain the structural dynamic response of the wind turbine in the time-invariant period.

A fan 1p signal identification device based on continuous monitoring includes:

An acquisition module configured to acquire a structural dynamic response of the wind turbine;

A decomposition module configured to decompose the structural dynamic response of the wind turbine into several eigenmode functions;

A transformation module configured to transform each eigenmode function to obtain a corresponding instantaneous frequency, and calculate an average value of the instantaneous frequency of each eigenmode function;

A screening module configured to use the eigenmode function with the instantaneous frequency mean value located in a low frequency section as the effective eigenmode function, and use the sum of the effective eigenmode functions as the target eigenmode function, so The low frequency range is a frequency range below 1 Hz;

The identification module is configured to identify a frequency corresponding to the target eigenmode function and establish a steady state map, and extract a 1p frequency according to the steady state map.

As an improvement of the above technical solution, the transform is a Hilbert transform.

As a further improvement to the above technical solution, the identification module includes:

A modeling sub-module configured to establish a discrete state space equation;

An identification sub-module configured to calculate a frequency, a damping ratio, and a modal shape of the fan according to the discrete state space equation and a structural dynamic response of the fan;

A steady-state graph sub-module configured to establish a steady-state graph according to the frequency and modal shape of the fan;

An extraction submodule configured to extract a 1p frequency according to the steady-state map.

A terminal includes a memory and a processor. The memory is configured to store a computer program, and the processor executes the computer program to enable the terminal to implement the continuous monitoring-based fan 1p signal identification method according to any one of the above. .

A computer-readable storage medium stores the computer program executed by the terminal.

The beneficial effects of this application are:

The corresponding Hilbert spectrum is obtained by decomposing and transforming the structural dynamic response of the wind turbine, and the eigenmode function corresponding to the highest concentration in the low frequency range is selected as the target eigenmode function. The eigenmode function is used to identify the corresponding frequency and establish a steady-state map. The 1p frequency is extracted according to the steady-state map to realize the automatic recognition of the 1p signal, which has significant effectiveness and recognition accuracy.

In order to make the above-mentioned objects, features, and advantages of this application more comprehensible, preferred embodiments are described below in conjunction with the accompanying drawings and described in detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to explain the technical solutions of the embodiments of the present application more clearly, the drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application, so It should be regarded as a limitation on the scope. For those of ordinary skill in the art, other related drawings can be obtained according to these drawings without paying creative work.

1 is a flowchart of a method for identifying a 1p signal of a fan based on continuous monitoring according to Embodiment 1 of the present application;

FIG. 2 is a flowchart of step A of a method for identifying a fan 1p signal based on continuous monitoring according to Embodiment 1 of the present application;

3 is a schematic diagram of an axonometric structure of sensors involved in a method for identifying a fan 1p signal based on continuous monitoring according to Embodiment 1 of the present application;

4 is a time-domain distribution diagram of an acceleration response based on a continuous monitoring of a 1p signal recognition method of a fan provided in Embodiment 1 of the present application;

FIG. 5 is a time-domain distribution diagram of an eigenmode function based on a continuous monitoring of a fan 1p signal recognition method provided in Embodiment 1 of the present application; FIG.

6 is a flowchart of step E of a method for identifying a fan 1p signal based on continuous monitoring provided in Embodiment 1 of the present application;

7 is a steady state diagram obtained by the method for identifying a 1p signal of a fan based on continuous monitoring provided in Embodiment 1 of the present application;

8 is a steady state diagram obtained based on a conventional method;

9 is a Campbell diagram obtained by the method for identifying a 1p signal of a wind turbine based on continuous monitoring provided in Embodiment 1 of the present application;

FIG. 10 is a schematic structural diagram of a fan 1p signal recognition device based on continuous monitoring provided in Embodiment 2 of the present application;

11 is a schematic structural diagram of an acquisition module of a fan 1p signal identification device based on continuous monitoring provided in Embodiment 2 of the present application;

12 is a schematic structural diagram of an identification module of a fan 1p signal identification device based on continuous monitoring provided in Embodiment 2 of the present application;

FIG. 13 is a schematic structural diagram of a terminal provided in Embodiment 3 of the present application.

Explanation of main component symbols:

100-continuous monitoring-based fan 1p signal identification device, 110-acquisition module, 111-cycle submodule, 112-acquisition submodule, 120-decomposition module, 130-transformation module, 140-screening module, 150-identification module, 151 -Modeling sub-module, 152-recognition sub-module, 153-steady-state graph sub-module, 154-extraction sub-module, 200-terminal, 210-memory, 220-processor, 230-input unit, 240-display unit, 300 -Fan, 310-tower, 320-cabin, 400-sensor.

detailed description

In order to facilitate the understanding of the present application, a method, a device, a terminal, and a computer-readable storage medium for identifying a fan 1p signal based on continuous monitoring will be described more fully below with reference to related drawings. Preferred embodiments of a method, a device, a terminal, and a computer-readable storage medium for identifying a fan 1p signal based on continuous monitoring are shown in the drawings. However, the method, device, terminal, and computer-readable storage medium for fan 1p signal recognition based on continuous monitoring can be implemented in many different forms and are not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the method, device, terminal and computer-readable storage medium of the fan 1p signal recognition based on continuous monitoring more thorough and comprehensive.

It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a centered element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or intervening elements may be present concurrently. In contrast, when an element is referred to as being "directly on" another element, there are no intervening elements present. The terms "vertical", "horizontal", "left" and "right" and similar expressions used herein are for illustrative purposes only.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the method, device, terminal, and computer-readable storage medium for fan 1p signal recognition based on continuous monitoring herein is only for the purpose of describing specific embodiments and is not intended to limit the application. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

Example 1

Referring to FIG. 1, this embodiment provides a method for identifying a 1p signal of a fan based on continuous monitoring. The method includes steps A to E:

Step A: Obtain the structural dynamic response of the fan 300. Among them, the structural dynamic response refers to the speed, acceleration, and displacement generated by the fan 300 under the dynamic load. In practical applications, the structural dynamic response to be obtained may be an acceleration signal collected by an acceleration sensor, or a speed signal or a displacement signal measured by a displacement sensor.

Exemplarily, in this embodiment, the structural dynamic response of the fan 300 is an acceleration response. Referring to FIG. 2, under continuous monitoring conditions, step A may include steps A1 to A2:

Step A1: Determine the time constant period of the fan 300. Among them, the time constant period is determined according to the rotation speed of the fan 300, the pitch angle and the turning angle of the nacelle 320. The fan 300 needs to adjust the relevant parameters such as the rotation speed, the pitch angle, and the cabin 320 in real time according to the wind direction and wind speed to ensure better energy conversion efficiency, which belongs to a time-varying structure that is difficult to accurately measure. For effective and accurate monitoring, a time-invariant cycle needs to be determined. During the time-invariant period, the fan 300 conforms to the time-invariant assumption, that is, its structural characteristics can be considered to not change with time.

Step A2: Obtain the structural dynamic response of the fan 300 in the time-invariant period.

Please refer to FIG. 3. In an actual monitoring example, the tower 310 of the wind turbine 300 is divided into three parts along the height, and the acquisition units are respectively arranged at four points of the three parts. Among them, the bottom end of the tower 310 is 4Q. In a simple structure, only one sensor 400 needs to be arranged on the periphery of the 4Q to realize the structural dynamic response collection. Exemplarily, the sensor 400 may be an acceleration sensor or a displacement sensor. Additionally, FIG. 4 shows the acceleration response signal collected by the sensor 400.

Step B: Decompose the structural dynamic response of the wind turbine 300 into several eigenmode functions. The eigenmode function, Intrinsic Mode Function (imf), should satisfy the following conditions: (1) the number of extreme points of the function and the zero crossings are equal or at most one difference over the entire signal length; (2) at any time, The average value of the upper envelope defined by the maximum value of the function and the lower envelope defined by the minimum value is zero, that is, the upper and lower envelopes are symmetrical to the time axis. In addition, FIG. 5 shows time-domain distribution diagrams (imf1 to 6) of each eigenmode function obtained by decomposition.

Step C: Transform each eigenmode function to obtain a corresponding instantaneous frequency, and calculate the mean instantaneous frequency value of each eigenmode function. Exemplarily, the transform is a Hilbert transform. It should be understood that after each eigenmode function undergoes Hilbert transformation, a series of instantaneous frequencies will be obtained. The mean value of the instantaneous frequency of the eigenmode function is obtained by averaging the instantaneous frequencies corresponding to the same eigenmode function.

Step D: Use the eigenmode function whose mean frequency is in the low frequency region as the effective eigenmode function, and use the sum of the effective eigenmode functions as the target eigenmode function. The band is a frequency range below 1 Hz.

In other words, the eigenmode functions that satisfy the following conditions are all valid eigenmode functions: the mean instantaneous frequency is located in the low frequency section. Then add all the effective eigenmode functions, and the sum is the target eigenmode function.

Step E: Identify the frequency corresponding to the target eigenmode function and establish a steady-state map, and extract a 1p frequency according to the steady-state map. Among them, the steady state diagram is used to indicate the position of the poles of the system. Since the poles are the global characteristics of the system (represented by the frequency of the wind turbine 300 here), as the model order increases, the system poles extracted by the mathematical model of the order increase will appear repeatedly and be characterized on the same graph in order to Find the physical poles of the structure by observing the pole distribution. Furthermore, by performing feature extraction on the steady-state graph, a frequency of 1p can be obtained. It should be understood that the 1p frequency is less than the fundamental frequency of the fan 300.

Referring to FIG. 6, for example, step E includes steps E1 to E4:

Step E1: Establish a discrete state space equation. The discrete state space equation is established based on the discretized structural dynamic response, and a mathematical relationship is established between the structural dynamic response of the wind turbine 300 and the frequency, damping ratio, and modal shape. In this embodiment, the specific form of the discrete state space equation is:

Where x _k is a discrete state vector, y _k is a discrete output vector, w _k and ν _k are white noise terms, ε is one of the acceleration, speed, and displacement of the fan 300, A _ε is a discrete state matrix, C _ε is a discrete output matrix.

Step E2: Calculate the frequency, damping ratio and modal shape of the fan 300 according to the discrete state space equation and the structural dynamic response of the fan 300.

Step E3: Establish a steady-state map according to the frequency and modal shapes of the fan 300. FIG. 7 shows an actual steady state diagram obtained by performing step E3.

Step E4: Extract the 1p frequency according to the steady-state map. According to FIG. 7, the fundamental frequency of the fan 300 can be accurately extracted to be 0.6 Hz, and the 1p frequency is 0.41 Hz. FIG. 8 shows a steady-state map obtained based on the conventional method. The steady-state map lacks an obvious extreme point distribution in a low frequency region, and it is difficult to extract a 1p frequency. The steady-state map (Figure 7) obtained based on the continuous monitoring of the 1p signal recognition method of the fan provided in this embodiment has a significant and concentrated extreme point distribution in the low-frequency region, which provides reliable observation and extraction of the 1p frequency. The basics.

It is added that the 1p frequency is caused by slight quality differences between the leaves. When the blade rotates at a high speed, the slight mass difference will generate an excitation force with a fixed frequency to the wind tower, and the fixed frequency is an excitation frequency with a double speed.

Based on the aforementioned recognition results, further correlation analysis can be performed. For example, the average speed of the fan 300 during the time-invariant period is calculated, and a required number of frequency-average speed numbers are obtained, and a Campbell diagram is established according to the frequency-average speed numbers. Specifically, a preset number of time-invariant periods are continuously monitored, and the frequency and average speed of the fan 300 corresponding to each time-invariant period are obtained, so as to obtain a frequency-average speed number pair. According to a plurality of frequency-average speed number pairs, a Campbell diagram based on continuous monitoring can be established to analyze the structural mode of the fan 300.

For example, based on continuous acceleration monitoring for up to two years, a Campbell diagram obtained from long-term monitoring can be established. FIG. 9 shows a Campbell diagram obtained based on continuous monitoring of acceleration. According to the Campbell diagram, the 1p frequency (0.41 Hz) of the fan 300 can be obtained.

Example 2

Referring to FIG. 10, this embodiment provides a device 1p signal identification device 100 based on continuous monitoring. The device includes:

An obtaining module 110 configured to obtain a structural dynamic response of the wind turbine 300;

A decomposition module 120 configured to decompose the structural dynamic response of the wind turbine 300 into several eigenmode functions;

The transformation module 130 is configured to transform each eigenmode function to obtain an instantaneous frequency corresponding thereto, and calculate an average instantaneous frequency value of each eigenmode function;

The screening module 140 is configured to use the eigenmode function with the instantaneous frequency mean value located in the low-frequency section as the effective eigenmode function, and use the sum of the effective eigenmode functions as the target eigenmode function, The low frequency section is a frequency range below 1 Hz;

The identification module 150 is configured to identify a frequency corresponding to the target eigenmode function and establish a steady-state map, and extract a 1p frequency according to the steady-state map.

Referring to FIG. 11, for example, the obtaining module 110 includes:

A period sub-module 111 configured to determine a time-invariant period of the fan 300;

The obtaining sub-module 112 is configured to obtain a structural dynamic response of the wind turbine 300 in the time-invariant period.

Referring to FIG. 12, for example, the identification module 150 includes:

A modeling sub-module 151 configured to establish a discrete state space equation;

An identification submodule 152 configured to calculate a frequency, a damping ratio, and a modal shape of the fan 300 according to the discrete state space equation and the structural dynamic response of the fan 300;

A steady-state map sub-module 153 configured to establish a steady-state map according to the frequency and modal shapes of the fan 300;

The extraction sub-module 154 is configured to extract a 1p frequency according to the steady-state map.

Example 3

13, this embodiment provides a terminal 200. The terminal 200 includes a memory 210 and a processor 220. The memory 210 is configured to store a computer program, and the processor 220 executes the computer program to enable the terminal 200 to implement the continuous-based Identification method of the monitored 1p signal of the fan.

The terminal 200 includes terminal devices (such as computers and servers) that do not have mobile communication capabilities, and also includes mobile terminals (such as smart phones, tablet computers, on-board computers, and smart wearable devices).

The memory 210 may include a storage program area and a storage data area. The storage program area can store an operating system and applications required for at least one function (such as a sound playback function and an image playback function), and the like; the storage data area can store data (such as audio data and Backup files, etc.). In addition, the memory 210 may include a high-speed random access memory, and may further include a non-volatile memory (for example, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage device).

Preferably, the terminal 200 further includes an input unit 230 and a display unit 240. The input unit 230 is configured to receive various instructions or parameters (including a preset scrolling method, a preset time interval, and a preset scrolling number) input by a user, including a mouse, a keyboard, a touch panel, and other input devices. The display unit 240 is configured to display various output information (including a webpage page and a parameter configuration interface, etc.) of the terminal 200, including a display panel.

A computer-readable storage medium is also provided, which stores the computer program executed by the terminal 200.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and structure diagrams in the accompanying drawings show the possible architectures and functions of the devices, methods, and computer program products according to various embodiments of the present application. And operation. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, which contains one or more components for implementing a specified logical function Executable instructions.

It should also be noted that in alternative implementations, the functions labeled in the blocks may also occur in a different order than those labeled in the figures. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved.

It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart can be a dedicated hardware-based system that performs a specified function or action To achieve, or can be implemented by a combination of dedicated hardware and computer instructions.

In addition, the functional modules or units in the various embodiments of the present application may be integrated together to form an independent part, or each of the modules may exist separately, or two or more modules may be integrated to form an independent part.

If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially a part that contributes to the existing technology or a part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a smart phone, a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiments of the present application. The foregoing storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks and other media that can store program codes .

In all the examples shown and described herein, any specific value should be construed as exemplary only and not as a limitation, so other examples of the exemplary embodiments may have different values.

It should be noted that similar reference numerals and letters indicate similar items in the following drawings, so once an item is defined in one drawing, it need not be further defined and explained in subsequent drawings.

The above-mentioned embodiments only express several implementation manners of the present application, and the descriptions thereof are more specific and detailed, but cannot be understood as limiting the scope of the present application. It should be noted that, for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of this application shall be subject to the appended claims.

Claims

A method for identifying a 1p signal of a fan based on continuous monitoring is characterized in that it includes:

Obtain the structural dynamic response of the fan;

Decomposing the structural dynamic response of the wind turbine into several eigenmode functions;

Transform each eigenmode function to obtain the instantaneous frequency corresponding to it, and calculate the mean instantaneous frequency value of each eigenmode function;

The eigenmode function with the instantaneous frequency mean value located in the low frequency section is taken as the effective eigenmode function, and the sum of the effective eigenmode functions is used as the target eigenmode function. Frequency range below 1Hz;

The frequency corresponding to the target eigenmode function is identified and a steady state map is established, and a 1p frequency is extracted according to the steady state map.
The method for identifying a 1p signal of a fan based on continuous monitoring according to claim 1, characterized in that "identifying the frequency corresponding to the target eigenmode function and establishing a steady state map" includes:

Establish discrete state space equations;

Calculating the fan frequency, damping ratio and modal shape according to the discrete state space equation and the structural dynamic response of the fan;

A steady state diagram is established according to the frequency and modal shape of the fan.
The method for identifying a fan 1p signal based on continuous monitoring according to claim 1, wherein the transform is a Hilbert transform.
The method for identifying a 1p signal of a fan based on continuous monitoring according to claim 1, wherein the structural dynamic response of the fan is an acceleration, speed, or displacement response.
The method for identifying a 1p signal of a wind turbine based on continuous monitoring according to claim 4, wherein "obtaining a structural dynamic response of the wind turbine" includes:

Determine the time constant period of the fan;

Obtain the structural dynamic response of the wind turbine in the time-invariant period.
A fan 1p signal identification device based on continuous monitoring is characterized in that it includes:

An acquisition module configured to acquire a structural dynamic response of the wind turbine;

A decomposition module configured to decompose the structural dynamic response of the wind turbine into several eigenmode functions;

A transformation module configured to transform each eigenmode function to obtain a corresponding instantaneous frequency, and calculate an average value of the instantaneous frequency of each eigenmode function;

A screening module configured to use the eigenmode function with the instantaneous frequency mean value located in a low frequency section as the effective eigenmode function, and use the sum of the effective eigenmode functions as the target eigenmode function The low frequency range is a frequency range below 1 Hz;

The identification module is configured to identify a frequency corresponding to the target eigenmode function and establish a steady state map, and extract a 1p frequency according to the steady state map.
The fan 1p signal identification device based on continuous monitoring according to claim 6, wherein the transformation is a Hilbert transformation.
The fan 1p signal identification device based on continuous monitoring according to claim 7, wherein the identification module comprises:

A modeling sub-module configured to establish a discrete state space equation;

An identification sub-module configured to calculate a frequency, a damping ratio, and a modal shape of the fan according to the discrete state space equation and a structural dynamic response of the fan;

A steady-state graph sub-module configured to establish a steady-state graph according to the frequency and modal shape of the fan;

An extraction submodule configured to extract a 1p frequency according to the steady-state map.
A terminal, comprising a memory and a processor, wherein the memory is configured to store a computer program, and the processor executes the computer program to enable the terminal to implement the device according to any one of claims 1 to 5. Method for identifying 1p signal of wind turbine based on continuous monitoring.
A computer-readable storage medium, characterized in that it stores the computer program executed by the terminal according to claim 9.