A fan operation health degree monitoring management system

Abstract

The application relates to the technical field of fan health degree monitoring, and particularly discloses a fan operation health degree monitoring management system, which comprises an information acquisition module, a prediction module, an analysis module and a warning module; the information acquisition module is used for acquiring real-time fan operation data and environmental data; the prediction module is used for predicting the theoretical operation parameters of the fan; the analysis module is used for comparing and analyzing the predicted fan operation parameters and the real-time fan operation parameters, and obtaining the fan rotating state according to the comparison and analysis result; and the warning module is used for issuing a warning when the fan rotating state is abnormal, the fan vibration state is abnormal or the fan health degree is low; the fan rotating speed value and the fan power generated are compared with the predicted theoretical operation parameters, the acquired fan vibration parameters are compared with the preset standard fan vibration parameters, the fan operation state and the health degree can be monitored in real time according to the fan operation data and the environmental data collected by the acquisition module, and a warning can be issued in time when the operation state is abnormal or the health degree is low.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fan health monitoring, in particular to a fan operation health monitoring management system. BACKGROUND

[0002] A fan is a device that generates wind force by rotating blades, which is usually used for ventilation, ventilation or cooling applications, and can also be used for power generation or driving mechanical equipment, and with the proposal of the double carbon target, the installed capacity of wind turbines for wind power generation in China grows exponentially, and there is a mismatch between the growth of installed capacity and the growth of operation and maintenance capability, which poses a higher challenge to the operation and maintenance capability of wind farms.

[0003] Currently, the operation and maintenance process of power generation fans is carried out in the data acquisition and monitoring control SCADA system, after real-time reporting of warning or shutdown information, the wind farm technical personnel take a series of maintenance actions, this operation and maintenance method cannot arrange corresponding maintenance or prepare spare parts such as fan blades, generators and other major components through early monitoring of the health status of the fan, and the waiting time for spare parts may be up to one or two months, which will cause serious loss of power generation. SUMMARY

[0004] The purpose of the present application is to provide a fan operation health monitoring management system to solve the following technical problems:

[0005] How to monitor the running state and health degree in the running process of the fan in real time and issue timely warning.

[0006] The purpose of the present application can be achieved by the following technical solutions:

[0007] A fan operation health monitoring management system, the system comprises:

[0008] An information acquisition module for acquiring real-time fan operation data and environmental data;

[0009] The fan operation data includes fan speed value, fan vibration amplitude, fan vibration frequency and fan power generation power, and the environmental data includes environmental wind speed and wind direction;

[0010] An analysis module for predicting and analyzing based on the environmental data, obtaining predicted fan operation parameters, comparing and analyzing the predicted fan operation parameters and real-time fan operation parameters, obtaining the fan rotation state according to the comparison and analysis result; comparing the real-time vibration parameters of the fan with the preset standard vibration parameters, obtaining the fan vibration state according to the comparison and analysis result; obtaining the fan health value according to the fan rotation state and the fan vibration state, comparing the fan health value with the fan standard health value, and obtaining the fan health degree according to the comparison and analysis result;

[0011] an early warning module for issuing an early warning when the fan rotation state is abnormal, the fan vibration state is abnormal, or the fan health degree is low.

[0012] In an embodiment, the prediction analysis includes:

[0013] The predicted rotation speed value V e (t) of the fan is obtained through formula V r = f d (R(t))*f e (θ(t));

[0014] wherein R(t) is a real-time change curve of the environmental wind speed, θ(t) is a function of the angle change of the environmental wind direction relative to the standard wind direction over time, f r is a function of converting the environmental wind speed into the fan rotation speed, and f d is a function of converting the environmental wind direction into the standard wind direction.

[0015] The predicted power generation P e(t) of the fan is obtained through formula P e = V e(t) (t)*μ;

[0016] wherein μ is a coefficient of converting the fan rotation speed into the power generation.

[0017] In an embodiment, the fan rotation state analysis process includes:

[0018] The rotation speed deviation value Q during the fan operation from t1 to t2 is obtained through formula Q = ∫V s (t)dt;

[0019] wherein V s (t) is a real-time rotation speed curve of the fan.

[0020] The power generation deviation value W during the fan operation from t1 to t2 is obtained through formula W = ∫P e (t)dt;

[0021] wherein P e (t) is a real-time power generation curve of the fan.

[0022] The deviation coefficient S during the fan operation from t1 to t2 is obtained through formula S = Q*α+W*β;

[0023] wherein α is a deviation weight coefficient of the rotation speed deviation value, and β is a deviation weight coefficient of the power generation deviation value.

[0024] The deviation coefficient S and the health deviation coefficient S e are compared in terms of deviation.

[0025] If S ≥ S e , an early warning is issued.If S < S

[0026] If S < S e , the rotating state of the fan is further judged.

[0027] In an embodiment, the further judgment of the rotating state of the fan comprises:

[0028] If S < S e , a deviation coefficient S(t) curve is obtained by the formula S(t) = (V e (t) - V s (t))*a + (P e (t) - P s (t))*b.

[0029] In a preset time period, N deviation coefficients S i are taken on the deviation coefficient S(t) curve at equal time intervals.

[0030] A dispersion coefficient s of the deviation coefficients in the rotating process of the fan is obtained by the formula .

[0031] Wherein, i e [1, N], S i is the i-th deviation coefficient value, is the average value of the N deviation coefficients S i .

[0032] The dispersion coefficient s and a standard dispersion coefficient s e are compared.

[0033] If s > s e , it is judged that the fan has a rotating fault, and the warning module issues an intermittent warning.

[0034] If s < s e , it is judged that the rotating state of the fan is normal.

[0035] In an embodiment, the fan vibration state analysis process comprises:

[0036] In a preset time, M vibration amplitude Z j data and vibration frequency K values are taken on the vibration amplitude Z(t) curve at equal time intervals.

[0037] A vibration parameter O in the rotating process of the fan is obtained by the formula .

[0038] Wherein, j e [1, M], Z j is the j-th vibration amplitude value, is the average value of the M vibration amplitudes Z j , and Z(t) maxis a maximum amplitude of the amplitude Z curve in the preset time, K is a maximum amplitude of the corresponding standard amplitude curve, e τ1, τ2, τ3 are preset weight coefficients, and

[0039] The vibration parameter O is compared with the standard vibration interval, and whether the fan vibration is abnormal is determined according to the vibration comparison result.

[0040] In an embodiment, the vibration comparison process includes:

[0041] The preset standard vibration parameter interval is [O e , O s ], O s is a maximum standard vibration parameter, and O e is a minimum standard vibration parameter difference;

[0042] If O e , it is determined that the information acquisition module collects data incorrectly, and the information acquisition module is repaired;

[0043] If O e , O s ], it is determined that the fan vibration state is normal;

[0044] If O s , it is determined that the fan vibration state is abnormal, and a warning is issued.

[0045] In an embodiment, the analysis of the standard vibration parameter interval includes:

[0046] The fan rotation process is divided into D preset time intervals according to a fixed time interval t0;

[0047] The average value E x of the rotating speed in the xth time interval [t x , t x+1 ] is obtained by the formula ;

[0048] Wherein, x∈[1, D];

[0049] The rotating speed dispersion parameter ω in the fan rotation process is obtained by the formula ;

[0050] Wherein, is the average value of the rotating speed in the whole fan rotation process;

[0051] The maximum standard vibration parameter O s is obtained by the formula O s =g(max(V s (t)), ω);

[0052] wherein g is a function of the maximum rotational speed and the rotational speed dispersion parameter into the maximum standard vibration parameter, max(V s (t)) is the maximum value of the rotational speed of the fan;

[0053] The minimum standard vibration parameter O e is obtained by the formula O s = h(max(V e (t)), ω).

[0054] wherein h is a function of the maximum rotational speed and the rotational speed dispersion parameter into the minimum standard vibration parameter.

[0055] In an embodiment, the fan health analysis process comprises:

[0056] The health H during the operation of the fan is obtained by the formula H = B e / (S*γ+O*δ).

[0057] wherein γ is the weight of the deviation coefficient during the rotation of the fan, δ is the weight of the vibration parameter during the rotation of the fan, B e is the standard health value of the fan.

[0058] If H ≥ 1, it is judged that the health of the fan is normal.

[0059] If H < 1, it is judged that the health of the fan is low, and a warning is issued.

[0060] The beneficial effects of the present application are:

[0061] (1) The present application collects the fan operation data and environmental data during the operation of the fan through the information collection module, compares the collected fan rotational speed value and fan power generation power with the predicted theoretical operation parameters, judges whether the fan rotation state is normal, compares the collected fan vibration parameters with the preset fan standard vibration parameters, judges whether the fan vibration state is normal, compares the fan health value with the fan standard health value, obtains the fan health, judges whether the fan health meets the standard, can monitor the running state and health of the fan in real time according to the fan operation data and environmental data collected by the collection module, and timely issue a warning when the running state is abnormal or the health is low.

[0062] (2) The present application obtains the rotational speed deviation value Q during the operation of the fan within the time t1 to t2 by the formula , obtains the power generation power deviation value W during the operation of the fan within the time t1 to t2 by the formula , and obtains the deviation coefficient S during the operation of the fan within the time t1 to t2 by the formula S = Q*α+W*β, compares the deviation coefficient S with the health deviation coefficient S eThe comparison is implemented to realize the pre-warning when the fan has the rotating fault.

[0063] (3) The formula of the present application is The real-time dispersion coefficient σ of the deviation coefficient in the preset time period during the rotation of the fan is obtained, and the dispersion coefficient σ and the standard dispersion coefficient σ e The comparison is implemented to realize the intermittent pre-warning when the fan has the rotating fault.

[0064] (4) The formula of the present application is The vibration parameter O during the rotation of the fan is obtained, and the vibration parameter O is compared with the preset standard vibration parameter interval [O e , O s ] to realize the pre-warning when the vibration state of the fan is abnormal. BRIEF DESCRIPTION OF DRAWINGS

[0065] The present application will be further described below in combination with the drawings.

[0066] Figure 1 is a schematic diagram of the data security pre-warning system of the present application. DETAILED DESCRIPTION

[0067] The technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0068] Please refer to Figure 1 shown, in one embodiment, a fan operation health monitoring management system is provided, the system comprises:

[0069] An information collection module is used to collect real-time fan operation data and environmental data;

[0070] The fan operation data includes fan rotating speed value, fan vibration amplitude, fan vibration frequency and fan power generation power, and the environmental data includes environmental wind speed and wind direction.

[0071] An analysis module is used to perform prediction analysis according to the environmental data, obtain predicted fan operation parameters, compare and analyze the predicted fan operation parameters and real-time fan operation parameters, obtain the fan rotating state according to the comparison and analysis result, compare the real-time fan vibration parameters with the preset standard vibration parameters, obtain the fan vibration state according to the comparison and analysis result, obtain the fan health value according to the fan rotating state and the fan vibration state, compare the fan health value with the fan standard health value, and obtain the fan health degree according to the comparison and analysis result.

[0072] The early warning module is configured to issue a warning when the rotation state of the fan is abnormal, the vibration state of the fan is abnormal, or the health of the fan is low.

[0073] According to the technical solution, the rotation speed value, the vibration amplitude, the vibration frequency and the power generation of the fan during the operation of the fan are collected by the information collection module, the environmental wind speed and the wind direction during the operation of the fan are collected, the theoretical operation parameters of the fan are obtained through prediction and analysis of the environmental data, the rotation speed value and the power generation of the fan collected by the collection module are compared with the predicted theoretical operation parameters, it is determined whether the rotation state of the fan is normal, a warning is issued by the early warning module when the rotation state of the fan is abnormal, the vibration parameters of the fan are obtained through the collected vibration amplitude and the vibration frequency, the vibration parameters of the fan are compared with the preset standard vibration parameters of the fan, it is determined whether the vibration state of the fan is normal, a warning is issued by the early warning module when the vibration state of the fan is abnormal, the health value of the fan is obtained through the rotation state and the vibration state of the fan, the health value of the fan is compared with the standard health value of the fan, the health of the fan is obtained, and a warning is issued by the early warning module when the health of the fan is not up to standard. The operation state and the health of the fan can be monitored in real time according to the operation data and the environmental data of the fan collected by the collection module, and a warning can be issued in time when the operation state is abnormal or the health is low.

[0074] The prediction and analysis include:

[0075] The predicted rotation speed value V e (t) of the fan is obtained through the formula V r (t)=f d (θ(t)); e

[0076] wherein R(t) is the real-time change curve of the environmental wind speed, θ(t) is the angle change function of the environmental wind direction relative to the standard wind direction with time, f r is the function of converting the environmental wind speed into the rotation speed of the fan, and f d is the function of converting the environmental wind direction into the standard wind direction.

[0077] The predicted power generation P e(t) of the fan is obtained through the formula P e =V e(t) ;

[0078] wherein μ is the coefficient of converting the rotation speed of the fan into the power generation.

[0079] According to the technical solution, the method for predicting the rotation speed and the power generation of the fan is provided, specifically, the environmental wind speed and the wind direction are used, the predicted rotation speed value V e (t) of the fan is obtained through the formula V r ​(R(t))*f d (θ(t)) calculates the predicted speed value V of the fan e (t), wherein R(t) is the real-time change curve of the environmental wind speed, which can be measured by the wind speed sensor of the acquisition module, θ(t) is the function of the angle change of the environmental wind direction relative to the standard wind direction over time, which can be measured by the wind direction sensor of the acquisition module, f r is the function of the environmental wind speed converted into the speed of the fan, which can be obtained by fitting the speed data of the standard fan under different wind speeds, and is related to the parameters of the fan itself, f d is the function of the environmental wind direction converted into the standard wind direction, which can be obtained by fitting the simulation experiment data, and then the formula P e(t) =V e (t)*μ is used to obtain the predicted power P e(t) of the fan, wherein μ is the coefficient of the speed of the fan converted into the power of the fan, which can be obtained by fitting the power data of the standard fan under different speeds.

[0080] The fan rotating state analysis process includes:

[0081] The speed deviation value Q of the fan during the running process from t1 to t2 is obtained by the formula ;

[0082] wherein V s (t) is the real-time speed curve of the fan;

[0083] The power deviation value W of the fan during the running process from t1 to t2 is obtained by the formula ;

[0084] wherein P s (t) is the real-time power curve of the fan;

[0085] The deviation coefficient S of the fan during the running process from t1 to t2 is obtained by the formula S=Q*α+W*β;

[0086] wherein α is the deviation weight coefficient of the speed deviation value, and β is the deviation weight coefficient of the power deviation value;

[0087] The deviation coefficient S and the health deviation coefficient S e are compared;

[0088] If S≥S e , it is judged that the fan has rotating failure, and the warning module issues a warning;

[0089] If S<S e , the rotating state of the fan is further judged.

[0090] Through the technical solution, the embodiment provides a specific process of fan rotating speed state analysis, and the rotating speed deviation value Q of the fan during the t1-t2 period is obtained through a formula The power generation deviation value W of the fan during the t1-t2 period is obtained through a formula The deviation coefficient S of the fan during the t1-t2 period is obtained through a formula S=Q*α+W*β, wherein V s (t) is a real-time rotating speed curve of the fan, which can be measured by a rotating speed sensor of the acquisition module, P s (t) is a real-time power generation curve of the fan, which can be measured according to a power meter, α is a deviation weight coefficient of the rotating speed deviation value, β is a deviation weight coefficient of the power generation deviation value, and α and β can be set according to experimental data fitting, the deviation coefficient S and the health deviation coefficient S e are compared to determine whether the fan has rotating faults and issue a warning, S e is the health deviation coefficient, which can be set according to experimental data fitting.

[0091] Further determining the rotating state of the fan includes:

[0092] When S e , a deviation coefficient S(t) curve is obtained through a formula S(t)=(V e (t)-V s (t))*α+(P e (t)-P s (t))*β.

[0093] In a preset time period, N deviation coefficients S i are taken at equal time intervals on the deviation coefficient S(t) curve.

[0094] A dispersion coefficient σ of the deviation coefficient during the rotating process of the fan is obtained through a formula .

[0095] Wherein, i∈[1, N], S i is an i-th deviation coefficient value, is an average value of the N deviation coefficients S i .

[0096] The dispersion coefficient σ and the standard dispersion coefficient σ e are compared in dispersion.

[0097] If σ≥σ e , it is determined that the fan has rotating fault risks, and the warning module intermittently issues a warning.

[0098] If σ e , it is determined that the rotating state of the fan is normal.

[0099] By the technical solution, the embodiment provides a process of further analyzing the rotating state of the fan. e When S e (t) is obtained by a formula S(t) = (V s (t) - V e (t))*a + (P s (t) - P i (t))*b, and a and b are preset coefficients. A real-time dispersion coefficient s of the deviation coefficient in the preset time period during the rotating process of the fan is obtained by a formula s = (S i i - S e i-1) / S e i-1, wherein i e [1, N], S i i is the i-th deviation coefficient value, N values of the deviation coefficient S are taken at equal time intervals on the deviation coefficient S curve in the preset time period, j S j is the average value of the N deviation coefficients S j , and s max is a standard dispersion coefficient. e The dispersion coefficient s and the standard dispersion coefficient s e are compared to determine whether the fan has a rotating fault risk and issue an intermittent early warning, wherein s s is a standard dispersion coefficient, which can be set according to experimental data.

[0100] The fan vibration state analysis process includes:

[0101] M vibration amplitude Z s values are taken at equal time intervals on the vibration amplitude Z j curve in a preset time period, and vibration frequency K values are obtained.

[0102] A vibration parameter O is obtained by a formula O = (Z (t) - Z (t))*c + (K

[0103] - K )*d, wherein c and d are preset coefficients. j Z j i is the j-th vibration amplitude value, max Z e is the average value of the M vibration amplitudes Z e , Z s (t) is the maximum amplitude of the vibration amplitude Z curve in the preset time period, s K e is the maximum amplitude of the corresponding standard vibration amplitude curve, K e is the corresponding standard vibration frequency value, and t1, t2, and t3 are preset weight coefficients. The vibration parameter O is compared with a standard vibration interval, and whether the fan vibration is abnormal is determined according to the vibration comparison result.

[0105] The vibration comparison process includes:

[0106] The preset standard vibration parameter interval is [O e , O s ], O sO e is the maximum standard vibration parameter;

[0107] If O e , it is judged that the data collected by the information collection module is wrong, and the information collection module is repaired;

[0108] If O e ∈ [O s ], it is judged that the vibration state of the fan is normal;

[0109] If O s , it is judged that the vibration state of the fan is abnormal, and a warning is issued.

[0110] The analysis of the standard vibration parameter interval includes:

[0111] The rotation process of the fan is divided into D preset time intervals according to a fixed time interval t0;

[0112] The average value E x of the rotation speed in the xth time interval [t x , t x+1 ] is obtained through the formula ;

[0113] Wherein, x ∈ [1, D];

[0114] The rotation speed dispersion parameter ω in the rotation process of the fan is obtained through the formula ;

[0115] Wherein, is the average value of the rotation speed in the whole rotation process of the fan, which can be obtained through the formula ;

[0116] The maximum standard vibration parameter O s is obtained through the formula O s = g(max(V s (t)), ω);

[0117] Wherein, g is a function of the maximum rotation speed and the rotation speed dispersion parameter converted into the maximum standard vibration parameter, and max(V s (t)) is the maximum value of the rotation speed of the fan;

[0118] The minimum standard vibration parameter O e is obtained through the formula O e = h(max(V s (t)), ω);

[0119] Wherein, h is a function of the maximum rotation speed and the rotation speed dispersion parameter converted into the minimum standard vibration parameter.

[0120] Through the above technical solution, this embodiment provides a process for analyzing the vibration parameters of a wind turbine, using formulas. Obtain the vibration parameter O during the fan rotation process, and preset the standard vibration parameter range of O as [O]. e O s The comparison is performed to issue an early warning when the fan vibration state is abnormal, where j∈[1,M],Z j For the j-th vibration amplitude value, within a preset time period, M numerical points are taken at equal time intervals on the vibration amplitude Z-curve. For M vibration amplitudes Z j The average value, Z(t) max The maximum amplitude of the vibration amplitude Z-curve within a preset time period can be obtained from the vibration amplitude Z-curve. The maximum amplitude of the corresponding standard vibration amplitude curve can be obtained by fitting experimental data, K. e The corresponding standard vibration frequency value can be obtained from the vibration sensor of the acquisition module. τ1, τ2, and τ3 are all preset weighting coefficients, which can be set by fitting experimental data. The maximum standard vibration parameter O at both ends of the vibration parameter range being compared is... s and minimum vibration parameter O e The settings can be achieved through formula O. s =g(max(V) s (t)), ω) obtain the maximum standard vibration parameter O s And formula O e =h(max(V) s (t)), ω) obtain the minimum standard vibration parameter O e Where g is a function of the maximum rotational speed and the discrete parameters of rotational speed transformed into the maximum standard vibration parameter, and h is a function of the maximum rotational speed and the discrete parameters of rotational speed transformed into the minimum standard vibration parameter. Both g and h can be obtained by fitting experimental data to a model, max(V s (t) represents the maximum speed of the fan, which can be obtained from V. s The point is obtained by taking points on the (t) curve, where ω is the discrete parameter of the rotational speed, which can be obtained through the formula. E x For the x-th time interval [t] x , t x+1 The average rotational speed within the range can be obtained using the formula... The average rotational speed of the fan throughout its entire rotational period can be obtained using the formula... Obtain the preset time interval [t] during the rotation of fan D. a , t b The number of elements, x∈[1,D].

[0121] The fan health degree analysis process comprises:

[0122] The health degree H of the fan during operation is obtained by the formula H=B e / (S*γ+O*δ);

[0123] Wherein, γ is the weight of the deviation coefficient during the rotation of the fan, δ is the weight of the vibration parameter during the rotation of the fan, B e is the standard health value of the fan;

[0124] If H>=1, it is judged that the fan health degree is normal;

[0125] If H<1, it is judged that the fan health degree is low, and a warning is issued.

[0126] Through the above technical solution, the embodiment provides a process for analyzing the fan health degree, the health degree H of the fan during operation is obtained by the formula H=B e / (S*γ+O*δ), the health degree H is compared with 1 to realize the warning when the fan health degree is low, wherein, γ is the weight of the deviation coefficient during the rotation of the fan, δ is the weight of the vibration parameter during the rotation of the fan, γ and δ can be set by experimental data fitting, B e is the standard health value of the fan, which can be set according to the parameters of the fan itself.

[0127] The above has carried out the detailed explanation to one embodiment of the application, but the content described is only the preferred embodiment of the application, cannot be considered for limiting the implementation scope of the application. All equivalent changes and improvements made according to the application scope should still belong to the patent coverage range of the application.

Claims

1. A wind turbine operation health monitoring and management system, characterized in that, The system includes: The information acquisition module is used to collect real-time wind turbine operation data and environmental data; The wind turbine operating data includes wind turbine speed, wind turbine vibration amplitude, wind turbine vibration frequency, and wind turbine power generation; the environmental data includes ambient wind speed and wind direction. The analysis module is used to perform predictive analysis based on environmental data to obtain predicted wind turbine operating parameters. It compares these predicted parameters with real-time wind turbine operating parameters to determine the wind turbine rotation status. It also compares real-time wind turbine vibration parameters with preset standard vibration parameters to determine the wind turbine vibration status. Finally, it obtains a wind turbine health value based on the wind turbine rotation and vibration statuses, compares this health value with the standard health value, and determines the wind turbine health degree based on the comparison results. The early warning module is used to issue warnings when the fan rotation is abnormal, the fan vibration is abnormal, or the fan health is low. The process of analyzing the vibration status of a wind turbine includes: Within a preset time period, M vibration amplitudes are taken at equal time intervals on the vibration amplitude Z(t) curve. Data and vibration frequency K value; Through formula Obtain vibration parameters during the fan rotation process O ; in, , For the j-th vibration amplitude value, M vibration amplitudes The average value, The maximum amplitude of the Z-curve within a preset time period. This represents the maximum amplitude of the corresponding standard vibration amplitude curve. This corresponds to the standard vibration frequency value. , , All are preset weighting coefficients; Vibration parameters Compare the vibration with the standard vibration range, and determine whether the fan vibration is abnormal based on the vibration comparison results; The vibration comparison process includes: The preset standard vibration parameter range is , For the maximum standard vibration parameters, The minimum standard vibration parameter difference; like If the error is found, the information acquisition module is judged to be collecting data incorrectly, and the information acquisition module is inspected and repaired. like If so, the fan vibration status is considered normal; like If this is detected, the fan vibration state is determined to be abnormal, and an early warning is issued. Analysis of the standard vibration parameter range includes: The fan rotation process is scheduled at fixed time intervals. Divided into D A preset time interval; Through formula Get the time intervals Average rotational speed within ; in, ; Through formula Obtain the discrete parameters of the fan speed during the fan rotation process ; in, This represents the average rotational speed of the fan throughout its entire rotational process. Through formula Obtain the maximum standard vibration parameters ; in, This is a function that transforms the maximum rotational speed and the discrete parameters of rotational speed into the maximum standard vibration parameters. This represents the maximum speed of the fan. Through formula Obtain the minimum standard vibration parameters ; in, It is a function that transforms the maximum rotational speed and the discrete parameters of rotational speed into the minimum standard vibration parameters.

2. The wind turbine operation health monitoring and management system according to claim 1, characterized in that, The predictive analysis includes: Through formula Obtain the predicted speed value of the wind turbine. ; in, This is a curve showing the real-time changes in ambient wind speed. Let be the function representing the change of the angle of the ambient wind direction relative to the standard wind direction over time. This is a function that converts ambient wind speed into fan speed. This is a function that converts ambient wind direction into standard wind direction. Through formula Obtain the predicted power generation capacity of the wind turbine. ; in, This is the coefficient that converts the wind turbine speed into power generation.

3. The wind turbine operation health monitoring and management system according to claim 2, characterized in that, The fan rotation status analysis process includes: Through formula get arrive Speed ​​deviation during the operation of the fan over a period of time ; in, This is the real-time speed curve of the fan; Through formula get arrive Power generation deviation during wind turbine operation over a period of time W ; in, This is the real-time power generation curve of the wind turbine; Through formula get arrive The deviation coefficient S during the operation of the fan within a given time period; in, This is the deviation weighting coefficient for the speed deviation value. The deviation weighting coefficient for the power generation deviation value; The deviation coefficient S and the health deviation coefficient Perform deviation comparison; like If the fan has a rotation fault, the warning module will issue a warning. like Then, the rotation status of the fan can be further determined.

4. The wind turbine operation health monitoring and management system according to claim 3, characterized in that, Further assessment of the fan's rotation status includes: when At that time, through the formula Obtain the deviation coefficient curve; Within the preset time period, at the deviation coefficient N deviation coefficients are taken at equal time intervals on the curve. data; Through formula Obtain the coefficient of variation of the deviation coefficient during the fan rotation process ; in, , For the first Each deviation coefficient value, N deviation coefficients The average value; Discrete coefficients and standard coefficient of variation Perform discrete comparison; like If the wind turbine is suspected of having a rotational malfunction, the warning module will issue an intermittent warning. like If so, it can be determined that the fan is rotating normally.

5. The wind turbine operation health monitoring and management system according to claim 1, characterized in that, The wind turbine health analysis process includes: Through formula Obtain the health status H of the wind turbine during operation; in, This represents the weight of the deviation coefficient during the fan rotation process. The weights of the vibration parameters during the fan rotation process. This represents the standard health value for the fan. like If so, the fan is considered to be in normal health. like If the wind turbine's health is deemed low, an early warning will be issued.

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