CN102352824A - Monitoring system based on electric information for health status of wind driven generator and monitoring method thereof - Google Patents

Abstract

The invention discloses a monitoring system based on electric information for the health status of a wind driven generator and a monitoring method thereof. The monitoring system comprises a wind driven generator data acquisition module, a signal preprocessing module, a signal processing module and a monitoring output module. The monitoring method disclosed by the invention comprises the following steps: (1) acquiring wind driven generator environmental data, running status physical quantity and output electric information from the wind driven generator; (2) processing the wind driven generator environmental data and the running status physical quantity acquired by the wind driven generator data acquisition module, estimating to obtain the due output power of the wind driven generator, comparing the due output power with actual output power, and if the deviation is overgreat, outputting an alarm signal; and (3) processing a wind driven generator output current signal acquired by the wind driven generator data acquisition module. By adopting the monitoring system based on the electric information for the health status of the wind driven generator and the monitoring method thereof, the health status of the wind driven generator can be efficiently and economically monitored, mechanical failure and electrical failure can be monitored simultaneously, and a sufficient and reliable safety guarantee is provided for the wind driven generator.

Description

A kind of wind-driven generator health status monitoring system and method based on electric information

Technical field

The present invention relates to a kind of wind-driven generator health status monitoring system that the wind-driven generator health status is monitored, this system can monitor the health status of wind power generating set efficiently.The invention still further relates to the monitoring method that adopts above-mentioned wind-driven generator health status monitoring system.

Background technique

China's wind-power electricity generation development is swift and violent, but faces the multiple challenge of fault in the primary stage.Survey report shows: the relative stipulated time difference of the annual mean generating dutation of certain 7 hundreds of typhoon machine of wind field is about 20%, and the generating economic loss of bringing thus reaches 1.7 hundred million yuan, and reason then is that fault is shut down.If the calculating maintenance cost, economic loss will be multiplied.

The expectation of wind-powered electricity generation industry changes to intensive farming from extensive operation, not only need improve electric motor power, more need realize high-yield and high-efficiency.And the fan trouble pilosity has become the obstacle of realizing this goal, even has the tens of typhoon machines of certain wind field that failover over several years is installed and the report of generating never.More situation is that the blower fan reliability is lower; And lack the fault pre-alarming function, consequently less fault is failed to find, is keeped in repair and develops into great safety and equipment accident, not only causes shutdown loss; And maintenance expenses sharply rises, even possibly surpass its output expense.Therefore, occurred certain blower fan MANUFACTURER abroad and caused enterprise's report of going bankrupt unable to make ends meet because of guaranteeing to keep in good repair the multiple product of its fault, the wind-powered electricity generation industry must be walked out this type of awkward situation.

The health status monitoring technology provides technical specifications for the wind-powered electricity generation industry breaks away from above-mentioned awkward situation.The basic purpose of health status monitoring is in time to find fault, the alarm prompt maintenance.Though it can not prevent that fault from taking place, and can prevent trouble before it happens.The health status monitoring technology is carried out health examination for wind power equipment incessantly; Find that in time the state of an illness and guides timely treatment, can prevent that little disease from developing into serious disease, gets well with less cost; Thereby keep fit and life-saving, bring into play bigger economic benefit.

Existing health status monitoring system; The overwhelming majority all is based on the fan vibration physical quantity; Through splitting the vibration transducer on several parts of wind-powered electricity generation unit; The monitoring vibration amount is also sent signal to the vibration evaluation module; By comparing of the preset fault of evaluation module basis, send corresponding fault and non-trouble signal.This method has high input and the technical requirements height, and the collection environment of signal is had the requirement of strictness, so do not have how many real input reality uses.

In the prior motor Condition Monitoring Technology field, there has been the electric information of simple dependence motor output to monitor the technology of the health status of motor, and dropped into motor monitoring practical application.Therefore the motor of comparing and using in the other field, blower fan rely on the new more efficiently method of electric information monitoring wind-powered electricity generation unit health status needs because randomness and the wave properties of its running state seem more special.

Summary of the invention

Technical problem to be solved by this invention provides a kind of wind-driven generator health status monitoring system based on electric information that can carry out on-line monitoring to the wind-driven generator real-time running state; With this monitoring system, the present invention also provides the wind-driven generator health status monitoring method based on electric information.

In order to solve above-mentioned technical problem, the present invention is based on the wind-driven generator health status monitoring system of electric information, comprising:

The wind-driven generator data acquisition module is used for extracting required electric current and power signal from wind power generating set, and the relevant essential information of wind-driven generator; The relevant basic information packet of said wind-driven generator is drawn together essential informations such as obtaining wind speed, rotor speed.

Signal pre-processing module is used for the wind-driven generator power signal is carried out pretreatment.Extrapolate wind-driven generator through mechanical quantity such as wind speed and fan rotor rotation speeds output power should be arranged; Relatively real output with output power should be arranged, if deviation is excessive, output alarm signal and export monitoring information then, otherwise entering signal puocessing module to staff's telecontrol interface.

Signal processing module; Be used for the wind-driven generator output current signal is handled; Remove signal noise through discrete wavelet; Extract the signal characteristic frequency through continuous wavelet again; Contrast normal eigen frequency frequency spectrum and actual signal eigen frequency frequency spectrum; The output monitoring result, if find the fault spectrum component, warning and output fault message and fault spectrum are to the telecontrol interface.

The monitoring output module is used for output monitoring information and alarm signal.

Wind-driven generator health status monitoring method based on electric information of the present invention comprises the steps:

1) obtains wind-driven generator environmental data, running state physical quantity and output electric information from wind-driven generator;

2) wind-driven generator environmental data and the running state physical quantity that the wind-driven generator data acquisition module is obtained handled, and estimates to such an extent that wind-driven generator should have output power, contrasts real output, if deviation is excessive, and output alarm signal then;

3) the wind-driven generator output current signal that the wind-driven generator data acquisition module is obtained is handled; At first remove signal noise through discrete wavelet; Extract the signal characteristic frequency through continuous wavelet again; Contrast normal eigen frequency frequency spectrum and actual signal eigen frequency frequency spectrum; The output monitoring result; If find the fault spectrum component, report to the police and output fault message and fault spectrum.

In said step 1), obtaining the wind-driven generator environmental data from wind-driven generator is wind speed, and the running state physical quantity is a rotor speed, and electric information is output current and power.

In said step 2) in, it is P that wind-driven generator should have output power _e,

$P_e=\frac{1}{2}\frac{\mathrm{\ρ\π}{R}^2{V}^3{C}_p(\mathrm{\λ},\mathrm{\β})}{{\mathrm{\ω}}_t}$

ρ is an air density in the formula, C _pBe power factor, ω _tBe rotation speed of fan, R is a blade radius, and V is a wind speed.Actual power P is the blower fan tip speed ratio with power deviation ε, λ should be arranged, and β is a propeller pitch angle.

$\mathrm{\ϵ}=\left|\frac{P-P_e}{P_e}\right|$

ε ₀For setting secure threshold.As ε≤ε ₀The time, show that fan operation is normal, otherwise output alarm signal.

In said step 3), used continuous wavelet transform (continuous-wavelet-transform CWT) is expressed as:

$\mathrm{CWT}(a,b)=\frac{1}{\sqrt{\left|a\right|}}\underset{-\∞}{\overset{\∞}{\∫}}x\left(t\right){\mathrm{\ψ}}^{*}\left(\frac{t-b}{a}\right)\mathrm{dt}$

In the formula, ψ (t) is a wavelet function, and asterisk * representes complex conjugate, and a is the small echo scope, and b is the small echo time sequence parameter.Wavelet transformation time-frequency window function representation is:

$\left\{\begin{array}{c}{\mathrm{\ω}}_{\mathrm{upper}}={\mathrm{\ω}}_c+{\mathrm{\ω}}_f/2\\ {\mathrm{\ω}}_{\mathrm{lower}}={\mathrm{\ω}}_c-{\mathrm{\ω}}_f/2\\ {\mathrm{\ω}}_f={\mathrm{\η\ω}}_{\mathrm{fg}}\end{array}\right.$

ω is useful angular frequency in the formula, ω _UpperBe window top frequency, ω _LowerBe window bottom frequency, ω _fBe the fluctuation of useful frequency, ω _cBe mean frequency, ω _FgBe the fluctuation of generator rotational frequency.

Said ω _fBe time-varying parameter, depend on the fluctuation of generator rotational frequency, depend on fluctuations in wind speed in essence.The land fluctuations in wind speed is about 6%, and the ocean fluctuations in wind speed is 12%～20%.

Relation between continuous wavelet transform parameter a and the useful angular frequency is:

$a=\frac{{\mathrm{\ω}}_0}{\mathrm{\ω}}$

ω ₀Be time-frequency window CF center frequency.Wavelet function parameter a definitional domain does thus,

a∈[a _min?a _max]

$\left\{\begin{array}{c}{a}_{\min }=\frac{{\mathrm{\ω}}_0}{{\mathrm{\ω}}_{\mathrm{upper}}}\\ a_{\min }=\frac{{\mathrm{\ω}}_0}{{\mathrm{\ω}}_{\mathrm{lower}}}\end{array}\right.$

Local continuous wavelet transform is expressed as,

${\mathrm{CWT}}_{\mathrm{local}}(a,b)=\frac{1}{\sqrt{\left|a\right|}}\underset{-\∞}{\overset{\∞}{\∫}}x\left(t\right){\mathrm{\ψ}}^{*}\left(\frac{t-b}{a}\right)\mathrm{dt}$

So, in useful frequency signal, extract spectrum energy A and be expressed as,

$A({\mathrm{<figure-callout\; id="0"\; label="t"\; filenames="HSA00000589562300021.png,HSA00000589562300023.png"\; state="\{\{state\}\}">t</figure-callout>}}_0+T/2)=\max \left(\right|{\mathrm{CWT}}_{\mathrm{local}}(a,b)\left|\right)\left\{\begin{array}{c}a\&Element;\left[\begin{array}{cc}{a}_{\min }& a_{\max }\end{array}\right]\\ b\&Element;\left[\begin{array}{cc}{t}_0& {\mathrm{<figure-callout\; id="0"\; label="t"\; filenames="HSA00000589562300021.png,HSA00000589562300023.png"\; state="\{\{state\}\}">t</figure-callout>}}_0+T\end{array}\right]\end{array}\right.$

The time-frequency window number advances with the letter, and the maximin of window is followed motor rotational frequency ω _FgChange.Through cycle calculations in signal length, can obtain the oscillogram of spectrum energy A.

Through analyzing the oscillogram of A, can be easy to find the fault spectrum component.

Use wind-driven generator health status monitoring system and the method based on electric information of the present invention; The health status of the monitoring wind power generating set of high-efficiency and economic more; And can monitor mechanical failure and electrical failure simultaneously, for wind power generating set provides fully, reliable safety guarantee.

Description of drawings

Below in conjunction with accompanying drawing and embodiment the present invention is done further detailed explanation.

Fig. 1 wind-driven generator health status of the present invention monitoring system structural drawing.

Fig. 2 wind-driven generator health status of the present invention monitoring method flow chart.

The slight mechanical disturbance frequency energy of Fig. 3 oscillogram.

A kind of slight electric disturbance correlation frequency oscillogram of Fig. 4.

A kind of slight electric disturbance spectrum energy oscillogram of Fig. 5.

Embodiment

As shown in Figure 1, the wind-driven generator health status monitoring system based on electric information comprises:

The wind-driven generator data acquisition module is used for extracting required electric current and power signal from wind power generating set, and the relevant essential information of wind-driven generator, and wherein the relevant basic information packet of wind-driven generator is drawn together essential informations such as obtaining wind speed, rotor speed.

Signal pre-processing module is used for the wind-driven generator power signal is carried out pretreatment, and extrapolating wind-driven generator through wind speed and fan rotor rotation speed mechanical quantity should have output power; Relatively real output with output power should be arranged, if deviation is excessive, output alarm signal and export monitoring information then, otherwise entering signal puocessing module to staff's telecontrol interface.

Signal processing module; Be used for the wind-driven generator output current signal is handled; Remove signal noise through discrete wavelet; Extract the signal characteristic frequency through continuous wavelet again; Contrast normal eigen frequency frequency spectrum and actual signal eigen frequency frequency spectrum; The output monitoring result, if find the fault spectrum component, warning and output fault message and fault spectrum are to the telecontrol interface.

The monitoring output module is used for output monitoring information and alarm signal.

As shown in Figure 2, the wind-driven generator health status monitoring method based on electric information of the present invention comprises the steps:

1) obtains wind-driven generator environmental data, running state physical quantity and output electric information from wind-driven generator;

2) wind-driven generator environmental data and the running state physical quantity that the wind-driven generator data acquisition module is obtained handled, and estimates to such an extent that wind-driven generator should have output power, contrasts real output, if deviation is excessive, and output alarm signal then;

3) the wind-driven generator output current signal that the wind-driven generator data acquisition module is obtained is handled; At first remove signal noise through discrete wavelet; Extract the signal characteristic frequency through continuous wavelet again; Contrast normal eigen frequency frequency spectrum and actual signal eigen frequency frequency spectrum; The output monitoring result; If find the fault spectrum component, report to the police and output fault message and fault spectrum.

In said step 1), obtaining the wind-driven generator environmental data from wind-driven generator is wind speed, and the running state physical quantity is a rotor speed, and electric information is output current and power.

In said step 2) in, it is P that wind-driven generator should have output power _e,

$P_e=\frac{1}{2}\frac{\mathrm{\&rho;\&pi;}{R}^2{V}^3{C}_p(\mathrm{\&lambda;},\mathrm{\&beta;})}{{\mathrm{\&omega;}}_t}$

ρ is an air density in the formula, C _pBe power factor, ω _tBe rotation speed of fan, R is a blade radius, and V is a wind speed.Actual power P with power deviation ε should be arranged, λ is the blower fan tip speed ratio, β is a propeller pitch angle.

$\mathrm{\&epsiv;}=\left|\frac{P-P_e}{P_e}\right|$

ε ₀For setting secure threshold.As ε≤ε ₀The time, show that fan operation is normal, otherwise output alarm signal.

In said step 3), used continuous wavelet transform (continuous-wavelet-transform CWT) is expressed as:

$\mathrm{CWT}(a,b)=\frac{1}{\sqrt{\left|a\right|}}\underset{-\&infin;}{\overset{\&infin;}{\&Integral;}}x\left(t\right){\mathrm{\&psi;}}^{*}\left(\frac{t-b}{a}\right)\mathrm{dt}$

In the formula, ψ (t) is a wavelet function, and asterisk * representes complex conjugate, and a is the small echo scope, and b is the small echo time sequence parameter.Wavelet transformation time-frequency window function representation is:

$\left\{\begin{array}{c}{\mathrm{\&omega;}}_{\mathrm{upper}}={\mathrm{\&omega;}}_c+{\mathrm{\&omega;}}_f/2\\ {\mathrm{\&omega;}}_{\mathrm{lower}}={\mathrm{\&omega;}}_c-{\mathrm{\&omega;}}_f/2\\ {\mathrm{\&omega;}}_f={\mathrm{\&eta;\&omega;}}_{\mathrm{fg}}\end{array}\right.$

ω is useful angular frequency in the formula, ω _UpperBe window top frequency, ω _LowerBe window bottom frequency, ω _fBe the fluctuation of useful frequency, ω _cBe mean frequency, ω _FgBe the fluctuation of generator rotational frequency.

Said ω _fBe time-varying parameter, depend on the fluctuation of generator rotational frequency, depend on fluctuations in wind speed in essence.The land fluctuations in wind speed is about 6%, and the ocean fluctuations in wind speed is 12%～20%.

Relation between continuous wavelet transform parameter a and the useful angular frequency is:

$a=\frac{{\mathrm{\&omega;}}_0}{\mathrm{\&omega;}}$

ω ₀Be time-frequency window CF center frequency.Wavelet function parameter a definitional domain does thus,

a∈[a _min?a _max]

$\left\{\begin{array}{c}{a}_{\min }=\frac{{\mathrm{\&omega;}}_0}{{\mathrm{\&omega;}}_{\mathrm{upper}}}\\ a_{\min }=\frac{{\mathrm{\&omega;}}_0}{{\mathrm{\&omega;}}_{\mathrm{lower}}}\end{array}\right.$

Local continuous wavelet transform is expressed as,

${\mathrm{CWT}}_{\mathrm{local}}(a,b)=\frac{1}{\sqrt{\left|a\right|}}\underset{-\&infin;}{\overset{\&infin;}{\&Integral;}}x\left(t\right){\mathrm{\&psi;}}^{*}\left(\frac{t-b}{a}\right)\mathrm{dt}$

So, in useful frequency signal, extract spectrum energy A and be expressed as,

$A({\mathrm{<figure-callout\; id="0"\; label="t"\; filenames="HSA00000589562300021.png,HSA00000589562300023.png"\; state="\{\{state\}\}">t</figure-callout>}}_0+T/2)=\max \left(\right|{\mathrm{CWT}}_{\mathrm{local}}(a,b)\left|\right)\left\{\begin{array}{c}a\&Element;\left[\begin{array}{cc}{a}_{\min }& a_{\max }\end{array}\right]\\ b\&Element;\left[\begin{array}{cc}{t}_0& {\mathrm{<figure-callout\; id="0"\; label="t"\; filenames="HSA00000589562300021.png,HSA00000589562300023.png"\; state="\{\{state\}\}">t</figure-callout>}}_0+T\end{array}\right]\end{array}\right.$

The time-frequency window number advances with the letter, and the maximin of window is followed motor rotational frequency ω _FgChange.Through cycle calculations in signal length, can obtain the oscillogram of spectrum energy A.

Embodiment 1

1 10kW wind-driven generator, simulation wind speed 10 meter per seconds, it is 10% that setting should have generated output and actual power power deviation threshold value.Through simulating mechanical disturbance or fault at the additional certain mass object of rotor; Be specially: normally move second at 0-60; Beginning in the 61st second; An additional 1kg addition (is approximately 0.3% on the rotor of its 290.7kg; The balancing mass grade that causes is G7.8, less than the low speed blower fan transmission shaft balance level G16 of ISO1940-1:2003 regulation) continue 60 seconds.According to said method step 1) acquisition system electric information.2) through pretreatment, it is 5.1%＜10% that generated output and actual power power deviation should be arranged, and meets the threshold value requirement.3) handle through system data of the present invention, the frequency analysis result as shown in Figure 3.Y coordinate is represented the spectrum energy of signal behind the mechanical disturbance.Can find out obviously that by Fig. 3 this mechanical disturbance can be detected very easily.

Embodiment 2

1 30kW wind-driven generator, simulation wind speed 10 meter per seconds, it is 10% that setting should have generated output and actual power power deviation threshold value.Come analog electrical gas disturbance or fault through changing its alternate resistance, be specially: normally move second at 0-60; In 60-120 second, change its certain alternate resistance 4%; In 120-180 second, normal operation; In 180-240 second, change its certain alternate resistance 9%; Normally move second at 240-300.According to the inventive method step 1) acquisition system electric information.2) through pretreatment, it is 4.3%＜10% that generated output and actual power power deviation should be arranged, and meets the threshold value requirement.3) handle through system data of the present invention, the frequency analysis result is like Fig. 4, shown in 5.Fig. 4 y coordinate is represented the disturbance correlation frequency after the electric disturbance, and Fig. 5 y coordinate is represented the spectrum energy of signal after the electric disturbance.Can find out that by Fig. 4,5 4% electric disturbance can be detected out, but not clearly, and 9% electric disturbance just can detect very easily.

In the above-described embodiments, in slight machinery and the electric disturbance of the artificial generation of wind-powered electricity generation unit diverse location, system and method for the present invention can detect the amount of unbalance of system before system mechanics or electrical failure.

The foregoing description does not limit the present invention in any way, and every employing is equal to the technological scheme that replacement or the mode of equivalent transformation obtain and all drops in protection scope of the present invention.

Claims (8)

1. the wind-driven generator health status monitoring system based on electric information is characterized in that, comprising:

The wind-driven generator data acquisition module is used for extracting required electric current and power signal from wind power generating set, and the relevant essential information of wind-driven generator;

Signal pre-processing module is used for the wind-driven generator power signal is carried out pretreatment, and extrapolating wind-driven generator through wind speed and fan rotor rotation speed mechanical quantity should have output power; Relatively real output with output power should be arranged, if deviation is excessive, output alarm signal and export monitoring information then, otherwise entering signal puocessing module to staff's telecontrol interface;

Signal processing module; Be used for the wind-driven generator output current signal is handled; Remove signal noise through discrete wavelet; Extract the signal characteristic frequency through continuous wavelet again; Contrast normal eigen frequency frequency spectrum and actual signal eigen frequency frequency spectrum; The output monitoring result, if find the fault spectrum component, warning and output fault message and fault spectrum are to the telecontrol interface;

The monitoring output module is used for output monitoring information and alarm signal.

2. the wind-driven generator health status monitoring method based on electric information is characterized in that, comprises the steps:

1) obtains wind-driven generator environmental data, running state physical quantity and output electric information from wind-driven generator;

2) wind-driven generator environmental data and the running state physical quantity that the wind-driven generator data acquisition module is obtained handled, and estimates to such an extent that wind-driven generator should have output power, contrasts real output, if deviation is excessive, and output alarm signal then;

3) the wind-driven generator output current signal that the wind-driven generator data acquisition module is obtained is handled; At first remove signal noise through discrete wavelet; Extract the signal characteristic frequency through continuous wavelet again; Contrast normal eigen frequency frequency spectrum and actual signal eigen frequency frequency spectrum; The output monitoring result; If find the fault spectrum component, report to the police and output fault message and fault spectrum.

3. the wind-driven generator health status monitoring method based on electric information according to claim 2; It is characterized in that: in said step 1); Obtaining the wind-driven generator environmental data from wind-driven generator is wind speed; The running state physical quantity is a rotor speed, and electric information is output current and power.

4. the wind-driven generator health status monitoring method based on electric information according to claim 2 is characterized in that: in said step 2) in, it is P that wind-driven generator should have output power _e,

$P_e=\frac{1}{2}\frac{\mathrm{\&rho;\&pi;}{R}^2{V}^3{C}_p(\mathrm{\&lambda;},\mathrm{\&beta;})}{{\mathrm{\&omega;}}_t}$

ρ is an air density in the formula, C _pBe power factor, ω _tBe rotation speed of fan, R is a blade radius, and V is a wind speed.Actual power P with power deviation ε should be arranged, λ is the blower fan tip speed ratio, β is a propeller pitch angle; Wherein

$\mathrm{\&epsiv;}=\left|\frac{P-P_e}{P_e}\right|$

ε ₀For setting secure threshold, as ε≤ε ₀The time, show that fan operation is normal, otherwise output alarm signal.

5. the wind-driven generator health status monitoring method based on electric information according to claim 2, it is characterized in that: in said step 3), said continuous wavelet transform is expressed as:

$\mathrm{CWT}(a,b)=\frac{1}{\sqrt{\left|a\right|}}\underset{-\&infin;}{\overset{\&infin;}{\&Integral;}}x\left(t\right){\mathrm{\&psi;}}^{*}\left(\frac{t-b}{a}\right)\mathrm{dt}$

In the formula, ψ (t) is a wavelet function, and asterisk * representes complex conjugate, and a is the small echo scope, and b is the small echo time sequence parameter; Wavelet transformation time-frequency window function representation is:

$\left\{\begin{array}{c}{\mathrm{\&omega;}}_{\mathrm{upper}}={\mathrm{\&omega;}}_c+{\mathrm{\&omega;}}_f/2\\ {\mathrm{\&omega;}}_{\mathrm{lower}}={\mathrm{\&omega;}}_c-{\mathrm{\&omega;}}_f/2\\ {\mathrm{\&omega;}}_f={\mathrm{\&eta;\&omega;}}_{\mathrm{fg}}\end{array}\right.$

ω is useful angular frequency in the formula, ω _UpperBe window top frequency, ω _LowerBe window bottom frequency, ω _fBe the fluctuation of useful frequency, ω _cBe mean frequency, ω _FgBe the fluctuation of generator rotational frequency.

6. the wind-driven generator health status monitoring method based on electric information according to claim 5 is characterized in that: said ω _fBe time-varying parameter, depend on the fluctuation of generator rotational frequency, depend on fluctuations in wind speed in essence; The land fluctuations in wind speed is 6%, and the ocean fluctuations in wind speed is 12%～20%.

7. the wind-driven generator health status monitoring method based on electric information according to claim 5 is characterized in that: the relation between said continuous wavelet transform parameter a and the useful angular frequency is:

$a=\frac{{\mathrm{\&omega;}}_0}{\mathrm{\&omega;}}$

Wherein, ω ₀Be time-frequency window CF center frequency, wavelet function parameter a definitional domain is thus:

a∈[a _min?a _max]

$\left\{\begin{array}{c}{a}_{\min }=\frac{{\mathrm{\&omega;}}_0}{{\mathrm{\&omega;}}_{\mathrm{upper}}}\\ a_{\min }=\frac{{\mathrm{\&omega;}}_0}{{\mathrm{\&omega;}}_{\mathrm{lower}}}\end{array}\right..$

8. according to claim 5,6 or 7 each described wind-driven generator health status monitoring methods based on electric information, it is characterized in that: local continuous wavelet transform is expressed as:

${\mathrm{CWT}}_{\mathrm{local}}(a,b)=\frac{1}{\sqrt{\left|a\right|}}\underset{-\&infin;}{\overset{\&infin;}{\&Integral;}}x\left(t\right){\mathrm{\&psi;}}^{*}\left(\frac{t-b}{a}\right)\mathrm{dt}$

In useful frequency signal, extracting spectrum energy A is expressed as:

$A(t_0+T/2)=\max \left(\right|{\mathrm{CWT}}_{\mathrm{local}}(a,b)\left|\right)\left\{\begin{array}{c}a\&Element;\left[\begin{array}{cc}{a}_{\min }& a_{\max }\end{array}\right]\\ b\&Element;\left[\begin{array}{cc}{t}_0& t_0+T\end{array}\right]\end{array}\right.$

The time-frequency window number advances with the letter, and the maximin of window is followed motor rotational frequency ω _FgChange,, can obtain the oscillogram of spectrum energy A through cycle calculations in signal length.

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