CN111173688A - Wind driven generator fault diagnosis and isolation method based on adaptive observer - Google Patents

Wind driven generator fault diagnosis and isolation method based on adaptive observer Download PDF

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CN111173688A
CN111173688A CN202010031883.8A CN202010031883A CN111173688A CN 111173688 A CN111173688 A CN 111173688A CN 202010031883 A CN202010031883 A CN 202010031883A CN 111173688 A CN111173688 A CN 111173688A
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generator
fault
value
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滕婧
李常玲
杨韬燃
冯一展
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North China Electric Power University
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/04—Automatic control; Regulation
    • F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
    • F03D7/043—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic
    • F03D7/045—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic with model-based controls
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/04—Automatic control; Regulation
    • F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
    • F03D7/043—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic
    • F03D7/046—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic with learning or adaptive control, e.g. self-tuning, fuzzy logic or neural network
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction

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Abstract

The invention relates to a wind driven generator fault diagnosis and isolation method based on a self-adaptive observer, which can diagnose eight faults proposed in a fan reference model and comprises the following concrete implementation steps: step 1: and modeling a fan reference model, wherein the fan reference model comprises eight basic faults. Step 2: and (3) simulating the fan reference model, connecting each part with two sensors to meet the requirement of physical redundancy, and recording actual measurement values obtained by the sensors. And step 3: and (3) transmitting the value obtained after the simulation of the reference model in the step (2) to a designed adaptive observer, comparing the estimated value obtained by the adaptive observer with the measured value in the sensor to obtain the value of a residual signal, comparing the value of the residual signal with a threshold value, and judging whether a fault is generated and the specific part is generated in a certain time period. The invention not only ensures the realization of the fault diagnosis and isolation process, but also can use the estimated value for the later fault-tolerant control.

Description

Wind driven generator fault diagnosis and isolation method based on adaptive observer
Technical Field
The invention belongs to the field of fan fault diagnosis, and particularly relates to a wind driven generator fault diagnosis and isolation method adopting an adaptive observer and a FAFE algorithm.
Background
The sliding mode observer is a dynamic system which obtains state variable estimated values according to measured values of external variables of the system. In the document [1], a wind driven generator foot fault diagnosis and isolation scheme based on a sliding mode observer is used, brake faults in a fan variable pitch system are converted into sensor faults, a transmission system reduced-order model is established for eliminating interference influence in aerodynamics, and a group of sliding mode observers are designed by using a new system representation.
In the literature, a sliding mode observer is used for modeling, but the sliding mode observer is extremely sensitive to measurement noise and can cause the problem of inaccurate result.
The document [2] provides a fault diagnosis and isolation scheme in a fan variable pitch system and a frequency converter based on an adaptive observer, the scheme converts the fault of a sensor on the variable pitch system into the fault of a brake, and the fault generated at different parts can be accurately judged according to the value of a residual signal.
The scheme only diagnoses partial faults in the reference model, and a solution is not provided for related faults in a transmission system.
Disclosure of Invention
The model-based adaptive observer method can be used for system online estimation, and can be used for an adaptive system which can determine unknown parameters and estimate state variables. The observer is used for obtaining an estimated value of a related signal, the estimated value is compared with a measured value to obtain a residual error, if the residual error exceeds a certain threshold value, the fault is judged to be generated, otherwise, the fault can be judged to be absent, and the adaptive observer has better performance in linear and nonlinear systems.
A Fast Adaptive Fault Estimation (FAFE) algorithm improves the traditional adaptive fault estimation algorithm and meets the Lyapunov stability condition. Although the traditional adaptive fault estimation algorithm can ensure constant fault unbiased. However, selecting a larger learning rate may enable fast fault estimation, but cannot avoidAvoiding large overshoot. If a smaller learning rate is selected, the effect of overshoot is overcome at the expense of sluggish response. FAFE algorithm in traditional method
Figure BDA0002364615190000023
On the basis of e is addedyDerivative of (t)
Figure BDA0002364615190000022
And the speed and the accuracy of fault diagnosis are improved.
Because interference and noise exist in wind speed, the aerodynamic processing in a transmission system becomes a difficulty in fault diagnosis, and in some observer designs and Kalman filtering-based methods, an aerodynamic model is divided into an estimation part and an unknown part for processing, so that the influence of the interference in the wind speed can be reduced.
Compared with the existing fault diagnosis method based on the model, the method improves the performance of the observer, has robustness to interference, considers linear and nonlinear systems at the same time, and can diagnose all faults in the reference model.
In order to achieve the above purposes, the technical scheme adopted by the invention is as follows:
a wind driven generator fault diagnosis and isolation method based on a self-adaptive observer can diagnose eight faults proposed in a wind driven generator reference model, and comprises the following concrete implementation steps:
step 1: and establishing a fan reference system, and dividing the fan reference system into eight basic faults.
Further comprising:
step 11: the fan reference system comprises a variable pitch system, a transmission system, a generator and frequency converter system and a controller;
the controller references β by using the blade pitch anglerTo control the pitch system by using the generator torque reference τg,rTo control the generator and frequency converter system. PrFor reference power, the value is 4.8 × 106。
Vw denotes the wind speed, Vw passes through the pitch system, which pitch systemThe blade-rotating rotor producing a rotor torque taurTo the transmission system, the transmission system is connected with a power transmission system,
generator and frequency converter system using generator torque reference τg,rTo obtain taugThe transmission system transmits the rotor torque taurAnd generator torque tau produced by the generator and frequency converter systemgConversion to rotor speed omegarAnd generator speed omegag. Generator and frequency converter system referenced by generator torque τg,rCombined with generator speed omegagCan obtain power Pg；
ωr,mIndicating rotor speed sensor measurements, omegag,mRepresenting measured values, tau, of generator speed sensorsg,mrepresenting a measured value of generator torque, betamRepresenting pitch angle measurements, while meeting physical redundancy requirements, ωr,m,ωg,m,βmMeasurement is carried out using two sensors, respectively, [ tau ]g,mMeasuring with a sensor; and transferred to the controller.
Step 12, subdividing the fan reference model into eight basic faults which are respectively as follows:
2000-2100s of fault, measured value beta of pitch angle on sensor 1 of blade 11,m1Generating a fixed value fault of 5 °;
2300-2400s of fault, measuring the pitch angle beta on the sensor 2 of the blade 22,m2Generating a gain factor fault value of 1.2;
2600-2700s Fault, Pitch Angle measurement β on blade 3 sensor 13,m1Generating a fixed value fault of 10 °;
and (4) failure four: at 1500-1600s, the rotor speed sensor 1 omegar,m1Generating a fixed value fault with a value of 1.4 rad/s;
and (5) failure: at 1000-1100s, the rotor speed sensor 2 omegar,m2And generator speed sensor 1 omegag,m1Gain factor failures with values 1.1 and 0.9 are generated;
and (6) failure six: 2900-3000s, brake failure caused by excessive air content in oil;
and a seventh fault: 3500-3600s, brake failure due to low pressure;
and eighth failure: 3800-3900s, brake failure due to an offset in converter torque control.
Step 2: the method comprises the following steps of simulating a fan reference model, and measuring three parts, namely rotor rotating speed, generator rotating speed and each blade pitch angle, by using two sensors respectively in order to meet the requirement of physical redundancy, wherein the sensors comprise: sensor 1, sensor 2; the generator torque is measured using a sensor, and the actual measurements made by the sensor are recorded separately.
Further comprising:
step 21: establishing an aerodynamic model of the fan as the torque acting on the blades:
Figure BDA0002364615190000041
where ρ represents air density, R represents blade radius, Cqrepresenting a table of torque coefficients, λ being the tip speed ratio, βiRepresents the corresponding torsion angle (i ═ 1, 2, 3), vmIs the wind speed.
The state space model of the variable pitch system is as follows:
Figure BDA0002364615190000042
yp＝Cpx (3)
wherein x represents a state vector having a value of
Figure BDA0002364615190000043
Is the first derivative of x, ApRepresenting a system matrix of values
Figure BDA0002364615190000051
BpRepresenting an input matrix of values
Figure BDA0002364615190000052
CpRepresenting an output matrix of values
Figure BDA0002364615190000053
ypto output the vector, betaiIn order to be the pitch angle,
Figure BDA0002364615190000054
is angular velocity, zeta is damping factor, omeganIs the natural frequency.
Step 22: the transmission system model is established as follows:
Figure BDA0002364615190000055
Figure BDA0002364615190000056
wherein, JrFor low rotational inertia of the shaft, KdtFor torsional stiffness of the drive train, BdtIs high-speed shaft viscous friction, NgIs a gear ratio, Jgis the rotational inertia of the high-speed shaft, etadtFor efficiency of the transmission system, thetaΔIs the driveline twist angle;
Figure BDA0002364615190000057
respectively rotor speed omegarGenerator speed omegag、θΔFirst derivative of, BdtFor the torsional damping coefficient of the drive train, BrIs low speed shaft viscous friction, BgFor high-speed shaft viscous friction, ydtThe output vector of the state space;
step 23: the system model of the generator and the frequency converter is established as
Figure BDA0002364615190000058
yc＝τg(7)
Wherein the content of the first and second substances,
Figure BDA0002364615190000059
for rotating generatorsMoment taugFirst derivative of, ycto output the vector, αgcModel parameters of the generator and the frequency converter are obtained;
the power generated by the generator is described as:
Pg(t)＝ηgωg(t)τg(t) (8)
wherein eta isgIs the generator efficiency.
And step 3: and (3) transmitting the value obtained after the simulation of the reference model in the step (2) to a designed adaptive observer, comparing the estimated value obtained by the adaptive observer with the measured value in the sensor to obtain the value of a residual signal, comparing the value of the residual signal with a threshold value, and judging whether a fault is generated and the specific part is generated in a certain time period.
Further comprising:
step 31, FAFE algorithm:
Figure BDA0002364615190000061
where F is the matrix, Γ is the learning rate, eyObtained by differencing the observer output vector with the state space model output variable, i.e.
Figure BDA0002364615190000062
A is a scalar quantity of a number of words,
Figure BDA0002364615190000063
is eyThe introduction of the derivative of (a), which enhances the speed and accuracy of fault diagnosis, is applicable in both linear and non-linear systems, the traditional method being a special case of the algorithm.
Theorem: if there is a symmetric positive definite matrix P, G, matrix Y, F and scalar σ > 0, μ > 0, the brake fault estimator is stable, such that
P＝PT＞0 (10)
Figure BDA0002364615190000064
PB＝CTYT(12)
Wherein, represents symmetrical terms in the symmetrical matrix, i.e.
Figure BDA0002364615190000065
Transpose of (P)TIs the transpose of matrix P, A is the system matrix in the state space, ATIs the transpose of matrix A, B is the input matrix in the state space, C is the output matrix in the state space, CTAs a transpose of matrix C, E is a constant matrix of brake failures in state space, ETIs the transpose of E, YTIs the transpose of Y.
When the above conditions are satisfied, the observer gain matrix is:
L＝P-1Y (13)
step 32: designing a variable pitch system self-adaptive observer:
Figure BDA0002364615190000071
Δβm＝0.5(βi,m1+βi,m2)＝Cpx+Δβi,m(14)
wherein x is an input vector,
Figure BDA0002364615190000072
is the first derivative of x, ApIs a system matrix, BpAs an input matrix, Cpas an output matrix, βi,m1representative of blade i sensor 1 obtaining pitch angle measurement, βi,m2representative of blade i sensor 2 obtaining a pitch angle measurement, Δ βmis the average of the two sensor measurements, Δ βi,mAnd the failure average value of the pitch angle sensor is obtained.
Order to
Figure BDA0002364615190000073
Then
Figure BDA0002364615190000074
In combination with the formula (14), the state can be obtainedAnd (3) space model:
Figure BDA0002364615190000075
Figure BDA0002364615190000076
wherein, ypRepresenting an output matrix, Z1Which represents the input vector, is,
Figure BDA0002364615190000077
is Z1The first derivative of (a).
From this, the adaptive observer of the pitch system can be designed:
Figure BDA0002364615190000078
Figure BDA0002364615190000079
wherein the content of the first and second substances,
Figure BDA00023646151900000710
in order to input the vector, the vector is input,
Figure BDA00023646151900000711
is composed of
Figure BDA00023646151900000712
The first derivative of (a) is,
Figure BDA00023646151900000713
in order to obtain an output vector estimation value by the variable pitch system adaptive observer,
Figure BDA00023646151900000714
to obtain an estimate of the fault as calculated by the FAFE algorithm,
Figure BDA00023646151900000715
which represents the input vector, is,
Figure BDA00023646151900000716
is composed of
Figure BDA00023646151900000717
The first derivative of (a).
Step 33, obtaining a residual signal of the variable pitch system:
Figure BDA0002364615190000081
Figure BDA0002364615190000082
Figure BDA0002364615190000083
a residual signal close to 0 represents no fault, and a significant deviation of the residual signal from 0 represents a fault;
if r1,1Deviation of 0, r1,2If the value is close to 0, the generation of a fault I is proved;
if r2,1Near 0, r2,2If the deviation is 0, the generation of a second fault is proved;
if r3,1Deviation of 0, r3,2If the value is close to 0, the generation of a third fault is proved;
if r2,1，r2,2Meanwhile, if the deviation is 0, the generation of a fault six is proved;
if r3,1，r3,2While deviating from 0, a fault seven is proved to occur.
Step 34, designing the adaptive observer of the transmission system
Due to the presence of disturbances and noise in the wind speed, the measurement has uncertainty and the aerodynamic model is divided into an estimation part
Figure BDA0002364615190000084
And unknown part
Figure BDA0002364615190000085
Treatment of, i.e.
Figure BDA0002364615190000086
Equation (3) can thus be written:
Figure BDA0002364615190000087
Figure BDA0002364615190000088
where E is the column full naive matrix, dn is the unknown input, ωr,m1Obtaining a measured value, omega, for a rotor speed sensor 1r,m2Obtaining a measured value, omega, for a rotor speed sensor 2g,m1Obtaining a measured value, omega, for a generator speed sensor 1g,m2Obtaining a measured value, Δ ω, for a rotor speed sensor 2r,mAs an average of rotor speed sensor measurements, Δ ωg,mAs an average value, Δ ω, of the generator speed sensor measurementsr,miAs mean value of rotor speed sensor faults, Δ ωg,miAnd the average value of the faults of the generator speed sensor is obtained.
Defining a new state
Figure BDA0002364615190000091
Then
Figure BDA0002364615190000092
Thus, combining (20) results in a state space model:
Figure BDA0002364615190000093
Figure BDA0002364615190000094
wherein A isbIs a state matrix of value
Figure BDA0002364615190000095
BbFor inputting a matrix, the value is
Figure BDA0002364615190000096
CbIs an output matrix of value
Figure BDA0002364615190000097
Z2For a new definition of the input vector, the first derivative of the input vector is
Figure BDA0002364615190000101
From this, can design transmission system self-adaptation observer
Figure BDA0002364615190000102
Figure BDA0002364615190000103
Wherein the content of the first and second substances,
Figure BDA0002364615190000104
are respectively an input vector omegar、ωg、θΔ、Z2Is determined by the estimated value of (c),
Figure BDA0002364615190000105
is composed of
Figure BDA0002364615190000106
The first derivative of (a);
Figure BDA0002364615190000107
an estimated output value is obtained for the transmission system adaptive observer.
Step 35, obtaining a residual signal of the transmission system:
Figure BDA0002364615190000108
Figure BDA0002364615190000109
if r1,1Significant deviation from 0, r1,2If the value is close to 0, judging that a fourth fault is generated;
if r1,2，r1,2And if the error signal deviates from 0 and other residual error signals are close to 0, judging that a fault five occurs.
Step 36, designing the self-adaptive observer of the generator and frequency converter system
Figure BDA00023646151900001010
Figure BDA00023646151900001011
Wherein the content of the first and second substances,
Figure BDA00023646151900001012
for generator torque τgIs determined by the estimated value of (c),
Figure BDA00023646151900001013
is composed of
Figure BDA00023646151900001014
L is a gain matrix,
Figure BDA00023646151900001015
to obtain an estimated output vector, y, from an adaptive observercIs an output vector obtained by equation (7).
Step 37, obtaining a residual signal of the generator and the frequency converter system
Figure BDA0002364615190000111
Wherein, taug,mRepresenting a generator torque measurement.
If r is close to 0 no fault is generated,
if r deviates significantly from 0, a fault eight is generated.
The invention has the beneficial effects that:
according to the method, the adaptive observer is used for processing related signals in the fan model, the adaptive observer can estimate parameters in the model, the implementation of fault diagnosis and isolation processes is guaranteed, and the estimated values can be used for later fault-tolerant control. Meanwhile, the accuracy and the speed of the fault estimation process are improved by introducing the rapid self-adaptive fault estimation algorithm, and the performance of the fault estimation algorithm is improved compared with that of the traditional fault estimation algorithm. Uncertainty caused by interference and noise in the wind speed is a difficulty in fan fault diagnosis, and the method processes the aerodynamic model, so that the influence of the uncertainty in the wind speed on the result is reduced. And finally, comparing the residual error signal with a set threshold, and judging that a fault occurs if the residual error signal exceeds the threshold. In addition, the whole process of the method is free from human intervention, and diagnosis and isolation of all faults proposed in the fan reference model are realized.
Simulation experiment conditions are as follows: the reference model is shown in FIGS. 2 and 3, the wind speed sequence used by the model is shown in FIG. 4, and it can be seen that the wind speed range from 0s to 4400s is 5 m/s to 20m/s, and the range covers the allowable wind speed range for normal operation of the wind turbine. The relevant parameter values are: j. the design is a squarer＝55e6，Kdt＝2.7e9，Bdt＝775.45，Ng＝95，Jg＝45.6，ηdt＝0.97，αgc50. And injecting the fault into the reference model in a required time range.
In summary, the method for diagnosing and isolating the fault of the wind driven generator by using the adaptive observer provided by the invention is feasible.
The technical key points and points to be protected of the invention are as follows:
(1) the method solves the problem of uncertainty caused by interference and noise in the wind speed, and the problem is a difficult point in the current model-based fan fault diagnosis.
(2) Use of the FAFE algorithm in an adaptive observer.
Reference to the literature
[1]J.Zhang,O.Bennouna,A.K.Swain,and S.K.Nguang,“Detection andisolation of sensor faults of wind turbines using sliding mode observers,”inPRO-CEEDINGS OF 2013INTERNATIONAL RENEWABLE AND SUSTAIN-ABLE ENERGYCONFERENCE(IRSEC),2013.
[2]A.Mokhtari and M.Belkhiri,“An adapative observer based fdi forwind turbine benchmark model,”in 2016 8th International Conference onModelling,Identification and Control(ICMIC),2016.
Drawings
The invention has the following drawings:
FIG. 1 Structure of the invention
FIG. 2 reference model simulation diagram one
FIG. 3 reference model simulation Diagram two
FIG. 4 wind speed sequence chart
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings 1 to 4.
A wind driven generator fault diagnosis and isolation method based on an adaptive observer can diagnose eight kinds of faults proposed in a wind driven generator reference model. The method comprises the following concrete steps:
step 1: a wind turbine reference system is established, and the system comprises eight basic faults.
Further comprising:
step 11: with reference to the first drawing, the fan reference model comprises a variable pitch system, a transmission system, a generator and frequency converter system and a controller;
the controller references β by using the blade pitch anglerTo control the pitch system by using the generator torque reference τg,rTo control the generator and frequency converter system. PrFor reference power, the value is 4.8 × 106。
Vw represents the wind speed, which passes through the pitch system whose blades turn the rotor producing a rotor torque τrTo the transmission system, the transmission system is connected with a power transmission system,
generator and frequency converter system using generator torque reference τg,rTo obtain taugThe transmission system will trAnd generator torque tau produced by the generator and frequency converter systemgConversion to rotor speed omegarAnd generator speed omegag. Generator and frequency converter system through taug,rCombined with generator speed omegagCan obtain power Pg；
ωr,mIndicating rotor speed sensor measurements, omegag,mRepresenting measured values, tau, of generator speed sensorsg,mrepresenting a measured value of generator torque, betamRepresenting pitch angle measurements, while meeting physical redundancy requirements, ωr,m,ωg,m,βmMeasurement is carried out using two sensors, respectively, [ tau ]g,mMeasuring with a sensor;
step 12, subdividing the reference system into eight faults which are respectively as follows:
2000-2100s of fault, measured value beta of pitch angle on sensor 1 of blade 11,m1Generating a fixed value fault of 5 °;
2300-2400s of fault, measuring the pitch angle beta on the sensor 2 of the blade 22,m2Generating a gain factor fault value of 1.2;
2600-2700s Fault, Pitch Angle measurement β on blade 3 sensor 13,m1Generating a fixed value fault of 10 °;
and (4) failure four: at 1500-1600s, the rotor speed sensor 1 omegar,m1Generating a fixed value fault with a value of 1.4 rad/s;
and (5) failure: at 1000-1100s, the rotor speed sensor 2 omegar,m2And generator speed sensor 1 omegag,m1Gain factor failures with values 1.1 and 0.9 are generated;
and (6) failure six: 2900-3000s, brake failure caused by excessive air content in oil;
and a seventh fault: 3500-3600s, brake failure due to low pressure;
and eighth failure: 3800-3900s, brake failure due to an offset in converter torque control.
Step 2: the reference system is simulated, and simulation diagrams are shown in fig. 2 and 3. In order to meet the requirement of physical redundancy, three parts of rotor rotating speed, generator rotating speed and each blade pitch angle are measured by two sensors, wherein the sensors comprise: sensor 1, sensor 2; the generator torque is measured using a sensor, and the actual measurements made by the sensor are recorded separately.
Further comprising:
step 21: the aerodynamics of the fan are modeled as the torque acting on the blades:
Figure BDA0002364615190000141
where ρ represents air density, R represents blade radius, Cqrepresenting a table of torque coefficients, λ being the tip speed ratio, βiRepresents the corresponding torsion angle (i ═ 1, 2, 3), vmIs the wind speed.
The state space model of the variable pitch system is as follows:
Figure BDA0002364615190000142
yp＝Cpx (3)
wherein x represents a state vector having a value of
Figure BDA0002364615190000151
Is the first derivative of x, ApRepresenting a system matrix of values
Figure BDA0002364615190000152
BpRepresenting an input matrix of values
Figure BDA0002364615190000153
CpRepresenting an output matrix of values
Figure BDA0002364615190000154
ypto output the vector, betaiIn order to be the pitch angle,
Figure BDA0002364615190000155
is angular velocity, zeta is damping factor, omeganIs the natural frequency.
Step 22: the transmission system can be modeled as:
Figure BDA0002364615190000156
Figure BDA0002364615190000157
wherein, JrFor low rotational inertia of the shaft, KdtFor torsional stiffness of the drive train, BdtIs high-speed shaft viscous friction, NgIs a gear ratio, Jgis the rotational inertia of the high-speed shaft, etadtFor efficiency of the transmission system, thetaΔIs the driveline twist angle;
Figure BDA0002364615190000158
respectively rotor speed omegarGenerator speed omegag、θΔFirst derivative of, BdtFor the torsional damping coefficient of the drive train, BrIs low speed shaft viscous friction, BgFor high-speed shaft viscous friction, ydtThe output vector of the state space;
step 23: the generator and frequency converter system can be modeled as
Figure BDA0002364615190000159
yc＝τg(7)
Wherein the content of the first and second substances,
Figure BDA00023646151900001510
for generator torque τgFirst derivative of, ycto output the vector, αgcAre model parameters of the generator and the frequency converter,
the power generated by the generator is described as:
Pg(t)＝ηgωg(t)τg(t) (8)
wherein eta isgIs the generator efficiency.
And step 3: and (3) transmitting the value obtained after the simulation of the reference model in the step (2) to a designed adaptive observer, comparing the estimated value obtained by the observer with the measured value in the sensor to obtain the value of a residual signal, comparing the value of the residual signal with a threshold value, and judging whether a fault is generated and the specific part is generated in a certain time period.
Further comprising:
step 31, FAFE algorithm:
Figure BDA0002364615190000161
where F is the matrix, Γ is the learning rate, eyObtained by differencing the observer output vector with the state space model output variable, i.e.
Figure BDA0002364615190000162
A is a scalar quantity of a number of words,
Figure BDA0002364615190000163
is eyThe introduction of the derivative of (a), which enhances the speed and accuracy of fault diagnosis, is applicable in both linear and non-linear systems, the traditional method being a special case of the algorithm.
Theorem: if there is a symmetric positive definite matrix P, G, matrix Y, F and scalar σ > 0, μ > 0, the brake fault estimator is stable, such that
P＝PT＞0 (10)
Figure BDA0002364615190000164
PB＝CTYT(12)
Wherein, represents symmetrical terms in the symmetrical matrix, i.e.
Figure BDA0002364615190000165
Transpose of (P)TIs the transpose of matrix P, A is the system matrix in the state space, ATIs the transpose of matrix A, B is the input matrix in the state space, C is the output matrix in the state space, CTAs a transpose of matrix C, E is a constant matrix of brake failures in state space, ETIs the transpose of E, YTIs the transpose of Y.
When the above conditions are satisfied, the observer gain matrix is:
L＝P-1Y (13)
step 32: variable pitch system adaptive observer design
Figure BDA0002364615190000171
Δβm＝0.5(βi,m1+βi,m2)＝Cpx+Δβi,m(14)
Wherein x is an input vector,
Figure BDA0002364615190000172
is the first derivative of x, ApIs a system matrix, BpAs an input matrix, Cpas an output matrix, βi,m1representative of blade i sensor 1 obtaining pitch angle measurement, βi,m2representative of blade i sensor 2 obtaining a pitch angle measurement, Δ βmis the average of the two sensor measurements, Δ βi,mAnd the failure average value of the pitch angle sensor is obtained.
Order to
Figure BDA0002364615190000173
Then
Figure BDA0002364615190000174
In conjunction with equation (14), a state space model is obtained:
Figure BDA0002364615190000175
Figure BDA0002364615190000176
wherein, ypRepresenting an output matrix, Z1Which represents the input vector, is,
Figure BDA0002364615190000177
is Z1The first derivative of (a).
From this, the adaptive observer of the pitch system can be designed:
Figure BDA0002364615190000178
Figure BDA0002364615190000179
wherein the content of the first and second substances,
Figure BDA00023646151900001710
in order to input the vector, the vector is input,
Figure BDA00023646151900001711
is composed of
Figure BDA00023646151900001712
The first derivative of (a) is,
Figure BDA00023646151900001713
in order to obtain an output vector estimation value by the variable pitch system adaptive observer,
Figure BDA00023646151900001714
to obtain an estimate of the fault as calculated by the FAFE algorithm,
Figure BDA00023646151900001715
which represents the input vector, is,
Figure BDA00023646151900001716
is composed of
Figure BDA00023646151900001717
The first derivative of (a).
Step 33, residual signal:
Figure BDA0002364615190000181
Figure BDA0002364615190000182
Figure BDA0002364615190000183
a residual signal close to 0 indicates no fault is generated and a significant deviation of the residual signal from 0 indicates a fault is generated.
If r1,1Deviation of 0, r1,2Near 0, it is verified that a failure of one occurred;
if r2,1Near 0, r2,2A deviation from 0, a failure of two is demonstrated,
if r3,1Deviation of 0, r3,2If the value is close to 0, the generation of a third fault is proved;
if r2,1，r2,2Meanwhile, if the deviation is 0, the generation of a fault six is proved;
if r3,1，r3,2While a deviation of 0 would prove the generation of fault seven.
Step 34, design of the self-adaptive observer of the transmission system
Due to the presence of disturbances and noise in the wind speed, the measurement has uncertainty and the aerodynamic model is divided into an estimation part
Figure BDA0002364615190000184
And unknown part
Figure BDA0002364615190000185
Treatment of, i.e.
Figure BDA0002364615190000186
Thus, formula (3) can be written as
Figure BDA0002364615190000187
Figure BDA0002364615190000188
Where E is the column full naive matrix, dn is the unknown input, ωr,m1For rotor speed sensor 1To the measured value, ωr,m2Obtaining a measured value, omega, for a rotor speed sensor 2g,m1Obtaining a measured value, omega, for a generator speed sensor 1g,m2Obtaining a measured value, Δ ω, for a rotor speed sensor 2r,mAs an average of rotor speed sensor measurements, Δ ωg,mAs an average value, Δ ω, of the generator speed sensor measurementsr,miAs mean value of rotor speed sensor faults, Δ ωg,miAnd the average value of the faults of the generator speed sensor is obtained.
Defining a new state
Figure BDA0002364615190000191
Then
Figure BDA0002364615190000192
Thus, combining (20) results in a state space model:
Figure BDA0002364615190000193
Figure BDA0002364615190000194
wherein A isbIs a state matrix of value
Figure BDA0002364615190000195
BbFor inputting a matrix, the value is
Figure BDA0002364615190000196
CbIs an output matrix of value
Figure BDA0002364615190000197
Z2For a new definition of the input vector, the first derivative of the input vector is
Figure BDA0002364615190000201
From this, a drive system adaptive observer can be designed:
Figure BDA0002364615190000202
Figure BDA0002364615190000203
wherein the content of the first and second substances,
Figure BDA0002364615190000204
are respectively an input vector omegar、ωg、θΔ、Z2Is determined by the estimated value of (c),
Figure BDA0002364615190000205
is composed of
Figure BDA0002364615190000206
The first derivative of (a);
Figure BDA0002364615190000207
an estimated output value is obtained for the transmission system adaptive observer.
Step 35, residual signal:
Figure BDA0002364615190000208
Figure BDA0002364615190000209
if r1,1Significant deviation from 0, r1,2If the value is close to 0, judging that a fourth fault is generated;
if r1,2，r1,2And if the error signal deviates from 0 and other residual error signals are close to 0, judging that a fault five occurs.
Step 36, design of self-adaptive observer of generator and frequency converter system
Figure BDA00023646151900002010
Figure BDA00023646151900002011
Wherein the content of the first and second substances,
Figure BDA00023646151900002012
for generator torque τgIs determined by the estimated value of (c),
Figure BDA00023646151900002013
is composed of
Figure BDA00023646151900002014
L is a gain matrix,
Figure BDA00023646151900002015
to obtain an estimated output vector, y, from an adaptive observercIs an output vector obtained by equation (7).
Step 37 residual signal
Figure BDA0002364615190000211
Wherein, taug,mRepresenting a generator torque measurement.
If r is close to 0, no failure occurs,
if r deviates significantly from 0, a fault eight is generated.
Those not described in detail in this specification are within the skill of the art.

Claims (5)

1. A wind driven generator fault diagnosis and isolation method based on an adaptive observer is characterized by comprising the following steps:
step 1: establishing a fan reference system, and dividing the fan reference system into eight basic faults;
step 2: modeling simulation is carried out on a fan reference system, and in order to meet the requirement of physical redundancy, the three parts of the rotor rotating speed, the generator rotating speed and each blade pitch angle are measured by two sensors respectively, wherein the sensors comprise: sensor 1, sensor 2; measuring the torque of the generator by using a sensor, and respectively recording actual measurement values obtained by the sensor;
and step 3: and (3) transmitting the value obtained after the simulation of the fan reference model in the step (2) to a designed adaptive observer, comparing the estimated value obtained by the adaptive observer with the measured value in the sensor to obtain the value of a residual signal, comparing the value of the residual signal with a threshold value, and judging whether a fault is generated and the specific part generated in a certain time period.
2. The adaptive-observer-based wind turbine generator fault diagnosis and isolation method according to claim 1, wherein the wind turbine reference system of step 1 comprises a pitch system, a transmission system, a generator and frequency converter system, and a controller;
the controller references β by using the blade pitch anglerTo control the pitch system by using the generator torque reference τg,rTo control the generator and frequency converter system;
vw represents the wind speed, which passes through the pitch system whose blades turn the rotor producing a rotor torque τrAnd transmitted to the transmission system, the generator and frequency converter system using the generator torque reference taug,rObtaining the generator torque taugThe transmission system transmits the rotor torque taurAnd generator torque tau produced by the generator and frequency converter systemgConversion to rotor speed omegarAnd generator speed omegag(ii) a Generator and frequency converter system referenced by generator torque τg,rCombined with generator speed omegagTo obtain power Pg，ωr,mIndicating rotor speed sensor measurements, omegag,mRepresenting measured values, tau, of generator speed sensorsg,mrepresenting a measured value of generator torque, betamRepresenting pitch angle measurements, while meeting physical redundancy requirements, ωr,m,ωg,m,βmMeasurement is carried out using two sensors, respectively, [ tau ]g,mMeasuring with a sensor; and communicates the measurements to the controller.
3. The adaptive-observer-based wind turbine generator fault diagnosis and isolation method according to claim 2, wherein the eight basic faults of the wind turbine reference model in the step 1 are:
2000-2100s of fault, measured value beta of pitch angle on sensor 1 of blade 11,m1Generating a fixed value fault of 5 °;
2300-2400s of fault, measuring the pitch angle beta on the sensor 2 of the blade 22,m2Generating a gain factor fault value of 1.2;
2600-2700s Fault, Pitch Angle measurement β on blade 3 sensor 13,m1Generating a fixed value fault of 10 °;
and (4) failure four: at 1500-1600s, the rotor speed sensor 1 omegar,m1Generating a fixed value fault with a value of 1.4 rad/s;
and (5) failure: at 1000-1100s, the rotor speed sensor 2 omegar,m2And generator speed sensor 1 omegag,m1Gain factor failures with values 1.1 and 0.9 are generated;
and (6) failure six: 2900-3000s, brake failure caused by excessive air content in oil;
and a seventh fault: 3500-3600s, brake failure due to low pressure;
and eighth failure: 3800-3900s, brake failure due to an offset in converter torque control.
4. The adaptive-observer-based wind turbine generator fault diagnosis and isolation method according to claim 1, wherein the step 2 specifically comprises the steps of:
step 21: establishing an aerodynamic model of the fan as the torque acting on the blades:
Figure FDA0002364615180000031
where ρ represents air density, R represents blade radius, Cqrepresenting a table of torque coefficients, λ being the tip speed ratio, βiRepresents the corresponding torsion angle i ═ 1, 2, 3, vmIs the wind speed;
the state space model of the variable pitch system is as follows:
Figure FDA0002364615180000032
yp＝Cpx (3)
wherein x represents a state vector having a value of
Figure FDA0002364615180000033
Figure FDA0002364615180000034
Is the first derivative of x, ApRepresenting a system matrix of values
Figure FDA0002364615180000035
BpRepresenting an input matrix of values
Figure FDA0002364615180000036
CpRepresenting an output matrix of values
Figure FDA0002364615180000037
ypto output the vector, betaiIn order to be the pitch angle,
Figure FDA0002364615180000038
is angular velocity, zeta is damping factor, omeganIs a natural frequency;
step 22: establishing a transmission system model:
Figure FDA0002364615180000039
Figure FDA00023646151800000310
wherein, JrFor low rotational inertia of the shaft, KdtFor torsional stiffness of the drive train, BdtIs high-speed shaft viscous friction, NgIs a gear ratio, Jgis the rotational inertia of the high-speed shaft, etadtFor efficiency of the transmission system, thetaΔIs the driveline twist angle;
Figure FDA0002364615180000041
respectively rotor speed omegarGenerator speed omegag、θΔFirst derivative of, BdtFor the torsional damping coefficient of the drive train, BrIs low speed shaft viscous friction, BgFor high-speed shaft viscous friction, ydtThe output vector of the state space;
step 23: the method comprises the following steps of establishing a generator and frequency converter system model:
Figure FDA0002364615180000042
yc＝τg(7)
wherein the content of the first and second substances,
Figure FDA0002364615180000043
for generator torque τgFirst derivative of, ycto output the vector, αgcModel parameters of the generator and the frequency converter are obtained;
the power generated by the generator is described as:
Pg(t)＝ηgωg(t)τg(t) (8)
wherein eta isgIs the generator efficiency.
5. The adaptive-observer-based wind turbine generator fault diagnosis and isolation method according to claim 4, wherein the step 3 specifically comprises the steps of:
step 31: designing a variable pitch system self-adaptive observer:
Figure FDA0002364615180000044
Δβm＝0.5(βi,m1+βi,m2)＝Cpx+Δβi,m(14)
wherein x is an input vector,
Figure FDA0002364615180000045
is the first derivative of x, ApIs a system matrix, BpAs an input matrix, Cpas an output matrix, βi,m1representative of blade i sensor 1 obtaining pitch angle measurement, βi,m2representative of blade i sensor 2 obtaining a pitch angle measurement, Δ βmis the average of the two sensor measurements, Δ βi,mThe fault average value of the pitch angle sensor is obtained;
order to
Figure FDA0002364615180000046
Then
Figure FDA0002364615180000047
Combining equation (14), we get the state space model:
Figure FDA0002364615180000051
Figure FDA0002364615180000052
wherein, ypRepresenting an output matrix, Z1Which represents the input vector, is,
Figure FDA0002364615180000053
is Z1The first derivative of (a);
therefore, the self-adaptive observer of the variable pitch system is designed:
Figure FDA0002364615180000054
Figure FDA0002364615180000055
wherein the content of the first and second substances,
Figure FDA0002364615180000056
in order to input the vector, the vector is input,
Figure FDA0002364615180000057
is composed of
Figure FDA0002364615180000058
The first derivative of (a) is,
Figure FDA0002364615180000059
in order to obtain an output vector estimation value by the variable pitch system adaptive observer,
Figure FDA00023646151800000510
to obtain an estimate of the fault as calculated by the FAFE algorithm,
Figure FDA00023646151800000511
which represents the input vector, is,
Figure FDA00023646151800000512
is composed of
Figure FDA00023646151800000513
The first derivative of (a);
step 32, obtaining a residual signal of the variable pitch system:
Figure FDA00023646151800000514
Figure FDA00023646151800000515
Figure FDA00023646151800000516
a residual signal close to 0 means no fault is generated, and the residual signal deviates significantly0 represents the occurrence of a failure;
if r1,1Deviation of 0, r1,2If the value is close to 0, the generation of a fault I is proved;
if r2,1Near 0, r2,2If the deviation is 0, the generation of a second fault is proved;
if r3,1Deviation of 0, r3,2If the value is close to 0, the generation of a third fault is proved;
if r2,1，r2,2Meanwhile, if the deviation is 0, the generation of a fault six is proved;
if r3,1，r3,2Meanwhile, if the deviation is 0, the fault seven is proved to be generated;
step 33, designing the transmission system adaptive observer:
due to the presence of disturbances and noise in the wind speed, the measurement has uncertainty and the aerodynamic model is divided into an estimation part
Figure FDA00023646151800000517
And unknown part
Figure FDA00023646151800000518
And (3) treatment:
Figure FDA0002364615180000061
equation (3) is thus written:
Figure FDA0002364615180000062
Figure FDA0002364615180000063
where E is the column full naive matrix, dn is the unknown input, ωr,m1Obtaining a measured value, omega, for a rotor speed sensor 1r,m2Obtaining a measured value, omega, for a rotor speed sensor 2g,m1Obtaining a measured value, omega, for a generator speed sensor 1g,m2Obtaining a measured value, Δ ω, for a rotor speed sensor 2r,mAs an average of rotor speed sensor measurements, Δ ωg,mAs an average value, Δ ω, of the generator speed sensor measurementsr,miAs mean value of rotor speed sensor faults, Δ ωg,miThe average value of the faults of the generator speed sensor is obtained;
defining a new state
Figure FDA0002364615180000064
Then
Figure FDA0002364615180000065
Thus, the state space model is obtained in combination (15):
Figure FDA0002364615180000066
Figure FDA0002364615180000071
Figure FDA0002364615180000072
wherein A isbIs a state matrix of value
Figure FDA0002364615180000073
BbFor inputting a matrix, the value is
Figure FDA0002364615180000074
CbIs an output matrix of value
Figure FDA0002364615180000075
Z2For a new definition of the input vector, the first derivative of the input vector is
Figure FDA0002364615180000076
Thus, the transmission system adaptive observer is designed:
Figure FDA0002364615180000077
Figure FDA0002364615180000078
wherein the content of the first and second substances,
Figure FDA0002364615180000079
are respectively an input vector omegar、ωg、θΔ、Z2Is determined by the estimated value of (c),
Figure FDA00023646151800000710
is composed of
Figure FDA00023646151800000711
The first derivative of (a);
Figure FDA00023646151800000712
obtaining an estimated output value for the transmission system adaptive observer;
step 34, obtaining a residual signal of the transmission system:
Figure FDA0002364615180000081
Figure FDA0002364615180000082
if r1,1Significant deviation from 0, r1,2If the value is close to 0, judging that a fourth fault is generated;
if r1,2，r1,2If the error signal deviates from 0 and other residual error signals are close to 0, judging that a fault five occurs;
step 35, designing a self-adaptive observer of the generator and frequency converter system:
Figure FDA0002364615180000083
Figure FDA0002364615180000084
wherein the content of the first and second substances,
Figure FDA0002364615180000085
for generator torque τgIs determined by the estimated value of (c),
Figure FDA0002364615180000086
is composed of
Figure FDA0002364615180000087
L is a gain matrix,
Figure FDA0002364615180000088
to obtain an estimated output vector, y, from an adaptive observercIs an output vector obtained by equation (7);
step 36, obtaining a residual signal of the generator and the frequency converter system
Figure FDA0002364615180000089
Wherein, taug,mRepresenting a generator torque measurement;
if r is close to 0 no fault is generated,
if r deviates significantly from 0, a fault eight is generated.
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CN112081715A (en) * 2020-09-07 2020-12-15 浙江浙能技术研究院有限公司 Method for flexibly inhibiting torsional vibration of driving chain of wind generating set
CN112502900A (en) * 2020-10-16 2021-03-16 浙江工业大学 Wind power gear box transient load active suppression method based on nonlinear damping control
CN112727678A (en) * 2020-12-29 2021-04-30 重庆电子工程职业学院 Fan variable pitch control system based on multiple fault-tolerant modes

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112081715A (en) * 2020-09-07 2020-12-15 浙江浙能技术研究院有限公司 Method for flexibly inhibiting torsional vibration of driving chain of wind generating set
CN112081715B (en) * 2020-09-07 2021-08-13 浙江浙能技术研究院有限公司 Method for flexibly inhibiting torsional vibration of driving chain of wind generating set
CN112502900A (en) * 2020-10-16 2021-03-16 浙江工业大学 Wind power gear box transient load active suppression method based on nonlinear damping control
CN112502900B (en) * 2020-10-16 2021-10-15 浙江工业大学 Wind power gear box transient load active suppression method based on nonlinear damping control
CN112727678A (en) * 2020-12-29 2021-04-30 重庆电子工程职业学院 Fan variable pitch control system based on multiple fault-tolerant modes
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