CN113935408A - Wind power generation equipment state monitoring method based on simplified nuclear principal component network - Google Patents

Wind power generation equipment state monitoring method based on simplified nuclear principal component network Download PDF

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CN113935408A
CN113935408A CN202111156322.1A CN202111156322A CN113935408A CN 113935408 A CN113935408 A CN 113935408A CN 202111156322 A CN202111156322 A CN 202111156322A CN 113935408 A CN113935408 A CN 113935408A
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陈杨
陈勇旗
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Abstract

The invention discloses a wind power generation equipment state monitoring method based on a simplified nuclear principal component network. Specifically, the method disclosed by the invention implements sample distribution characteristic analysis on a training data set of the wind power generation equipment, and uses edge data points and clustering center data points to construct a hidden layer of the principal component network, so that the calculated amount during online monitoring can be greatly reduced, and the application feasibility and the real-time property of the state monitoring method based on the simplified principal component network are ensured. In addition, the method respectively calculates corresponding outputs through the two output layer coefficient matrixes, and compared with the traditional kernel principal component analysis algorithm which only has one output, the diversity of feature extraction is further guaranteed.

Description

Wind power generation equipment state monitoring method based on simplified nuclear principal component network
Technical Field
The invention relates to a state monitoring method for wind power generation equipment, in particular to a state monitoring method for wind power generation equipment based on a simplified nuclear principal component network.
Background
Due to the fluctuation of the price of primary energy such as petroleum and the like and the environmental protection characteristic of wind power generation, the advantage of wind power generation is more and more obvious. At present, the top five countries of the world with the wind power installed capacity are China, America, Germany, Spain and India. China is rich in wind energy resources, the wind energy storage capacity is 32 hundred million kilowatts, the developable installed capacity is about 2.5 hundred million kilowatts, and the wind energy resources live at the first place in the world. The installed capacity of wind power in China is rapidly increased in recent years, and meanwhile, the related support and the happy planning of domestic new energy are continuously provided, so that the development of domestic wind power generation is further promoted. Wind power generation equipment (namely a wind turbine) is a power machine for converting wind energy into mechanical energy, meanwhile, the main utilization form of wind energy is power generation at present, and the wind power generation is fastest growing in the industries of new energy and renewable energy.
Generally speaking, most wind power generation equipment operates in regions with bad weather, and the operating conditions are extremely poor, so that the wind power generation set has more faults, and a large number of faults cause the wind power generation set to stop operating or not reach rated output. Therefore, the running state of the wind power generation equipment is monitored in real time, the running abnormality is found in time, the maintenance of the equipment is organized rapidly, and the method has important practical significance for improving the wind power utilization rate and the wind power generation service quality. In the existing wind power generation equipment set, a plurality of sensors are usually installed in a matching manner, and data information such as the rotating speed of a generator, generated electric power, acceleration and the like are measured and fed back in real time. The data collected by these sensors provide a solid data base for implementing data-driven wind power plant condition monitoring. Under the current wind tide of intelligent manufacturing and big data, the scheme of monitoring the state of the wind power generation equipment by using the sampled data is very suitable.
However, the operating state of the wind turbine is directly influenced by the wind speed of the external environment, and changes along with the change of the wind speed. Because the intermittent characteristics, the non-linear characteristics and the time sequence variation characteristics of the wind power are not manually and accurately predictable or controllable, the operating characteristics of the wind power generator, which are directly affected by the wind speed, provide challenges for implementing the state monitoring of the data-driven wind power generation equipment. The development of the equipment fault diagnosis technology in the field of wind power equipment at present in China is in an attempt stage, large-scale wind generating sets in China develop rapidly in recent years, but the state monitoring system products of the corresponding large-scale wind generating sets are in a primary stage, the running state monitoring system products of the large-scale wind generating sets in China are immature, and autonomous real-time intelligent monitoring equipment is lacked.
In recent years, in the field of data-driven process monitoring, nuclear principal component analysis algorithms have been successfully applied to monitor the operating state of non-linear industrial processes. Although the kernel principal component analysis algorithm can realize the monitoring of the running state by mining the nonlinear characteristics of the sampled data, a key problem exists: the computational complexity of implementing online monitoring is proportional to the size of the training data set, but larger training data sets are more advantageous for implementing condition monitoring. Therefore, if the condition monitoring of the wind power generation equipment is implemented by using the kernel principal component analysis algorithm, the on-line calculation amount is a problem to be considered in order to ensure the feasibility and real-time performance of the practical application.
Disclosure of Invention
The invention aims to solve the main technical problems that: how to build a simplified principal component network model and implement real-time state monitoring on the wind power generation equipment on the basis of the model. Specifically, the method disclosed by the invention implements sample distribution characteristic analysis on a training data set of the wind power generation equipment, and uses edge data points and clustering center data points to construct a hidden layer of the principal component network, so that the calculated amount during online monitoring can be greatly reduced, and the application feasibility and the real-time property of the state monitoring method based on the simplified principal component network are ensured.
The technical scheme adopted by the method for solving the problems is as follows: a wind power generation equipment state monitoring method based on a simplified core principal component network comprises the following steps:
step (1): after data which can be measured in real time by the wind power generation equipment is determined, acquiring the data according to the inherent sampling time interval of the data acquisition system of the wind power generation equipment in the normal operation state of the wind power generation equipment; wherein, 11 data that can real-time measurement at each sampling moment are specifically in turn: wind speed, rotor speed, generator speed, mechanical torque, generated power, blade pitch angle, blade azimuth, blade root moment, top horizontal axis acceleration, top longitudinal axis acceleration and yaw error.
Step (2): n sample data vectors x with wind speed between cut-in wind speed (typically 3 meters per second) and cut-out wind speed (typically 25 meters per second)1，x2，...，xnThe composition matrix X ═ X1，x2，...，xn]And for X ∈ R11×nEach row vector is normalized to obtain a new matrix
Figure BSA0000254265160000021
Wherein the ith sample data vector xi∈R11×1The 11 data in (1) are arranged in sequence according to the sequence in step (1), i belongs to {1, 211×nRepresenting a matrix of real numbers in dimensions 11 Xn, R representing a set of real numbers, R11×1Representing a real number vector of dimension 11 x 1.
And (3): for new matrix
Figure BSA0000254265160000022
After kernel principal component analysis is implemented, the kernel parameter delta and the kernel score matrix Y epsilon R are reservedD×nThe specific implementation process is shown in the steps (3.1) to (3.5).
Step (3.1): according to the formula
Figure BSA0000254265160000023
Computing new matrices
Figure BSA0000254265160000024
Vector of the ith column
Figure BSA0000254265160000025
And j-th column vector
Figure BSA0000254265160000026
The squared distance ζ (i, j) therebetween; where j ∈ {1, 2., n }, the superscript T denoting the transpose of a matrix or vector.
Step (3.2): the kernel parameter δ is determined according to the formula (i) as follows:
Figure BSA0000254265160000027
step (3.3): calculating a kernel matrix K epsilon R according to the formula ∈ Rn×nRow i and column j element K (i, j):
Figure BSA0000254265160000028
in the above equation, e represents a natural constant, i.e., e is equal to about 2.718281828.
Step (3.4): calculating n eigenvalues λ of the kernel matrix K1≥λ2≥...≥λnAnd their respectively corresponding feature vectors alpha1，α2，...，αnThen, the lambda is added1，λ2，...，λnIn is not less than λ0The number of the characteristic values is recorded as D; wherein λ is0Representing the mean value of n characteristic values, i.e. λ0＝(λ1+λ2+...+λn) N, feature vector α1，α2，...，αnAre all 1.
Step (3.5): d feature vectors alpha1，α2，...，αDForming a characteristic transformation matrix A ═ alpha1，α2，...，αD]Then, according to the formula Y ═ ATK is calculated to obtain a kernel score matrix Y epsilon RD×n。
And (4): for new matrix
Figure BSA0000254265160000029
Column vector of
Figure BSA00002542651600000210
Carrying out edge characteristic point analysis to obtain E edge point vectors xi1，ξ2，...，ξEThe specific implementation process is shown in the steps (4.1) to (4.5).
Step (4.1): the initialization i is 1.
Step (4.2): will be provided with
Figure BSA0000254265160000031
The neutral column vector
Figure BSA0000254265160000032
Column vectors having a squared distance between them less than δ are recorded as
Figure BSA0000254265160000033
Wherein N isiTo represent
Figure BSA0000254265160000034
The number of column vectors satisfying the condition ζ (i, j) < δ.
Step (4.3): calculating a column vector according to the formula shown below
Figure BSA0000254265160000035
Normal vector f ofi：
Figure BSA0000254265160000036
In the above formula, the first and second carbon atoms are,
Figure BSA0000254265160000037
representation calculation
Figure BSA0000254265160000038
And
Figure BSA0000254265160000039
1, 2, N, is equal toi。
Step (4.4): calculating the column vector according to the formula
Figure BSA00002542651600000310
Corresponding edge point index gi：
Figure BSA00002542651600000311
In the above formula, b is in the form of {1, 2i}，θbIs binary number, and the value law is as follows:
Figure BSA00002542651600000312
step (4.5): judging whether a condition i is less than n; if yes, after i is set to i +1, returning to the step (4.2); if not, according to the value to the edge point index g1，g2，...，gnPerforming descending arrangement, and recording the column vectors corresponding to the largest E edge point indexes as edge point vectors xi1，ξ2，...，ξE。
And (5): for new matrix
Figure BSA00002542651600000313
Column vector of
Figure BSA00002542651600000314
Performing cluster analysis to obtain C cluster central point vectors h1，h2，...，hC。
It should be noted that the various clustering algorithms that can be used for performing the clustering analysis in the step (5) are not limited to the k-means clustering (k-means clustering) algorithm adopted in the specific embodiment.
And (6): respectively sequentially arranging the column vectors
Figure BSA00002542651600000315
Computing the hidden layer neuron output vector z of the simplified kernel principal component network according to the following formula (iv) as an input vector1，z2，...，zn：
Figure BSA00002542651600000316
In the above formula, zi∈R(E+C)×1Representing the ith hidden layer neuronThe output vector, i ∈ {1, 2., n }, R ∈(E+C)×1Represents a real number vector of (E + C) × 1 dimension,
Figure BSA00002542651600000317
and (7): will z1，z2，...，znMerging into a hidden layer output matrix Z ═ Z with (E + C) x n dimension1，z2，...，zn]Then according to formula B1＝(ZTZ)-1ZYTCalculating to obtain a first output layer coefficient matrix B of the simplified core principal component network1。
And (8): computing matrices
Figure BSA00002542651600000318
E + C eigenvalues η of1≥η2≥...≥ηE+CAnd their respectively corresponding feature vectors beta1，β2，...，βE+CThen, η1，η2，...，ηE+CIn is not less than eta0The number of the characteristic values of (a) is recorded as d; wherein eta is0Representing the mean of E + C characteristic values, i.e. eta0＝(η1+η2+...+ηE+C) /(E + C), feature vector β1，β2，...，βE+CAre all 1.
And (9): will beta1，β2，...，βdSecond output layer series matrix B combined into simplified core principal component network2＝[β1，β2，...，βd]Then, the first monitoring index vector Q is calculated according to the formula (v)1And a second monitoring index vector Q2：
Figure BSA0000254265160000041
In the above formula, diag { } denotes an operation of converting a matrix diagonal element in braces into a column vector,
Figure BSA0000254265160000042
and
Figure BSA0000254265160000043
respectively represent Λ1＝B1 TZZTB1And Λ2＝B2 TZZTB2The inverse matrix of (c).
Step (10): respectively combine Q1And Q2The element of the maximum value in (b) is recorded as the upper control limit q1And q is2Thereafter, the off-line modeling process is ended and step (11) is performed.
Step (11): the method comprises the steps of utilizing a data acquisition system of the wind driven generator to acquire 11 data of the wind driven generator at the latest sampling moment and forming a sample data vector x belonging to R11×1Judging whether the first data in x is between the cut-in wind speed and the cut-out wind speed or not; if yes, executing step (12); if not, the wind driven generator is in an offline state, and the step (11) is repeated to continue to utilize the sample data vector at the latest sampling moment to implement state monitoring; wherein, 11 data in x are arranged in sequence according to the sequence in the step (1).
Step (12): subjecting the elements of each row in x to the same normalization process as in step (2), thereby obtaining a data vector
Figure BSA0000254265160000044
Step (13): will be provided with
Figure BSA0000254265160000045
When the vector is made into an input vector, the hidden layer neuron output vector z E R is obtained by calculation according to the formula ∈ R(E+C)×1：
Figure BSA0000254265160000046
Step (14): according to the formula
Figure BSA0000254265160000047
And
Figure BSA0000254265160000048
calculating to obtain a monitoring index J1And J2Then, whether the conditions are met is judged again: j. the design is a square1≤q1And J2≤q2(ii) a If yes, the wind power generation equipment operates normally at the current sampling moment, and the step (11) is returned; if not, executing step (15).
Step (15): returning to the step (11) to continue to monitor the state of the wind power generation equipment by using the sample data vector at the latest sampling moment; if the monitoring indexes of the continuous 6 latest sampling moments do not meet the judgment condition in the step (14), triggering a working abnormity alarm, and arranging personnel to maintain in time; and otherwise, the wind power generation equipment normally operates, and the step (11) is returned to continue to monitor the state of the wind power generation equipment.
The main advantages of the process of the invention compared to conventional processes are detailed below.
It can be seen from the above step (13) that the online calculation amount of the method of the present invention mainly comes from the formula (C), i.e., a vector containing E + C data is calculated. When the traditional kernel principal component analysis algorithm is implemented on-line monitoring, the traditional kernel principal component analysis algorithm needs to be aimed at
Figure BSA0000254265160000049
A column vector containing n data is calculated as follows:
Figure BSA0000254265160000051
since n is equal to the total number of sample data vectors, and E and C represent the number of edge feature points and cluster center points, respectively, E + C is generally much smaller than n. Therefore, the online calculation amount of the method is greatly reduced.
In addition, corresponding outputs are respectively calculated through the two output layer coefficient matrixes, and compared with the traditional kernel principal component analysis algorithm with only one output, the diversity of feature extraction is optimized and guaranteed. Finally, the present specification will further explain the advantages of the process of the present invention by means of specific implementation procedures.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of a simplified kernel principal component network structure according to the method of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention discloses a wind power generation equipment state monitoring method based on a simplified core principal component network, and the specific implementation mode of the method is described by combining with an implementation flow diagram shown in figure 1.
Step (1): after the data which can be measured in real time by the wind power generation equipment is determined, the data are collected according to a fixed sampling time interval under the normal operation state of the wind power generation equipment.
Step (2): n sample data vectors x with wind speed between 3 and 25 meters per second1，x2，...，xnThe composition matrix X ═ X1，x2，...，xn]And for X ∈ R11×nEach row vector is normalized to obtain a new matrix
Figure BSA0000254265160000052
And (3): for new matrix
Figure BSA0000254265160000053
After kernel principal component analysis is implemented, the kernel parameter delta and the kernel score matrix Y epsilon R are reservedD×nThe specific implementation process is shown in the steps (3.1) to (3.5).
And (4): for new matrix
Figure BSA0000254265160000054
Column vector of
Figure BSA0000254265160000055
Carrying out edge characteristic point analysis to obtain E edge point vectors xi1，ξ2，...，ξEThe specific implementation process is shown in the steps (4.1) to (4.5).
And (5): for new matrix
Figure BSA0000254265160000056
Column vector of
Figure BSA0000254265160000057
Performing cluster analysis to obtain C cluster central point vectors h1，h2，...，hC。
In this embodiment, the k-means clustering (k-means clustering) algorithm is used in the step (5). It should be noted that the implementation step (5) of the method of the present invention is not limited to the use of k-means clustering algorithm, as long as the column vector can be implemented
Figure BSA0000254265160000058
Clustering analysis algorithms that cluster into C clusters can be used.
And (6): respectively sequentially arranging the column vectors
Figure BSA0000254265160000059
Computing the hidden layer neuron output vector z of the simplified kernel principal component network according to the formula (iv) as an input vector1，z2，...，zn。
And (7): will z1，z2，...，znMerging into a hidden layer output matrix Z ═ Z with (E + C) x n dimension1，z2，...，zn]Then according to formula B1＝(ZTZ)-1ZYTCalculating to obtain a first output layer coefficient matrix B of the simplified core principal component network1。
And (8): computing matrices
Figure BSA0000254265160000061
E + C eigenvalues η of1≥η2≥...≥ηE+CAnd their respectively corresponding feature vectors beta1，β2，...，βE+CThen, η1，η2，...，ηE+CIn is not less than eta0The number of the characteristic values of (a) is recorded as d;
and (9): will beta1，β2，...，βdSecond output layer series matrix B combined into simplified core principal component network2＝[β1，β2，...，βd]Then, according to the above-mentioned formula (v), respectively calculating first monitoring index vector Q1And a second monitoring index vector Q2。
Passing through two output layer series matrixes B in the step (7) and the step (9)1And B2A reduced core principal component network structure as shown in fig. 2 can be built. As can be seen from FIG. 2, the number of hidden layer neurons of the reduced kernel principal component network according to the method of the present invention is equal to E + C, and the output layer neurons are divided into two groups, respectively using B1And B2As their respective coefficient matrices.
Step (10): respectively combine Q1And Q2The element of the maximum value in (b) is recorded as the upper control limit q1And q is2Thereafter, the off-line modeling process is ended and step (11) is performed.
Step (11): by utilizing a data acquisition system of the wind driven generator, acquiring a sample data vector x ∈ R at the latest sampling moment11×1Judging whether the first data in x is between the cut-in wind speed and the cut-out wind speed or not; if yes, executing step (12); if not, the wind driven generator is in an offline state, and the step (11) is repeated to continue to utilize the sample data vector at the latest sampling moment to implement state monitoring; wherein, 11 data in x need to be arranged in sequence according to the sequence in the step (1).
Step (12): subjecting the elements of each row in x to the same normalization process as in step (2), thereby obtaining a data vector
Figure BSA0000254265160000062
Step (13): will be provided with
Figure BSA0000254265160000063
When the vector is made into an input vector, the hidden layer neuron output vector z E R is obtained by calculation according to the formula |(E+C)×1。
Step (14): according to the formula
Figure BSA0000254265160000064
And
Figure BSA0000254265160000065
calculating to obtain a monitoring index theta1And theta2Then, whether the conditions are met is judged again: theta1≤q1And theta2≤q2(ii) a If yes, the wind power generation equipment operates normally at the current sampling moment, and the step (11) is returned; if not, executing step (15).
Step (15): returning to the step (11) to continue to monitor the state of the wind power generation equipment by using the sample data vector at the latest sampling moment; if the monitoring indexes at the continuous 6 sampling moments do not meet the judgment condition in the step (14), triggering an abnormal alarm, and arranging personnel to maintain in time; and otherwise, the wind power generation equipment normally operates, and the step (11) is returned to continue to monitor the state of the wind power generation equipment.

Claims (1)

1. A wind power generation equipment state monitoring method based on a simplified core principal component network is characterized by comprising the following steps:
step (1): after data which can be measured in real time by the wind power generation equipment is determined, acquiring the data according to a fixed sampling time interval under the normal operation state of the wind power generation equipment; wherein, 11 data that can real-time measurement at each sampling moment are specifically in turn: wind speed, rotor speed, generator speed, mechanical torque, generated power, blade pitch angle, blade azimuth, blade root moment, top horizontal axis acceleration, top longitudinal axis acceleration and yaw error;
step (2): the wind speed is between the cut-in wind speed andn sample data vectors x between cut-out wind speeds1，x2，…，xnThe composition matrix X ═ X1，x2，…，xn]And for X ∈ R11×nEach row vector is normalized to obtain a new matrix
Figure FSA0000254265150000011
Wherein the ith sample data vector xi∈R11×1The 11 data in (1) are arranged in sequence, i belongs to {1, 2, …, n }, R belongs to11×nRepresenting a matrix of real numbers in dimensions 11 Xn, R representing a set of real numbers, R11×1A real number vector representing 11 × 1 dimensions;
and (3): for new matrix
Figure FSA0000254265150000012
After kernel principal component analysis is implemented, the kernel parameter delta and the kernel score matrix Y epsilon R are reservedD×nThe specific implementation process is shown in the steps (3.1) to (3.5);
step (3.1): according to the formula
Figure FSA0000254265150000013
Computing new matrices
Figure FSA0000254265150000014
Vector of the ith column
Figure FSA0000254265150000015
And j-th column vector
Figure FSA0000254265150000016
The squared distance ζ (i, j) therebetween; wherein j belongs to {1, 2, …, n }, and the upper label T represents the transpose of a matrix or a vector;
step (3.2): the kernel parameter δ is determined according to the formula (i) as follows:
Figure FSA0000254265150000017
step (3.3): calculating a kernel matrix K epsilon R according to the formula ∈ Rn×nRow i and column j element K (i, j):
Figure FSA0000254265150000018
in the above formula, e represents a natural constant;
step (3.4): calculating n eigenvalues λ of the kernel matrix K1≥λ2≥…≥λnAnd its corresponding feature vector alpha1，α2，…，αnThen, the lambda is added1，λ2，…，λnIn is not less than λ0The number of the characteristic values is recorded as D; wherein the feature vector alpha1，α2，…，αnAre all equal to 1, lambda0＝(λ1+λ2+…+λn)/n；
Step (3.5): d feature vectors alpha1，α2，…，αDForming a characteristic transformation matrix A ═ alpha1，α2，…，αD]Then, according to the formula Y ═ ATK is calculated to obtain a kernel score matrix Y epsilon RD×n；
And (4): for new matrix
Figure FSA0000254265150000019
Column vector of
Figure FSA00002542651500000110
Carrying out edge characteristic point analysis to obtain E edge point vectors xi1，ξ2，…，ξEThe specific implementation process is shown in the steps (4.1) to (4.5);
step (4.1): initializing i to 1;
step (4.2): will be provided with
Figure FSA00002542651500000111
The neutral column vector
Figure FSA00002542651500000112
N with square distance less than delta betweeniEach column vector is recorded as
Figure FSA00002542651500000113
Step (4.3): calculating the column vector according to the formula (c)
Figure FSA00002542651500000114
Normal vector f ofi：
Figure FSA00002542651500000115
In the above formula, the first and second carbon atoms are,
Figure FSA00002542651500000116
representation calculation
Figure FSA00002542651500000117
And
Figure FSA00002542651500000118
b is 1, 2, …, Ni；
Step (4.4): calculating the column vector according to the formula
Figure FSA0000254265150000021
Corresponding edge point index gi：
Figure FSA0000254265150000022
In the above formula, θbThe value law is as follows:
Figure FSA0000254265150000023
step (4.5): judging whether i is smaller than n; if yes, after i is set to i +1, returning to the step (4.2); if not, according to the value to the edge point index g1，g2，…，gnPerforming descending arrangement, and recording the column vectors corresponding to the largest E edge point indexes as edge point vectors xi1，ξ2，…，ξE；
And (5): for new matrix
Figure FSA0000254265150000024
Column vector of
Figure FSA0000254265150000025
Performing cluster analysis to obtain C cluster central point vectors h1，h2，…，hC；
And (6): respectively sequentially arranging the column vectors
Figure FSA0000254265150000026
As an input vector, computing a hidden layer neuron output vector z of the reduced kernel principal component network according to the formula1，z2，…，zn：
Figure FSA0000254265150000027
In the above formula, zi∈R(E+C)×1The representation corresponds to
Figure FSA0000254265150000028
Is the hidden layer neuron output vector of (i) is in the range of {1, 2, …, n }, R(E+C)×1Represents a real number vector of (E + C) × 1 dimension,
Figure FSA0000254265150000029
and (7): will z1，z2，…，znMerging into a hidden layer output matrix Z ═ Z with (E + C) x n dimension1，z2，…，zn]Then according to formula B1＝(ZTZ)-1ZYTCalculating to obtain a first output layer coefficient matrix B of the simplified core principal component network1；
And (8): computing matrices
Figure FSA00002542651500000210
E + C eigenvalues η of1≥η2≥…≥ηE+CAnd their respectively corresponding feature vectors beta1，β2，…，βE+CThen, η1，η2，…，ηE+CIn is not less than eta0The number of the characteristic values of (a) is recorded as d; wherein the feature vector beta1，β2，…，βE+CAll have a length of 1, eta0＝(η1+η2+…+ηE+C)/(E+C)；
And (9): will beta1，β2，…，βdSecond output layer series matrix B combined into simplified core principal component network2＝[β1，β2，…，βd]Then, the first monitoring index vectors Q are calculated according to the following formula1And a second monitoring index vector Q2：
Figure FSA00002542651500000211
In the above formula, diag { } denotes an operation of converting a matrix diagonal element in braces into a column vector,
Figure FSA00002542651500000212
and
Figure FSA00002542651500000213
respectively represent Λ1＝B1 TZZTB1And Λ2＝B2 TZZTB2The inverse matrix of (d);
step (10): respectively combine Q1And Q2The element of the maximum value in (b) is recorded as the upper control limit q1And q is2Then, ending the off-line modeling process and implementing the step (11);
step (11): acquiring 11 data of the wind power generation equipment at the latest sampling moment, and forming a sample data vector x ∈ R11×1Judging whether the first data in x is between the cut-in wind speed and the cut-out wind speed or not; if yes, executing step (12); if not, the wind driven generator is in an offline state, and the step (11) is repeated to continue to utilize the sample data vector at the latest sampling moment to implement state monitoring; wherein, 11 data in x need to be arranged in sequence according to the sequence in the step (1);
step (12): the same normalization process as in step (2) is applied to the data of each row in x, thereby obtaining a data vector
Figure FSA0000254265150000031
Step (13): will be provided with
Figure FSA0000254265150000032
When an input vector is made, the hidden layer neuron output vector z epsilon R is obtained by utilizing the following formula [ + ](E+C)×1：
Figure FSA0000254265150000033
Step (14): according to the formula
Figure FSA0000254265150000034
And
Figure FSA0000254265150000035
calculating to obtain a monitoring index J1And J2Then, whether the conditions are met is judged again: j. the design is a square1≤q1And J2≤q2(ii) a If yes, the wind power generation equipment operates normally at the current sampling moment, and the step (11) is returned; if not, executing the step (15);
step (15): returning to the step (11) to continue to monitor the state of the wind power generation equipment by using the sample data vector at the latest sampling moment; if the monitoring indexes of the continuous 6 latest sampling moments do not meet the judgment condition in the step (14), triggering an abnormal alarm and arranging personnel to maintain in time; and otherwise, the wind power generation equipment normally operates, and the step (11) is returned to continue to monitor the state of the wind power generation equipment.
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