CN112211795B - Wind driven generator fault detection method based on univariate feature extraction strategy - Google Patents

Wind driven generator fault detection method based on univariate feature extraction strategy Download PDF

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CN112211795B
CN112211795B CN202011142603.7A CN202011142603A CN112211795B CN 112211795 B CN112211795 B CN 112211795B CN 202011142603 A CN202011142603 A CN 202011142603A CN 112211795 B CN112211795 B CN 112211795B
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CN112211795A (en
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方浩杰
葛英辉
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Dragon Totem Technology Hefei Co ltd
Shanghai Sirui Online Monitoring Technology Co ltd
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Ningbo 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

Abstract

The invention discloses a wind driven generator fault detection method based on a univariate feature extraction strategy, which can extract univariate features beneficial to fault separation in real time when implementing fault detection of a data-driven wind driven generator so as to realize the purpose of detecting whether a wind driven generator fails in real time. Specifically, the method of the invention extracts the univariate characteristics which are most beneficial to separating the faults in real time through an online instant characteristic extraction and an optimization idea, and completes the fault detection task of the wind driven generator through upper and lower limit range judgment. The method of the invention calculates the corresponding projection transformation vector in real time aiming at the sample data at different moments, thereby extracting the univariate characteristic which can maximally distinguish the difference between the single variable characteristic and the normal data. Therefore, under the condition that the normal working condition data of the wind driven generator is abundant, the method can detect the fault of the wind driven generator in real time according to the sampling data.

Description

Wind driven generator fault detection method based on univariate feature extraction strategy
Technical Field
The invention relates to a wind driven generator fault detection method, in particular to a wind driven generator fault detection method based on a univariate feature extraction strategy.
Background
Green energy represented by wind energy and solar energy is receiving more and more attention due to the increasing shortage of natural resources, and a large amount of manpower and material resources are invested in the aspects of energy conversion efficiency, system design, optimized scheduling, maintenance and the like to develop corresponding research and improvement work. The wind power generator is a typical device which utilizes wind energy on the market at present, and under the condition of continuous wind power, the wind power generator can continuously provide electric energy. Wind power generators are therefore widely popularized and applied in islands or mountainous areas. In order to ensure efficient power output of the wind driven generator, the method has important research significance for timely detecting faults occurring in the running process of the wind driven generator, and can timely schedule maintainers to go to maintenance, so that waste of labor cost caused by periodical and purposeless maintenance is avoided. However, considering that the wind turbine is a large and complex system, it is very difficult to establish a corresponding dynamic model and a circuit model through mechanism knowledge such as physics. Therefore, a scheme of performing failure detection using a mechanism model is not preferable.
Generally, a plurality of sensors are usually installed in the wind turbine system, and data information such as the rotational speed of the generator, the generated electric power, the acceleration, etc. is fed back in real time. These sensors and the associated data storage devices provide a solid data base for implementing data-driven wind turbine fault detection. Under the current tide of intelligent manufacturing and big data, the scheme of utilizing the sampling data to implement the fault detection of the wind driven generator is worth developing corresponding research by researchers, and various data-driven technologies for implementing the fault detection and diagnosis of the wind driven generator according to the mode classification idea exist. 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 wind power is not artificially accurately predictable or controllable, this operational characteristic of wind generators, in which the operating state is directly affected by wind speed, adds a challenge to data-driven fault detection schemes.
Generally, the wind power generator is in a normal working state most of the time, and the wind power generator generates electric energy with a wind speed interval. When the ambient wind speed is not in the wind speed interval, the wind driven generator is in a standby state due to the self-protection function. Therefore, the fault detection of the wind driven generator detects whether the wind driven generator has a fault in the process of generating electric energy. The wind driven generator fault detection is implemented from the data driving perspective, and available training data are generally collected from a normal working state and are sampling data in a fault state. Therefore, the data-driven wind driven generator fault detection method is designed from the aspect of feature extraction. Meanwhile, in order to ensure the success rate of fault detection, the extracted features utilize the features which have the function of optimally distinguishing normal and fault, rather than feature extraction according to the traditional thought.
Disclosure of Invention
The invention aims to solve the main technical problems that: when the data-driven wind driven generator fault detection is implemented, the univariate characteristics which are beneficial to fault separation can be extracted in real time, so that an abnormal change monitoring idea for converting monitoring a plurality of measurement data into univariate can be realized, and the purpose of detecting whether the wind driven generator has faults or not in real time can be realized. Specifically, the method of the invention extracts the univariate characteristics which are most beneficial to separating the faults in real time through an online instant characteristic extraction and an optimization idea, and completes the fault detection task of the wind driven generator through upper and lower limit range judgment.
The technical scheme adopted by the method for solving the problems is as follows: a wind driven generator fault detection method based on a univariate feature extraction strategy comprises the following steps:
step (1): under the normal operation state of the wind driven generator, a data acquisition system is used for acquiring sample data every 1 minute, wherein the sample data specifically comprises 11 data, and the 11 data acquired every time form a column vector; wherein, 11 data in the column vector are: 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, yaw error.
Step (2): according to the time sequence of collection, N column vectors x with the wind speed between 5m/s and 20m/s1，x2，...，xNThe composition matrix X ═ X1，x2，...，xN]And for X ∈ R11×NEach row vector is normalized to obtain a new matrix
Figure BSA0000222620640000021
Wherein R is11×NRepresenting a matrix of real numbers of dimension 11 XN, R11×1The real number vector of 11 × 1 dimension is expressed, and the normalization process is specifically performed as shown in step (2.1) to step (2.2).
Step (2.1): let zj∈R1×NA row vector representing the jth row in matrix X; where j ∈ {1, 2., 11 }.
Step (2.2): calculating a row vector zjAverage value μ of all elements injAnd standard deviation deltajThen according to
Figure BSA0000222620640000022
Calculating to obtain a new matrix
Figure BSA0000222620640000023
Row vector of the j-th row
Figure BSA0000222620640000024
And (3): according to the formula
Figure BSA0000222620640000025
Calculating a base matrix C; wherein the superscript T represents the transpose of a matrix or vector.
And (4): determining an upper bound ζ of a univariate featuremaxAnd a lower limit ζminThe specific implementation process is shown in the steps (4.1) to (4.5).
Step (4.1): is provided with
Figure BSA0000222620640000026
Representing a new matrix
Figure BSA0000222620640000027
A column vector of the ith column; wherein i ∈ {1, 2.,. N }.
Step (4.2): according to the formula
Figure BSA0000222620640000028
Computing matrix GiThen, solve G againiThe eigenvector p corresponding to the largest eigenvaluei∈R11×1。
Step (4.3): according to the formula wi＝CpiComputing projective transformation vector wi∈R11×1Then, again according to
Figure BSA0000222620640000029
Calculating univariate characteristics yi。
Step (4.4): respectively setting i equal to 1, 2.. times.N, and executing the steps (4.1) to (4.3) so as to correspondingly obtain the univariate characteristics y1，y2，...，yNAnd calculate y1，y2，...，yNAverage value of (d)0And standard deviation delta0。
Step (4.5): according to ζmax＝μ0+3×δ0And ζmin＝μ0-3×δ0Separately determining the upper limit ζ of univariate characteristicsmaxAnd a lower limit ζmin。
And (5): acquiring sample data of the latest sampling moment by using a data acquisition system of the wind driven generator, specifically comprising 11 data listed in the step (1), and forming the 11 data into a column vector xnew∈R11×1。
And (6): judgment of xnewWhether or not the first data (i.e., wind speed) in (1) is 20 or less and 5 or more; if yes, executing the step (7); if not, the wind driven generator does not generate electric energy and is in a standby state, and the step (4) is returned to continue to carry out the fault detection of the wind driven generator at the latest sampling moment.
And (7): according to the formula
Figure BSA00002226206400000210
For xnewThe elements of each row in the system are subjected to standardization processing, so that a new data vector is obtained
Figure BSA0000222620640000031
Wherein the content of the first and second substances,
Figure BSA0000222620640000032
and
Figure BSA0000222620640000033
respectively represent xnewAnd
Figure BSA0000222620640000034
the element of the j-th row.
And (8): according to the formula
Figure BSA0000222620640000035
Computing matrix GnewThen, solve G againnewThe eigenvector p corresponding to the largest eigenvaluenew∈R11×1。
And (9): according to the formula wnew＝CpnewComputing projective transformation vector wnew∈R11×1Then, the sheet is recalculatedVariable characteristics
Figure BSA0000222620640000036
Step (10): judging whether the conditions are met: zetamin≤ynew≤ζmax(ii) a If so, the wind driven generator normally operates, and returns to the step (5) to continue to utilize the sample data at the latest sampling moment to carry out fault detection on the wind driven generator; if not, executing step (11).
Step (11): returning to the step (5) to continue to use the sample data at the latest sampling moment to implement fault detection; if the univariate characteristics corresponding to the sample data at the continuous 6 sampling moments do not meet the judgment condition in the step (10), triggering a fault alarm; otherwise, the wind driven generator normally operates, and returns to the step 5) to continue to utilize the sample data at the latest sampling moment to carry out fault detection on the wind driven generator.
It is to be noted that the matrix and its maximum features obtained in the above steps (4.2) and (4.3) and in the above steps (8) and (9) are derived from the reasoning process of the algorithm aimed at searching projective transformation vectors to maximize the distinction between new data and training data, the objective function and its constraints of the algorithm are as follows:
Figure BSA0000222620640000037
in the above formula, after the normal working condition data is transformed by the projection transformation vector w, the variance or length is 1. And the objective function aims to ensure that the data vectors sampled on line are better as far as the far point after the same projective transformation. In other words, the online data is separated from the normal working condition data as much as possible, so that the characteristic components most suitable for monitoring the fault can be extracted.
The optimization solution of the formula I can be completed by a classical Lagrange multiplier method, and an intermediate quantity p (XX) needs to be introduced firstT)1/2w makes a transition, and pay attention to
Figure BSA0000222620640000038
Where tr () represents finding the trace of the matrix within the parenthesis.
By carrying out the steps described above, the advantages of the method of the invention are presented below.
The method of the invention calculates the corresponding projection transformation vector in real time aiming at the sample data at different moments, thereby extracting the univariate characteristic which can maximally distinguish the difference between the single variable characteristic and the normal data. Therefore, under the condition that the normal working condition data of the wind driven generator is abundant, the method can detect the fault of the wind driven generator in real time according to the sampling data. In the specific implementation case to be stated, the feasibility and the advantages of the method are verified through practical application.
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FIG. 1 is a schematic flow chart of the method of the present invention.
Fig. 2 is a detailed diagram of the fault detection of 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 driven generator fault detection method based on a univariate feature extraction strategy, and the specific implementation mode of the method is described below by combining with an implementation flow schematic diagram shown in fig. 1.
Step (1): under the normal operating condition of the wind driven generator, sample data is collected once every 1 minute, and the method specifically comprises the following steps: 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, yaw error.
Step (2): n column vectors x with wind speed between 5m/s and 20m/s1，x2，...，xNThe composition matrix X ═ X1，x2，...，xN]And for X ∈ R11×NEach row vector is normalized to obtain a new matrix
Figure BSA0000222620640000041
And (3): according to the formula
Figure BSA0000222620640000042
Calculating a base matrix C; wherein the superscript T represents the transpose of a matrix or vector.
And (4): determining an upper bound ζ of a univariate featuremaxAnd a lower limit ζminThe specific implementation process is shown in the steps (4.1) to (4.5).
And (5): acquiring sample data of the latest sampling moment by using a data acquisition system of the wind driven generator, specifically comprising the 11 data in the step (1), and forming the 11 data into a column vector xnew∈R11×1。
And (6): judgment of xnewWhether or not the first data (i.e., wind speed) in (1) is 20 or less and 5 or more; if yes, executing the step (7); if not, the wind driven generator does not generate electric energy and is in a standby state, and the step (4) is returned to continue to carry out the fault detection of the wind driven generator at the latest sampling moment.
And (7): according to the formula
Figure BSA0000222620640000043
For xnewThe elements of each row in the system are subjected to standardization processing, so that a new data vector is obtained
Figure BSA0000222620640000044
Wherein the content of the first and second substances,
Figure BSA0000222620640000045
and
Figure BSA0000222620640000046
respectively represent xnewAnd
Figure BSA0000222620640000047
the element of the j-th row.
And (8): according to the formula
Figure BSA0000222620640000048
Computing matrix GnewThen, solve G againnewThe eigenvector p corresponding to the largest eigenvaluenew∈R11×1。
And (9): according to the formula wnew＝CpnewComputing projective transformation vector wnew∈R11×1Then, the univariate characteristics are calculated
Figure BSA0000222620640000049
Step (10): judging whether the conditions are met: zetamin≤ynew≤ζmax(ii) a If so, the wind driven generator normally operates, and returns to the step (5) to continue to utilize the sample data at the latest sampling moment to carry out fault detection on the wind driven generator; if not, executing step (11).
Step (11): returning to the step (5) to continue to use the sample data at the latest sampling moment to implement fault detection; if the univariate characteristics corresponding to the sample data at the continuous 6 sampling moments do not meet the judgment condition in the step (10), triggering a fault alarm; otherwise, the wind driven generator normally operates, and returns to the step 5) to continue to utilize the sample data at the latest sampling moment to carry out fault detection on the wind driven generator.
Specific values of the univariate, upper and lower limits are plotted in figure 2 for clarity to show the details of the variation of the univariate characteristics between the upper and lower limits. As can be seen from fig. 2, when a fault does not occur, the univariate feature is located between the upper limit and the lower limit; after a fault occurs, the univariate characteristics exceed the lower limit of the limit range, so that a fault alarm can be triggered.

Claims (1)

1. A wind driven generator fault detection method based on a univariate feature extraction strategy is characterized by comprising the following steps:
step (1): under the normal operation state of the wind driven generator, a data acquisition system is used for acquiring sample data every 1 minute, wherein the sample data specifically comprises 11 data, and the 11 data acquired every time form a column vector; wherein, 11 data in the column vector are: wind speed, rotor speed, generator speed, mechanical torque, power generation power, blade pitch angle, blade azimuth angle, blade root moment, top horizontal axis acceleration, top longitudinal axis acceleration, yaw error;
step (2): n column vectors x with wind speed between 5m/s and 20m/s1，x2，…，xNThe composition matrix X ═ X1，x2，…，xN]And for X ∈ R11×NEach row vector is normalized to obtain a new matrix
Figure FSB0000193675780000011
Wherein R is11×NA real number matrix of 11 × N dimensions is represented, and the normalization process is specifically performed as shown in steps (2.1) to (2.2);
step (2.1): let zj∈R1×NA row vector representing the jth row in matrix X; wherein j ∈ {1, 2, …, 11 };
step (2.2): calculating a row vector zjAverage value μ of all elements injAnd standard deviation deltajThen according to
Figure FSB0000193675780000012
Calculating to obtain a new matrix
Figure FSB0000193675780000013
Row vector of the j-th row
Figure FSB0000193675780000014
And (3): according to the formula
Figure FSB0000193675780000015
Calculating a base matrix C; wherein the superscript T represents the transpose of a matrix or vector;
and (4): determining an upper bound ζ of a univariate featuremaxAnd a lower limit ζminThe specific implementation process is as shown in the step (4.1) to the step (4.5)) Shown;
step (4.1): is provided with
Figure FSB0000193675780000016
Representing a new matrix
Figure FSB0000193675780000017
A column vector of the ith column; wherein R is11×1Represents a real number vector of 11 x 1 dimensions, i ∈ {1, 2, …, N };
step (4.2): according to the formula
Figure FSB0000193675780000018
Computing matrix GiThen, solve G againiThe eigenvector p corresponding to the largest eigenvaluei∈R11×1；
Step (4.3): according to the formula wi＝CpiComputing projective transformation vector wi∈R11×1Then, again according to
Figure FSB0000193675780000019
Calculating univariate characteristics yi；
Step (4.4): respectively setting i equal to 1, 2, … and N, and executing the steps (4.1) to (4.3) so as to correspondingly obtain the univariate characteristics y1，y2，…，yNAnd calculate y1，y2，…，yNAverage value of (d)0And standard deviation delta0；
Step (4.5): according to ζmax＝μ0+3×δ0And ζmin＝μ0-3×δ0Separately determining the upper limit ζ of univariate characteristicsmaxAnd a lower limit ζmin；
And (5): acquiring sample data of the latest sampling moment by using a data acquisition system of the wind driven generator, specifically comprising 11 data listed in the step (1), and forming the 11 data into a column vector xnew∈R11×1；
And (6): judgment of xnewThe first data in (1) isWhether or not 20 or less and 5 or more; if yes, executing the step (7); if not, the wind driven generator does not generate electric energy and is in a standby state, and the step (5) is returned to continue to carry out fault detection on the wind driven generator at the latest sampling moment;
and (7): according to the formula
Figure FSB00001936757800000110
For xnewThe elements of each row in the system are subjected to standardization processing, so that a new data vector is obtained
Figure FSB00001936757800000111
Wherein the content of the first and second substances,
Figure FSB00001936757800000112
and
Figure FSB00001936757800000113
respectively represent xnewAnd
Figure FSB00001936757800000114
the element of line j;
and (8): according to the formula
Figure FSB00001936757800000115
Computing matrix GnewThen, solve G againnewThe eigenvector p corresponding to the largest eigenvaluenew∈R11×1；
And (9): according to the formula wnew＝CpnewComputing projective transformation vector wnew∈R11×1Then, the univariate characteristics are calculated
Figure FSB0000193675780000021
Step (10): judging whether the conditions are met: zetamin≤ynew≤ζmax(ii) a If yes, the wind driven generator normally operates, and returns to the step (5) to continue to utilize the sample data at the latest sampling momentImplementing fault detection of the wind turbine; if not, executing the step (11);
step (11): returning to the step (5) to continue to use the sample data at the latest sampling moment to implement fault detection; if the univariate characteristics corresponding to the sample data at the continuous 6 sampling moments do not meet the judgment condition in the step (10), triggering a fault alarm; and otherwise, the wind driven generator normally operates, and the step (5) is returned to continue to utilize the sample data at the latest sampling moment to carry out fault detection on the wind driven generator.
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CN110374822A (en) * 2019-08-07 2019-10-25 电子科技大学 Blade of wind-driven generator method for diagnosing faults based on depth convolution self-encoding encoder and XGBoost
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Publication number Priority date Publication date Assignee Title
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CN102434387A (en) * 2011-11-16 2012-05-02 三一电气有限责任公司 Draught fan detection and diagnosis system
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