CN117494476B - Measuring point optimization method for improving pneumatic load identification stability of fan tower - Google Patents
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Abstract
The invention discloses a measuring point optimization method for improving the pneumatic load identification stability of a fan tower, which belongs to the technical field of the pneumatic load identification of the fan tower, and comprises the steps of arranging a plurality of initial strain monitoring points on the inner wall of the fan tower, and arranging a strain sensor group on the strain monitoring points; establishing a mathematical model between the pneumatic load and the structural response according to the initial monitoring point position arrangement condition; taking the worst frequency response function matrix of the pathological degree in the load frequency domain as an optimization matrix to perform mathematical model optimization; optimizing the measuring points by adopting a successive addition method to obtain an optimal measuring point matrix; and reestablishing a mathematical model according to the optimal measuring point matrix, and identifying the pneumatic load born by the tower barrel by adopting an inverse pseudo-excitation method. The method can realize effective identification of the pneumatic load of the tower drum of the fan, obtain the optimal measuring point arrangement and reduce the pathological degree of the matrix.
Description
Technical Field
The invention relates to the field of pneumatic load identification of a fan tower, in particular to a measuring point optimization method for improving the pneumatic load identification stability of the fan tower.
Background
In the on-line monitoring and safety evaluation of engineering structures, accurately acquiring external loads to which the structures are subjected is a key step in performing structural analysis. However, the external load of most structures is difficult to measure directly, requiring indirect measurement through structural response data. For the wind turbine tower structure, the pneumatic load generated during the operation of the wind turbine is the main load applied to the wind turbine, and is also the main cause of structural fatigue. Dynamic load identification techniques can use the monitoring data of the fan structure to identify the aerodynamic load it is subjected to on top of.
Dynamic load recognition is mainly divided into time domain dynamic load recognition and frequency domain dynamic load recognition. The time domain method is difficult to solve the problems of non-zero initial state, error accumulation and the like. When the load duration is long, the recognition accuracy is significantly degraded. And the time domain method needs to solve the load value of each time point, so that the calculated amount is very large, and real-time load identification is difficult to carry out. The theory and the calculation method of the frequency domain dynamic load identification method are perfect, the calculated amount is small, and the frequency domain dynamic load identification method is widely applied to engineering.
In the field of frequency domain dynamic load identification, how to solve the pathological problem of the frequency response matrix, the method mainly adopted at present comprises the following steps:
and adopting an enumeration method to optimize the measuring points. However, under the condition that the number of the measuring points is large, the enumerated number is very large, and 10 measuring point combinations are enumerated in 100 measuring points, so that the measuring point optimization difficulty is high under the condition of.7E13. And the number of frequency response function matrices is very large, and increases if all frequencies are calculated.
The mathematical methods such as regularization, singular value truncation and the like are adopted to reduce the pathological problems of the mathematical model. When the position of the measuring point is not good, the mathematical model is a pathological model, and a stable solution cannot be solved by a regularization or singular value cutting method. Particularly, under the condition of simultaneously identifying various loads, the condition of overfitting is very easy to occur, so that the identification precision of only part of the loads is improved, and the identification precision of the rest of the loads is reduced. And the adoption of regularization and singular value truncation methods can reduce the calculation efficiency.
The DOD adopts a successive exchange algorithm, and a matrix with the lowest pathological degree is obtained by continuously exchanging measuring points, the convergence speed is high, but the matrix is easy to fall into a local optimal solution, and the sensitivity of the matrix is only suitable for a real matrix.
COD adopts successive reduction method, and the matrix with the minimum condition number is searched by continuously reducing measuring points, so that the mathematical model is reduced to the target dimension. However, when the target dimension is too small, the degree of pathology of the matrix may rather rise. And has poor effect on complex matrix application.
In view of the foregoing, it is necessary to provide a new solution to the above-mentioned problems.
Disclosure of Invention
In order to solve the technical problems, the application provides a measuring point optimizing method for improving the identification stability of the pneumatic load of the fan tower, which can effectively identify the pneumatic load of the fan tower, obtain the optimal measuring point arrangement and reduce the pathological degree of a matrix.
A measuring point optimizing method for improving the pneumatic load identification stability of a fan tower comprises the following steps:
arranging a plurality of initial strain monitoring points on the inner wall of a fan tower, and setting a strain sensor group on the strain monitoring points;
establishing a mathematical model between the pneumatic load and the structural response according to the initial monitoring point position arrangement condition;
taking the worst frequency response function matrix of the pathological degree in the load frequency domain as an optimization matrix to perform mathematical model optimization;
optimizing the measuring points by adopting a successive addition method to obtain an optimal measuring point matrix;
and reestablishing a mathematical model according to the optimal measuring point matrix, and identifying the pneumatic load born by the tower barrel by adopting an inverse pseudo-excitation method.
Preferably, a plurality of initial strain monitoring points are arranged on the inner wall of the fan tower, strain sensor groups are arranged on the strain monitoring points, and the strain sensor groups are fixedly arranged on the inner surface of the fan tower; the distance between adjacent strain monitoring points in the height direction is 2m, and the included angle between the adjacent strain monitoring points in the height direction is 30 degrees;
the strain monitoring point is provided with a strain sensor group; the strain sensor group comprises a strain sensor arranged vertically and two strain sensors arranged obliquely; the two strain sensors which are obliquely arranged are symmetrically distributed on two sides of the strain sensor which is vertically arranged; the included angle between the obliquely arranged strain sensor and the vertically arranged strain sensor is 45 degrees.
Preferably, in the establishing a mathematical model between the pneumatic load and the structural response according to the initial monitoring point position arrangement condition, the mathematical model between the pneumatic load and the structural response is:
；
in the method, in the process of the invention,is a power spectrum density function matrix output by the system; />Is a frequency response function matrix;is a system input power spectral density function matrix; the superscript H is a conjugate transpose of the representation matrix.
Preferably, the mathematical model optimization using the most severe frequency response function matrix of the pathological degree in the load frequency domain as the optimization matrix includes:
calculating the condition number of the frequency response function matrix under each frequency, and drawing a condition number change curve;
and taking a frequency response function matrix corresponding to the frequency point with the largest condition number in the whole frequency range as an optimization matrix.
Preferably, the optimizing the measuring point by adopting a successive addition method to obtain an optimal measuring point matrix includes:
determining the number of measuring points of an initial matrix, and obtaining a matrix of all measuring points by adopting an enumeration method;
calculating the condition number of all the measurement point matrixes respectively, and taking the matrix with the minimum condition number as an initial moment;
sequentially selecting one measuring point from the rest measuring points, adding the measuring points into the initial matrix to obtain all expansion matrixes, and calculating the condition number of the expansion matrixes;
taking a matrix with the minimum condition number as a new matrix, and finishing one-time measuring point increase;
and repeatedly increasing the number of the measuring points until the number of the measuring points reaches the target number, and obtaining a final matrix.
Preferably, the reconstructing a mathematical model according to the optimal measurement point matrix, and identifying the aerodynamic load suffered by the tower by using an inverse pseudo-excitation method includes:
reestablishing a mathematical model according to the optimal measuring point matrix;
carrying out spectrum decomposition on the load spectrum;
according to the relation between the virtual input and the virtual output, the corresponding virtual input is obtained;
and solving a power spectrum density function matrix input by the system.
Preferably, the formula for performing spectral decomposition on the load spectrum is as follows:
；
in the method, in the process of the invention,a conjugate matrix that is non-negative positive; />And->Respectively is a matrix->Is defined, and corresponding feature vectors; />For matrix->Is a rank of (c).
Preferably, the method calculates the correspondence based on the relationship between the virtual input and the virtual outputThe formula of (2) is:
；
in the method, in the process of the invention,is a virtual input; />Is virtual output; superscript + denotes the Moore-Penrose matrix pseudo-inverse of the original matrix.
Preferably, the formula for solving the power spectrum density function matrix input by the system is as follows:
。
compared with the prior art, the application has the following beneficial effects:
1. the method can realize effective identification of the pneumatic load of the tower drum of the fan, obtain the optimal measuring point arrangement and reduce the pathological degree of the matrix.
2. According to the invention, the optimal measuring point arrangement obtained by enumerating the initial measuring point matrix is continuously increased, the optimal sensor arrangement is searched on the basis of the optimal arrangement, and the optimal orientation arrangement of the strain sensor is determined.
3. The invention can determine the optimal initial matrix by enumerating the initial matrix by a successive addition method when the load quantity is far smaller than the measuring point quantity, and avoid discarding the optimal initial measuring point.
4. The invention only enumerates the initial measuring points, and can avoid overlarge calculated amount.
5. According to the invention, when the number of the target measuring points is small, the sinking of the local optimal solution can be effectively avoided, the dimensionality of the mathematical model is reduced to the greatest extent, and the load identification efficiency is improved.
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Some specific embodiments of the invention will be described in detail hereinafter by way of example and not by way of limitation with reference to the accompanying drawings. The same reference numbers will be used throughout the drawings to refer to the same or like parts or portions. It will be appreciated by those skilled in the art that the drawings are not necessarily drawn to scale. In the accompanying drawings:
FIG. 1 is a schematic overall flow chart of the present invention;
FIG. 2 is a layout of initial sensing points of the present invention;
FIG. 3 is an orientation layout of an initial sensor set of the present invention;
FIG. 4 is a condition diagram of the condition number of the frequency response function matrix at the initial measuring point of the invention;
FIG. 5 is a graph of the incremental addition station optimization process of the present invention;
FIG. 6 is a diagram of an optimized station arrangement of the present invention;
FIG. 7 is a graph of condition number change after optimization in accordance with the present invention.
Detailed Description
For the purposes, technical solutions and advantages of the present application, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
1-7, a measuring point optimization method for improving the pneumatic load identification stability of a fan tower comprises the following steps:
step S1, arranging a plurality of initial strain monitoring points on the inner wall of a fan tower, and setting a strain sensor group on the strain monitoring points;
s2, establishing a mathematical model between the pneumatic load and the structural response according to the initial monitoring point position arrangement condition;
s3, carrying out mathematical model optimization by taking a frequency response function matrix with the worst pathological degree in the load frequency domain as an optimization matrix;
s4, optimizing the measuring points by adopting a successive addition method to obtain an optimal measuring point matrix;
and S5, reestablishing a mathematical model according to the optimal measuring point matrix, and identifying the pneumatic load born by the tower barrel by adopting an inverse pseudo-excitation method.
Preferably, a plurality of initial strain monitoring points are arranged on the inner wall of the fan tower, strain sensor groups are arranged on the strain monitoring points, and the strain sensor groups are fixedly arranged on the inner surface of the fan tower; the distance between adjacent strain monitoring points in the height direction is 2m, and the included angle between the adjacent strain monitoring points in the height direction is 30 degrees;
the strain monitoring point is provided with a strain sensor group; the strain sensor group comprises a strain sensor arranged vertically and two strain sensors arranged obliquely; the two strain sensors which are obliquely arranged are symmetrically distributed on two sides of the strain sensor which is vertically arranged; the included angle between the obliquely arranged strain sensor and the vertically arranged strain sensor is 45 degrees.
Preferably, in the establishing a mathematical model between the pneumatic load and the structural response according to the initial monitoring point position arrangement condition, the mathematical model between the pneumatic load and the structural response is:
；
in the method, in the process of the invention,is a power spectrum density function matrix output by the system; />Is a frequency response function matrix;is a system input power spectral density function matrix; the superscript H is a conjugate transpose of the representation matrix.
Preferably, the mathematical model optimization using the most severe frequency response function matrix of the pathological degree in the load frequency domain as the optimization matrix includes:
calculating the condition number of the frequency response function matrix under each frequency, and drawing a condition number change curve;
and taking a frequency response function matrix corresponding to the frequency point with the largest condition number in the whole frequency range as an optimization matrix.
Preferably, the optimizing the measuring point by adopting a successive addition method to obtain an optimal measuring point matrix includes:
determining the number of measuring points of an initial matrix, and obtaining a matrix of all measuring points by adopting an enumeration method;
calculating the condition number of all the measurement point matrixes respectively, and taking the matrix with the minimum condition number as an initial moment;
sequentially selecting one measuring point from the rest measuring points, adding the measuring points into the initial matrix to obtain all expansion matrixes, and calculating the condition number of the expansion matrixes;
taking a matrix with the minimum condition number as a new matrix, and finishing one-time measuring point increase;
and repeatedly increasing the number of the measuring points until the number of the measuring points reaches the target number, and obtaining a final matrix.
Preferably, the reconstructing a mathematical model according to the optimal measurement point matrix, and identifying the aerodynamic load suffered by the tower by using an inverse pseudo-excitation method includes:
reestablishing a mathematical model according to the optimal measuring point matrix;
carrying out spectrum decomposition on the load spectrum;
according to the relation between the virtual input and the virtual output, the corresponding virtual input is obtained;
and solving a power spectrum density function matrix input by the system.
Preferably, due toThe formula for carrying out spectrum decomposition on the load spectrum is as follows:
；
in the method, in the process of the invention,for matrix->Is composed of feature vectors of (1); />And->Respectively is a matrix->Is defined, and corresponding feature vectors; />For matrix->Is a rank of (c).
Preferably, the method calculates the correspondence based on the relationship between the virtual input and the virtual outputThe formula of (2) is:
；
in the method, in the process of the invention,is a virtual input; />Is virtual output; superscript + denotes the Moore-Penrose matrix pseudo-inverse of the original matrix.
The virtual inputs and virtual outputs here are made virtual because the number obtained by spectral decomposition and the real number may not be identical.
Preferably, the formula for solving the power spectrum density function matrix input by the system is as follows:
。
the above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A measuring point optimizing method for improving the identification stability of the pneumatic load of a fan tower is characterized by comprising the following steps:
arranging a plurality of initial strain monitoring points on the inner wall of a fan tower, and setting a strain sensor group on the strain monitoring points;
establishing a mathematical model between the pneumatic load and the structural response according to the initial monitoring point position arrangement condition;
taking the worst frequency response function matrix of the pathological degree in the load frequency domain as an optimization matrix to perform mathematical model optimization;
optimizing the measuring points by adopting a successive addition method to obtain an optimal measuring point matrix;
reestablishing a mathematical model according to the optimal measuring point matrix, and identifying the pneumatic load born by the tower barrel by adopting an inverse pseudo-excitation method;
arranging a plurality of initial strain monitoring points on the inner wall of the fan tower, wherein strain sensor groups are arranged on the strain monitoring points and fixedly arranged on the inner surface of the fan tower; the distance between adjacent strain monitoring points in the height direction is 2m, and the included angle between the adjacent strain monitoring points in the height direction is 30 degrees;
the strain monitoring point is provided with a strain sensor group; the strain sensor group comprises a strain sensor arranged vertically and two strain sensors arranged obliquely; the two strain sensors which are obliquely arranged are symmetrically distributed on two sides of the strain sensor which is vertically arranged; the included angle between the obliquely arranged strain sensor and the vertically arranged strain sensor is 45 degrees;
according to the initial monitoring point position arrangement condition, establishing a mathematical model between the pneumatic load and the structural response, wherein the mathematical model between the pneumatic load and the structural response is as follows:
；
in the method, in the process of the invention,is a power spectrum density function matrix output by the system; />Is a frequency response function matrix;is a system input power spectral density function matrix; the superscript H is a conjugate transpose of the representation matrix;
the mathematical model optimization is carried out by taking the most serious pathological degree frequency response function matrix in the load frequency domain as an optimization matrix, and the mathematical model optimization method comprises the following steps:
calculating the condition number of the frequency response function matrix under each frequency, and drawing a condition number change curve;
and taking a frequency response function matrix corresponding to the frequency point with the largest condition number in the whole frequency range as an optimization matrix.
2. The method for optimizing measurement points according to claim 1, wherein the step of optimizing measurement points by using a successive addition method to obtain an optimal measurement point matrix comprises:
determining the number of measuring points of an initial matrix, and obtaining a matrix of all measuring points by adopting an enumeration method;
calculating the condition number of all the measurement point matrixes respectively, and taking the matrix with the minimum condition number as an initial moment;
sequentially selecting one measuring point from the rest measuring points, adding the measuring points into the initial matrix to obtain all expansion matrixes, and calculating the condition number of the expansion matrixes;
taking a matrix with the minimum condition number as a new matrix, and finishing one-time measuring point increase;
and repeatedly increasing the number of the measuring points until the number of the measuring points reaches the target number, and obtaining a final matrix.
3. The station optimization method of claim 2, wherein the reconstructing a mathematical model from the optimal station matrix and identifying the aerodynamic load of the tower by using an inverse pseudo-excitation method comprises:
reestablishing a mathematical model according to the optimal measuring point matrix;
carrying out spectrum decomposition on the load spectrum;
according to the relation between the virtual input and the virtual output, the corresponding virtual input is obtained;
and solving a power spectrum density function matrix input by the system.
4. A station optimization method as claimed in claim 3, characterized in that the formula for the spectral decomposition of the load spectrum is:
；
in the method, in the process of the invention,a conjugate matrix that is non-negative positive; />And->Respectively is a matrix->Is defined, and corresponding feature vectors; />For matrix->Rank of (c); />Is a virtual output.
5. The site optimization method of claim 4 wherein the formula for finding the corresponding virtual input based on the relationship between the virtual input and the virtual output is:
；
in the method, in the process of the invention,is a virtual input; superscript + denotes the Moore-Penrose matrix pseudo-inverse of the original matrix.
6. The station optimization method of claim 5, wherein the formula for solving the power spectral density function matrix input by the system is:
。
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