CN113933043B - Method and system for monitoring mechanical state of isolating switch based on stroke curve form - Google Patents
Method and system for monitoring mechanical state of isolating switch based on stroke curve form Download PDFInfo
- Publication number
- CN113933043B CN113933043B CN202111404622.7A CN202111404622A CN113933043B CN 113933043 B CN113933043 B CN 113933043B CN 202111404622 A CN202111404622 A CN 202111404622A CN 113933043 B CN113933043 B CN 113933043B
- Authority
- CN
- China
- Prior art keywords
- curve
- matrix
- body travel
- isolating switch
- calculating
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 53
- 238000012544 monitoring process Methods 0.000 title claims abstract description 34
- 239000011159 matrix material Substances 0.000 claims abstract description 181
- 230000009466 transformation Effects 0.000 claims abstract description 65
- 238000004364 calculation method Methods 0.000 claims description 19
- 230000008569 process Effects 0.000 claims description 18
- 238000005070 sampling Methods 0.000 claims description 5
- 238000007781 pre-processing Methods 0.000 claims description 4
- 238000010606 normalization Methods 0.000 claims description 2
- 230000001131 transforming effect Effects 0.000 claims description 2
- 238000006073 displacement reaction Methods 0.000 description 6
- 230000008859 change Effects 0.000 description 5
- 230000007246 mechanism Effects 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 3
- 230000033001 locomotion Effects 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 2
- 230000009471 action Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 238000011418 maintenance treatment Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000007704 transition Effects 0.000 description 2
- 230000000903 blocking effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 239000012212 insulator Substances 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000017105 transposition Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
- G01B21/22—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring angles or tapers; for testing the alignment of axes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Computing Systems (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Testing And Monitoring For Control Systems (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention relates to a method and a system for monitoring the mechanical state of an isolating switch based on a stroke curve form. And then carrying out inner product transformation on the body travel curve to obtain a transformation matrix, iteratively calculating a base matrix of the transformation matrix based on a Lagrange algorithm, and calculating a gray level co-occurrence matrix of the base matrix. And finally, calculating the reverse difference moment of the gray level co-occurrence matrix, judging the mechanical state of the GIS equipment isolating switch according to the reverse difference moment and the historical reverse difference moment, and further, diagnosing the mechanical state of the GIS equipment isolating switch by analyzing the body travel curve, so that whether the mechanical state of the GIS equipment isolating switch changes or not can be judged efficiently and accurately, effective measures can be taken for the GIS equipment isolating switch in time, and the operation reliability of the GIS equipment isolating switch is improved.
Description
Technical Field
The invention relates to the technical field of isolating switch state monitoring, in particular to a GIS equipment isolating switch mechanical state monitoring method and system based on a travel curve form.
Background
GIS (GAS Insulated SWITCH GEAR) is a GAS Insulated fully sealed switch device, has very wide application in modern power grid, integrates isolating switch, circuit breaker, mutual inductor, grounding device, lightning arrester, cable and bus, connecting piece and outgoing terminal, and has the advantages of small volume, light weight, modularized design integration of functions, high reliability, less maintenance workload and the like.
The isolating switch is a switch device with an isolating function, mainly serves as an isolating circuit and is one of important components in GIS equipment. In recent years, the problem of internal faults of GIS equipment caused by the defects in the isolating switch continuously occurs, so that large-area power failure accidents are often caused, even the whole transformer substation is powered off, thereby causing serious accidents of power failure of an upper power grid, and bringing great trouble and loss to power equipment and users. The faults of the isolating switch mainly comprise faults of a conductive loop, faults of a transmission mechanism, faults of an operating mechanism, faults of a post insulator and system mechanical faults. In addition, various human or environmental factors in the actual installation and operation processes cause the problems that the related disconnecting link isolating switch of GIS equipment cannot be effectively closed in place and the like in the actual operation process, and the power supply reliability of a local power grid and even the whole power grid is greatly affected.
The GIS equipment isolating switch mainly comprises a connecting rod mechanism, a driving motor, a movable contact and other parts, and the action process is that after the isolating switch judges on or off according to received signals, a crank arm starts to rotate and drives a movable contact to realize on or off under the drive of the motor, so that a body stroke curve of the isolating switch can be obtained through measuring the rotation of the crank arm and through a certain conversion relation, and obviously, the body stroke curve is closely related to whether the on or off of the GIS equipment isolating switch contact is in place or not. In addition, the body travel curve of the GIS equipment isolating switch can be conveniently obtained through the rotation angle displacement sensor and the fixed support thereof, which are arranged on the crank arm and used for obtaining the movement track of the rotation shaft head, the working procedure is simple, the precision is high, the actual operation condition of the GIS equipment isolating switch can be comprehensively and fully mastered, and the operation reliability of the GIS equipment isolating switch is improved. In practical application, how to judge the mechanical state of the GIS equipment isolating switch according to the body travel curve of the GIS equipment isolating switch and improve the pertinence of operation and maintenance work of the GIS equipment is a research difficulty.
Disclosure of Invention
The invention aims to provide a mechanical state monitoring method and system for a disconnecting switch based on a stroke curve form, which can efficiently and accurately judge the mechanical state of the disconnecting switch of GIS equipment by analyzing a body stroke curve generated in the switching-on and switching-off process of the disconnecting switch of GIS equipment.
In order to achieve the above object, the present invention provides the following solutions:
A method for monitoring the mechanical state of an isolating switch based on a stroke curve form, comprising the following steps:
Collecting a body travel curve in the switching-off or switching-on process of a GIS equipment isolating switch;
performing inner product transformation on the body travel curve to obtain a transformation matrix;
iteratively calculating a base matrix of the transformation matrix based on a Lagrangian algorithm, and calculating a gray level co-occurrence matrix of the base matrix;
Calculating the reverse moment of the gray level co-occurrence matrix, and judging the mechanical state of the GIS equipment isolating switch according to the reverse moment and the historical reverse moment; the historical reverse difference moment is obtained by calculating a historical body travel curve.
An isolating switch mechanical state monitoring system based on a trip curve morphology, the monitoring system comprising:
the acquisition module is used for acquiring a body travel curve in the switching-off or switching-on process of the GIS equipment isolating switch;
the inner product transformation module is used for carrying out inner product transformation on the body travel curve to obtain a transformation matrix;
The gray level co-occurrence matrix calculation module is used for iteratively calculating a base matrix of the transformation matrix based on a Lagrange algorithm and calculating a gray level co-occurrence matrix of the base matrix;
The judging module is used for calculating the reverse difference moment of the gray level co-occurrence matrix and judging the mechanical state of the GIS equipment isolating switch according to the reverse difference moment and the historical reverse difference moment; the historical reverse difference moment is obtained by calculating a historical body travel curve.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
The invention provides a method and a system for monitoring mechanical state of an isolating switch based on a stroke curve form. And then carrying out inner product transformation on the body travel curve to obtain a transformation matrix, iteratively calculating a base matrix of the transformation matrix based on a Lagrange algorithm, and calculating a gray level co-occurrence matrix of the base matrix. And finally, calculating the reverse difference moment of the gray level co-occurrence matrix, judging the mechanical state of the GIS equipment isolating switch according to the reverse difference moment and the historical reverse difference moment, and further, diagnosing the mechanical state of the GIS equipment isolating switch by analyzing the body travel curve, so that whether the mechanical state of the GIS equipment isolating switch changes or not can be judged efficiently and accurately, effective measures can be taken for the GIS equipment isolating switch in time, and the operation reliability of the GIS equipment isolating switch is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a monitoring method according to embodiment 1 of the present invention;
FIG. 2 is a detailed flowchart of the monitoring method according to embodiment 1 of the present invention;
Fig. 3 is a system block diagram of a monitoring system according to embodiment 2 of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a method and a system for monitoring the mechanical state of a isolating switch based on a stroke curve form, which can realize the efficient and accurate judgment of the mechanical state of the isolating switch of GIS equipment by carrying out real-time monitoring, calculation and analysis on a stroke signal in the switching-on and switching-off process of the isolating switch of the GIS equipment.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1:
the embodiment is used for providing a method for monitoring the mechanical state of an isolating switch based on a stroke curve form, as shown in fig. 1 and 2, the monitoring method comprises the following steps:
S1: collecting a body travel curve in the switching-off or switching-on process of a GIS equipment isolating switch;
Specifically, after the GIS equipment isolating switch judges to open or close according to the received signal, under the drive of the motor, the crank arm starts to rotate and drives the moving contact to open or close, the angular displacement of the crank arm is obtained through a rotation angle displacement sensor which is arranged on the crank arm and used for obtaining the motion track of the rotating shaft head, and then the body stroke curve of the isolating switch can be obtained, and the body stroke curve is the change curve of the angular displacement along with time. The monitoring method is simultaneously suitable for a body travel curve obtained in the opening process and a body travel curve obtained in the closing process, and whether the mechanical state of the GIS equipment isolating switch changes can be judged by analyzing the body travel curve in the opening or closing process.
As an optional implementation manner, the monitoring method of this embodiment further includes: and (2) preprocessing the body travel curve obtained in the step (S1) to obtain a preprocessed body travel curve, and executing the step (S2) by taking the preprocessed body travel curve as a new body travel curve.
The preprocessing the body travel curve to obtain the preprocessed body travel curve may include:
1) Dividing the travel curve of the body into a plurality of sections of continuous curves according to the time sequence;
the body travel curve is equally divided into a plurality of sections of continuous curves according to the sampling sequence of each sampling point in the body travel curve, the time period length of each section of continuous curve is deltat, the length of each section of continuous curve is M 0, namely, one section of continuous curve totally comprises M 0 data points, and the kth continuous curve is the curve sampled in the time period [ t k-Δt,tk ].
2) And respectively calculating the grid number required when each section of continuous curve is covered by square grids with preset side length, determining a curve starting point and a curve ending point according to all the grid numbers, and taking the curve between the curve starting point and the curve ending point as a preprocessed body travel curve.
For each continuous curve, the mesh number N (δ) represents the mesh number required when a square mesh with the length δ as a side length covers the continuous curve, and its calculation formula is as follows:
In formula (1), δ=Δt/M 0;j＝1,2,...,M0;xj is the jth data point of the continuous curve; x j+1 is the j+1st data point of the continuous curve. And then the grid number corresponding to each section of continuous curve can be calculated by using the formula (1).
Wherein determining the curve start point and the curve end point according to all the grid numbers may include:
Judging whether each grid number is larger than a first preset threshold value or not; if the grid number is larger than a first preset threshold value, recording a continuous curve corresponding to the grid number as a first curve; judging whether the grid number is larger than a second preset threshold value or not; if the grid number is larger than a second preset threshold value, the continuous curve corresponding to the grid number is recorded as a second curve. Determining the arrangement sequence of the first curve and the second curve according to the sequence of sampling the body travel curve, namely arranging the first curve according to the sequence from small to large of the starting time of the time period corresponding to the first curve, arranging the second curve according to the sequence from small to large of the starting time of the time period corresponding to the second curve, selecting the starting point of the first curve as the starting point of the curve, and selecting the ending point of the last second curve as the ending point of the curve. The first preset threshold is epsilon 1 and the second preset threshold is epsilon 2.
According to the embodiment, the body travel curve of the GIS equipment isolating switch is preprocessed based on grid fractal, so that travel data reflecting mechanical action characteristics of the GIS equipment isolating switch can be further acquired, and the calculation efficiency is improved.
S2: performing inner product transformation on the body travel curve to obtain a transformation matrix;
When the preprocessed body travel curve is used as the body travel curve in the step S2, the preprocessed body travel curve is subjected to inner product transformation, and a transformation matrix A with the dimension of MxM can be constructed. The calculation formula corresponding to the inner product transformation is as follows:
In the formula (2), T represents transposition; s represents a pretreated body travel curve, and the pretreated body travel curve has M data points; Representing a column vector consisting of M data points; s 1×M denotes the row vector consisting of M data points.
According to the embodiment, the travel curve of the GIS equipment isolating switch body is converted into a matrix form, so that the fluctuation mode of the travel curve can be effectively obtained, and the information transmission process of the travel curve can be accurately described.
As an optional implementation manner, the monitoring method of this embodiment further includes: normalizing the transformation matrix according to columns to obtain a normalized transformation matrix, taking the normalized transformation matrix as a new transformation matrix, and executing S3. Specifically, the transformation matrix a M×M is normalized according to columns to obtain a normalized transformation matrix B M×M, and the normalized calculation formula is as follows:
In the formula (3), s' ij is the element value of the ith row and the jth column in the normalized transformation matrix B M×M; s ij is the element value of the ith row and jth column in the transformation matrix a; i=1, 2,; j=1, 2,..m.
S3: iteratively calculating a base matrix of the transformation matrix based on a Lagrangian algorithm, and calculating a gray level co-occurrence matrix of the base matrix;
In S3, iteratively calculating the base matrix of the transformation matrix based on the lagrangian algorithm may include:
1) Establishing a base matrix solving model according to the transformation matrix;
the base matrix is initialized, the base matrix W M×M is initialized, the elements on the main diagonal are set to be 1, and the other elements are set to be zero.
The basis matrix solution model is:
In the formula (4): g is an objective function; B-WW TB||F represents the Frobenius norm of the matrix; lambda is the regularization coefficient; the |w| 1 denotes a 1-norm of the base matrix W; lambda W 1 represents a sparse constraint term for eliminating redundant terms in the basis matrix W; w.gtoreq.0, B.gtoreq.0 represents that each element of the matrices W and B is a non-negative real number.
2) Transforming the base matrix solving model to obtain a base matrix solving function;
The basis matrix solving function L is:
in the formula (5), tr represents the trace of the matrix, and refers to the sum of elements on the main diagonal of the matrix; beta is the Lagrangian multiplier.
3) And taking the base matrix solving function as a Lagrange function, and carrying out iterative calculation on the Lagrange function based on a Lagrange algorithm until the KKT condition is met, so as to obtain the base matrix of the transformation matrix.
The lagrangian function L is biased with:
Its Karush-Kuhn-Tucker (KKT) condition is:
(-BBTW+WWTBBTW)ijWij-βijWij-λWij＝0； (7)
And solving an optimal solution of the base matrix W by using a Lagrangian algorithm, performing iterative computation on each element W ij in the base matrix W and a Lagrangian multiplier beta ij corresponding to the element W ij, and if the KKT condition is met, ending the iteration to obtain the base matrix W, wherein the dimension of the base matrix W is M multiplied by r.
By using the method, the base matrix W of the normalized transformation matrix under the sparse constraint can be calculated based on the Lagrange algorithm in an iterative mode.
In S3, calculating the gray co-occurrence matrix of the base matrix may include:
1) Intercepting each row of the base matrix by utilizing rectangular windows with preset widths, intercepting each row to obtain a plurality of windows, and constructing a wavelet coefficient matrix; the number of lines of the wavelet coefficient matrix is equal to the number of lines of the base matrix, and the number of columns is equal to the number of windows obtained by intercepting each line;
specifically, the width is set to Is sequentially truncated starting from the start position of each row of the base matrix W, where,The representation is rounded down, and each row may be truncated to yield C 2 windows. For example, if r is equal to 10 and C 2 is equal to 4, then C 1 is equal to 2, where there are two elements in a row that are not counted in the window, and this case directly ignores the remaining two elements, the first window includes the first 4 elements in a row, the second window includes the 5 th, 6 th, 7 th, 8 th elements in a row, and the 9 th, 10 th elements in a row are ignored.
After window interception is completed, calculating the average value of each window of each row according to the element values of the base matrix to construct a wavelet coefficient matrix. The average value of the wavelet coefficients in the jth row and the jth window is recorded as H 'i (j, p), and the average value of the wavelet coefficients in the jth row and the jth window is used as the element value of the jth row and the jth column of the wavelet coefficient matrix, so that a new wavelet coefficient matrix H' i with the dimension of MxC 2 can be formed. The calculation formula of the average value H' i (j, p) of the wavelet coefficients in the jth row and the p window is as follows:
In the formula (8), W i (j, k) is the element value of the j-th row and the k-th column in the base matrix; j=1, 2,..m; p=1, 2,..c 2.
2) Calculating the gray value of each pixel point in the acoustic signal feature vector gray image based on the wavelet coefficient matrix;
According to the average value H' i (j, p) of wavelet coefficients in the jth row and p window in the wavelet coefficient matrix, calculating the gray value G i (j, p) of the pixel point of the jth row and p column in the acoustic signal feature vector gray image, wherein the calculation formula is as follows:
in the formula (9), the ceil function represents an upward rounding, and q is the gray scale bit depth.
3) Traversing all pixel points in the acoustic signal feature vector gray level image according to a preset step length and a preset angle to obtain a gray level co-occurrence matrix.
Specifically, traversing all pixel points in the acoustic signal feature vector gray level image according to a distance step d (i.e. a preset step) between pixel pairs of the gray level image and an adjacent angle theta (i.e. a preset angle) between the pixel pairs to obtain a gray level co-occurrence matrix of the acoustic signal feature vector gray level image. The calculation formula of the element value P (i, j|d, theta) of the ith row and jth column of the gray level co-occurrence matrix is as follows:
P(i,j|d,θ)＝{(x,y)|f(x,y)＝i,f(x+dx,y+dy)＝j}； (10)
In the formula (10), i, j are gray values corresponding to the gray image pairs (x, y) and (x+dx, y+dy), respectively; dx, dy represents the offset in the horizontal direction and the vertical direction, respectively, and dx=dcos θ and dy=dsin θ; d=1 and the number of the groups, θ=135°.
As an optional implementation manner, the monitoring method of the present embodiment further includes, when traversing all pixels in the acoustic signal feature vector gray scale image according to a preset step size and a preset angle: and for each pixel point which is traversed, thresholding the gray value of the pixel point in the acoustic signal feature vector gray image to obtain a thresholded gray value, and taking the thresholded gray value as a new gray value of the pixel point, namely taking the thresholded gray value as the gray value in the formula (10) to calculate a gray co-occurrence matrix.
More specifically, thresholding the gray values of the pixel points in the acoustic signal feature vector gray scale image may include:
a) Defining a pixel window of c×c in the acoustic signal feature vector gray scale image; the pixel point is the central pixel point of the pixel window;
b) Comparing w c with w ci, and obtaining an 8-bit binary number according to a comparison result; w c is the gray value of the central pixel point of the pixel window, namely the gray value of the central pixel point in the acoustic signal feature vector gray image; w ci is the gray value of the ith adjacent pixel point of the central pixel point, namely the gray value of the adjacent pixel point in the acoustic signal feature vector gray image; i=1, 2, 8;
Specifically, w c and w ci are sequentially compared to obtain an 8-bit binary number K lb＝{l1,l2,...,l8, and the formula used for obtaining the comparison result is as follows:
c) And converting the 8-bit binary number into a decimal number, wherein the decimal number is the thresholded gray value of the pixel point.
According to the embodiment, the Lagrange algorithm is used for iterative computation of the base matrix of the normalized transformation matrix under the sparse constraint, the influence of the travel curve interference component of the isolating switch body of the GIS equipment can be effectively restrained, and meanwhile, the gray level co-occurrence matrix of the gray level image matrix is obtained by adopting the local binary mode, so that the gray level information of the image is not lost while the local characteristic information of the gray level image is accurately obtained, and the computing efficiency and the computing precision are improved.
S4: calculating the reverse moment of the gray level co-occurrence matrix, and judging the mechanical state of the GIS equipment isolating switch according to the reverse moment and the historical reverse moment; the historical reverse difference moment is obtained by calculating a historical body travel curve.
The calculation formula of the inverse difference moment I of the gray level co-occurrence matrix is as follows:
in the formula (12), g is the number of elements of the gray level co-occurrence matrix.
Wherein, distinguishing the mechanical state of the GIS equipment isolating switch according to the reverse torque and the historical reverse torque can comprise:
1) Calculating the variation of the inverse difference moment relative to the historical inverse difference moment; the variation is equal to the ratio of the difference value of the reverse torque and the historical reverse torque to the historical reverse torque;
2) Judging whether the variation is larger than a preset variation or not; if so, the mechanical state of the GIS equipment isolating switch is changed.
The embodiment provides a quantitative judgment standard for the mechanical state of the GIS equipment isolating switch, provides a basis for overhauling and maintaining the GIS equipment isolating switch, compares the reverse difference moment of the gray level co-occurrence matrix with the reverse difference moment of the gray level co-occurrence matrix of the history body travel curve, and judges the mechanical state of the GIS equipment isolating switch according to the change of the reverse difference moment. When the variation of the inverse difference moment of the gray level co-occurrence matrix exceeds 5%, the mechanical state of the GIS equipment isolating switch is judged to be changed, and at the moment, maintenance treatment is needed in time, so that serious faults are avoided.
According to the monitoring method provided by the embodiment, the mechanical state of the GIS equipment isolating switch is judged by calculating and analyzing the change of the reverse difference moment of the gray level co-occurrence matrix of the base matrix of the body travel curve in the switching-on and switching-off process of the GIS equipment isolating switch, and the mechanical state of the GIS equipment isolating switch can be accurately monitored through the travel curve in the switching-on and switching-off process of the GIS equipment isolating switch, so that the hidden danger of the mechanical fault of the GIS equipment isolating switch can be effectively identified, and an effective operation and maintenance strategy is adopted, so that serious faults are avoided.
And then, testing a body travel curve in the opening and closing process by taking a certain 2200kV GIS equipment isolating switch as a test object, thereby explaining a mechanical state monitoring method of the GIS equipment isolating switch. Fig. 2 is a detailed flow chart of the method of the present example.
As shown in fig. 2, it includes the steps of:
(1) In a transformer substation site, a rotation angle displacement sensor is arranged on an adjustable universal crank arm of a GIS equipment isolating switch through a fixed support and a transition screw, a movement track of a rotation shaft head is transmitted to a shaft core of the rotation angle displacement sensor through the transition screw, a connecting wire is connected to a signal acquisition and analysis system, and a body travel curve x (n) in the switching-on and switching-off process of the GIS equipment isolating switch is acquired, wherein the length of the body travel curve x (n) is M 0, and M 0 =4000;
(2) The body travel curve is preprocessed, and the specific steps are as follows:
calculating the grid number N (delta) of the body travel curve in different time periods [ t k-Δt,tk ], wherein the grid number N (delta) represents the grid number required by covering the body travel curve by square grids with delta as side length in the time period [ t k-Δt,tk ], and the calculation formula is as follows:
Where δ=Δt/M 0.
And 2b, respectively taking the positions where the grid number N (delta) is greater than the threshold epsilon 1 and epsilon 2 as a starting point and an ending point of a travel curve, and recording the preprocessed travel curve of the body as s (N), wherein the length of the travel curve is M.
(3) Performing inner product transformation on the preprocessed GIS equipment isolating switch body travel curve to construct a transformation matrix A with dimension of MxM, wherein a corresponding calculation formula is as follows
Wherein: t represents a transpose;
(4) Normalizing the transformation matrix A M×M according to columns to obtain a normalized transformation matrix B M×M, wherein the normalization calculation formula is as follows
(5) The method comprises the following steps of iteratively calculating a base matrix W of a normalized transformation matrix under sparse constraint based on a Lagrange algorithm:
Initializing a base matrix W M×M, wherein the element on a main diagonal is 1, and the other elements are zero;
5b, establishing a base matrix W solving model to be
s.t.W≥0,B≥0
Wherein: g is an objective function; B-WW TB||F represents the Frobenius norm of the matrix; w is a base matrix; lambda is the regularization coefficient; the |w| 1 denotes a 1-norm of the base matrix W; lambda W 1 represents a sparse constraint term for eliminating redundant terms in the basis matrix W;
5c, solving the optimal solution of the base matrix W by using a Lagrange algorithm, and defining a Lagrange function L as
Wherein: lambda is the Lagrangian multiplier; tr represents the trace of the matrix, referring to the sum of the elements on the main diagonal of the matrix;
the lagrangian function L is biased with:
Its Karush-Kuhn-Tucker (KKT) condition is:
(-BBTW+WWTBBTW)ijWij-βijWij-λWij＝0
Performing iterative computation on each element W ij and the corresponding Lagrangian multiplier lambda ij, and if the KKT condition is met, ending the iteration to obtain a base matrix W, wherein the dimension of the base matrix W is M multiplied by r;
(6) The gray level co-occurrence matrix and the inverse difference moment of the base matrix are calculated, and the specific steps are as follows:
6a, use width of Is sequentially truncated starting from the start of each row of matrix H, where,Representing downward rounding, each row can be truncated to obtain C 2 windows, and the average value of wavelet coefficients in the jth row and the p window is recorded as H ' i (j, k), so that a new wavelet coefficient matrix H ' i can be formed, its dimension is M×C 2, and the calculation formula of the wavelet coefficient matrix H ' i is
Calculating the gray value G i (j, p) of the pixel point at the j-th row and p-th column of the acoustic signal feature vector gray image according to the wavelet coefficient matrix H' i, as
In the formula, the ceil function represents upward rounding, and q is the bit depth of the gray level map;
Defining a pixel window of c×c, wherein the gray value of the central pixel point of the pixel window is w c,wc, and the gray value of any adjacent pair of pixel points is w ci, i=1, 2, 8;
Sequentially performing difference operation on w c and w ci to obtain an 8-bit binary number K lb＝{l1,l2,...,l8, wherein the difference operation formula is as follows
6E, taking a distance step d=1 between pixel pairs of the gray image and an adjacent angle theta=135 between the pixel pairs, and traversing all pixel points of the gray image matrix according to the local binary pattern defined in the step 6c and the step 6d to obtain a gray co-occurrence matrix of the gray image;
6f, calculating the inverse difference moment of the gray level co-occurrence matrix, wherein the calculation formula of the inverse difference moment of the gray level co-occurrence matrix is as follows
Wherein: g is the number of elements of the gray level co-occurrence matrix; p represents a gray level co-occurrence matrix.
(7) Comparing the inverse difference moment of the gray level co-occurrence matrix with the inverse difference moment of the gray level co-occurrence matrix of the history travel curve, and judging the mechanical state of the GIS equipment isolating switch according to the change of the inverse difference moment: when the variation of the inverse difference moment of the gray level co-occurrence matrix exceeds 5%, the mechanical state of the isolating switch is judged to be changed, and at the moment, maintenance treatment is needed in time, so that serious faults are avoided. Here, the change of the reverse difference moment of the gray level co-occurrence matrix of the travel curve of the GIS equipment isolating switch is 8%, and the blocking of the GIS equipment isolating switch transmission mechanism is found through on-site confirmation.
Example 2:
the embodiment is used for providing a monitoring system for mechanical state of an isolating switch based on stroke curve form, as shown in fig. 3, the monitoring system includes:
The acquisition module M1 is used for acquiring a body travel curve in the switching-off or switching-on process of the isolating switch of the GIS equipment;
The inner product transformation module M2 is used for performing inner product transformation on the body travel curve to obtain a transformation matrix;
The gray level co-occurrence matrix calculation module M3 is used for iteratively calculating a base matrix of the transformation matrix based on a Lagrange algorithm and calculating a gray level co-occurrence matrix of the base matrix;
The judging module M4 is used for calculating the reverse difference moment of the gray level co-occurrence matrix and judging the mechanical state of the GIS equipment isolating switch according to the reverse difference moment and the historical reverse difference moment; the historical reverse difference moment is obtained by calculating a historical body travel curve.
In this specification, each embodiment is mainly described in the specification as a difference from other embodiments, and the same similar parts between the embodiments are referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (8)
1. The method for monitoring the mechanical state of the isolating switch based on the stroke curve form is characterized by comprising the following steps of:
Collecting a body travel curve in the switching-off or switching-on process of a GIS equipment isolating switch;
performing inner product transformation on the body travel curve to obtain a transformation matrix;
iteratively calculating a base matrix of the transformation matrix based on a Lagrangian algorithm, and calculating a gray level co-occurrence matrix of the base matrix;
Calculating the reverse moment of the gray level co-occurrence matrix, and judging the mechanical state of the GIS equipment isolating switch according to the reverse moment and the historical reverse moment; the historical reverse difference moment is obtained by calculating a historical body travel curve;
Before performing inner product transformation on the body travel curve to obtain a transformation matrix, the monitoring method further comprises: preprocessing the body travel curve to obtain a preprocessed body travel curve, and taking the preprocessed body travel curve as a new body travel curve;
The body travel curve is preprocessed, and the preprocessed body travel curve specifically comprises:
Dividing the body travel curve into a plurality of sections of continuous curves according to the time sequence;
respectively calculating the grid number required when each section of continuous curve is covered by square grids with preset side length, determining a curve starting point and a curve ending point according to all the grid numbers, and taking a curve between the curve starting point and the curve ending point as a preprocessed body travel curve;
the determining the curve starting point and the curve ending point according to all the grid numbers specifically comprises:
Judging whether the grid number is larger than a first preset threshold value or not for each grid number; if the grid number is larger than the first preset threshold value, recording a continuous curve corresponding to the grid number as a first curve; judging whether the grid number is larger than a second preset threshold value or not; if the grid number is larger than the second preset threshold value, recording a continuous curve corresponding to the grid number as a second curve;
Determining the arrangement sequence of the first curve and the second curve according to the sequence of the body travel curve during sampling, selecting the starting point of the first curve as the starting point of the curve, and selecting the ending point of the last second curve as the ending point of the curve.
2. The monitoring method according to claim 1, wherein before iteratively calculating the basis matrix of the transformation matrix based on a lagrangian algorithm, the monitoring method further comprises: normalizing the transformation matrix according to columns to obtain a normalized transformation matrix, and taking the normalized transformation matrix as a new transformation matrix.
3. The method according to claim 1, wherein iteratively calculating the basis matrix of the transformation matrix based on the lagrangian algorithm specifically comprises:
establishing a base matrix solving model according to the transformation matrix;
transforming the base matrix solving model to obtain a base matrix solving function;
Taking the base matrix solving function as a Lagrange function, and carrying out iterative calculation on the Lagrange function based on a Lagrange algorithm until a KKT condition is met, so as to obtain a base matrix of the transformation matrix; the KKT condition is (-BB TW+WWTBBTW)ijWij-βijWij-λWij =0), wherein B is a transformation matrix A constructed by carrying out inner product transformation on a preprocessed body travel curve, normalization processing is carried out on the transformation matrix A, the normalized transformation matrix B is obtained, B T is a transposed matrix of the transformation matrix B, W is a base matrix, W T is a transposed matrix of the base matrix, beta ij is a Lagrange multiplier, W ij is an element in the base matrix, and lambda is a regularization coefficient.
4. The method of monitoring according to claim 1, wherein the calculating the gray level co-occurrence matrix of the base matrix specifically includes:
intercepting each row of the base matrix by utilizing a rectangular window with a preset width, intercepting each row to obtain a plurality of windows, and constructing a wavelet coefficient matrix; the number of lines of the wavelet coefficient matrix is equal to the number of lines of the base matrix, and the number of columns is equal to the number of windows obtained by intercepting each line;
calculating the gray value of each pixel point in the acoustic signal feature vector gray image based on the wavelet coefficient matrix;
traversing all pixel points in the acoustic signal feature vector gray level image according to a preset step length and a preset angle to obtain a gray level co-occurrence matrix.
5. The method of monitoring of claim 4, wherein the method further comprises, while traversing all pixels in the acoustic signal feature vector gray scale image according to a preset step size and a preset angle:
And for each pixel point which is traversed, thresholding the gray value of the pixel point in the acoustic signal feature vector gray image to obtain a thresholded gray value, and calculating a gray co-occurrence matrix by taking the thresholded gray value as a new gray value of the pixel point.
6. The method according to claim 5, wherein thresholding the gray level of the pixel point in the acoustic signal feature vector gray level image specifically comprises:
Defining a pixel window of c×c in the acoustic signal feature vector gray scale image; the pixel point is the central pixel point of the pixel window;
Comparing wc with wci, and obtaining an 8-bit binary number according to a comparison result; wc is the gray value of the center pixel point; wci is the gray value of the ith adjacent pixel point of the central pixel point; i=1, 2, 8;
And converting the 8-bit binary number into a decimal number, wherein the decimal number is the thresholded gray value of the pixel point.
7. The monitoring method according to claim 1, wherein the distinguishing the mechanical state of the GIS device isolating switch according to the reverse torque and the historical reverse torque specifically includes:
calculating the variation of the inverse difference moment relative to the historical inverse difference moment; the variation is equal to the ratio of the difference between the inverse moment and the historical inverse moment to the historical inverse moment;
Judging whether the variation is larger than a preset variation or not;
If yes, the mechanical state of the GIS equipment isolating switch is changed.
8. An isolating switch mechanical state monitoring system based on a stroke curve form, which is characterized by comprising:
the acquisition module is used for acquiring a body travel curve in the switching-off or switching-on process of the GIS equipment isolating switch;
the inner product transformation module is used for carrying out inner product transformation on the body travel curve to obtain a transformation matrix;
The gray level co-occurrence matrix calculation module is used for iteratively calculating a base matrix of the transformation matrix based on a Lagrange algorithm and calculating a gray level co-occurrence matrix of the base matrix;
The judging module is used for calculating the reverse difference moment of the gray level co-occurrence matrix and judging the mechanical state of the GIS equipment isolating switch according to the reverse difference moment and the historical reverse difference moment; the historical reverse difference moment is obtained by calculating a historical body travel curve;
Before performing inner product transformation on the body travel curve to obtain a transformation matrix, the monitoring system further comprises: preprocessing the body travel curve to obtain a preprocessed body travel curve, and taking the preprocessed body travel curve as a new body travel curve; the body travel curve is preprocessed, and the preprocessed body travel curve specifically comprises: dividing the body travel curve into a plurality of sections of continuous curves according to the time sequence; respectively calculating the grid number required when each section of continuous curve is covered by square grids with preset side length, determining a curve starting point and a curve ending point according to all the grid numbers, and taking a curve between the curve starting point and the curve ending point as a preprocessed body travel curve; the determining the curve starting point and the curve ending point according to all the grid numbers specifically comprises: judging whether the grid number is larger than a first preset threshold value or not for each grid number; if the grid number is larger than the first preset threshold value, recording a continuous curve corresponding to the grid number as a first curve; judging whether the grid number is larger than a second preset threshold value or not; if the grid number is larger than the second preset threshold value, recording a continuous curve corresponding to the grid number as a second curve; determining the arrangement sequence of the first curve and the second curve according to the sequence of the body travel curve during sampling, selecting the starting point of the first curve as the starting point of the curve, and selecting the ending point of the last second curve as the ending point of the curve.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111404622.7A CN113933043B (en) | 2021-11-24 | 2021-11-24 | Method and system for monitoring mechanical state of isolating switch based on stroke curve form |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111404622.7A CN113933043B (en) | 2021-11-24 | 2021-11-24 | Method and system for monitoring mechanical state of isolating switch based on stroke curve form |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113933043A CN113933043A (en) | 2022-01-14 |
| CN113933043B true CN113933043B (en) | 2024-08-09 |
Family
ID=79288342
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111404622.7A Active CN113933043B (en) | 2021-11-24 | 2021-11-24 | Method and system for monitoring mechanical state of isolating switch based on stroke curve form |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113933043B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008295298A (en) * | 2001-02-09 | 2008-12-04 | Toshiba Corp | System for protecting and controlling transformer apparatus |
| CN206864379U (en) * | 2017-05-27 | 2018-01-09 | 余姚市华宇电器有限公司 | A kind of three position mechanism control and accessory drive structure |
Family Cites Families (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201803857U (en) * | 2010-08-20 | 2011-04-20 | 中国西电电气股份有限公司 | On-line monitoring and diagnosing system for mechanical properties of GIS (Gas-Insulated Metal-Enclosed Switchgear) control mechanism |
| CN104019975B (en) * | 2014-06-13 | 2017-02-15 | 西安交通大学 | On-line monitoring system for high-voltage GIS mechanical states |
| CN105093059B (en) * | 2015-08-26 | 2018-12-11 | 云南电网有限责任公司电力科学研究院 | A kind of transformer winding working state detecting method based on normalization singular spectrum entropy |
| CN105372059B (en) * | 2015-12-15 | 2018-07-27 | 河海大学常州校区 | High voltage isolator mechanical property on-Line Monitor Device and its monitoring method |
| CN205785500U (en) * | 2016-05-25 | 2016-12-07 | 国网宁夏电力公司固原供电公司 | GIS device vibration signal detector |
| CN107515108B (en) * | 2017-07-26 | 2019-10-18 | 国网江苏省电力公司南京供电公司 | The mechanical property monitoring device and monitoring method of high voltage isolating switch operating mechanism |
| CN108489717B (en) * | 2018-01-25 | 2019-11-22 | 国网浙江省电力有限公司电力科学研究院 | On-load tap changers of transformers machine performance monitoring method and system |
| CN109839263B (en) * | 2019-02-26 | 2020-06-09 | 北京航空航天大学 | GIS (geographic information System) feature extraction and mechanical defect diagnosis method based on vibration information |
| CN110376455B (en) * | 2019-06-26 | 2021-11-16 | 深圳供电局有限公司 | Transformer working state detection method and device, computer equipment and storage medium |
| CN110378407A (en) * | 2019-07-12 | 2019-10-25 | 台州宏创电力集团有限公司 | Power equipment image-recognizing method and device based on parametric texture and neural network |
| CN111024233B (en) * | 2019-11-14 | 2021-10-08 | 国网上海市电力公司 | Simulation device for poor contact of GIS internal contact and infrared calibration method |
| CN112966630B (en) * | 2021-03-18 | 2022-09-30 | 国网安徽省电力有限公司电力科学研究院 | Method, system and equipment for evaluating running state of ultra/extra-high voltage GIS switch equipment |
| CN113504020A (en) * | 2021-05-24 | 2021-10-15 | 国网江苏省电力有限公司电力科学研究院 | GIS vibration defect simulation system and method |
| CN113591985B (en) * | 2021-07-30 | 2023-12-15 | 国网安徽省电力有限公司电力科学研究院 | Ultra-high voltage GIS running state comprehensive diagnosis method and system based on data fusion |
-
2021
- 2021-11-24 CN CN202111404622.7A patent/CN113933043B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008295298A (en) * | 2001-02-09 | 2008-12-04 | Toshiba Corp | System for protecting and controlling transformer apparatus |
| CN206864379U (en) * | 2017-05-27 | 2018-01-09 | 余姚市华宇电器有限公司 | A kind of three position mechanism control and accessory drive structure |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113933043A (en) | 2022-01-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111404274B (en) | Transmission system displacement on-line monitoring and early warning system | |
| CN112560888B (en) | Method and system for evaluating short circuit resistance of transformer winding based on octyl geometric transformation | |
| CN112069930A (en) | Vibration signal processing method and device for improving GIS equipment fault diagnosis accuracy | |
| CN117009770A (en) | Bearing fault diagnosis method based on SDP and visual transducer codes | |
| CN114114001B (en) | GIS equipment isolating switch mechanical state monitoring method and system | |
| CN116186520A (en) | Multi-task combined high-voltage isolating switch fault diagnosis method | |
| CN117113243B (en) | Photovoltaic equipment abnormality detection method | |
| CN114655247A (en) | MATT-GNN-based peripheral vehicle track prediction method and system | |
| CN113436184A (en) | Power equipment image defect judging method and system based on improved twin network | |
| CN115236451A (en) | Power distribution network overhead line fault monitoring device and method | |
| CN113933043B (en) | Method and system for monitoring mechanical state of isolating switch based on stroke curve form | |
| CN117607672A (en) | Intelligent monitoring method and system for GIS circuit breaker | |
| CN116523835A (en) | Improved U-Net high temperature abnormality detection method integrating multiscale and attention mechanisms | |
| CN113284103A (en) | Substation equipment defect online detection system based on space transformation fast R-CNN model | |
| CN111932007A (en) | Power prediction method and device for photovoltaic power station and storage medium | |
| CN117576074A (en) | Transmission line insulator defect detection system based on deep learning | |
| CN114048762B (en) | Double-attention-guided rotary machine health assessment method | |
| CN115684908A (en) | State monitoring method for power GIS equipment, storage medium and electronic device | |
| CN115294094A (en) | Extra-high voltage line insulator fault diagnosis method and device based on heterogeneous data | |
| CN116610940A (en) | Equipment fault diagnosis system based on wavelet transformation and deep neural network | |
| CN114112017B (en) | Vibration signal extraction method and system of GIS isolating switch | |
| CN117648634B (en) | Method and system for predicting performance of connecting hardware fitting of power distribution network based on time domain and frequency domain information | |
| CN118611059B (en) | Offshore wind power prediction method of attention mechanism double-channel neural network | |
| CN216144360U (en) | Production site real-time monitoring system based on Internet of things | |
| Liu et al. | PTZ control system of indoor rail inspection robot based on neural network prediction model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |