Health assessment method and system for hydropower units based on characteristic statistical analysis

Abstract

The present application relates to a method and system for health evaluation of a hydropower unit based on characteristic statistical analysis, wherein the method comprises: obtaining sample data of each associated measuring point in the evaluation project, and determining new sample variables of each evaluation item in the hydropower unit components by principal component analysis; calculating the eigenvalues ​​and eigenvectors of the original sample variables in the sample data, and obtaining the principal component eigenvalues ​​and load matrix corresponding to the new sample variables; calculating the project health index of the evaluation project based on the eigenvalues, eigenvectors, principal component eigenvalues ​​and load matrix; determining the weight coefficient of the evaluation item in the hydropower unit components by hierarchical analysis, and then calculating the component health index of the hydropower unit components. Through the present application, the problem of poor health status evaluation effect of the hydropower unit is solved, and the cause of the failure from the associated measuring point to the hydropower unit equipment is determined, the health status of the hydropower unit is accurately grasped, and the failure is prevented in advance.

Description

Technical Field

[0001] The present application relates to the technical field of hydropower unit status assessment, and in particular to a hydropower unit health assessment method and system based on feature statistical analysis. Background Art

[0002] With the continuous development of my country's power industry, hydropower has gradually increased its share of the country's power generation supply structure. Due to the continuous construction and commissioning of ultra-high voltage direct current (UHVDC) transmission lines, large-scale hydropower plants are increasingly serving as direct power supply terminals for industrial consumption. Hydropower energy transmission has entered a new era of large units, ultra-high voltage transmission, and intelligent management. Hydropower units, as key power generation equipment in hydropower plants, are also evolving towards larger capacity, higher specific speeds, and higher loads. Their component structures and layouts are becoming increasingly complex, and the coupling between hydraulic, mechanical, and electrical systems is becoming increasingly prominent. This has led to an increasing number of unplanned outages and sudden impact failures of hydropower equipment. Therefore, health assessments of hydropower units are particularly important.

[0003] The current existing technology can generally only alarm when a hydropower unit fails, but cannot determine the cause of the failure in a timely manner. The health status assessment effect of the hydropower unit is poor, and it cannot accurately determine the health status of the hydropower unit and prevent failures in advance.

[0004] Currently, no effective solution has been proposed to address the problem of poor health status assessment results for hydropower units in related technologies. Summary of the Invention

[0005] The embodiments of the present application provide a method and system for evaluating the health of a hydropower unit based on characteristic statistical analysis, so as to at least solve the problem of poor health status evaluation effect of a hydropower unit in the related art.

[0006] In a first aspect, an embodiment of the present application provides a method for health assessment of a hydropower unit based on characteristic statistical analysis, the method comprising:

[0007] Obtain sample data of each associated measuring point in the evaluation project at different times;

[0008] According to the sample data, a new sample variable of each evaluation item in the hydroelectric motor components is determined by principal component analysis;

[0009] Calculating the eigenvalues ​​and eigenvectors of the original sample variables in the sample data, and obtaining the principal component eigenvalues ​​and loading matrix corresponding to the new sample variables;

[0010] Calculating a project health index of the evaluation project according to the eigenvalue, the eigenvector, the principal component eigenvalue, and the load matrix;

[0011] The weight coefficients of the evaluation items in the hydroelectric motor components are determined by the hierarchical analysis method, and then the component health index of the hydroelectric motor components is calculated.

[0012] In some embodiments, calculating the eigenvalues ​​and eigenvectors of the original sample variables in the sample data includes:

[0013] Performing data preprocessing on the sample data by matrix standardization to obtain a standardized data matrix;

[0014] Calculating a correlation coefficient matrix of the sample data according to the standardized data matrix;

[0015] The Jacobi method is used to calculate the correlation coefficient matrix to obtain the eigenvalues ​​and eigenvectors of the original sample variables in the sample data, and the principal component eigenvalues ​​and loading matrix corresponding to the new sample variables are obtained.

[0016] In some embodiments, calculating the project health index of the evaluation project based on the eigenvalue, the eigenvector, the principal component eigenvalue, and the loading matrix includes:

[0017] Calculating the number of the new sample variables according to the eigenvalue, the principal component eigenvalue and a preset contribution rate threshold;

[0018] Calculating the contribution rate of each new sample variable according to the number of the new sample variables, the eigenvalues ​​and the principal component eigenvalues;

[0019] The project health index of the evaluation project is calculated based on the contribution rate of the new sample variable, the load matrix and the preset degradation evaluation index of the new sample variable.

[0020] In some embodiments, determining the weight coefficient of the evaluation item in the hydroelectric motor component by using the hierarchical analysis method, and then calculating the component health index of the hydroelectric motor component includes:

[0021] According to the judgment of relative importance, corresponding scale values ​​are introduced to form a judgment matrix, and the maximum eigenvalue of the judgment matrix and the eigenvector corresponding to the maximum eigenvalue are calculated. The eigenvector corresponding to the maximum eigenvalue is the weight vector of the evaluation item in the hydroelectric motor component;

[0022] The weight vector is normalized to obtain a normalized weight vector, and then the component health index of the hydroelectric motor component is calculated.

[0023] In some embodiments, obtaining sample data of each associated measurement point in the evaluation project at different times includes:

[0024] Obtain sample data at different times for each associated measurement point in the evaluation project, and set a preset degradation evaluation index based on expert experience in hydropower unit health evaluation;

[0025] The measuring point health index of the associated measuring point is directly calculated based on whether the actual measurement value in the sample data is within the threshold range of the preset degradation evaluation index.

[0026] In some embodiments, calculating the project health index of the evaluation project according to the contribution rate of the new sample variable, the load matrix, and the preset degradation evaluation index of the new sample variable includes:

[0027] Calculate the minimum value and the maximum value of each new sample variable in the evaluation item according to the loading matrix;

[0028] Calculating the standard minimum value and the standard maximum value of the new sample variable according to the preset degradation evaluation index of the associated measuring point;

[0029] Calculating a principal component health index of the new sample variable according to the minimum value, the maximum value, the standard minimum value, and the standard maximum value;

[0030] The project health index of the evaluation project is calculated based on the principal component health index and the contribution rate.

[0031] In some embodiments, after calculating the maximum eigenvalue of the judgment matrix and the eigenvector corresponding to the maximum eigenvalue, the method further includes:

[0032] Check whether the constructed judgment matrix is ​​reasonable, and calculate the consistency index formula The consistency index is calculated, where λ max is the maximum characteristic root, and m is the number of evaluation items;

[0033] If the consistency index CI=0, the judgment matrix consistency test passes;

[0034] If CI≠0, then the test consistency ratio Among them, RI is the random consistency index;

[0035] If the consistency ratio CR is less than 0.1, the judgment matrix consistency test passes; otherwise, the test fails and the judgment matrix is ​​revised.

[0036] In some embodiments, after determining the weight coefficients of the evaluation items in the hydroelectric motor components by using the analytic hierarchy process and then calculating the component health index of the hydroelectric motor components, the method further includes:

[0037] The weight coefficients of the hydroelectric motor components in the hydroelectric motor equipment are determined by the same analytic hierarchy process, and then the equipment health index of the hydroelectric motor equipment is calculated.

[0038] In some embodiments, before obtaining sample data of each associated measurement point in the evaluation project at different times, the method further includes:

[0039] Determine whether the hydropower unit is in a shutdown state according to the operating state data of the hydropower unit, and if so, return a preset identification value;

[0040] If not, determine whether the hydropower unit has defects or faults based on the defect and fault information, and if so, return the corresponding preset health evaluation value;

[0041] If not, it is determined whether there is an alarm for the hydropower unit according to the alarm information. If so, the corresponding preset health evaluation value is returned. If not, a health evaluation of the hydropower unit is performed.

[0042] In a second aspect, an embodiment of the present application provides a hydropower unit health assessment system based on feature statistical analysis, the system comprising a data acquisition module, a principal component analysis module, and a health assessment module;

[0043] The data acquisition module acquires sample data of each associated measurement point in the evaluation project at different times;

[0044] The principal component analysis module determines new sample variables for each evaluation item in the hydroelectric motor components by principal component analysis based on the sample data;

[0045] The principal component analysis module calculates the eigenvalues ​​and eigenvectors of the original sample variables in the sample data, and obtains the principal component eigenvalues ​​and loading matrix corresponding to the new sample variables;

[0046] The health evaluation module calculates the project health index of the evaluation project according to the eigenvalue, the eigenvector, the principal component eigenvalue and the load matrix;

[0047] The health evaluation module determines the weight coefficient of the evaluation item in the hydroelectric motor component through the hierarchical analysis method, and then calculates the component health index of the hydroelectric motor component.

[0048] Compared with the related art, the embodiment of the present application provides a hydropower unit health evaluation method and system based on characteristic statistical analysis, which obtains sample data of each associated measuring point in the evaluation project, and determines the new sample variables of each evaluation item in the hydropower unit component through the principal component analysis method; calculates the eigenvalues ​​and eigenvectors of the original sample variables in the sample data, and obtains the principal component eigenvalues ​​and load matrix corresponding to the new sample variables; calculates the project health index of the evaluation project based on the eigenvalues, eigenvectors, principal component eigenvalues ​​and load matrix; determines the weight coefficient of the evaluation project in the hydropower unit component through the hierarchical analysis method, and then calculates the component health index of the hydropower unit component, which solves the problem of poor health status evaluation effect of the hydropower unit, realizes the determination of the cause of the failure from the associated measuring point to the hydropower unit equipment, accurately grasps the health status of the hydropower unit, and prevents the failure in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0050] Figure 1 This is a flowchart of the steps of the hydropower unit health assessment method based on characteristic statistical analysis according to an embodiment of the present application;

[0051] Figure 2 It is a flowchart of the steps for calculating the eigenvalues ​​and characteristic variables of the original sample variables;

[0052] Figure 3 It is a flow chart of the steps for calculating the project health index of the evaluation project;

[0053] Figure 4 is a flow chart of the steps for calculating the component health index of a hydroelectric motor component;

[0054] Figure 5 This is a flowchart of the steps of the hydropower unit health assessment system based on feature statistical analysis according to an embodiment of the present application;

[0055] Figure 6 Schematic diagram of the internal structure of an electronic device according to an embodiment of the present application.

[0056] Description of the accompanying drawings: 51. Data acquisition module; 52. Principal component analysis module; 53. Health assessment module. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is described and illustrated below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely used to explain this application and are not intended to limit this application. Based on the embodiments provided in this application, all other embodiments obtained by those of ordinary skill in the art without making any creative efforts are within the scope of protection of this application.

[0058] Obviously, the drawings described below are merely examples or embodiments of the present application. Those skilled in the art can, without inventive effort, apply the present application to other similar scenarios based on these drawings. Furthermore, it is also understood that, although the effort involved in such a development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, changes in design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as an insufficiency of the content disclosed in this application.

[0059] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments unless there is a conflict.

[0060] Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by a person of ordinary skill in the technical field to which this application belongs. The words "one", "a", "the" and the like used in this application do not indicate a limit on quantity and may indicate the singular or plural. The terms "include", "comprise", "have" and any variations thereof used in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units that are inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The word "multiple" used in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0061] The present invention provides a method for evaluating the health of a hydropower unit based on characteristic statistical analysis. Figure 1 This is a flowchart of the steps of the hydropower unit health assessment method based on feature statistical analysis according to an embodiment of the present application. Figure 1 As shown, the method includes the following steps:

[0062] Step S102, obtaining sample data of each associated measurement point in the evaluation project at different times;

[0063] Specifically, Table 1 is a data table of real-time detection of associated measuring points in a hydropower unit. As shown in Table 1, the sample data includes the following types (characteristic state parameters):

[0064] Table 1

[0065]

[0066]

[0067] Step S104, determining new sample variables for each evaluation item in the hydroelectric motor components by principal component analysis based on the sample data;

[0068] Specifically, based on the sample data, the principal component analysis method is used to determine the new sample variables for each evaluation item in the hydroelectric motor components. The principal component analysis calculation process is as follows:

[0069] Assume there are n groups of sample data, and each group of data has m variables, then the sample data matrix V n×m as follows:

[0070]

[0071] In each set of sample data, let the single variable be X, then the sample data of variable X is: X j =(x 1j ,x 2j ,...,x nj ) T , (j=1,2,...,m), and V n×m =(X1,X2,...,X m ).

[0072] The purpose of using principal component analysis is to reduce the dimensionality of the sample data matrix, that is, each set of sample data consists of m original sample variables (X1, X2, ..., X m ) is reduced to p new sample variables (y1,y2,...,y p ), (p≤m), then:

[0073]

[0074] Finally, there are p new sample variables (y1, y2, ..., y p ) replaces m original sample variables (x1, x2, ..., x m ) is used as the data for a certain evaluation item of a component to solve the problem of too many sample variables in a certain evaluation item of a component.

[0075] Where y1 is (X1,X2,...,X m ) is the feature with the largest variance among all linear combinations of X1, X2, ..., X m ) among all linear combinations with the largest variance, and so on. i (i≤p) is related to y1,y2,...,y i-1 are all unrelated (X1,X2,...,X m ) among all linear combinations with the largest variance, and the new sample variables y1,y2,...,y p They are respectively called the original sample variables (X1, X2, ..., X m )’s first principal component, second principal component, …, pth principal component.

[0076] Step S106, calculating the eigenvalues ​​and eigenvectors of the original sample variables in the sample data, and obtaining the principal component eigenvalues ​​and loading matrix corresponding to the new sample variables;

[0077] Specifically, the eigenvalues ​​and eigenvectors of the original sample variables in the sample data are calculated, and the principal component eigenvalues ​​and loading matrices corresponding to the new sample variables are obtained. That is, the original sample variables (X1, X2, ..., X m ) in each new sample variable (y1,y2,...,y p ) Load factor The value of .

[0078] It can be shown mathematically that the load factor is the sample data matrix V n×m The eigenvectors corresponding to the p larger eigenvalues ​​of the correlation coefficient matrix.

[0079] Step S108, calculating the project health index of the evaluation project based on the eigenvalues, eigenvectors, principal component eigenvalues ​​and loading matrix;

[0080] Step S110 , determining the weight coefficients of the evaluation items in the hydroelectric motor components by using the hierarchical analysis method, and then calculating the component health index of the hydroelectric motor components.

[0081] Through steps S102 to S110 in the embodiment of the present application, the problem of poor health status assessment of the hydropower unit is solved, the cause of the fault from the associated measuring point to the hydropower unit equipment is determined, the health status of the hydropower unit is accurately grasped, and the fault is prevented in advance.

[0082] In some embodiments, Figure 2 It is a flowchart of the steps for calculating the eigenvalues ​​and characteristic variables of the original sample variables, such as Figure 2 As shown, step S106, calculating the eigenvalues ​​and eigenvectors of the original sample variables in the sample data includes:

[0083] Step S202, performing data preprocessing on the sample data by matrix standardization to obtain a standardized data matrix;

[0084] Specifically, in order to eliminate the data differences caused by the order of magnitude, the sample data matrix V needs to be n×m Perform data preprocessing to obtain a standardized data matrix

[0085]

[0086] Matrix standardization does not affect the correlation coefficient matrix, the sample data matrix V n×m The correlation coefficient matrix is ​​the standardized data matrix The covariance matrix of .

[0087] The purpose of matrix normalization is to preprocess the sample data through z-score normalization to obtain data that follows a standard normal distribution with a mean of 0 and a standard deviation of 1. For example, for one-dimensional data, that is, each column of the matrix, the matrix normalization method is to subtract the mean from the sample data and then divide it by the standard deviation. It should be noted that z-score normalization is only one of many matrix normalization methods. There are many existing matrix normalization methods, which will not be described in detail in the embodiments of this application.

[0088] Step S204, calculating the correlation coefficient matrix of the sample data according to the standardized data matrix;

[0089] Specifically, according to the standardized data matrix By formula Calculate the correlation coefficient matrix R of the sample data m×m ;

[0090] Among them, the matrix is the standardized data matrix The transposed matrix of . After matrix operation, the correlation coefficient matrix R is obtained m×m as follows:

[0091]

[0092] Step S206 , performing calculations using the Jacobi method based on the correlation coefficient matrix to obtain the eigenvalues ​​and eigenvectors of the original sample variables in the sample data, and obtaining the principal component eigenvalues ​​and loading matrix corresponding to the new sample variables.

[0093] Specifically, the calculation is performed using the Jacobi method |λE-R|=0, where E is the identity matrix and R represents the correlation coefficient matrix R m×m , the eigenvalues ​​and corresponding eigenvectors sorted by eigenvalue size are as follows:

[0094] λ1≥λ2≥...≥λ m ≥0,β1,β2,...,β m

[0095] where (λ1, λ2, ..., λ m ) are eigenvalues, (β1, β2, ..., β m ) is the eigenvector;

[0096] And obtain the principal component eigenvalues ​​and loading matrix corresponding to the new sample variables. It is called the loading matrix of the jth principal component, that is, the principal component equation is:

[0097]

[0098] In some embodiments, Figure 3 It is a flowchart of the steps to calculate the project health index of the evaluation project, such as Figure 3 As shown, in step S108, the project health index of the evaluation project is calculated based on the eigenvalue, eigenvector, principal component eigenvalue and load matrix, including:

[0099] Step S302, calculating the number of new sample variables based on the eigenvalues, principal component eigenvalues, and a preset contribution rate threshold;

[0100] Specifically, according to the eigenvalues ​​(λ1, λ2, ..., λ m ), principal component eigenvalues ​​(λ1, λ2, ..., λ p ) and the preset contribution rate threshold, the number of new sample variables is calculated by the cumulative contribution rate formula, that is, the cumulative contribution rate of the first p principal components (new sample variables) is:

[0101]

[0102] Generally, the characteristic value of the cumulative contribution rate is 85%-95%, that is, the preset contribution rate threshold θ p ≥0.85~0.95.

[0103] After the number of new variables p is determined, (β1,β2,...,β p ) is the load factor The load matrix composed of (y1,y2,...,y p ) is the new sample variable after the sample data is reduced in dimension.

[0104] Step S304, calculating the contribution rate of each new sample variable according to the number, eigenvalue and principal component eigenvalue of the new sample variable;

[0105] Specifically, according to the number p of new sample variables, the eigenvalues ​​(λ1, λ2, ..., λ m ) and the principal component eigenvalues ​​(λ1, λ2, ..., λ p ), calculate the contribution rate of each new sample variable respectively, such as the contribution rate of the i-th principal component (new sample variable) is:

[0106]

[0107] Among them, θ i is the contribution rate of the i-th new sample variable, λ i is the eigenvalue corresponding to the i-th new sample variable.

[0108] Furthermore, the contribution rate of each new sample variable is converted with the denominator being 1. For example, the contribution rate of the i-th principal component (new sample variable) after conversion is:

[0109]

[0110] Step S306 : Calculate the project health index of the evaluation project based on the contribution rate of the new sample variable, the load matrix, and the preset degradation evaluation index of the new sample variable.

[0111] In some embodiments, Figure 4 It is a flow chart of the steps for calculating the component health index of hydroelectric components, such as Figure 4 As shown, step S110, determining the weight coefficients of the evaluation items in the hydroelectric motor components by the hierarchical analysis method, and then calculating the component health index of the hydroelectric motor components includes:

[0112] Step S402: Based on the relative importance judgment, corresponding scale values ​​are introduced to form a judgment matrix, and the maximum eigenvalue of the judgment matrix and the eigenvector corresponding to the maximum eigenvalue are calculated. The eigenvector corresponding to the maximum eigenvalue is the weight vector of the evaluation item in the hydropower generator component.

[0113] Specifically, the corresponding scale values ​​are introduced according to the judgment of relative importance to form the judgment matrix as follows:

[0114]

[0115] Among them, e ij It represents the relative importance of evaluation item i to evaluation item j. Table 2 is a scale value table introduced based on the judgment of relative importance. As shown in Table 2, And when i=j, e ij =1. In the actual calculation process, e ij The value of needs to be determined by expert experience

[0116] Table 2

[0117] <![CDATA[Scale e ij > meaning 1 Indicator i and indicator j have the same impact 3 Indicator i is slightly more important than indicator j 5 Indicator i is significantly more important than indicator j 7 Indicator i is more important than indicator j 9 Indicator i is more important than indicator j 2,4,6,8 Indicates the middle value between the corresponding 1-9 scale

[0118] Calculate the maximum eigenvalue λ of the judgment matrix E max , and get the eigenvector corresponding to the largest eigenroot And W f is the desired weight vector.

[0119] In step S404 , the weight vector is normalized to obtain a normalized weight vector, and then the component health index of the hydroelectric motor component is calculated.

[0120] Specifically, the weight vector Perform normalization:

[0121]

[0122] Then the normalized weight vector is After normalization, each weight is a decimal between 0 and 1, and the sum of the indicator weights is equal to 1, that is, the weight of evaluation item i in the component is Then the component health index of the hydroelectric motor components is obtained.

[0123] In some embodiments, step S102, obtaining sample data of each associated measurement point in the evaluation project at different times, includes:

[0124] Obtain sample data at different times for each associated measurement point in the evaluation project, and set a preset degradation evaluation index based on expert experience in hydropower unit health evaluation;

[0125] The health index of the associated measuring point is directly calculated based on whether the actual measurement value in the sample data is within the threshold range of the preset degradation evaluation index.

[0126] Specifically, obtain sample data of each associated measuring point at different times in the evaluation project, and set a preset degradation evaluation index based on the expert experience of hydropower unit health evaluation and

[0127] The real-time value of the associated measuring point exceeds the limit threshold range and When the actual measured value is within the standard threshold range, the health index is 0; when the actual measured value is within the standard threshold range and When the values ​​coincide, the health index is 1. When the actual measured value is within the extreme threshold range, the value between (0, 1) can be obtained by comparing the distance with the standard threshold range to quantify the degree of degradation of a single indicator. j The health index of the measuring point f ij The calculation formula of (t) is as follows:

[0128]

[0129] Among them, x ij (t) is the measured value of the jth associated measurement point in the equipment evaluation item i at time t; the preset degradation evaluation index and are the minimum and maximum values ​​of the standard threshold range of the associated measurement point at time t, and The health index of the associated measuring point is f ij(t) is between 0 and 1, with higher values ​​indicating better equipment performance. 1 represents optimal performance, and 0 represents complete failure.

[0130] In some embodiments, step S306, calculating the project health index of the evaluation project based on the contribution rate of the new sample variable, the load matrix, and the preset degradation evaluation index of the new sample variable includes:

[0131] Calculate the minimum and maximum values ​​of each new sample variable in the evaluation project based on the loading matrix;

[0132] Specifically, the minimum value of the limit threshold range of the kth principal component (new sample variable) in a certain evaluation item of the component is calculated:

[0133]

[0134] in, is the load factor of the kth new sample variable;

[0135] Calculate the maximum value of the limit threshold range of the kth principal component (new sample variable) in a certain evaluation item of the component:

[0136]

[0137] in, is the load factor of the kth new sample variable.

[0138] According to the preset degradation evaluation index of the associated measuring points, the standard minimum value and standard maximum value of the new sample variable are calculated;

[0139] Specifically, according to the preset degradation evaluation index of the associated measurement point and Calculate the minimum value of the standard threshold range of the kth principal component (new sample variable) in a certain evaluation item of the component and maximum value

[0140] According to the minimum value, maximum value, standard minimum value and standard maximum value, the principal component health index of the new sample variable is calculated;

[0141] Specifically, according to the minimum and maximum values ​​of the extreme threshold range of the new sample variable and the minimum and maximum values ​​of the standard threshold range, the principal component health index hy of the kth principal component (new sample variable) of a certain evaluation item of the component is calculated. k (t):

[0142]

[0143] Among them, y k(t) is the measured value of the kth new sample variable in a certain evaluation project of the component at time t. The principal component health index hy of the kth new sample variable in a certain evaluation project k (t) is a single value between 0 and 1, where a higher value indicates better equipment performance. 1 represents optimal performance, and 0 represents complete failure.

[0144] The project health index of the evaluation project is calculated based on the principal component health index and contribution rate.

[0145] Specifically, based on the principal component health index and contribution rate of the new sample variable, the health of the evaluation item where the principal component is located at time t is calculated:

[0146]

[0147] Among them, hy k (t) is the overall health index of a component evaluation item at time t, hy k (t) is the principal component health index of the kth new sample variable, θ″ k is the contribution rate of the kth new sample variable.

[0148] In some embodiments, after calculating the maximum eigenvalue of the judgment matrix and the eigenvector corresponding to the maximum eigenvalue in step S402, the method further includes:

[0149] Check whether the constructed judgment matrix is ​​reasonable, and calculate the consistency index formula The consistency index is calculated, where λ max is the maximum characteristic root, m is the number of evaluation items;

[0150] If the consistency index CI = 0, the judgment matrix consistency test passes;

[0151] If CI≠0, then the test consistency ratio Among them, RI is the random consistency index;

[0152] If the consistency ratio CR is less than 0.1, the judgment matrix consistency test passes, otherwise the test fails and the judgment matrix is ​​revised.

[0153] It should be noted that Table 3 is a random consistency index table. As shown in Table 3, RI is a random consistency index, which can be obtained by looking up Table 3.

[0154] Table 3

[0155] Order m 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 RI value 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46 1.49 1.52 1.54 1.56 1.58 1.59

[0156] In some embodiments, in step S110, after determining the weight coefficients of the evaluation items in the hydroelectric motor components by the analytic hierarchy process and then calculating the component health index of the hydroelectric motor components, the following steps are further included:

[0157] The same analytic hierarchy process is used to determine the weight coefficients of hydroelectric motor components in hydroelectric motor equipment, and then the equipment health index of the hydroelectric motor equipment is calculated.

[0158] In some embodiments, before obtaining sample data of each associated measurement point in the evaluation project at different times in step S102, the method further includes:

[0159] Determine whether the hydropower unit is in a shutdown state according to the operating status data of the hydropower unit, and if so, return a preset identification value;

[0160] If not, determine whether the hydropower unit has defects or faults based on the defect and fault information. If so, return the corresponding preset health evaluation value;

[0161] Specifically, Table 4 is a table of defect or fault information for diagnosis of hydropower units. As shown in Table 4, the fault information diagnosed by the fault diagnosis module of the hydropower diagnosis system (which can be directly achieved through parameter transmission between system modules) or the defect or fault information entered through manual inspection can be used to determine whether the hydropower unit has defects or faults.

[0162] Table 4

[0163]

[0164]

[0165] If not, determine whether there is an alarm for the hydropower unit based on the alarm information. If so, return the corresponding preset health evaluation value. If not, perform a health evaluation of the hydropower unit.

[0166] Specifically, the alarm information is obtained through relevant equipment in the computer monitoring system, the main equipment status online monitoring system and the water and electricity fault diagnosis system.

[0167] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0168] The present invention provides a hydropower unit health assessment system based on feature statistical analysis. Figure 5 This is a flowchart of the steps of the hydropower unit health assessment system based on feature statistical analysis according to an embodiment of the present application. Figure 5As shown, the system includes a data acquisition module 51, a principal component analysis module 52 and a health assessment module 53;

[0169] The data acquisition module 51 acquires sample data of each associated measurement point in the evaluation project at different times;

[0170] The principal component analysis module 52 determines new sample variables for each evaluation item in the hydroelectric motor components by principal component analysis based on the sample data;

[0171] The principal component analysis module 52 calculates the eigenvalues ​​and eigenvectors of the original sample variables in the sample data, and obtains the principal component eigenvalues ​​and loading matrices corresponding to the new sample variables;

[0172] The health evaluation module 53 calculates the project health index of the evaluation project based on the eigenvalue, eigenvector, principal component eigenvalue and load matrix;

[0173] The health evaluation module 53 determines the weight coefficients of the evaluation items in the hydroelectric motor components through the hierarchical analysis method, and then calculates the component health index of the hydroelectric motor components.

[0174] Through the data acquisition module 51, principal component analysis module 52 and health evaluation module 53 in the embodiment of the present application, the problem of poor health status assessment of the hydropower unit is solved, the cause of the fault from the associated measuring point to the hydropower unit equipment is determined, the health status of the hydropower unit is accurately grasped, and the fault is prevented in advance.

[0175] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0176] This embodiment further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0177] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0178] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be repeated here.

[0179] In addition, in conjunction with the hydropower unit health assessment method based on feature statistical analysis in the above embodiments, embodiments of the present application may provide a storage medium for implementation. The storage medium stores a computer program; when the computer program is executed by a processor, it implements any of the hydropower unit health assessment methods based on feature statistical analysis in the above embodiments.

[0180] In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, memory, a network interface, a display screen, and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for evaluating the health of a hydropower unit based on feature statistical analysis is implemented. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen. The input device of the computer device may be a touch layer covering the display screen, or may be a key, trackball, or touchpad provided on the computer device housing, or may be an external keyboard, touchpad, or mouse.

[0181] In one embodiment, Figure 6 is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application, such as Figure 6 As shown, an electronic device is provided, which may be a server, and its internal structure diagram may be as shown in FIG. Figure 6 As shown. This electronic device includes a processor, a network interface, an internal memory, and a non-volatile memory connected via an internal bus, wherein the non-volatile memory stores an operating system, a computer program, and a database. The processor is used to provide computing and control capabilities, the network interface is used to communicate with external terminals via a network connection, the internal memory is used to provide an environment for the operation of the operating system and the computer program. When the computer program is executed by the processor, it implements a hydropower unit health assessment method based on characteristic statistical analysis, and the database is used to store data.

[0182] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0183] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0184] Those skilled in the art should understand that the various technical features of the above-described embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0185] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A hydropower unit health assessment method based on characteristic statistical analysis, characterized in that: The method comprises: Obtain sample data of each associated measuring point in the evaluation project at different times; According to the sample data, a new sample variable of each evaluation item in the hydroelectric motor components is determined by principal component analysis; Calculating the eigenvalues ​​and eigenvectors of the original sample variables in the sample data, and obtaining the principal component eigenvalues ​​and loading matrix corresponding to the new sample variables; Calculating the number of the new sample variables according to the eigenvalue, the principal component eigenvalue and a preset contribution rate threshold; Calculating the contribution rate of each new sample variable according to the number of the new sample variables, the eigenvalues ​​and the principal component eigenvalues; Calculate the minimum value and the maximum value of each new sample variable in the evaluation item according to the loading matrix; Calculating the standard minimum value and the standard maximum value of the new sample variable according to the preset degradation evaluation index of the associated measuring point; Calculating a principal component health index of the new sample variable according to the minimum value, the maximum value, the standard minimum value, and the standard maximum value; Calculating a project health index of the evaluation project based on the principal component health index and the contribution rate; The weight coefficients of the evaluation items in the hydroelectric motor components are determined by the hierarchical analysis method, and then the component health index of the hydroelectric motor components is calculated.

2. The method according to claim 1, characterized in that Calculating the eigenvalues ​​and eigenvectors of the original sample variables in the sample data, and obtaining the principal component eigenvalues ​​and loading matrix corresponding to the new sample variables includes: Performing data preprocessing on the sample data by matrix standardization to obtain a standardized data matrix; Calculating a correlation coefficient matrix of the sample data according to the standardized data matrix; The Jacobi method is used to calculate the correlation coefficient matrix to obtain the eigenvalues ​​and eigenvectors of the original sample variables in the sample data, and the principal component eigenvalues ​​and loading matrix corresponding to the new sample variables are obtained.

3. The method according to claim 1, characterized in that Determining the weight coefficient of the evaluation item in the hydroelectric motor component by using the hierarchical analysis method, and then calculating the component health index of the hydroelectric motor component includes: According to the judgment of relative importance, corresponding scale values ​​are introduced to form a judgment matrix, and the maximum eigenvalue of the judgment matrix and the eigenvector corresponding to the maximum eigenvalue are calculated. The eigenvector corresponding to the maximum eigenvalue is the weight vector of the evaluation item in the hydroelectric motor component; The weight vector is normalized to obtain a normalized weight vector, and then the component health index of the hydroelectric motor component is calculated.

4. The method according to claim 1, wherein Obtaining sample data for each associated measurement point in the evaluation project at different times includes: Obtain sample data at different times for each associated measurement point in the evaluation project, and set a preset degradation evaluation index based on expert experience in hydropower unit health evaluation; The measuring point health index of the associated measuring point is directly calculated based on whether the actual measurement value in the sample data is within the threshold range of the preset degradation evaluation index.

5. The method according to claim 3, characterized in that After calculating the maximum eigenvalue of the judgment matrix and the eigenvector corresponding to the maximum eigenvalue, the method further includes: Check whether the constructed judgment matrix is ​​reasonable, and calculate the consistency index formula The consistency index is calculated, where λ max is the maximum characteristic root, and m is the number of evaluation items; If the consistency index CI=0, the judgment matrix consistency test passes; If CI≠0, then the test consistency ratio Among them, RI is the random consistency index; If the consistency ratio CR<0.1, the judgment matrix consistency test passes; otherwise, the test fails and the judgment matrix is ​​revised.

6. The method according to claim 1, characterized in that After determining the weight coefficients of the evaluation items in the hydroelectric motor components by using the hierarchical analysis method and then calculating the component health index of the hydroelectric motor components, the method further includes: The weight coefficients of the hydroelectric motor components in the hydroelectric motor equipment are determined by the same analytic hierarchy process, and then the equipment health index of the hydroelectric motor equipment is calculated.

7. The method according to claim 1, characterized in that Before obtaining sample data of each associated measurement point in the evaluation project at different times, the method further includes: Determine whether the hydropower unit is in a shutdown state according to the operating state data of the hydropower unit, and if so, return a preset identification value; If not, determine whether the hydropower unit has defects or faults based on the defect and fault information, and if so, return the corresponding preset health evaluation value; If not, it is determined whether there is an alarm for the hydropower unit according to the alarm information. If so, the corresponding preset health evaluation value is returned. If not, a health evaluation of the hydropower unit is performed.

8. A hydropower unit health assessment system based on characteristic statistical analysis, characterized in that: The system includes a data acquisition module, a principal component analysis module and a health assessment module; The data acquisition module acquires sample data of each associated measurement point in the evaluation project at different times; The principal component analysis module determines new sample variables for each evaluation item in the hydroelectric motor components by principal component analysis based on the sample data; The principal component analysis module calculates the eigenvalues ​​and eigenvectors of the original sample variables in the sample data, and obtains the principal component eigenvalues ​​and loading matrix corresponding to the new sample variables; The health evaluation module calculates the number of the new sample variables according to the eigenvalue, the principal component eigenvalue and a preset contribution rate threshold; The health evaluation module calculates the contribution rate of each new sample variable according to the number of the new sample variables, the eigenvalues ​​and the principal component eigenvalues; The health evaluation module calculates the minimum value and the maximum value of each new sample variable in the evaluation item according to the load matrix; The health evaluation module calculates the standard minimum value and the standard maximum value of the new sample variable according to the preset degradation evaluation index of the associated measurement point; The health evaluation module calculates the principal component health index of the new sample variable according to the minimum value, the maximum value, the standard minimum value and the standard maximum value; Calculating a project health index of the evaluation project based on the principal component health index and the contribution rate; The health evaluation module determines the weight coefficient of the evaluation item in the hydroelectric motor component through the hierarchical analysis method, and then calculates the component health index of the hydroelectric motor component.

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