Hydraulic power plant transformer operation state evaluation method and system based on grading index system

Abstract

The invention discloses a hydraulic power plant transformer operation state evaluation method and system based on a grading index system, and relates to the field of big data, and the method comprises the steps: building a transformer operation state evaluation index system, so as to obtain a first-level index and a second-level index; determining the weight of the first-level index and the weight of the second-level index by using an analytic hierarchy process; constructing a running state evaluation model based on principal component analysis to obtain a comprehensive evaluation score according to the weight; and based on the output results of the analytic hierarchy process and the principal component analysis, constructing a running state prediction model based on regression analysis to obtain the running trend of the target transformer. Through the scheme of the invention, the accuracy and efficiency of transformer state evaluation can be improved, and the health condition of the transformer can be comprehensively reflected.

Description

Technical Field

[0001] The present application relates to the field of big data, and in particular to a method and system for evaluating the operating status of a hydropower plant transformer based on a hierarchical indicator system. Background Art

[0002] In modern hydropower plants, transformers are an important part of the power system. Their stable and efficient operation is crucial to ensure safe, continuous and efficient power supply. Transformer failures often lead to serious economic losses and power supply interruptions. Therefore, it is particularly important to accurately evaluate the operating status of hydropower plant transformers. The evaluation method of the operating status of hydropower plant transformers aims to monitor and analyze the health of transformers through technical means to prevent potential failures, ensure the stable operation of the power grid and improve the reliability of power supply. At present, the operating status evaluation technology of hydropower plant transformers mainly includes oil quality analysis, vibration analysis, temperature monitoring and electrical testing. These technologies judge the operating status of transformers by monitoring the physical and chemical indicators of transformers. For example, oil quality analysis can detect dissolved gases in transformer oil, thereby identifying whether insulation degradation or overheating has occurred inside the transformer; vibration analysis identifies mechanical faults by analyzing the vibration frequency of the transformer; temperature monitoring helps monitor the operating temperature of the transformer to prevent failures caused by overheating. Although these traditional methods are effective to a certain extent, they have certain limitations. For example, oil quality analysis requires regular sampling, which is not only time-consuming but may also lead to misjudgment due to non-representative samples; vibration analysis and temperature monitoring may be affected by the external environment, affecting the accuracy of the assessment.

[0003] In response to the above problems, the industry has begun to explore more advanced technologies, such as sensor-based real-time monitoring systems and data-driven predictive maintenance strategies. Real-time monitoring technology installs a variety of sensors at key parts of the transformer to continuously track its operating status, such as temperature, humidity, vibration and other parameters, to capture abnormal phenomena that may cause failures in real time. Data-driven methods use machine learning algorithms to analyze historical operating data to predict the type and time of possible transformer failures, thereby achieving proactive prevention of failures. Although these emerging technologies have improved the efficiency and accuracy of evaluation to a certain extent, they still have shortcomings. For example, the initial investment cost of real-time monitoring systems is high and maintenance is complex; data-driven predictive maintenance is highly dependent on data quality and quantity, and data accumulation may be difficult for small-scale power stations.

[0004] Therefore, there is an urgent need for a technical solution that can improve the accuracy and efficiency of transformer status assessment and fully reflect the health status of the transformer. Summary of the invention

[0005] In order to solve the deficiencies of the prior art, the embodiment of the present application provides a method and system for evaluating the operating status of a hydropower plant transformer based on a hierarchical index system. The present application solves the technical problems of the prior art that the transformer status evaluation is low in accuracy and the transformer health status is not fully reflected.

[0006] The embodiment of the present application provides a method for evaluating the operating status of a hydropower plant transformer based on a hierarchical indicator system, including: establishing a transformer operating status evaluation index system to obtain primary indicators and secondary indicators; using a hierarchical analysis method to determine the weights of the primary indicators and the weights of the secondary indicators; constructing an operating status evaluation model based on principal component analysis to obtain a comprehensive evaluation score according to the weights; based on the output results of the hierarchical analysis and the principal component analysis, constructing an operating status prediction model based on regression analysis to obtain the operating trend of the target transformer.

[0007] In a possible implementation, establishing a transformer operating status evaluation index system to obtain primary and secondary indicators includes: constructing a primary indicator; the primary indicator includes temperature, load rate, dissolved gas in oil, insulation resistance and local discharge; constructing a secondary indicator corresponding to the primary indicator; wherein the temperature includes top oil temperature, bottom oil temperature and ambient temperature, the load rate includes active power, reactive power and apparent power, the dissolved gas in oil includes total combustible gas, hydrogen, methane, ethane, ethylene and acetylene, the insulation resistance includes high-voltage winding to ground, low-voltage winding to ground and between high and low voltage windings, and the local discharge includes discharge amplitude, discharge frequency and discharge phase.

[0008] In a possible implementation, the use of hierarchical analysis method to determine the weights of the first-level indicators and the weights of the second-level indicators includes: constructing a judgment matrix to calculate the corresponding weight vector; based on the judgment matrix and the weight vector, calculating the consistency index to obtain the corresponding average random consistency index; based on the consistency index and the average random consistency index, determining whether the judgment matrix passes the consistency test; wherein, if the ratio of the consistency index to the average random consistency index is less than the target threshold, it is determined that the consistency test has passed.

[0009] In a possible implementation, constructing an operating status assessment model based on principal component analysis includes: performing data standardization to calculate a correlation coefficient matrix; obtaining eigenvalues ​​and eigenvectors based on the correlation coefficient matrix; selecting a target number of principal components based on the cumulative contribution rate of the principal components so that the cumulative contribution rate reaches a preset contribution value; and calculating a comprehensive score of the principal components as a comprehensive assessment score of the transformer operating status.

[0010] In a possible implementation method, based on the output results of hierarchical analysis and principal component analysis, an operating status prediction model based on regression analysis is constructed to obtain the operating trend of the target transformer, including: constructing a regression equation based on the comprehensive evaluation score to obtain regression parameters; using the least squares method to calculate the regression parameters to obtain the target regression coefficient; performing a significance analysis on the regression model; substituting the current transformer operating data into the regression model, and calculating the target comprehensive evaluation score to obtain the operating trend of the target transformer.

[0011] In a possible implementation, the constructing of the judgment matrix includes:

[0012] Among them, a ij Indicates the importance of indicator i relative to indicator j.

[0013] In a possible implementation, determining whether the judgment matrix passes the consistency check based on the consistency index and the average random consistency index includes: Where CR represents the consistency score, λ max represents the maximum eigenvalue of the judgment matrix, n represents the maximum index value of the judgment matrix, and RI represents the average random consistency index.

[0014] An embodiment of the present application also provides a hydropower plant transformer operating status assessment system based on a hierarchical indicator system, including an indicator construction unit, a hierarchy unit, a component unit and a regression unit; wherein the indicator construction unit is used to establish a transformer operating status assessment indicator system to obtain primary indicators and secondary indicators; the hierarchy unit is used to determine the weights of the primary indicators and the weights of the secondary indicators using a hierarchical analysis method; the component unit is used to construct an operating status assessment model based on principal component analysis to obtain a comprehensive assessment score according to the weights; the regression unit is used to construct an operating status prediction model based on regression analysis based on the output results of the hierarchical analysis and the principal component analysis to obtain the operating trend of the target transformer.

[0015] An embodiment of the present application also provides a hydropower plant transformer operating status assessment device based on a hierarchical indicator system, comprising: a processor, a memory, and a system bus; wherein the processor and the memory are connected via the system bus; the memory is used to store one or more programs, and the one or more programs include instructions, and when the instructions are executed by the processor, the processor executes the method described in the above embodiment.

[0016] In the hydropower plant transformer operating status assessment method, system and device based on the hierarchical index system provided above, the embodiment of the present application constructs an index system that comprehensively reflects the health status of the transformer by integrating multiple sensor data and operation records. This system not only includes traditional physical and electrical parameters, but also should integrate more micro-level indicators, such as chemical analysis indicators and microstructure changes, and introduce data fusion technology and artificial intelligence algorithms to more effectively process and analyze large amounts of data from different sources, improve the accuracy and timeliness of fault diagnosis, and can improve the accuracy and efficiency of transformer status assessment, and comprehensively reflect the health status of the transformer. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A schematic diagram of a flow chart of a method for evaluating the operating status of a hydropower plant transformer based on a hierarchical index system provided in an embodiment of the present application;

[0019] Figure 2 A schematic diagram of a process flow of a principal component analysis operating state method provided in an embodiment of the present application;

[0020] Figure 3 Schematic diagram of a hydropower plant transformer operating status assessment system based on a graded indicator system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present application.

[0022] Those skilled in the art will appreciate that the terms "first", "second" and the like in the embodiments of the present application are only used to distinguish different steps, devices or modules, etc., and do not represent any specific technical meaning, nor do they represent the necessary logical order between them. It should also be understood that in the embodiments of the present application, "multiple" may refer to two or more, and "at least one" may refer to one, two or more. It should also be understood that for any component, data or structure mentioned in the embodiments of the present application, in the absence of explicit limitation or contrary revelation given in the context, it can generally be understood as one or more. In addition, the term "and / or" in the present application is only a kind of association relationship describing the associated objects, indicating that there can be three kinds of relationships, for example, A and / or B, which can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present application generally indicates that the associated objects before and after are an "or" relationship. It should also be understood that the description of each embodiment in the present application emphasizes the differences between the embodiments, and the same or similar parts can refer to each other. For the sake of brevity, they will not be repeated one by one.

[0023] At the same time, it should be understood that, for ease of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present application and its application or use. The techniques, methods and devices known to ordinary technicians in the relevant fields may not be discussed in detail, but where appropriate, the techniques, methods and devices should be considered as part of the specification. It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0024] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0025] Figure 1 A schematic diagram of a method for evaluating the operating status of a hydropower plant transformer based on a hierarchical index system provided in an embodiment of the present application. Figure 1 As shown, at step S101, a transformer operation status evaluation index system is established to obtain primary indicators and secondary indicators.

[0026] Specifically, it includes constructing a first-level indicator; the first-level indicator includes temperature, load rate, dissolved gas in oil, insulation resistance and local discharge; constructing a second-level indicator corresponding to the first-level indicator; wherein the temperature includes top oil temperature, bottom oil temperature and ambient temperature, the load rate includes active power, reactive power and apparent power, the dissolved gas in oil includes total combustible gas, hydrogen, methane, ethane, ethylene and acetylene, the insulation resistance includes high-voltage winding to ground, low-voltage winding to ground and between high and low voltage windings, and the local discharge includes discharge amplitude, discharge frequency and discharge phase.

[0027] In one embodiment, the primary indicators are set as: temperature (T), load rate (L), dissolved gas analysis DGA (D), insulation resistance (I), and partial discharge (P). The secondary indicators are set as: temperature: top oil temperature (T1), bottom oil temperature (T2), and ambient temperature (T3). Load rate: active power (L1), reactive power (L2), and apparent power (L3). DGA: total combustible gas TDCG (D1), hydrogen H2 (D2), methane CH4 (D3), ethane C2H6 (D4), ethylene C2H4 (D5), and acetylene C2H2 (D6). Insulation resistance: high-voltage winding to ground (I1), low-voltage winding to ground (I2), and between high and low voltage windings (I3). Partial discharge: discharge amplitude (P1), discharge frequency (P2), and discharge phase (P3).

[0028] In step S102, the weight of the first-level index and the weight of the second-level index are determined using the hierarchical analysis method. Specifically, a judgment matrix is ​​first constructed to calculate the corresponding weight vector; the construction of the judgment matrix includes:

[0029]

[0030] Among them, a ij Indicates the importance of index i relative to index j, and takes values ​​from 1 to 9 and its reciprocal. Then, based on the judgment matrix and the weight vector, the consistency index is calculated to obtain the corresponding average random consistency index; wherein the weight vector W is calculated as [w1, w2, ..., w n ] T , where w i is the weight of index i, which can be obtained by solving the eigenvalue equation AW = λ max W got.

[0031] Based on the consistency index and the average random consistency index, it is determined whether the judgment matrix passes the consistency test; wherein, if the ratio of the consistency index to the average random consistency index is less than the target threshold, it is determined that the consistency test is passed. In one embodiment, the consistency index is calculated. Where CR represents the consistency score, λmax represents the maximum eigenvalue of the judgment matrix, n represents the maximum index value of the judgment matrix, and RI represents the average random consistency index. The average random consistency index RI is obtained by looking up the table. If Then the judgment matrix A passes the consistency test.

[0032] At step S103, an operation status assessment model based on principal component analysis is constructed to obtain a comprehensive assessment score according to the weight. First, the original data is normalized to unify the index data of different dimensions to the same order of magnitude to eliminate the dimensional influence between the data. Common normalization methods include maximum and minimum value normalization, Z-Score normalization, etc. Calculate the correlation coefficient matrix R. The correlation coefficient matrix reflects the degree of linear correlation between the variables, where the diagonal elements are 1 and the remaining element values ​​are between -1 and 1. Solve the eigenvalues ​​and eigenvectors of the correlation coefficient matrix R. Arrange the eigenvalues ​​from large to small, and select the eigenvectors corresponding to the largest m eigenvalues ​​as the principal component load matrix P. Then project the original data X into the principal component space to obtain the principal component score matrix Y=XP. Each column of the principal component score matrix Y corresponds to a principal component, and each row corresponds to the principal component score of a sample. Determine the number of principal components m. There are many methods to determine the number of principal components, such as the cumulative contribution rate method, the parallel analysis method, etc. Usually, the number of principal components that can reflect more than 90% of the variation information of the original data is selected. Calculate the comprehensive evaluation score. Add the first m columns of the principal component score matrix Y to obtain the comprehensive evaluation score vector of each sample (such as Figure 2 The detailed method will be shown in Figure 2 (see the description for details).

[0033] At step S104, based on the output results of the hierarchical analysis and the principal component analysis, an operating state prediction model based on regression analysis is constructed to obtain the operating trend of the target transformer. Specifically, it includes: based on the comprehensive evaluation score, a regression equation is constructed to obtain the regression parameter: y = β0 + β1x1 + β2x2 + ... + β n x n +ε, where y is the comprehensive evaluation score of the transformer operating status, x1, x2, …, x n There are n evaluation indicators, β0, β1, …, β n is the regression coefficient, and ε is the random error term. Then the regression parameters are calculated using the least squares method to obtain the target regression coefficient: Where X is a sample matrix of size m×(n+1), and Y is an m×1 vector of dependent variable observations.

[0034] Secondly, the significance analysis of the regression model is carried out: the multiple correlation coefficient R, F statistic F, t statistic, etc. are calculated to test the significance of the regression model. The multiple correlation coefficient R represents the proportion of the variation explained by the model to the total variation. The closer it is to 1, the stronger the explanatory power of the model. The F statistic and t statistic are used to test the significance of each regression coefficient in the model, so as to determine whether the contribution of each indicator to the prediction is significant. Finally, the current transformer operation data is substituted into the regression model, and the target comprehensive evaluation score is calculated to obtain the operation trend of the target transformer.

[0035] In one application case, a main transformer of a hydropower plant has a rated capacity of 240MVA and a rated voltage of 220kV / 15.75kV. According to its operating data, the values ​​of various evaluation indicators are calculated as follows: top oil temperature T1 = 65℃, bottom oil temperature T2 = 58℃, ambient temperature T3 = 25℃, active power L1 = 180MW, reactive power L2 = 30Mvar, apparent power L3 = 200MVA, total combustible gas TDCG D1 = 380μL / L, hydrogen H2 D2 = 85μL / L, methane CH4 D3 = 65μL / L, ethane C2H6 D4 = 35μL / L, ethylene C2H4 D5 = 85μL / L, acetylene C2H2 D6 = 0μL / L, high-voltage winding to ground I1 = 1200MΩ, low-voltage winding to ground I2 = 800MΩ, between high and low voltage windings I3 = 1500MΩ, discharge amplitude P1 = 650Pc, discharge frequency P2 = 8 times / s, discharge phase P3 = 45°.

[0036] First, the indicators are determined by AHP and the weights are calculated. The weight vector W = (w1, w2, ..., w 18 ) T . Standardize the original data to obtain the standardized matrix X. Calculate the weighted standardized matrix Y = X·W. Then perform principal component analysis on the matrix Y to obtain the eigenvalues ​​of the principal components λ1≥λ2≥…≥λ 18 and the corresponding eigenvectors α1, α2, …, α 18 . Calculate the contribution rate and cumulative contribution rate of each principal component, and select the first k principal components so that the cumulative contribution rate reaches the preset value (such as 85%). Calculate the scores of k principal components F1, F2, ..., F k The weighted sum of the principal component scores and the corresponding contribution rates is used to obtain the comprehensive evaluation score S of the transformer operating status.

[0037] Finally, regression analysis is performed to establish the regression equation y=β0+β1x1+β2x2+…+β n x n +ε, where y is the comprehensive evaluation score, x nis the evaluation index. The regression coefficient is estimated using the least squares method. The regression model is tested for significance and status prediction is performed. The current transformer operation data is substituted into the regression model to calculate the comprehensive evaluation score. The current operating status of the transformer is judged based on the comprehensive evaluation score, and its future development trend is predicted.

[0038] Figure 2 A schematic diagram of a principal component analysis operation status method provided in an embodiment of the present application. Figure 2 As shown, at step S201, data standardization is performed to calculate the correlation coefficient matrix. It should be noted that data standardization is an important preprocessing step in principal component analysis, the purpose of which is to eliminate the dimensional influence of each indicator in the data so that they are comparable and also reduce the influence of outliers. The specific formula for data standardization is: i = 1, 2, ..., m; j = 1, 2, ..., n, where x ij is the jth index value of the ith sample, and σ j are the mean and standard deviation of the j-th indicator respectively.

[0039] At step S202, based on the correlation coefficient matrix, eigenvalues ​​and eigenvectors are obtained. The correlation coefficient matrix R is calculated as follows: R = (r ij ) n×n , Solving for the eigenvalue λ i and the eigenvector α i , |R-λI|=0, Rα i =λ i α i , i = 1, 2, ..., n. The eigenvector describes the direction of the data in the new coordinate system, and the eigenvalue represents the variance of the data in that direction, that is, the amount of information. Selecting the eigenvectors corresponding to the largest eigenvalues ​​can achieve effective dimensionality reduction of the data.

[0040] In order to ensure the efficiency and accuracy of the algorithm, numerical methods such as the Power Method or the Jacobi Method can be used to solve the eigenvalues ​​and eigenvectors. In addition, considering the stability of the calculation and error control, singular value decomposition (SVD) may also be used as an alternative method, especially when dealing with large-scale data sets.

[0041] In step S203, according to the cumulative contribution rate of the principal components, a target number of principal components are selected so that the cumulative contribution rate reaches a preset contribution value. Specifically, the principal components are selected according to the cumulative contribution rate of the principal components, and the first k principal components are selected so that the cumulative contribution rate reaches a preset value (such as 85%). i = 1, 2, ..., k, where α ji is the eigenvector α i The jth component of .

[0042] At step S204, the comprehensive score of the principal components is calculated as the comprehensive evaluation score of the transformer operation status. The main goal of this process is to ensure that the selected principal components can cover most of the information in the original data set, so as to achieve data dimensionality reduction while retaining the original data characteristics as much as possible. Calculate the comprehensive score and sum the scores F1, F2, ..., F of the k principal components. k and the corresponding contribution rates η1, η2, …, η k Perform weighted summation to obtain the comprehensive evaluation score of the transformer operation status S = η1F1+η2F2+…+η k F k ,

[0043] In practical applications, determining the k value usually requires combining specific business needs and data analysis goals. In addition, visualization methods such as screen plots can also be used to assist in determining the appropriate number of principal components. A screen plot is a chart that plots the eigenvalues ​​of each principal component to view their relative sizes. The point before the eigenvalue drops rapidly is usually selected as the number of principal components. In addition, mathematical modeling and simulation can be used to further verify the impact of the selected k principal components on the overall structure of the data. The effect of dimensionality reduction can be evaluated by comparing the statistical properties of the original data and the reduced data, such as mean, variance, and distribution.

[0044] Figure 3Schematic diagram of a hydropower plant transformer operating status assessment system based on a hierarchical index system provided for an embodiment of the present application. It should be understood that the system shown in the figure is exemplary and not restrictive. This means that the system architecture involved is not limited to a specific form or design, but is presented as an example. In other words, the architecture shown in the figure can be regarded as a way of expression to clearly describe related concepts and relationships, and does not exclude other forms of architecture. Therefore, when interpreting the architecture in the picture, it should be understood that the model is flexible and diverse, and its purpose is to provide an exemplary description rather than a restrictive provision on a specific form. Specifically, in an embodiment of the present application, a system for evaluating the operating status of a hydropower plant transformer based on a hierarchical indicator system comprises an indicator construction unit, a hierarchy unit, a component unit and a regression unit; wherein the indicator construction unit is used to establish an indicator system for evaluating the operating status of a transformer to obtain primary indicators and secondary indicators; the hierarchy unit is used to determine the weights of the primary indicators and the weights of the secondary indicators using a hierarchical analysis method; the component unit is used to construct an operating status evaluation model based on principal component analysis to obtain a comprehensive evaluation score according to the weights; the regression unit is used to construct an operating status prediction model based on regression analysis based on the output results of the hierarchical analysis and the principal component analysis to obtain the operating trend of the target transformer.

[0045] Furthermore, an embodiment of the present application also provides a hydropower plant transformer operating status assessment device based on a hierarchical indicator system, including: a processor, a memory, and a system bus; the processor and the memory are connected via the system bus; the memory is used to store one or more programs, and the one or more programs include instructions, which, when executed by the processor, enable the processor to execute any of the above-mentioned assessment methods.

[0046] Furthermore, an embodiment of the present application also provides a computer program product, which, when executed on a terminal device, enables the terminal device to execute any one of the above-mentioned evaluation methods.

[0047] It can be known from the description of the above implementation mode that those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment method can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a storage medium such as ROM / RAM, a disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network communication device such as a media gateway, etc.) to execute the evaluation method described in each embodiment of the present application or some parts of the embodiment.

[0048] It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0049] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0050] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating the operating status of a hydropower plant transformer based on a hierarchical index system, characterized in that: include: Establish a transformer operating status evaluation index system to obtain primary and secondary indicators; Determine the weight of the first-level index and the weight of the second-level index by using the hierarchical analysis method; Construct an operation status assessment model based on principal component analysis to obtain a comprehensive assessment score according to the weights; Based on the output results of hierarchical analysis and principal component analysis, an operating status prediction model based on regression analysis is constructed to obtain the operating trend of the target transformer.

2. The evaluation method according to claim 1, characterized in that: The transformer operation status evaluation index system is established to obtain the first-level and second-level indicators including: Constructing primary indicators; the primary indicators include temperature, load rate, dissolved gas in oil, insulation resistance and partial discharge; Construct a secondary indicator corresponding to the primary indicator; wherein the temperature includes the top oil temperature, the bottom oil temperature and the ambient temperature, the load rate includes active power, reactive power and apparent power, the gas dissolved in the oil includes total combustible gas, hydrogen, methane, ethane, ethylene and acetylene, the insulation resistance includes the high-voltage winding to the ground, the low-voltage winding to the ground and between the high and low voltage windings, and the local discharge includes the discharge amplitude, discharge frequency and discharge phase.

3. The evaluation method according to claim 2, characterized in that: The method of using the analytic hierarchy process to determine the weight of the primary index and the weight of the secondary index includes: Construct a judgment matrix to calculate the corresponding weight vector; Based on the judgment matrix and the weight vector, a consistency index is calculated to obtain a corresponding average random consistency index; Based on the consistency index and the average random consistency index, it is determined whether the judgment matrix passes the consistency test; wherein, if the ratio of the consistency index to the average random consistency index is less than a target threshold, it is determined that the consistency test is passed.

4. The evaluation method according to claim 3, characterized in that: The operation status evaluation model based on principal component analysis includes: Data normalization was performed to calculate the correlation coefficient matrix; Based on the correlation coefficient matrix, obtain eigenvalues ​​and eigenvectors; According to the cumulative contribution rate of the principal components, a target number of principal components are selected so that the cumulative contribution rate reaches a preset contribution value; The comprehensive score of the principal components is calculated as the comprehensive evaluation score of the transformer operating status.

5. The evaluation method according to claim 4, characterized in that: Based on the output results of hierarchical analysis and principal component analysis, an operating status prediction model based on regression analysis is constructed to obtain the operating trend of the target transformer, including: Based on the comprehensive evaluation score, construct a regression equation to obtain regression parameters; The regression parameters are calculated using the least squares method to obtain a target regression coefficient; Conduct significance analysis on the regression model; Substitute the current transformer operation data into the regression model and calculate the target comprehensive evaluation score to obtain the operation trend of the target transformer.

6. The evaluation method according to claim 5, characterized in that: The construction of the judgment matrix comprises: Among them, a ij Indicates the importance of indicator i relative to indicator j.

7. The evaluation method according to claim 3, characterized in that: Wherein determining whether the judgment matrix passes the consistency test based on the consistency index and the average random consistency index comprises: Where CR represents the consistency score, λ max represents the maximum eigenvalue of the judgment matrix, n represents the maximum index value of the judgment matrix, and RI represents the average random consistency index.

8. The hydropower plant transformer operation status evaluation system based on the hierarchical index system is characterized by: It includes indicator construction unit, hierarchical unit, component unit and regression unit; among them, The index construction unit is used to establish a transformer operation status evaluation index system to obtain primary indicators and secondary indicators; The hierarchical unit is used to determine the weight of the first-level index and the weight of the second-level index by using the hierarchical analysis method; The component unit is used to construct an operation status evaluation model based on principal component analysis to obtain a comprehensive evaluation score according to the weight; The regression unit is used to construct an operation status prediction model based on regression analysis based on the output results of hierarchical analysis and principal component analysis, so as to obtain the operation trend of the target transformer.

9. Hydropower plant transformer operating status assessment equipment based on a hierarchical index system, characterized in that: include: A processor, a memory, and a system bus; wherein the processor and the memory are connected via the system bus; The memory is used to store one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by the processor, the processor executes the evaluation method according to any one of claims 1 to 7.

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