Thermal power plant electrical equipment state evaluation method and system

Abstract

The invention relates to the technical field of electrical equipment state evaluation, and particularly discloses a thermal power plant electrical equipment state evaluation method and system, and the method comprises the steps: obtaining the vibration signals of electrical equipment, and carrying out the clustering and partitioning of the electrical equipment according to the vibration signals of the electrical equipment; acquiring historical fault data of the electrical equipment, and mining a strong association rule of the historical fault data based on an association rule algorithm; obtaining the current operation data of the electrical equipment, and determining the operation state parameters of the current electrical equipment according to the current operation data of the electrical equipment and the clustering subarea in which the electrical equipment is located in combination with the strong association rule of the historical fault data; and acquiring the environment temperature when the electrical equipment runs, correcting the running state parameters according to the environment temperature, and determining the evaluation grade of the electrical equipment according to the corrected running state parameters. According to the method, the operation state parameters are calculated after the electrical equipment is clustered, state evaluation is carried out on the electrical equipment in the thermal power plant, and the fault risk of the electrical equipment is accurately evaluated.

Description

Technical Field

[0001] The present application relates to the technical field of electrical equipment status evaluation, and more specifically, to a method and system for evaluating the status of electrical equipment in a thermal power plant. Background Art

[0002] With the continuous expansion of the power system scale of thermal power plants and the continuous development of the power market, the safety and reliability issues of the power system have gradually become prominent. In order to solve these problems, it has become necessary to evaluate the operating health of power supply equipment. Combined with the monitoring items of the equipment, the current operating status of the equipment is analyzed in real time, the operating status and health level of the equipment are accurately grasped, and potential safety hazards and faults are discovered and resolved in a timely manner to ensure the safe and stable operation of the equipment. At the same time, maintenance management is optimized, the service life of the equipment is extended, the operating efficiency of the equipment is improved, and the equipment replacement and maintenance costs are reduced.

[0003] As the structure of equipment in thermal power plants becomes more and more complex, the accuracy requirements for equipment maintenance are also increasing. The time and manpower costs required to repair faults are also rising. The upgrade and iteration of electrical equipment fault monitoring systems are imperative. In the existing technology, maintenance is performed after equipment failure, which will cause loss of manpower and time costs, resulting in a technical problem of large lag in equipment maintenance. Summary of the invention

[0004] The present invention provides a method and system for evaluating the state of electrical equipment in a thermal power plant, which is used to solve the problem of large hysteresis in electrical equipment maintenance in the prior art, and includes: Acquire vibration signals of electrical equipment, and cluster and partition each electrical equipment according to the vibration signals of the electrical equipment; Obtain historical fault data of electrical equipment and mine strong association rules of historical fault data based on association rule algorithm; Obtain the current operating data of the electrical equipment, and determine the operating status parameters of the current electrical equipment according to the current operating data of the electrical equipment and the cluster partition in which it is located combined with the strong association rules of historical fault data; The ambient temperature of the electrical equipment during operation is obtained, the operating status parameters are corrected according to the ambient temperature, and the evaluation level of the electrical equipment is determined according to the corrected operating status parameters.

[0005] Furthermore, clustering and partitioning each electrical device according to the vibration signal of the electrical device includes: Preprocess the vibration signal of the electrical equipment, perform fast Fourier transform on the preprocessed vibration signal, and obtain a vibration signal spectrum diagram; The fault parameters are determined according to the vibration signal spectrum of the electrical equipment, and the electrical equipment is clustered according to the fault parameters to obtain the cluster partitions of each electrical equipment.

[0006] Further, the determining of fault parameters according to the vibration signal spectrum of the electrical equipment includes: Obtaining peak points in a vibration signal spectrum diagram, screening out peak points in the vibration signal spectrum diagram whose peak points are greater than a first preset threshold, and determining a first fault parameter according to an average peak point greater than the first preset threshold; Calculate the absolute value of the slope between the peak point and the adjacent valley point in the vibration signal spectrum diagram, and determine the second fault parameter according to the variance of all the absolute values ​​of the slope in the vibration signal spectrum diagram; Obtain a preset standard vibration signal spectrum of the electrical equipment, calculate the area value between the vibration signal spectrum during the operation cycle of the electrical equipment and the preset standard vibration signal spectrum and the coordinate axis, and determine the third fault parameter according to the area difference between the vibration signal spectrum and the preset standard vibration signal spectrum; Normalizing the first fault parameter, the second fault parameter, and the third fault parameter, and setting the normalized first fault parameter, the second fault parameter, and the third fault parameter to the first length, the second length, and the third length according to a preset ratio; The first length and the second length are used as the lower bottom surface and the upper bottom surface respectively, and the third length is used as the height. A truncated cone is constructed according to the lower bottom surface, the upper bottom surface and the height, and the volume value of the truncated cone is set as the comprehensive fault parameter of the electrical equipment.

[0007] Furthermore, clustering the electrical equipment according to the fault parameters to obtain cluster partitions of each electrical equipment includes: A comprehensive fault parameter data set is established according to the comprehensive fault parameters of the electrical equipment, an initial k value is set, and an initial clustering center of the comprehensive fault parameter data set is randomly selected according to the initial k value; Calculate the Euclidean distance between each comprehensive fault parameter in the comprehensive fault parameter data set and the k initial cluster centers, and cluster the comprehensive fault parameters into the cluster partition with the closest Euclidean distance to the initial cluster center; Calculate the average value of the comprehensive fault parameters in each cluster partition, and set a new cluster center according to the average value of the comprehensive fault parameters in each cluster partition; Repeat the iteration of the new cluster center until the cluster center no longer changes, and obtain the final cluster partitions of k electrical equipment.

[0008] Furthermore, the method of mining strong association rules of historical fault data based on association rule algorithm includes: Obtain historical operation data of electrical equipment and corresponding fault types, discretize the historical operation data of electrical equipment and corresponding fault types, and obtain a fault data set; Based on the association rule algorithm, the frequent itemsets of the fault data set are mined, and the strong association rules are determined according to the frequent itemsets of the fault data set.

[0009] Furthermore, the frequent item sets of the fault data set are mined based on the association rule algorithm, and strong association rules are determined according to the frequent item sets of the fault data set, including: Set a minimum support threshold, and mine historical operation data with a support greater than or equal to the minimum support threshold based on the historical operation data of the electrical equipment and the corresponding fault type; A minimum confidence threshold is set, and historical operation data with a confidence greater than or equal to the minimum confidence threshold is mined from historical operation data with a support greater than or equal to the minimum support threshold according to the historical operation data of the electrical equipment and the corresponding fault type, to obtain strong association rules.

[0010] Furthermore, the operating state parameters of the current electrical equipment are determined according to the current operating data of the electrical equipment and the cluster partition in which it is located combined with the strong association rules of the historical fault data, including: The cluster partition where the electrical equipment is located is obtained, and the cluster center of the cluster partition is normalized to obtain the fault weight value of the electrical equipment; Obtain the current operation data of the electrical equipment, and obtain the fault probability of each fault type of the current electrical equipment according to the confidence of the corresponding fault type in the strong association rule set of the current operation data; The fault weight value of the electrical equipment is multiplied by the fault probability of each fault type and then added to obtain the operating status parameters of each electrical equipment.

[0011] Furthermore, the correcting of the operating state parameters according to the ambient temperature includes: Calculate the difference between the ambient temperature when the electrical equipment is running and the real-time temperature inside the electrical equipment, draw a temperature difference change curve according to the difference between the ambient temperature and the real-time temperature inside the electrical equipment, and screen out the temperature difference in the temperature difference change curve that is greater than a fourth preset threshold; Calculate the proportion of temperature differences greater than a second preset threshold in the temperature difference change curve, and use the ratio of the proportion of temperature differences greater than the second preset threshold in the temperature difference change curve to the preset standard proportion as the operating state correction coefficient; The operating state correction coefficient is multiplied by the operating state parameter of the electrical equipment to obtain the corrected operating state parameter.

[0012] Furthermore, determining the evaluation level of the electrical equipment according to the corrected operating status parameters includes: Obtaining a preset operating state standard value, calculating a difference between an operating state parameter of the electrical equipment and the preset operating state standard value, and determining whether the difference between the operating state parameter of the electrical equipment and the preset operating state standard value is greater than a third preset threshold value; If the difference between the operating state parameter of the electrical equipment and the preset operating state standard value is greater than a third preset threshold, the first level is used as the evaluation level of the electrical equipment; If the difference between the operating state parameter of the electrical equipment and the preset operating state standard value is less than or equal to the third preset threshold, then determining whether the difference between the operating state parameter of the electrical equipment and the preset operating state standard value is greater than a fourth preset threshold; If the difference between the operating state parameter of the electrical equipment and the preset operating state standard value is greater than a fourth preset threshold, the second level is used as the evaluation level of the electrical equipment; If the difference between the operating state parameter of the electrical equipment and the preset operating state standard value is less than or equal to the fourth preset threshold, the third level is used as the evaluation level of the electrical equipment.

[0013] In order to achieve the above object, the present invention also provides a thermal power plant electrical equipment status evaluation system, comprising: A clustering module is used to obtain vibration signals of electrical equipment and cluster and partition each electrical equipment according to the vibration signals of the electrical equipment; Mining module, used to obtain historical fault data of electrical equipment and mine strong association rules of historical fault data based on association rule algorithm; A calculation module is used to obtain the current operation data of the electrical equipment, and determine the operation status parameters of the current electrical equipment according to the current operation data of the electrical equipment and the cluster partition in which it is located combined with the strong association rules of the historical fault data; The evaluation module is used to obtain the ambient temperature of the electrical equipment during operation, to correct the operating status parameters according to the ambient temperature, and to determine the evaluation level of the electrical equipment according to the corrected operating status parameters.

[0014] The beneficial effects of the present invention are: By applying the above technical scheme, the present invention clusters electrical equipment through the vibration signals of the electrical equipment, and mines the historical fault data at the same time, effectively utilizing the massive high-density operation monitoring historical data of the thermal power plant, and evaluating the operating status of the electrical equipment according to the mining results and the clustering partitions of each electrical equipment. It can accurately assess the failure risk of the electrical equipment and facilitate timely maintenance when the electrical equipment is abnormal or may be abnormal. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1The overall flow chart of a method for evaluating the state of electrical equipment in a thermal power plant proposed in an embodiment of the present invention is shown; Figure 2 A structural schematic diagram of a thermal power plant electrical equipment status evaluation system proposed in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only 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.

[0018] The present application embodiment provides a method for evaluating the state of electrical equipment in a thermal power plant, such as Figure 1 As shown, including: S101, obtaining vibration signals of electrical equipment, and clustering and partitioning each electrical equipment according to the vibration signals of the electrical equipment.

[0019] In some embodiments of the present application, clustering and partitioning each electrical device according to the vibration signal of the electrical device includes: preprocessing the vibration signal of the electrical device, performing fast Fourier transform on the preprocessed vibration signal to obtain a vibration signal spectrum diagram; determining fault parameters according to the vibration signal spectrum diagram of the electrical device, clustering the electrical equipment according to the fault parameters to obtain cluster partitions of each electrical device.

[0020] In this embodiment, the vibration signal of each electrical equipment in the thermal power plant is detected by an acceleration sensor, the vibration signal is preprocessed to reduce the noise of the vibration signal, and the preprocessed vibration signal is transformed from the time domain to the frequency domain based on the fast Fourier transform to obtain a vibration signal spectrum diagram.

[0021] In some embodiments of the present application, the method of determining the fault parameter according to the vibration signal spectrum of the electrical equipment includes: obtaining the peak point in the vibration signal spectrum, screening out the peak points in the vibration signal spectrum whose peak points are greater than a first preset threshold, and determining the first fault parameter according to the average peak point greater than the first preset threshold; calculating the absolute value of the slope of the peak point and the adjacent valley point in the vibration signal spectrum, and determining the second fault parameter according to the variance of all the absolute values ​​of the slope in the vibration signal spectrum; obtaining the preset standard vibration signal spectrum of the electrical equipment, and calculating the variance between the vibration signal spectrum of the electrical equipment during the operation cycle and the preset standard The area value of the quasi-vibration signal spectrum diagram and the coordinate axis, and the third fault parameter are determined according to the area difference between the vibration signal spectrum diagram and the preset standard vibration signal spectrum diagram; the first fault parameter, the second fault parameter and the third fault parameter are normalized, and the normalized first fault parameter, the second fault parameter and the third fault parameter are set to the first length, the second length and the third length according to the preset ratio; the first length and the second length are used as the lower bottom surface and the upper bottom surface respectively, and the third length is used as the height, a truncated cone is constructed according to the lower bottom surface, the upper bottom surface and the height, and the volume value of the truncated cone is set as the comprehensive fault parameter of the electrical equipment.

[0022] In this embodiment, the first fault parameter is determined by the average peak point greater than the first preset threshold value, the second fault parameter is determined by the variance of the absolute values ​​of all slopes in the vibration signal spectrum diagram, and the third fault parameter is determined by the area difference between the vibration signal spectrum diagram and the preset standard vibration signal spectrum diagram. A frustum is constructed by combining the first fault parameter, the second fault parameter and the third fault parameter, and the volume value of the frustum is set as the comprehensive fault parameter of the electrical equipment, so that the comprehensive fault parameter can more comprehensively represent the vibration state of the electrical equipment.

[0023] In some embodiments of the present application, the electrical equipment is clustered according to fault parameters to obtain cluster partitions of each electrical equipment, including: establishing a comprehensive fault parameter data set according to the comprehensive fault parameters of the electrical equipment, setting an initial k value, and randomly selecting an initial cluster center of the comprehensive fault parameter data set according to the initial k value; calculating the Euclidean distance between each comprehensive fault parameter in the comprehensive fault parameter data set and the k initial cluster centers, and clustering the comprehensive fault parameters into cluster partitions with the closest Euclidean distance to the initial cluster center; calculating the average value of the comprehensive fault parameters in each cluster partition, and setting a new cluster center according to the average value of the comprehensive fault parameters in each cluster partition; repeatedly iterating the new cluster center until the cluster center no longer changes, and obtaining the final cluster partitions of k electrical equipment.

[0024] In this embodiment, the initial k value is specifically 8, and the comprehensive fault parameters are clustered based on the k-means clustering algorithm, so as to facilitate the subsequent calculation of operating status parameters for electrical equipment in different cluster partitions.

[0025] S102, obtaining historical fault data of electrical equipment, and mining strong association rules of the historical fault data based on an association rule algorithm.

[0026] In some embodiments of the present application, the strong association rules of historical fault data mined based on the association rule algorithm include: obtaining historical operation data of electrical equipment and corresponding fault types, discretizing the historical operation data of electrical equipment and the corresponding fault types to obtain a fault data set; mining frequent item sets of the fault data set based on the association rule algorithm, and determining strong association rules based on the frequent item sets of the fault data set.

[0027] In some embodiments of the present application, the frequent item sets of the fault data set are mined based on the association rule algorithm, and strong association rules are determined based on the frequent item sets of the fault data set, including: setting a minimum support threshold, and mining historical operation data with a support greater than or equal to the minimum support threshold based on the historical operation data of the electrical equipment and the corresponding fault types; setting a minimum confidence threshold, and mining historical operation data with a confidence greater than or equal to the minimum confidence threshold from the historical operation data with a support greater than or equal to the minimum support threshold based on the historical operation data of the electrical equipment and the corresponding fault types, to obtain strong association rules.

[0028] In this embodiment, historical operation data of electrical equipment and corresponding fault types are determined through historical fault data and a fault data set is established. The historical operation data of electrical equipment includes voltage, current, power, speed, etc. The fault types include discharge faults, overheating faults and other electrical equipment fault types. Strong association rules of the fault data set are mined based on an association rule algorithm to achieve effective utilization of massive and high-density historical operation data of thermal power plants.

[0029] S103, obtaining current operation data of the electrical equipment, and determining the operation status parameters of the current electrical equipment according to the current operation data of the electrical equipment and the cluster partition in which the electrical equipment is located in combination with the strong association rule of historical fault data.

[0030] In some embodiments of the present application, the operating status parameters of the current electrical equipment are determined based on the current operating data of the electrical equipment and the cluster partition in which it is located in combination with the strong association rules of historical fault data, including: obtaining the cluster partition in which the electrical equipment is located, normalizing the cluster center of the cluster partition, and obtaining the fault weight value of the electrical equipment; obtaining the current operating data of the electrical equipment, and obtaining the failure probability of each fault type of the current electrical equipment based on the confidence of the corresponding fault type in the strong association rule set of the current operating data; multiplying the fault weight value of the electrical equipment by the failure probability of each fault type and adding them to obtain the operating status parameters of each electrical equipment.

[0031] In this embodiment, the cluster center of the cluster partition where the electrical equipment is located can represent the vibration state of the current electrical equipment. Therefore, the fault weight value of the electrical equipment is determined by the cluster center, and the confidence of the corresponding fault type of the current operating data in the strong association rule set is used as the failure probability of each fault type of the current electrical equipment. The failure probability of each fault type of the electrical equipment is multiplied by the fault weight value and then added to obtain the operating status parameters of each electrical equipment.

[0032] S104, obtaining the ambient temperature of the electrical equipment during operation, correcting the operating status parameters according to the ambient temperature, and determining the evaluation level of the electrical equipment according to the corrected operating status parameters.

[0033] In some embodiments of the present application, the correction of the operating status parameters according to the ambient temperature includes: calculating the difference between the ambient temperature when the electrical equipment is running and the real-time temperature inside the electrical equipment, drawing a temperature difference change curve according to the difference between the ambient temperature and the real-time temperature inside the electrical equipment, and screening out the temperature difference change curve that is greater than a fourth preset threshold; calculating the proportion of temperature differences greater than the second preset threshold in the temperature difference change curve, and taking the ratio of the proportion of temperature differences greater than the second preset threshold in the temperature difference change curve to the preset standard proportion as an operating status correction coefficient; multiplying the operating status correction coefficient by the operating status parameter of the electrical equipment to obtain the corrected operating status parameter.

[0034] In this embodiment, the fourth preset threshold is used as the maximum acceptable temperature difference of the electrical equipment. The operating status parameters are corrected by the ratio of the proportion of temperature differences greater than the fourth preset threshold in the temperature difference change curve to the preset standard proportion, so that the calculation of the operating status parameters of the electrical equipment is more accurate.

[0035] In some embodiments of the present application, determining the evaluation level of the electrical equipment based on the corrected operating status parameters includes: obtaining a preset operating status standard value, calculating the difference between the operating status parameter of the electrical equipment and the preset operating status standard value, and judging whether the difference between the operating status parameter of the electrical equipment and the preset operating status standard value is greater than a third preset threshold value; if the difference between the operating status parameter of the electrical equipment and the preset operating status standard value is greater than the third preset threshold value, taking the first level as the evaluation level of the electrical equipment; if the difference between the operating status parameter of the electrical equipment and the preset operating status standard value is less than or equal to the third preset threshold value, judging whether the difference between the operating status parameter of the electrical equipment and the preset operating status standard value is greater than a fourth preset threshold value; if the difference between the operating status parameter of the electrical equipment and the preset operating status standard value is greater than the fourth preset threshold value, taking the second level as the evaluation level of the electrical equipment; if the difference between the operating status parameter of the electrical equipment and the preset operating status standard value is less than or equal to the fourth preset threshold value, taking the third level as the evaluation level of the electrical equipment.

[0036] In this embodiment, the operating status of the electrical equipment is evaluated by the operating status parameters and the corresponding level is set, which can accurately assess the failure risk of the electrical equipment and facilitate timely maintenance when the electrical equipment is abnormal or may be abnormal.

[0037] Based on the same technical concept, such as Figure 2 As shown, the present invention also provides a system for evaluating the status of electrical equipment in a thermal power plant, including: a clustering module, used to obtain vibration signals of electrical equipment, and cluster and partition each electrical equipment according to the vibration signals of the electrical equipment; a mining module, used to obtain historical fault data of electrical equipment, and mine strong association rules of historical fault data based on an association rule algorithm; a calculation module, used to obtain current operating data of electrical equipment, and determine the operating status parameters of the current electrical equipment according to the current operating data of the electrical equipment and the cluster partition in which it is located combined with the strong association rules of historical fault data; an evaluation module, used to obtain the ambient temperature when the electrical equipment is running, correct the operating status parameters according to the ambient temperature, and determine the evaluation level of the electrical equipment according to the corrected operating status parameters.

[0038] By applying the above technical scheme, the present invention obtains the vibration signal of the electrical equipment, clusters and partitions each electrical equipment according to the vibration signal of the electrical equipment; obtains the historical fault data of the electrical equipment, and mines the strong association rules of the historical fault data based on the association rule algorithm; obtains the current operation data of the electrical equipment, and determines the operation status parameters of the current electrical equipment according to the current operation data of the electrical equipment and the cluster partition in which it is located combined with the strong association rules of the historical fault data; obtains the ambient temperature when the electrical equipment is running, corrects the operation status parameters according to the ambient temperature, and determines the evaluation level of the electrical equipment according to the corrected operation status parameters. The present invention evaluates the state of the electrical equipment in the thermal power plant by calculating the operation status parameters after clustering the electrical equipment, and accurately assesses the failure risk of the electrical equipment.

[0039] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for evaluating the state of electrical equipment in a thermal power plant, characterized in that: The method comprises: Acquire vibration signals of electrical equipment, and cluster and partition each electrical equipment according to the vibration signals of the electrical equipment; Obtain historical fault data of electrical equipment and mine strong association rules of historical fault data based on association rule algorithm; Obtain the current operating data of the electrical equipment, and determine the operating status parameters of the current electrical equipment according to the current operating data of the electrical equipment and the cluster partition in which it is located combined with the strong association rules of historical fault data; The ambient temperature of the electrical equipment during operation is obtained, the operating status parameters are corrected according to the ambient temperature, and the evaluation level of the electrical equipment is determined according to the corrected operating status parameters.

2. The method for evaluating the state of electrical equipment in a thermal power plant according to claim 1, characterized in that: The clustering and partitioning of each electrical device according to the vibration signal of the electrical device comprises: Preprocess the vibration signal of the electrical equipment, perform fast Fourier transform on the preprocessed vibration signal, and obtain a vibration signal spectrum diagram; The fault parameters are determined according to the vibration signal spectrum of the electrical equipment, and the electrical equipment is clustered according to the fault parameters to obtain the cluster partitions of each electrical equipment.

3. The method for evaluating the state of electrical equipment in a thermal power plant according to claim 2, characterized in that: Determining the fault parameters according to the vibration signal spectrum of the electrical equipment includes: Obtaining peak points in a vibration signal spectrum diagram, screening out peak points in the vibration signal spectrum diagram whose peak points are greater than a first preset threshold, and determining a first fault parameter according to an average peak point greater than the first preset threshold; Calculate the absolute value of the slope between the peak point and the adjacent valley point in the vibration signal spectrum diagram, and determine the second fault parameter according to the variance of all the absolute values ​​of the slope in the vibration signal spectrum diagram; Obtain a preset standard vibration signal spectrum of the electrical equipment, calculate the area value between the vibration signal spectrum during the operation cycle of the electrical equipment and the preset standard vibration signal spectrum and the coordinate axis, and determine the third fault parameter according to the area difference between the vibration signal spectrum and the preset standard vibration signal spectrum; Normalizing the first fault parameter, the second fault parameter, and the third fault parameter, and setting the normalized first fault parameter, the second fault parameter, and the third fault parameter to the first length, the second length, and the third length according to a preset ratio; The first length and the second length are used as the lower bottom surface and the upper bottom surface respectively, and the third length is used as the height. A truncated cone is constructed according to the lower bottom surface, the upper bottom surface and the height, and the volume value of the truncated cone is set as the comprehensive fault parameter of the electrical equipment.

4. The method for evaluating the state of electrical equipment in a thermal power plant according to claim 3, characterized in that: The step of clustering the electrical equipment according to the fault parameters to obtain cluster partitions of each electrical equipment includes: A comprehensive fault parameter data set is established according to the comprehensive fault parameters of the electrical equipment, an initial k value is set, and an initial clustering center of the comprehensive fault parameter data set is randomly selected according to the initial k value; Calculate the Euclidean distance between each comprehensive fault parameter in the comprehensive fault parameter data set and the k initial cluster centers, and cluster the comprehensive fault parameters into the cluster partition with the closest Euclidean distance to the initial cluster center; Calculate the average value of the comprehensive fault parameters in each cluster partition, and set a new cluster center according to the average value of the comprehensive fault parameters in each cluster partition; Repeat the iteration of the new cluster center until the cluster center no longer changes, and obtain the final cluster partitions of k electrical equipment.

5. The method for evaluating the state of electrical equipment in a thermal power plant according to claim 4, characterized in that: The strong association rules of historical fault data mined based on the association rule algorithm include: Obtain historical operation data of electrical equipment and corresponding fault types, discretize the historical operation data of electrical equipment and corresponding fault types, and obtain a fault data set; Based on the association rule algorithm, the frequent itemsets of the fault data set are mined, and the strong association rules are determined according to the frequent itemsets of the fault data set.

6. The method for evaluating the state of electrical equipment in a thermal power plant according to claim 5, characterized in that: The method of mining frequent item sets of the fault data set based on the association rule algorithm and determining strong association rules according to the frequent item sets of the fault data set includes: Set a minimum support threshold, and mine historical operation data with a support greater than or equal to the minimum support threshold based on the historical operation data of the electrical equipment and the corresponding fault type; A minimum confidence threshold is set, and historical operation data with a confidence greater than or equal to the minimum confidence threshold is mined from historical operation data with a support greater than or equal to the minimum support threshold according to the historical operation data of the electrical equipment and the corresponding fault type, to obtain strong association rules.

7. The method for evaluating the state of electrical equipment in a thermal power plant according to claim 6, characterized in that: The operating status parameters of the current electrical equipment are determined based on the current operating data of the electrical equipment and the cluster partition in which it is located, combined with the strong association rules of the historical fault data, including: The cluster partition where the electrical equipment is located is obtained, and the cluster center of the cluster partition is normalized to obtain the fault weight value of the electrical equipment; Obtain the current operation data of the electrical equipment, and obtain the fault probability of each fault type of the current electrical equipment according to the confidence of the corresponding fault type in the strong association rule set of the current operation data; The fault weight value of the electrical equipment is multiplied by the fault probability of each fault type and then added to obtain the operating status parameters of each electrical equipment.

8. The method for evaluating the state of electrical equipment in a thermal power plant according to claim 7, characterized in that: The step of correcting the operating state parameters according to the ambient temperature includes: Calculate the difference between the ambient temperature when the electrical equipment is running and the real-time temperature inside the electrical equipment, draw a temperature difference change curve according to the difference between the ambient temperature and the real-time temperature inside the electrical equipment, and screen out the temperature difference in the temperature difference change curve that is greater than a fourth preset threshold; Calculate the proportion of temperature differences greater than a second preset threshold in the temperature difference change curve, and use the ratio of the proportion of temperature differences greater than the second preset threshold in the temperature difference change curve to the preset standard proportion as the operating state correction coefficient; The operating state correction coefficient is multiplied by the operating state parameter of the electrical equipment to obtain the corrected operating state parameter.

9. The method for evaluating the state of electrical equipment in a thermal power plant according to claim 8, characterized in that: Determining the evaluation level of the electrical equipment according to the corrected operating status parameters includes: Obtaining a preset operating state standard value, calculating a difference between an operating state parameter of the electrical equipment and the preset operating state standard value, and determining whether the difference between the operating state parameter of the electrical equipment and the preset operating state standard value is greater than a third preset threshold value; If the difference between the operating state parameter of the electrical equipment and the preset operating state standard value is greater than a third preset threshold, the first level is used as the evaluation level of the electrical equipment; If the difference between the operating state parameter of the electrical equipment and the preset operating state standard value is less than or equal to the third preset threshold, then determining whether the difference between the operating state parameter of the electrical equipment and the preset operating state standard value is greater than a fourth preset threshold; If the difference between the operating state parameter of the electrical equipment and the preset operating state standard value is greater than a fourth preset threshold, the second level is used as the evaluation level of the electrical equipment; If the difference between the operating state parameter of the electrical equipment and the preset operating state standard value is less than or equal to the fourth preset threshold, the third level is used as the evaluation level of the electrical equipment.

10. A thermal power plant electrical equipment status evaluation system, characterized in that: include: A clustering module is used to obtain vibration signals of electrical equipment and cluster and partition each electrical equipment according to the vibration signals of the electrical equipment; Mining module, used to obtain historical fault data of electrical equipment and mine strong association rules of historical fault data based on association rule algorithm; A calculation module is used to obtain the current operation data of the electrical equipment, and determine the operation status parameters of the current electrical equipment according to the current operation data of the electrical equipment and the cluster partition in which it is located combined with the strong association rules of the historical fault data; The evaluation module is used to obtain the ambient temperature of the electrical equipment during operation, to correct the operating status parameters according to the ambient temperature, and to determine the evaluation level of the electrical equipment according to the corrected operating status parameters.

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