Method and system for evaluating health state of switch cabinet

Abstract

The invention belongs to the technical field of power equipment, and discloses a switch cabinet health state assessment method and system, and the method comprises the steps: extracting the operation feature data of a switch cabinet; key factors influencing the health state of the switch cabinet are identified; based on a state evaluation algorithm, the identified key factors influencing the health state of the switch cabinet are evaluated, and the change trend of the health state of the switch cabinet is obtained; and a maintenance plan and an operation strategy of the switch cabinet are optimized in combination with the change trend of the health state of the switch cabinet and the real-time operation state of the switch cabinet. Based on a state evaluation algorithm, the current health state can be evaluated in real time, the future operation state change can be predicted through trend analysis, support is provided for preventive maintenance, key problems are ensured to be processed preferentially, sudden equipment faults are avoided, and the reliability of a power system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power equipment, and in particular to a switch cabinet health status assessment method and system. Background Art

[0002] Switchgear is a key component in the power system. It is mainly used to control, protect and distribute electrical energy. It contains multiple high-voltage electrical equipment such as circuit breakers, busbars and current transformers. Since switchgear usually operates in a complex environment, it is easily affected by external factors such as temperature, humidity, dust and electromagnetic interference. These factors may cause abnormalities in the internal components of the switchgear, and then cause problems such as local overheating, insulation aging or partial discharge. If these problems are not discovered and handled in time, it may cause equipment failure. Therefore, real-time monitoring of switchgear is particularly important.

[0003] In the existing technology, it is not easy to accurately find the key parameters that affect the health status of the switch cabinet from the operating characteristic data, so it is not easy to quickly locate the main source of the problem, and it cannot provide a clear direction for the assessment of the health status, which increases the time for assessment and decision-making. It is also not easy to assess the changing trend of the health status, and thus it is not easy to identify hidden dangers that may cause equipment failures in advance, which increases the equipment failure rate and maintenance costs.

[0004] Therefore, how to provide a switch cabinet health status assessment method and system is a problem that needs to be solved urgently. Summary of the invention

[0005] The embodiments of the present invention provide a method and system for evaluating the health status of a switch cabinet to solve the problem in the prior art that it is not convenient to accurately find the key parameters that affect the health status of the switch cabinet from the operating characteristic data, making it difficult to quickly locate the main source of the problem, and failing to provide a clear direction for the evaluation of the health status, thereby increasing the time for evaluation and decision-making, and making it difficult to evaluate the changing trend of the health status, thereby making it difficult to identify hidden dangers that may cause equipment failures in advance, thereby increasing the equipment failure rate and maintenance costs.

[0006] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0007] According to a first aspect of an embodiment of the present invention, a method for evaluating the health status of a switch cabinet is provided.

[0008] In one embodiment, a switch cabinet health status assessment method includes:

[0009] Using feature extraction technology to pre-process the acquired switch cabinet operation data, and extract the switch cabinet operation feature data;

[0010] Based on the extracted operating characteristic data of the switch cabinet, the operating characteristic data is analyzed using the factor identification algorithm to identify the key factors affecting the health status of the switch cabinet;

[0011] Based on the status assessment algorithm, the key factors that affect the health status of the switchgear are evaluated to obtain the changing trend of the health status of the switchgear; and combined with the changing trend of the health status of the switchgear and the real-time operating status of the switchgear, the maintenance plan and operation strategy of the switchgear are optimized.

[0012] In one embodiment, based on the extracted operation characteristic data of the switch cabinet, the operation characteristic data is analyzed using a factor identification algorithm to identify key factors affecting the health status of the switch cabinet, including:

[0013] Discretize the operating characteristic data of the switch cabinet and use LASSO regression to reduce the dimension of the operating characteristic data;

[0014] Establish a node pool containing multiple analysis nodes, each node represents an analysis point of any operating characteristic of the switchgear;

[0015] A record is randomly selected from the switchgear operation characteristic data, and the best matching analysis node is selected for analysis through a screening algorithm;

[0016] Generate anomaly recognition signals based on simplified attributes, and extract relevant signal values ​​to calculate signal concentration;

[0017] Compare the semi-mature signal strength and mature signal strength of each node. If the semi-mature signal strength is greater than the mature signal strength, mark the node as an abnormal state node; otherwise, mark it as a normal state node; and use all nodes marked as abnormal states and their corresponding characteristics as key factors affecting the health status of the switchgear.

[0018] In one embodiment, randomly selecting a record from the switch cabinet operation characteristic data and selecting the most matching analysis node for analysis through a screening algorithm includes:

[0019] Initialize the parameters of the screening algorithm and use the search mechanism to generate an initial group of analysis nodes in the analysis node parameter space, where each analysis node corresponds to a different feature matching parameter combination;

[0020] Calculate the matching fitness value of each node in the analysis node group, and select the analysis node with the best current matching effect as the initial optimal node according to the fitness value;

[0021] According to the optimization formula of the screening algorithm, the parameter positions of all nodes in the analysis node group are updated to generate a new analysis node group;

[0022] Based on the learning mechanism, a group of opposing analysis nodes is generated, and a greedy selection strategy is adopted to retain the node with the highest fitness value between the original analysis node and the opposing analysis node to enter the next round;

[0023] Check whether the screening algorithm has reached the set maximum number of iterations. If not, re-optimize. Otherwise, select the node with the highest fitness value from the final analysis node group as the optimal matching analysis node for the switch cabinet operation characteristic data record.

[0024] In one embodiment, based on the status evaluation algorithm, the identified key factors affecting the health status of the switch cabinet are evaluated, and the change trend of the health status of the switch cabinet is obtained, including:

[0025] Establish an initial assessment matrix based on key factors affecting the health status of the switchgear;

[0026] Randomly select key operating parameters for analysis and calculate their matching scores with health status;

[0027] The health status weights of the key operating parameters are adjusted according to the matching scores, and new key operating parameters are inserted to repeat the evaluation process;

[0028] Based on the evaluation matrix, the overall health status score of the switchgear under the current operating conditions is calculated, and the switchgear operation and maintenance strategy is updated according to the evaluation results; the change trend of the health status of the switchgear obtained by the evaluation is output.

[0029] In one embodiment, randomly selecting key operating parameters for analysis and calculating their matching scores with health status includes:

[0030] Define the key operating parameters of the switch cabinet and their variation ranges, and randomly select the initial values ​​of the key operating parameters;

[0031] The operating data of the switchgear under randomly selected parameters is obtained, the obtained operating data is compared with the known health status influencing factors, and the matching score between each key operating parameter and the health status is calculated using the preset status assessment scoring model.

[0032] According to a second aspect of an embodiment of the present invention, a switch cabinet health status assessment system is provided.

[0033] In one embodiment, a switch cabinet health status assessment system includes:

[0034] A data processing module is used to pre-process the acquired operation data of the switch cabinet by using feature extraction technology to extract the operation feature data of the switch cabinet;

[0035] A factor identification module is used to analyze the operation characteristic data of the switch cabinet based on the extracted operation characteristic data by using a factor identification algorithm to identify key factors affecting the health status of the switch cabinet;

[0036] The status assessment module is used to evaluate the key factors that affect the health status of the switch cabinet based on the status assessment algorithm, and obtain the changing trend of the health status of the switch cabinet; and optimize the maintenance plan and operation strategy of the switch cabinet in combination with the changing trend of the health status of the switch cabinet and the real-time operation status of the switch cabinet.

[0037] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0038] In one embodiment, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0039] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0040] In one embodiment, the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0041] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0042] 1. The present invention utilizes a factor identification algorithm to accurately identify key parameters that affect the health status of the switch cabinet from the operating characteristic data, thereby avoiding wasting resources on minor factors. Based on the status assessment algorithm, it can not only assess the current health status in real time, but also predict future operating status changes through trend analysis, provide support for preventive maintenance, and optimize the maintenance plan based on the assessment results, thereby ensuring that key issues are handled as a priority, avoiding sudden equipment failures, and improving the reliability of the power system.

[0043] 2. The present invention selects the most matching nodes for analysis through a screening algorithm to ensure that the analysis results of the data are highly targeted and accurate. By calculating the signal concentration and generating abnormal identification signals, it dynamically captures abnormal behaviors in the feature data that may reflect changes in health status, laying the foundation for the identification of key factors. By identifying abnormal nodes and their signals, potential fault hazards can be perceived in advance, thereby improving the predictability of health status assessment.

[0044] 3. The present invention calculates the overall health status score through the evaluation matrix to intuitively reflect the current health status of the switch cabinet. The overall score can timely reflect the abnormal trend of the equipment and provide a decision-making basis for preventive maintenance and sudden fault handling. By outputting the changing trend of the health status of the switch cabinet, it can predict possible problems that may arise in the equipment in the future, so as to take measures in advance to reduce the risk of failure. Trend analysis helps the operation and maintenance team adjust strategies according to changes in health status, optimize resource allocation, and improve operational reliability.

[0045] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0047] Figure 1 is a flow chart of a method for evaluating the health status of a switch cabinet according to an exemplary embodiment;

[0048] Figure 2 is a principle block diagram of a switch cabinet health status assessment system according to an exemplary embodiment;

[0049] Figure 3 The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0050] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0051] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0052] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0053] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0054] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0055] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0056] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0057] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0058] Figure 1 An embodiment of a switch cabinet health status assessment method of the present invention is shown.

[0059] In this optional embodiment, the switch cabinet health status assessment method includes:

[0060] Step S101, preprocessing the acquired operation data of the switch cabinet by using feature extraction technology to extract the operation feature data of the switch cabinet;

[0061] Specifically, the operating data of the switchgear includes temperature data, partial discharge (PD) data, insulation status data, environmental data, vibration and noise data, time and frequency domain data, electrical data, etc.

[0062] Specifically, feature extraction technology mainly refers to a comprehensive signal processing method. The core technologies include empirical mode decomposition (EMD), local extreme point identification, gradient and direction analysis, feature descriptor generation and feature classification.

[0063] Step S103, based on the extracted operation characteristic data of the switch cabinet, the operation characteristic data is analyzed using a factor identification algorithm to identify key factors affecting the health status of the switch cabinet;

[0064] Specifically, the key factors affecting the health status of switchgear include: temperature-related factors, electrical performance-related factors, partial discharge (PD)-related factors, insulation performance-related factors, mechanical vibration and noise-related factors, environment-related factors, time and operating cycle-related factors, fault history and maintenance records, etc.

[0065] Step S105, based on the status assessment algorithm, evaluate the key factors that affect the health status of the switch cabinet to obtain the change trend of the health status of the switch cabinet; and optimize the maintenance plan and operation strategy of the switch cabinet in combination with the change trend of the health status of the switch cabinet and the real-time operation status of the switch cabinet.

[0066] Specifically, the changing trend of the health status of the switchgear refers to the dynamic evolution of the overall health status of the switchgear over time. It reflects the direction and magnitude of changes in the health status of the equipment from the present to the future, including the current level of health status, dynamic changes in health status, trend prediction of key factors, early warning of potential faults, and prediction of long-term health status.

[0067] It needs to be explained that for parameters that are in poor health or that trend analysis shows may deteriorate, maintenance and inspection should be prioritized; specific maintenance tasks should be formulated, such as cleaning, tightening connections, and replacing aging parts; maintenance schedules and priorities should be determined to ensure that key issues are dealt with in a timely manner; for parameters that are in poor health, operating parameters should be adjusted to reduce their load, such as reducing current load, increasing heat dissipation measures, etc.; the operating environment of the switchgear should be optimized, such as improving ventilation conditions, controlling temperature and humidity, etc.; operating parameters should be checked and adjusted regularly to ensure that the equipment is always operating in the best condition; after implementing the maintenance plan, the health status and operating parameters of the switchgear should continue to be monitored; based on the monitoring results, the maintenance effect should be evaluated, and the maintenance plan and operating strategy should be adjusted; the details of the maintenance and operating adjustments should be recorded to form a maintenance log to provide a reference for future optimization.

[0068] In this optional embodiment, when the acquired operating data of the switch cabinet is preprocessed using feature extraction technology to extract the operating feature data of the switch cabinet, the operating data of the switch cabinet can be decomposed at multiple scales using empirical mode decomposition to generate multi-level feature data reflecting different time and frequency characteristics; for the feature data of each scale, local extreme points in the data are identified by comparing adjacent data points, and the local extreme points are marked as preliminary feature points; for each preliminary feature point, the gradient and change direction of the operating data around it are calculated to generate a directional distribution map of the feature point, and the main direction of the feature point is determined by analyzing the peak value of the directional distribution map; with each feature point as the center, a neighborhood range of the feature point is constructed, the data gradient direction and gradient amplitude are calculated within the neighborhood, and the gradient direction and amplitude within the neighborhood of the feature point are combined into a feature vector as a descriptor of the feature point; based on the descriptor of the feature point, the feature point is classified to complete feature recognition, and the operating feature data of the switch cabinet is extracted.

[0069] In this optional embodiment, based on the extracted operation characteristic data of the switch cabinet, the operation characteristic data is analyzed using a factor identification algorithm to identify the key factors affecting the health status of the switch cabinet. The operation characteristic data of the switch cabinet can be discretized and the dimension of the operation characteristic data can be simplified using LASSO regression; a node pool containing multiple analysis nodes is established, and each node represents an analysis point of any operation characteristic of the switch cabinet; a record is randomly selected from the switch cabinet operation characteristic data, and the most matching analysis node is selected for analysis through a screening algorithm; an abnormal identification signal is generated according to the simplified attributes, and relevant signal values ​​are extracted to calculate the signal concentration; the semi-mature signal strength and mature signal strength of each node are compared, and if the semi-mature signal strength is greater than the mature signal strength, the node is marked as an abnormal state node; otherwise, it is marked as a normal state node; and all nodes marked as abnormal states and their corresponding characteristics are used as key factors affecting the health status of the switch cabinet.

[0070] In this optional embodiment, when a record is randomly selected from the switch cabinet operation characteristic data and the best matching analysis node is selected for analysis through the screening algorithm, the parameters of the screening algorithm can be initialized, and the search mechanism is used to generate an initial analysis node group in the analysis node parameter space, each analysis node corresponds to a different feature matching parameter combination; the matching fitness value of each node in the analysis node group is calculated, and the analysis node with the best current matching effect is selected as the initial optimal node according to the fitness value; according to the optimization formula of the screening algorithm, the parameter positions of all nodes in the analysis node group are updated to generate a new analysis node group; based on the learning mechanism, an opposing analysis node group is generated, and a greedy selection strategy is adopted to retain the node with the highest fitness value between the original analysis node and the opposing analysis node to enter the next round; check whether the screening algorithm has reached the set maximum number of iterations. If not, re-optimize; otherwise, select the node with the highest fitness value from the final analysis node group as the optimal matching analysis node for the switch cabinet operation characteristic data record.

[0071] It needs to be explained that the optimization formula is:

[0072]

[0073] In the formula, H a , d represents the parameter position of the a-th analysis node after updating in the d-th dimension; M represents the total number of the current analysis node group; δ1 and δ2 both represent adjustment coefficients; S d represents the upper limit of the d dimension; X d represents the lower limit of the d dimension; v represents the distance function; k a represents the current parameter position of the a-th analysis node in the d-th dimension; k b represents the current parameter position of the b-th analysis node in the d-th dimension; Q d ′ represents the global optimal analysis node in the dth dimension in the current analysis node group.

[0074] In this optional embodiment, when the identified key factors affecting the health status of the switch cabinet are evaluated based on the status assessment algorithm and the changing trend of the health status of the switch cabinet is obtained, an initial assessment matrix can be established based on the key factors affecting the health status of the switch cabinet; key operating parameters are randomly selected for analysis, and their matching scores with the health status are calculated; the health status weights of the key operating parameters are adjusted according to the matching scores, and new key operating parameters are inserted to repeat the assessment process; based on the assessment matrix, the overall health status score of the switch cabinet under the current operating conditions is calculated, and based on the assessment results, the switch cabinet operation and maintenance strategy is updated; and the changing trend of the health status of the switch cabinet obtained by the assessment is output.

[0075] In this optional embodiment, when randomly selecting key operating parameters for analysis and calculating their matching scores with the health status, the key operating parameters of the switch cabinet and their variation ranges can be defined, and the initial values ​​of the key operating parameters can be randomly selected; the operating data of the switch cabinet under the randomly selected parameters is obtained, the obtained operating data is compared with the known health status influencing factors, and the matching score between each key operating parameter and the health status is calculated using a preset status assessment scoring model.

[0076] It should be explained that the formula of the status assessment scoring model is:

[0077]

[0078] Where P i Indicates the matching score between the i-th key operating parameter and the health status (the score range is 0 to w i , the higher the value, the more consistent with the health status); w i represents the weight of the i-th key operating parameter (used to reflect the relative impact of the parameter on the health status); F i actual represents the actual operating value of the i-th key operating parameter; F i ideal It represents the ideal health value of the i-th key operating parameter (i.e., the benchmark value when the switchgear is in a healthy state).

[0079] In this optional embodiment, when obtaining the operating data of the switch cabinet under randomly selected parameters, comparing the obtained operating data with the known health status influencing factors, and using a preset status assessment scoring model to calculate the matching score between each key operating parameter and the health status, the operating environment and key parameters can be initialized, and the values ​​of the key operating parameters can be randomly selected; the real-time operating data of the switch cabinet under the selected operating parameter conditions is obtained, and the obtained real-time operating data is compared with the known health status influencing factors; based on the comparison results, combined with the importance of each key operating parameter, the degree of matching of the key operating parameter to the health status is evaluated and the matching score is calculated.

[0080] Figure 2 An embodiment of a switch cabinet health status assessment system of the present invention is shown.

[0081] In this optional embodiment, the switch cabinet health status assessment system includes:

[0082] The data processing module 201 is used to pre-process the acquired operation data of the switch cabinet by using feature extraction technology to extract the operation feature data of the switch cabinet;

[0083] A factor identification module 203 is used to analyze the operation characteristic data of the switch cabinet based on the extracted operation characteristic data using a factor identification algorithm to identify key factors affecting the health status of the switch cabinet;

[0084] The status assessment module 205 is used to evaluate the key factors that affect the health status of the switch cabinet based on the status assessment algorithm, and obtain the changing trend of the health status of the switch cabinet; and optimize the maintenance plan and operation strategy of the switch cabinet in combination with the changing trend of the health status of the switch cabinet and the real-time operation status of the switch cabinet.

[0085] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0086] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0087] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0088] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0089] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed 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 by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0090] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A switch cabinet health status assessment method, characterized in that: include: Using feature extraction technology to pre-process the acquired switch cabinet operation data, and extract the switch cabinet operation feature data; Based on the extracted operating characteristic data of the switch cabinet, the operating characteristic data is analyzed using the factor identification algorithm to identify the key factors affecting the health status of the switch cabinet; Based on the status assessment algorithm, the key factors that affect the health status of the switch cabinet are evaluated to obtain the change trend of the health status of the switch cabinet; And combined with the changing trend of the health status of the switch cabinet and the real-time operating status of the switch cabinet, the maintenance plan and operation strategy of the switch cabinet are optimized.

2. The switch cabinet health status assessment method according to claim 1, characterized in that: The method of preprocessing the acquired switch cabinet operation data by using feature extraction technology to extract the switch cabinet operation feature data includes: Use empirical mode decomposition to perform multi-scale decomposition on the switchgear operation data to generate multi-level feature data reflecting different time and frequency characteristics; For the feature data of each scale, local extreme points in the data are identified by comparing adjacent data points, and the local extreme points are marked as preliminary feature points; For each preliminary feature point, calculate the gradient and change direction of the running data around it, generate a directional distribution map of the feature point, and determine the main direction of the feature point by analyzing the peak value of the directional distribution map; Taking each feature point as the center, construct the neighborhood range of the feature point, calculate the data gradient direction and gradient amplitude in the neighborhood, and combine the gradient direction and amplitude in the neighborhood of the feature point into a feature vector as the descriptor of the feature point; Based on the descriptors of feature points, the feature points are classified, feature recognition is completed, and the operating feature data of the switch cabinet is extracted.

3. The switch cabinet health status assessment method according to claim 1, characterized in that: Based on the extracted operation characteristic data of the switch cabinet, the operation characteristic data is analyzed using a factor identification algorithm to identify key factors affecting the health status of the switch cabinet, including: Discretize the operating characteristic data of the switch cabinet and use LASSO regression to reduce the dimension of the operating characteristic data; Establish a node pool containing multiple analysis nodes, each node represents an analysis point of any operating characteristic of the switchgear; A record is randomly selected from the switchgear operation characteristic data, and the best matching analysis node is selected for analysis through a screening algorithm; Generate anomaly recognition signals based on simplified attributes, and extract relevant signal values ​​to calculate signal concentration; Compare the semi-mature signal strength and mature signal strength of each node. If the semi-mature signal strength is greater than the mature signal strength, mark the node as an abnormal state node; otherwise, mark it as a normal state node; and use all nodes marked as abnormal states and their corresponding characteristics as key factors affecting the health status of the switchgear.

4. The switch cabinet health status assessment method according to claim 3, characterized in that: The randomly selecting a record from the switch cabinet operation characteristic data and selecting the most matching analysis node for analysis through a screening algorithm includes: Initialize the parameters of the screening algorithm and use the search mechanism to generate an initial group of analysis nodes in the analysis node parameter space, where each analysis node corresponds to a different feature matching parameter combination; Calculate the matching fitness value of each node in the analysis node group, and select the analysis node with the best current matching effect as the initial optimal node according to the fitness value; According to the optimization formula of the screening algorithm, the parameter positions of all nodes in the analysis node group are updated to generate a new analysis node group; Based on the learning mechanism, a group of opposing analysis nodes is generated, and a greedy selection strategy is adopted to retain the node with the highest fitness value between the original analysis node and the opposing analysis node to enter the next round; Check whether the screening algorithm has reached the set maximum number of iterations. If not, re-optimize. Otherwise, select the node with the highest fitness value from the final analysis node group as the optimal matching analysis node for the switch cabinet operation characteristic data record.

5. The switch cabinet health status assessment method according to claim 4, characterized in that: The optimization formula is: In the formula, H a , d represents the parameter position of the a-th analysis node after updating in the d-th dimension; M represents the total number of the current analysis node group; δ1 and δ2 both represent adjustment coefficients; S d represents the upper limit of the d dimension; X d represents the lower limit of the d dimension; v represents the distance function; k a represents the current parameter position of the a-th analysis node in the d-th dimension; k b represents the current parameter position of the b-th analysis node in the d-th dimension; Q d ′ represents the global optimal analysis node in the dth dimension in the current analysis node group.

6. The switch cabinet health status assessment method according to claim 1, characterized in that: Based on the state assessment algorithm, the key factors that affect the health state of the switch cabinet are evaluated, and the change trend of the health state of the switch cabinet is obtained, including: Establish an initial assessment matrix based on key factors affecting the health status of the switchgear; Randomly select key operating parameters for analysis and calculate their matching scores with health status; The health status weights of the key operating parameters are adjusted according to the matching scores, and new key operating parameters are inserted to repeat the evaluation process; Based on the evaluation matrix, the overall health status score of the switchgear under the current operating conditions is calculated, and the switchgear operation and maintenance strategy is updated according to the evaluation results; the change trend of the health status of the switchgear obtained by the evaluation is output.

7. The switch cabinet health status assessment method according to claim 6, characterized in that: The randomly selected key operating parameters are analyzed and the matching scores with the health status are calculated, including: Define the key operating parameters of the switch cabinet and their variation ranges, and randomly select the initial values ​​of the key operating parameters; The operating data of the switchgear under randomly selected parameters is obtained, the obtained operating data is compared with the known health status influencing factors, and the matching score between each key operating parameter and the health status is calculated using the preset status assessment scoring model.

8. The switch cabinet health status assessment method according to claim 7, characterized in that: The step of obtaining the operating data of the switch cabinet under the randomly selected parameters, comparing the obtained operating data with the known health status influencing factors, and using a preset status evaluation scoring model to calculate the matching score between each key operating parameter and the health status includes: Initialize the operating environment and key parameters, and randomly select the values ​​of key operating parameters; Obtain the real-time operating data of the switch cabinet under the selected operating parameter conditions, and compare the obtained real-time operating data with the known health status influencing factors; Based on the comparison results and combined with the importance of each key operating parameter, the matching degree of the key operating parameters to the health status is evaluated and the matching score is calculated.

9. The switch cabinet health status assessment method according to claim 8, characterized in that: The formula of the state assessment scoring model is: Where P i represents the matching score between the i-th key operating parameter and the health status; w i represents the weight of the i-th key operating parameter; represents the actual operating value of the i-th key operating parameter; represents the ideal health value of the i-th key operating parameter.

10. A switch cabinet health status assessment system, characterized in that: The switchgear health status assessment system includes: A data processing module is used to pre-process the acquired operation data of the switch cabinet by using feature extraction technology to extract the operation feature data of the switch cabinet; A factor identification module is used to analyze the operation characteristic data of the switch cabinet based on the extracted operation characteristic data by using a factor identification algorithm to identify key factors affecting the health status of the switch cabinet; The status assessment module is used to evaluate the key factors that affect the health status of the switch cabinet based on the status assessment algorithm, and obtain the changing trend of the health status of the switch cabinet; and optimize the maintenance plan and operation strategy of the switch cabinet in combination with the changing trend of the health status of the switch cabinet and the real-time operation status of the switch cabinet.

Images