Electric power prefabricated cabin state on-line monitoring and early warning system and method

Abstract

The invention provides an electric power prefabricated cabin state on-line monitoring and early warning system and method, and the method comprises the steps: carrying out the processing of the original data of the insulation state of an electric power prefabricated cabin through a preset standardization method, and obtaining a standardized data matrix; respectively extracting insulation state characteristics of the partial discharge quantity, the dielectric loss angle and the surface impedance in the power prefabricated cabin from the standardized data matrix; extracting information entropy and variation characteristics of each insulation state parameter, and calculating an entropy weight balance value of each insulation state parameter according to each information entropy and variation characteristic; and according to the insulation state characteristics and each entropy weight balance value, determining an insulation closeness degree of a current insulation state in the power prefabricated cabin, and when the insulation state of the power prefabricated cabin is in an abnormal range, performing graded early warning on the insulation state of the power prefabricated cabin based on the insulation closeness degree. Based on the scheme, multi-parameter grading early warning of the insulation state in the electric power prefabricated cabin can be realized.

Description

Technical Field

[0001] The present application relates to the field of electric power early warning technology, and more specifically, to an online monitoring and early warning system and method for the status of an electric power prefabricated cabin. Background Art

[0002] The power prefabricated cabin is a modern solution for integrating power equipment, widely used in power systems such as substations and distribution stations. It pre-installs various power equipment (such as transformers, switchgear, and control equipment) within a prefabricated cabin, forming a complete power system module. This design not only saves space but also improves equipment installation efficiency and operational reliability.

[0003] The traditional binary method simply divides the insulation status into normal or abnormal. This extensive judgment model has significant flaws. It cannot identify the gradual process of insulation material degradation from initial degradation to complete failure, resulting in the inability to take differentiated maintenance measures for different degradation stages. The binary method will mask slight but continuous insulation performance degradation. When the abnormal alarm is finally triggered, the equipment is often in a critical state that must be shut down immediately, making it difficult to effectively connect with the preventive maintenance system. Operation and maintenance personnel cannot reasonably plan maintenance priorities according to the warning level. At the same time, the binary method is rigid in handling critical situations and is prone to frequent state jumps due to small fluctuations in parameters, seriously affecting the reliability of the warning. Therefore, how to achieve multi-parameter graded warning of the insulation status in the power prefabricated cabin has become a difficult problem facing the industry. Summary of the Invention

[0004] The present application provides an online monitoring and early warning system and method for the status of a power prefabricated cabin, which can realize multi-parameter graded early warning of the insulation status in the power prefabricated cabin.

[0005] In a first aspect, the present application provides a method for providing a graded early warning of insulation status of a power prefabricated cabin, which is used in an online monitoring and early warning system for the status of a power prefabricated cabin to provide a graded early warning of insulation status. The method comprises: Obtain real-time monitoring records of insulation state parameters such as partial discharge, dielectric loss angle, and surface impedance from the monitoring data of the power prefabricated cabin, and then obtain the original insulation state data; The raw insulation state data is processed by a preset standardization method to obtain a standardized data matrix, and the insulation state characteristics of the partial discharge amount, dielectric loss angle and surface impedance in the power prefabricated cabin are respectively extracted from the standardized data matrix; Extracting the information entropy and variation characteristics of each insulation state parameter, and then calculating the entropy weight balance value of each insulation state parameter based on the information entropy and variation characteristics; The insulation closeness of the current insulation state in the electric power prefabricated cabin is determined according to the insulation state characteristics and each entropy weight balance value. When the insulation state of the electric power prefabricated cabin is within an abnormal range, a graded early warning of the insulation state of the electric power prefabricated cabin is performed based on the insulation closeness.

[0006] In some embodiments, extracting the insulation state characteristics of partial discharge, dielectric loss angle, and surface impedance in the power prefabricated cabin from the standardized data matrix specifically includes: Decomposing the standardized data matrix into characteristic vectors of partial discharge amount, dielectric loss angle and surface impedance by columns; The insulation state characteristics of partial discharge, dielectric loss angle and surface impedance in the power prefabricated cabin are extracted from each eigenvector.

[0007] In some embodiments, extracting the information entropy and variation characteristics of each insulation state parameter specifically includes: Obtain parameter information of various insulation status parameters within a specified historical time period from monitoring data of the power prefabricated cabin; The information entropy and variation characteristics of each insulation state parameter are calculated through parameter information of each insulation state parameter.

[0008] In some embodiments, calculating the entropy weight balance value of each insulation state parameter based on each information entropy and variation feature specifically includes: For each insulation state parameter, obtaining a balance weight of the entropy weight in the insulation state parameter; Based on the balance weight, the information entropy and variation characteristics of the insulation state parameters are balanced and integrated to obtain the entropy weight balance value of the insulation state parameters, and then obtain the entropy weight balance value of each insulation state parameter.

[0009] In some embodiments, determining the insulation closeness of the current insulation state in the electric prefabricated cabin according to the insulation state characteristics and each entropy weight balance value specifically includes: An optimal evaluation of the current insulation state is performed using the insulation state characteristics and the respective entropy weight balance values ​​to obtain a positive ideal solution for the current insulation state in the power prefabricated cabin; Performing a worst-case evaluation of the current insulation state using the insulation state characteristics and each entropy weight balance value to obtain a negative ideal solution for the current insulation state in the power prefabricated cabin; The insulation closeness of the current insulation state in the electric power prefabricated cabin is determined based on the positive ideal solution and the negative ideal solution.

[0010] In some embodiments, providing a graded warning of the insulation status of the prefabricated power cabin based on the insulation proximity specifically includes: Obtain a hierarchical mapping table of warning levels and proximity in the power prefabricated cabin; Extracting the warning level of the insulation closeness from the grading mapping table; The warning level is used to provide a warning for the insulation status of the electric power prefabricated cabin.

[0011] In some embodiments, the graded warning is a four-level status warning.

[0012] In a second aspect, the present application provides an online monitoring and early warning system for the status of a power prefabricated cabin, comprising a monitoring and early warning unit, the monitoring and early warning unit comprising: An acquisition module is used to obtain real-time monitoring records of insulation state parameters such as partial discharge, dielectric loss angle, and surface impedance from the monitoring data of the power prefabricated cabin, and then obtain the original insulation state data; a processing module, configured to process the raw insulation state data using a preset standardization method to obtain a standardized data matrix, and extract insulation state characteristics of partial discharge, dielectric loss angle, and surface impedance in the power prefabricated cabin from the standardized data matrix; The processing module is further used to extract the information entropy and variation characteristics of each insulation state parameter, and then calculate the entropy weight balance value of each insulation state parameter based on the information entropy and variation characteristics; An execution module is used to determine the insulation closeness of the current insulation state in the power prefabricated cabin based on the insulation state characteristics and each entropy weight balance value, and when the insulation state of the power prefabricated cabin is within an abnormal range, a graded early warning of the insulation state of the power prefabricated cabin is performed based on the insulation closeness.

[0013] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned method for grading early warning of the insulation status of a prefabricated power cabin.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned method for graded early warning of insulation status of a prefabricated power cabin.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: In an online monitoring and early warning system and method for the status of a prefabricated electric power cabin provided in the present application, real-time monitoring records of partial discharge, dielectric loss angle and surface impedance in the insulation status parameters are obtained from the monitoring data of the prefabricated electric power cabin, thereby obtaining the original insulation status data; the original insulation status data are processed by a preset standardization method to obtain a standardized data matrix, and the insulation status characteristics of the partial discharge, dielectric loss angle and surface impedance in the prefabricated electric power cabin are respectively extracted from the standardized data matrix; the information entropy and variation characteristics of each insulation status parameter are extracted, and the entropy weighted balance value of each insulation status parameter is calculated based on each information entropy and variation characteristic; the insulation closeness of the current insulation status in the prefabricated electric power cabin is determined based on the insulation status characteristics and each entropy weighted balance value, and when the insulation status of the prefabricated electric power cabin is within an abnormal range, a graded early warning of the insulation status of the prefabricated electric power cabin is performed based on the insulation closeness.

[0016] It can be seen that in this application, the insulation closeness of the current insulation state in the power prefabricated cabin is determined based on the insulation state characteristics and each entropy weight balance value. When the insulation state of the power prefabricated cabin is in an abnormal range, the insulation state of the power prefabricated cabin is graded and warned based on the insulation closeness; first, by determining the insulation state characteristics, the key indicators reflecting the essence of equipment operation can be obtained. The insulation state characteristics contain both the absolute value information of the parameters and their dynamic change laws, so that the power prefabricated cabin can capture early signs of insulation degradation from different angles, thereby significantly improving the sensitivity and reliability of state monitoring; then, by determining the entropy weight balance value, the dynamic weight distribution scheme of each insulation state parameter can be obtained, thereby realizing the intelligent fusion decision of multi-parameter warning, measuring the degree of randomness of the parameters by calculating the information entropy, and evaluating the fluctuation characteristics of the parameters by combining the variation characteristics, and finally generating an entropy weight balance value with a clear physical meaning, which solves the problem that the parameter weights in the traditional warning system are fixed and cannot adapt to the dynamic changes of the equipment. At the same time, the entropy weight balance mechanism can also suppress the influence of interference parameters and avoid overall misjudgment of the system due to false alarm of a single parameter. The adaptive weight distribution strategy enables the multi-parameter graded early warning system to not only highlight the contribution of key parameters, but also take into account the comprehensive influence of various insulation status parameters, and still maintain a high evaluation accuracy in complex operating environments. In summary, based on the above scheme, a multi-parameter graded early warning of the insulation status in the power prefabricated cabin can be realized. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0018] Figure 1 is an exemplary flow chart of a method for hierarchical early warning of insulation status of a prefabricated power cabin according to some embodiments of the present application; Figure 2 is a schematic diagram of a process for determining insulation proximity according to some embodiments of the present application; Figure 3 is a schematic structural diagram of a monitoring and early warning unit according to some embodiments of the present application; Figure 4 It is a structural diagram of a computer device for implementing a graded early warning method for insulation status of a prefabricated power cabin according to some embodiments of the present application. DETAILED DESCRIPTION

[0019] In order to better understand the technical solution of the present application, the technical solution of the present application will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0020] refer to Figure 1 This figure is an exemplary flow chart of a method for hierarchical early warning of insulation status of a power prefabricated cabin according to some embodiments of the present application. The method mainly includes the following steps: In step 101, real-time monitoring records of insulation state parameters such as partial discharge, dielectric loss angle, and surface impedance are obtained from monitoring data of the power prefabricated cabin, thereby obtaining original insulation state data.

[0021] It should be noted that in this application, the original data of the insulation status is a real-time monitoring data set consisting of local discharge, dielectric loss angle and surface impedance; local discharge refers to the tiny discharge phenomenon occurring in a local area of ​​the insulation system of the power equipment. The value of the local discharge reflects the internal defects or aging of the insulating material, and is usually measured in picovolts or millivolts; the dielectric loss angle indicates the degree of energy loss of the insulating material under an alternating electric field. The larger the tangent value of the dielectric loss angle, the more serious the dielectric loss of the insulating material, which may indicate moisture or deterioration; surface impedance is an indicator reflecting the surface conductivity of the insulating material. A decrease in impedance value may indicate that the surface is dirty, damp or carbonized, resulting in a decrease in insulation performance.

[0022] In specific implementation, real-time monitoring records of insulation state parameters such as partial discharge, dielectric loss angle and surface impedance are obtained from the monitoring data of the power prefabricated cabin, so that the collection of all real-time monitoring records is used as the original insulation state data.

[0023] In step 102, the insulation state raw data is processed by a preset standardization method to obtain a standardized data matrix, and the insulation state characteristics of the partial discharge amount, dielectric loss angle and surface impedance in the power prefabricated cabin are respectively extracted from the standardized data matrix.

[0024] In some embodiments, the insulation state raw data is processed by a preset standardization method to obtain a standardized data matrix, which can be achieved in the following manner, namely: appropriate standardization methods (the default is decimal scaling standardization) are selected for the partial discharge amount, dielectric loss angle and surface impedance respectively to ensure that each insulation state parameter has the same importance in the matrix, and the processed data is arranged in time series to form a standardized data matrix, wherein each column in the standardized data matrix represents a parameter, and each row represents a standardized value at a time point. It should be noted that in this application, the standardized data matrix is ​​a data matrix after standardization processing, and the numerical range of each insulation state parameter in the standardized data matrix is ​​unified.

[0025] In some embodiments, extracting the insulation state characteristics of partial discharge, dielectric loss factor, and surface impedance in the power prefabricated cabin from the standardized data matrix can be achieved by using the following steps: Decomposing the standardized data matrix into characteristic vectors of partial discharge amount, dielectric loss angle and surface impedance by columns; The insulation state characteristics of partial discharge, dielectric loss angle and surface impedance in the power prefabricated cabin are extracted from each eigenvector.

[0026] It should be noted that in this application, the insulation state characteristics are time domain indicators that characterize the insulation state characteristics; in specific implementation, first, the standardized data matrix is ​​split into three independent one-dimensional arrays by column, corresponding to the characteristic vectors of local discharge, dielectric loss angle and surface impedance, respectively, where each characteristic vector saves the standardized values ​​of each insulation state parameter at all sampling time points to form a complete time series data; then, the maximum and minimum values ​​of each characteristic vector are obtained as the insulation state characteristics of local discharge, dielectric loss angle and surface impedance in the power prefabricated cabin.

[0027] In step 103, the information entropy and variation characteristics of each insulation state parameter are extracted, and then the entropy weight balance value of each insulation state parameter is calculated based on the information entropy and variation characteristics.

[0028] In some embodiments, extracting the information entropy and variation characteristics of each insulation state parameter can be achieved by using the following steps: Obtain parameter information of various insulation status parameters within a specified historical time period from monitoring data of the power prefabricated cabin; The information entropy and variation characteristics of each insulation state parameter are calculated through parameter information of each insulation state parameter.

[0029] It should be noted that in this application, information entropy is an indicator used to quantify the uncertainty of parameter data, reflecting the randomness of the parameters and the amount of information; the variation characteristic is a comprehensive indicator that characterizes the fluctuation characteristics of the parameters; the historical specified time period refers to a pre-set representative time range, usually selected to cover the period that can cover the typical operating status of the equipment (for example: one week, one month or one quarter); the parameter information is a complete monitoring data set of each insulation status parameter within the selected time period, including the correspondence between timestamps and parameter values.

[0030] In a specific implementation, first, for each insulation state parameter, a time series set of parameter values ​​of the insulation state parameter in the monitoring data of the power prefabricated cabin within a historically specified time period is used as parameter information for obtaining the insulation state parameter within the historically specified time period. The parameter information of each insulation state parameter within the historically specified time period can be obtained in the above manner; then, for each insulation state parameter, the information entropy of all parameter values ​​in the parameter information of the insulation state parameter is calculated as the information entropy of the insulation state parameter, and the ratio of the standard deviation to the mean of all parameters is used as the variation feature of the insulation state parameter. The information entropy and variation feature of each insulation state parameter can be obtained in the above manner.

[0031] In some embodiments, the entropy weight balance value of each insulation state parameter is calculated based on each information entropy and variation feature by using the following steps: For each insulation state parameter, obtaining a balance weight of the entropy weight in the insulation state parameter; Based on the balance weight, the information entropy and variation characteristics of the insulation state parameters are balanced and integrated to obtain the entropy weight balance value of the insulation state parameters, and then obtain the entropy weight balance value of each insulation state parameter.

[0032] It should be noted that, in the present application, the entropy weight balance value is a comprehensive evaluation parameter that reflects the relative importance of each insulation state parameter in the state assessment; in specific implementation, first, for each insulation state parameter, the balance weight of the entropy weight in the insulation state parameter is obtained from the control console of the power prefabricated cabin. The balance weight is an adjustment coefficient that reflects the importance of each insulation state parameter in the comprehensive evaluation, and is used to balance the contribution ratio of information entropy and variation characteristics; then, for each insulation state parameter, the information entropy and variation characteristics of the insulation state parameter are standardized to unify the dimensions, and the balance weight is used to calculate the weighted sum of the information entropy and variation characteristics of the unified dimension as the entropy weight balance value of the insulation state parameter. The entropy weight balance value of each insulation state parameter can be obtained in the above manner.

[0033] In step 104, the insulation closeness of the current insulation state in the power prefabricated cabin is determined based on the insulation state characteristics and each entropy weight balance value. When the insulation state of the power prefabricated cabin is in an abnormal range, a graded warning is issued for the insulation state of the power prefabricated cabin based on the insulation closeness.

[0034] In some embodiments, the insulation closeness of the current insulation state in the power prefabricated cabin is determined based on the insulation state characteristics and each entropy weight balance value, referring to Figure 2 As described above, the figure is a schematic diagram of the process of determining the insulation closeness in some embodiments of the present application. In this embodiment, the insulation closeness can be determined by the following steps: In step 1041, the current insulation state is optimally evaluated using the insulation state characteristics and each entropy weight balance value to obtain a positive ideal solution for the current insulation state in the power prefabricated cabin; In step 1042, the current insulation state is worst evaluated using the insulation state characteristics and each entropy weight balance value to obtain a negative ideal solution for the current insulation state in the power prefabricated cabin; In step 1043 , the insulation closeness of the current insulation state in the electric prefabricated cabin is determined based on the positive ideal solution and the negative ideal solution.

[0035] It should be noted that in this application, insulation closeness represents a comprehensive closeness index between the current insulation state and the positive and negative ideal solutions. The value range of insulation closeness is 0 to 1, and the larger the value, the better the insulation state. The positive ideal solution is a reference vector composed of the optimal values ​​of each insulation state parameter, representing the most ideal operating state of the insulation system; the negative ideal solution is a reference vector composed of the worst values ​​of each insulation state parameter, representing the most dangerous operating state of the insulation system.

[0036] In specific implementation, first, the minimum value of partial discharge, the minimum value of dielectric loss angle and the maximum value of surface impedance are screened out from the insulation state characteristics, and the products of the minimum value of partial discharge, the minimum value of dielectric loss angle and the maximum value of surface impedance with the corresponding entropy weighted balance value are calculated respectively, so that the set of all products is regarded as the positive ideal solution; then, the maximum value of partial discharge, the maximum value of dielectric loss angle and the minimum value of surface impedance are screened out from the insulation state characteristics, and the products of the minimum value of partial discharge, the minimum value of dielectric loss angle and the maximum value of surface impedance with the corresponding entropy weighted balance value are calculated respectively, so that the set of all products is regarded as the negative ideal solution; finally, the Euclidean distance between the current state vector and the positive ideal solution is calculated, and the Euclidean distance between the current state vector and the negative ideal solution is calculated at the same time, and the weighted sum of the two Euclidean distances is calculated using the preset weight as the insulation closeness of the current insulation state in the power prefabricated cabin.

[0037] In some embodiments, the following steps may be used to provide a graded warning of the insulation status of the prefabricated power cabin based on the insulation proximity: Obtain a hierarchical mapping table of warning levels and proximity in the power prefabricated cabin; Extracting the warning level of the insulation closeness from the grading mapping table; The warning level is used to provide a warning for the insulation status of the electric power prefabricated cabin.

[0038] It should be noted that in this application, the graded mapping table: a pre-set correspondence table between insulation proximity and warning level, clarifies the status level corresponding to different numerical intervals; the warning level is a classification indicator that characterizes the degree of danger of the insulation status, and the graded warning is a four-level status warning, which is divided into four levels: normal, attention, abnormal, and serious.

[0039] In the specific implementation, first, a hierarchical mapping table of warning levels and proximity in the power prefabricated cabin is obtained from the control console of the power prefabricated cabin; then, the warning level corresponding to the insulation proximity in the hierarchical mapping table is used as the warning level of the insulation proximity; finally, the warning level is used as the current warning level of the insulation status in the power prefabricated cabin to issue an alarm.

[0040] In addition, in another aspect of the present application, in some embodiments, the present application provides an online monitoring and early warning system for the state of a power prefabricated cabin, the online monitoring and early warning system for the state of a power prefabricated cabin includes a monitoring and early warning unit, Figure 3 , which is a schematic diagram of the structure of a monitoring and early warning unit according to some embodiments of the present application. The monitoring and early warning unit includes: an acquisition module 201, a processing module 202, and an execution module 203, which are described as follows: Acquisition module 201, in this application, acquisition module 201 is mainly used to obtain real-time monitoring records of partial discharge, dielectric loss angle and surface impedance among the insulation state parameters from the monitoring data of the power prefabricated cabin, and then obtain the original insulation state data; Processing module 202, in the present application, is used to process the raw insulation state data using a preset standardization method to obtain a standardized data matrix, and to extract insulation state characteristics of partial discharge, dielectric loss factor, and surface impedance in the power prefabricated cabin from the standardized data matrix; It should be noted that the processing module 202 is further configured to extract information entropy and variation characteristics of each insulation state parameter, and then calculate the entropy weight balance value of each insulation state parameter based on the information entropy and variation characteristics; Execution module 203. In this application, execution module 203 is mainly used to determine the insulation closeness of the current insulation state in the power prefabricated cabin based on the insulation state characteristics and each entropy weight balance value. When the insulation state of the power prefabricated cabin is in an abnormal range, the insulation state of the power prefabricated cabin is graded and warned based on the insulation closeness.

[0041] The above describes in detail an example of an online monitoring and early warning system and method for the status of a prefabricated power cabin provided in an embodiment of the present application. It can be understood that, in order to realize the above functions, the corresponding device includes a hardware structure and / or software module corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven hardware manner depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0042] In some embodiments, the present application also provides a computer device, which includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned electric prefabricated cabin insulation status graded warning method.

[0043] In some embodiments, reference Figure 4 The dotted line in the figure indicates that the unit or module is optional. The figure is a structural diagram of a computer device for implementing a hierarchical warning method for the insulation status of a power prefabricated cabin according to an embodiment of the present application. The hierarchical warning method for the insulation status of a power prefabricated cabin described in the above embodiment can be Figure 4 The computer device shown in the figure is implemented, and the computer device includes at least one processor 301, a memory 302 and at least one communication unit 305. The computer device can be a terminal device, a server or a chip.

[0044] The processor 301 may be a general-purpose processor or a dedicated processor. For example, the processor 301 may be a central processing unit (CPU), which may be used to control the computer device, execute software programs, and process data from the software programs. The computer device may also include a communication unit 305 for inputting (receiving) and outputting (transmitting) signals.

[0045] For example, the computer device may be a chip, the communication unit 305 may be an input and / or output circuit of the chip, or the communication unit 305 may be a communication interface of the chip, and the chip may be a component of a terminal device, a network device, or other device.

[0046] For another example, the computer device may be a terminal device or a server, and the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.

[0047] The computer device may include one or more memories 302, on which a program 304 is stored. The program 304 can be executed by the processor 301 to generate instructions 303, so that the processor 301 executes the method described in the above method embodiment according to the instructions 303. Optionally, data (such as a target audit model) can also be stored in the memory 302. Optionally, the processor 301 can also read data stored in the memory 302. The data can be stored at the same storage address as the program 304, or at a different storage address from the program 304.

[0048] The processor 301 and the memory 302 may be provided separately or integrated together, for example, integrated on a system on chip (SOC) of a terminal device.

[0049] It should be understood that each step of the above method embodiment can be completed by a hardware-based logic circuit or software-based instructions in the processor 301. The processor 301 can be a CPU, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0050] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0051] For example, in some embodiments, the present application also provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned method for graded early warning of insulation status of a prefabricated power cabin.

[0052] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0053] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A hierarchical early warning method for insulation status of a power prefabricated cabin, used for online monitoring and early warning system of the status of a power prefabricated cabin to provide hierarchical early warning of insulation status, characterized in that: The method comprises the following steps: Obtain real-time monitoring records of insulation state parameters such as partial discharge, dielectric loss angle, and surface impedance from the monitoring data of the power prefabricated cabin, and then obtain the original insulation state data; The raw insulation state data is processed by a preset standardization method to obtain a standardized data matrix, and the insulation state characteristics of the partial discharge amount, dielectric loss angle and surface impedance in the power prefabricated cabin are respectively extracted from the standardized data matrix; Extracting the information entropy and variation characteristics of each insulation state parameter, and then calculating the entropy weight balance value of each insulation state parameter based on the information entropy and variation characteristics; The insulation closeness of the current insulation state in the electric power prefabricated cabin is determined according to the insulation state characteristics and each entropy weight balance value. When the insulation state of the electric power prefabricated cabin is within an abnormal range, a graded early warning of the insulation state of the electric power prefabricated cabin is performed based on the insulation closeness.

2. The method according to claim 1, wherein Extracting the insulation state characteristics of partial discharge, dielectric loss angle and surface impedance in the power prefabricated cabin from the standardized data matrix specifically includes: Decomposing the standardized data matrix into characteristic vectors of partial discharge amount, dielectric loss angle and surface impedance by columns; The insulation state characteristics of partial discharge, dielectric loss angle and surface impedance in the power prefabricated cabin are extracted from each eigenvector.

3. The method according to claim 1, wherein Extracting the information entropy and variation characteristics of each insulation state parameter specifically includes: Obtain parameter information of various insulation status parameters within a specified historical time period from monitoring data of the power prefabricated cabin; The information entropy and variation characteristics of each insulation state parameter are calculated through parameter information of each insulation state parameter.

4. The method according to claim 1, wherein The entropy weight balance value of each insulation state parameter is calculated based on each information entropy and variation feature, specifically including: For each insulation state parameter, obtaining a balance weight of the entropy weight in the insulation state parameter; Based on the balance weight, the information entropy and variation characteristics of the insulation state parameters are balanced and integrated to obtain the entropy weight balance value of the insulation state parameters, and then obtain the entropy weight balance value of each insulation state parameter.

5. The method according to claim 1, wherein Determining the insulation closeness of the current insulation state in the electric prefabricated cabin according to the insulation state characteristics and each entropy weight balance value specifically includes: An optimal evaluation of the current insulation state is performed using the insulation state characteristics and the respective entropy weight balance values ​​to obtain a positive ideal solution for the current insulation state in the power prefabricated cabin; Performing a worst-case evaluation of the current insulation state using the insulation state characteristics and each entropy weight balance value to obtain a negative ideal solution for the current insulation state in the power prefabricated cabin; The insulation closeness of the current insulation state in the electric power prefabricated cabin is determined based on the positive ideal solution and the negative ideal solution.

6. The method according to claim 1, wherein The hierarchical early warning of the insulation status of the power prefabricated cabin based on the insulation proximity specifically includes: Obtain a hierarchical mapping table of warning levels and proximity in the power prefabricated cabin; Extracting the warning level of the insulation closeness from the grading mapping table; The warning level is used to provide a warning for the insulation status of the electric power prefabricated cabin.

7. The method according to claim 1, wherein The graded warning is a four-level status warning.

8. An online monitoring and early warning system for the status of a power prefabricated cabin, comprising a monitoring and early warning unit, characterized in that: The monitoring and early warning unit includes: An acquisition module is used to obtain real-time monitoring records of insulation state parameters such as partial discharge, dielectric loss angle, and surface impedance from the monitoring data of the power prefabricated cabin, and then obtain the original insulation state data; a processing module, configured to process the raw insulation state data using a preset standardization method to obtain a standardized data matrix, and extract insulation state characteristics of partial discharge, dielectric loss angle, and surface impedance in the power prefabricated cabin from the standardized data matrix; The processing module is further used to extract the information entropy and variation characteristics of each insulation state parameter, and then calculate the entropy weight balance value of each insulation state parameter based on the information entropy and variation characteristics; An execution module is used to determine the insulation closeness of the current insulation state in the power prefabricated cabin based on the insulation state characteristics and each entropy weight balance value, and when the insulation state of the power prefabricated cabin is within an abnormal range, a graded warning is issued for the insulation state of the power prefabricated cabin based on the insulation closeness.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the electric prefabricated cabin insulation status graded early warning method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions or codes, and when the instructions or codes are executed on a computer, the computer implements the method for graded early warning of insulation status of a prefabricated electric power cabin according to any one of claims 1 to 7.

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