Method and device for determining insulation state of equipment and electronic equipment

Abstract

The invention discloses a method and device for determining the insulation state of equipment and electronic equipment. The method comprises the following steps: acquiring an insulation state determination request; in response to the insulation state determination request, acquiring electric power data corresponding to the target equipment in a preset time period according to the equipment identifier; according to the power data, determining a plurality of actual feature values corresponding to the plurality of types of feature items of the target equipment in a predetermined time period; determining actual parameter coordinates respectively corresponding to the plurality of actual characteristic values in a unified coordinate system; determining an insulation influence parameter corresponding to the target equipment according to the plurality of actual parameter coordinates; and determining an insulation state corresponding to the target equipment according to the insulation influence parameter. The technical problem that the accuracy of the determined insulation state of the power equipment is low due to the fact that the correlation between the power data is difficult to determine in the related technology is solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method and device for determining the insulation status of equipment, and electronic equipment. Background Art

[0002] Determining the insulation status of equipment is crucial for ensuring safe operation and preventing electrical failures and accidents. Currently, insulation status assessment relies primarily on power equipment data. However, in the complex operating environments of power equipment, correlation between corresponding power data is difficult to determine, resulting in low accuracy in the insulation status of the equipment.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0004] Embodiments of the present invention provide a method, apparatus, and electronic device for determining the insulation status of equipment, to at least solve the technical problem in related technologies that it is difficult to determine the correlation between power data, resulting in low accuracy in determining the insulation status of power equipment.

[0005] According to one aspect of an embodiment of the present invention, a method for determining the insulation status of a device is provided, comprising: obtaining an insulation status determination request, wherein the insulation status determination request carries a device identification of a target device; in response to the insulation status determination request, obtaining power data corresponding to the target device within a predetermined time period based on the device identification; determining, based on the power data, a plurality of actual feature values corresponding to multiple categories of feature items of the target device within the predetermined time period, wherein feature indexes corresponding to the multiple categories of feature items are greater than an index threshold, and the corresponding feature indexes are used to indicate the degree of influence of the corresponding feature items on the insulation status of the device; determining actual parameter coordinates corresponding to the multiple actual feature values in a unified coordinate system; determining, based on the multiple actual parameter coordinates, an insulation influence parameter corresponding to the target device; and determining, based on the insulation influence parameter, the insulation status corresponding to the target device.

[0006] Optionally, the insulation influence parameter corresponding to the target device is determined based on multiple actual parameter coordinates, including: determining a target feature item from the multiple categories of feature items based on the multiple actual parameter coordinates, wherein the target feature item is a feature item whose influence index on the insulation state of the target device is greater than a predetermined influence threshold; calling a target model corresponding to the target feature item, wherein the target model is determined based on multiple sample parameter coordinates corresponding to the target feature item and the device insulation parameters corresponding to the multiple sample parameter coordinates respectively; determining the insulation influence parameter corresponding to the target device based on the actual parameter coordinates corresponding to the target feature item and the target model.

[0007] Optionally, determining the actual parameter coordinates corresponding to the multiple actual eigenvalues in a unified coordinate system includes: when the unified coordinate system is a polar coordinate system and the multiple categories of feature items respectively include corresponding first sub-feature items and corresponding second sub-feature items, determining the first sub-feature items corresponding to the multiple categories of feature items and the second sub-feature items corresponding to the multiple categories of feature items, wherein the corresponding first sub-feature item is a feature item for determining signal strength among the multiple sub-feature items of the corresponding feature item, and the corresponding second sub-feature item is a feature item for determining signal phase among the multiple sub-feature items of the corresponding feature item; determining the actual eigenvalue of the corresponding first sub-feature item in the predetermined time period to obtain a first eigenvalue, and determining the actual eigenvalue of the corresponding second sub-feature item in the predetermined time period to obtain a second eigenvalue; determining the polar diameter value of the corresponding first sub-feature item in the polar coordinate system based on the first eigenvalue, and determining the polar angle value of the corresponding first sub-feature item in the polar coordinate system based on the second eigenvalue; determining the corresponding actual parameter coordinates based on the polar diameter value and the polar angle value.

[0008] Optionally, determining the actual feature value of the corresponding first sub-feature item in the predetermined time period to obtain the first feature value, and determining the actual feature value of the corresponding second sub-feature item in the predetermined time period to obtain the second feature value, includes: when the corresponding target sub-feature item includes multiple, determining the first weight values corresponding to the multiple target sub-feature items respectively, wherein the target sub-feature item includes at least one of the following: the first sub-feature item, the second sub-feature item; determining the target feature value based on the actual feature values corresponding to the multiple target sub-feature items in the predetermined time period and the first weight values corresponding to the target sub-feature items respectively, wherein the target feature value includes at least one of the following: the first feature value, the second feature value.

[0009] Optionally, based on the power data, multiple actual characteristic values corresponding to multiple types of characteristic items of the target device within the predetermined time period are determined, including: determining characteristic data based on the power data, wherein the amount of interference data corresponding to the characteristic data is lower than a predetermined threshold; determining multiple initial characteristic values corresponding to multiple types of characteristic items of the target device within the predetermined time period based on the characteristic data; determining an error correction coefficient corresponding to the power data; and determining multiple actual characteristic values corresponding to multiple types of characteristic items of the target device within the predetermined time period based on the error correction coefficient and the multiple initial characteristic values.

[0010] Optionally, determining the error correction coefficient corresponding to the power data includes: determining an initial correction coefficient, wherein the initial correction coefficient is determined based on sample data, and the sample data includes sample test data and sample actual data corresponding to the sample test data; determining predicted characteristic data based on the initial correction coefficient and the sample test data; determining the difference between the predicted characteristic data and the sample actual data to obtain a data difference; and when the data difference is less than a difference threshold, determining the initial correction coefficient as the error correction coefficient.

[0011] Optionally, determining the insulation state corresponding to the target device based on the insulation influencing parameters includes: when the insulation influencing parameters include multiple, determining the second weight values corresponding to the multiple insulation influencing parameters respectively; determining the insulation state corresponding to the target device based on the multiple insulation influencing parameters and the second weight values corresponding to the multiple insulation influencing parameters respectively.

[0012] According to one aspect of an embodiment of the present invention, a device for determining the insulation status of a device is provided, comprising: an acquisition module for acquiring an insulation status determination request, wherein the insulation status determination request carries a device identification of a target device; a response module for acquiring, in response to the insulation status determination request, power data corresponding to the target device within a predetermined time period based on the device identification; a first determination module for determining, based on the power data, a plurality of actual characteristic values corresponding to multiple categories of characteristic items of the target device within the predetermined time period, wherein characteristic indexes corresponding to the multiple categories of characteristic items are greater than an index threshold, and the corresponding characteristic indexes are used to indicate the degree of influence of the corresponding characteristic items on the insulation status of the device; a second determination module for determining actual parameter coordinates corresponding to the multiple actual characteristic values in a unified coordinate system; a third determination module for determining, based on the multiple actual parameter coordinates, the insulation influence parameters corresponding to the target device; and a fourth determination module for determining the insulation status corresponding to the target device based on the insulation influence parameters.

[0013] According to one aspect of an embodiment of the present invention, an electronic device is provided, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement any of the above methods for determining the insulation status of a device.

[0014] According to one aspect of an embodiment of the present invention, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can execute any of the above-mentioned methods for determining the insulation status of a device.

[0015] In an embodiment of the present invention, an insulation state determination request is obtained, wherein the insulation state determination request carries a device identification of a target device; in response to the insulation state determination request, power data corresponding to the target device within a predetermined time period is obtained based on the device identification; based on the power data, a plurality of actual characteristic values corresponding to multiple types of characteristic items of the target device within the predetermined time period are determined, wherein characteristic indexes corresponding to the multiple types of characteristic items are greater than an index threshold, and the corresponding characteristic indexes are used to indicate the degree of influence of the corresponding characteristic items on the insulation state of the device; actual parameter coordinates corresponding to the multiple actual characteristic values in a unified coordinate system are determined; based on the multiple actual parameter coordinates, the insulation influence parameters corresponding to the target device are determined; based on the insulation influence parameters, the insulation state corresponding to the target device is determined. In this way, by determining the actual parameter coordinates corresponding to multiple actual eigenvalues in a unified coordinate system, the purpose of determining the insulation influence parameters corresponding to the target device based on multiple actual parameter coordinates is achieved. By converting the characteristic parameters corresponding to multiple types of signal characteristics of the target device into the target space coordinate system, the parameter coordinates corresponding to the multiple characteristics are obtained, the characteristic parameter information of multiple dimensions is simplified, the dimension of the data is reduced, the relationship between the characteristic parameters is intuitively reflected, and the accuracy of the determined insulation state is improved, thereby achieving the technical effect of determining the insulation state corresponding to the target device based on the insulation influence parameters, and further solving the technical problem in the related art that it is difficult to determine the correlation between the power data, resulting in low accuracy of the determined insulation state of the power equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0017] Figure 1 is a flow chart of a method for determining the insulation status of equipment according to an embodiment of the present invention;

[0018] Figure 2 This is a flow chart of a method for risk assessment of insulation status of power equipment provided by an optional embodiment of the present invention;

[0019] Figure 3 is a flow chart of a method for obtaining signal characteristic parameters provided by an optional embodiment of the present invention;

[0020] Figure 4 is a flow chart of a method for constructing a polar coordinate vector domain atlas provided by an optional embodiment of the present invention;

[0021] Figure 5 is a flow chart of a method for obtaining insulation influencing parameters provided by an optional embodiment of the present invention;

[0022] Figure 6is a flow chart of a method for determining the insulation status of an electric power device provided in an optional embodiment of the present invention;

[0023] Figure 7 It is a structural block diagram of a device for determining the insulation status of equipment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] Example 1

[0027] According to an embodiment of the present invention, an embodiment of a method for determining the insulation status of a device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0028] Figure 1 Flowchart of a method for determining the insulation status of a device according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0029] Step S102: Obtain an insulation status determination request, wherein the insulation status determination request carries a device identification of a target device.

[0030] In step S102 provided in the present application, an insulation state determination request is obtained.

[0031] Among them, an insulation status determination request is involved. The insulation status determination request refers to a request for obtaining insulation status assessment information of the target device, which is usually initiated when the health status, maintenance requirements or risk level of the power equipment needs to be assessed.

[0032] Among them, the target device is involved, and the target device refers to the power equipment whose insulation status needs to be determined.

[0033] Among them, the device identification is involved. The device identification refers to the device number that uniquely identifies the target device, such as the device's serial number, location code, type code, etc.

[0034] In this step, an insulation status determination request is received to determine the insulation status of the target device. This request contains the target device's device identifier, enabling the identification of the specific power device. The necessary information is extracted from the device's data stream to analyze and evaluate the insulation status. This is a key step in implementing device status monitoring and intelligent operation and maintenance.

[0035] Step S104 : In response to the insulation status determination request, power data corresponding to the target device within a predetermined time period is acquired according to the device identification.

[0036] In step S104 provided in the present application, power data corresponding to the target device within a predetermined period of time is obtained.

[0037] Among them, power data is involved. Power data refers to various measurement data related to the operation of power equipment and its insulation status, such as partial discharge data, insulation resistance data, dielectric loss factor, etc.

[0038] In this step, after receiving the insulation status determination request, all relevant power data corresponding to the target device within a predetermined time period in the database can be quickly located according to the device identifier specified in the request. The power data provides data support for the evaluation of the insulation status. By analyzing the changing trends and abnormal points of these power data, it can be determined whether the insulation performance of the target device is damaged.

[0039] Step S106: Determine, based on the power data, multiple actual characteristic values corresponding to multiple types of characteristic items of the target device within a predetermined time period, wherein the characteristic indexes corresponding to the multiple types of characteristic items are greater than the index threshold, and the corresponding characteristic indexes are used to indicate the degree of influence of the corresponding characteristic items on the insulation state of the device.

[0040] In step S106 provided in the present application, a plurality of actual feature values corresponding to the plurality of feature items of the target device within a predetermined time period are determined.

[0041] Among them, feature items are involved. Feature items refer to various indicators used to describe the insulation status of the target equipment, such as partial discharge characteristics, insulation resistance characteristics, dielectric loss factor characteristics, etc.

[0042] Among them, actual characteristic values are involved. Actual characteristic values refer to the specific values of multiple types of characteristic items obtained by analyzing power data within a predetermined time period, such as the discharge frequency and discharge amplitude of partial discharge, the resistance value of insulation resistance, the value of dielectric loss factor, etc.

[0043] Among them, the characteristic index is involved. The characteristic index refers to an indicator that quantifies the degree of influence of multiple characteristic items on the insulation state of power equipment. Different characteristic items have different characteristic indexes, which depends on their contribution and correlation to the insulation state.

[0044] Among them, the index threshold is involved. The index threshold refers to a preset value used to determine whether a feature item is important and whether it needs attention. Only feature items with a feature index greater than the index threshold will be further analyzed and used to avoid irrelevant or weakly influential data interfering with the evaluation results.

[0045] In this step, the characteristic indices corresponding to multiple feature items are first compared with the index thresholds to identify the feature items that significantly affect the insulation status. Next, the current values of the feature items that are highly correlated with the insulation status of the equipment are screened from the large amount of power data to obtain multiple actual feature values.

[0046] Through this step, the key characteristic items that significantly affect the insulation status of the equipment are identified, avoiding indiscriminate analysis of all data and improving evaluation efficiency and accuracy. At the same time, the actual characteristic values of multiple types of characteristic items are determined, providing data support for subsequent evaluation of the insulation status of the equipment.

[0047] Step S108: determining actual parameter coordinates corresponding to the plurality of actual eigenvalues in the unified coordinate system.

[0048] In step S108 provided in the present application, actual parameter coordinates corresponding to a plurality of actual eigenvalues in a unified coordinate system are determined.

[0049] Among them, a unified coordinate system is involved. The unified coordinate system refers to a common numerical space that can accommodate and represent different types of eigenvalues, such as the polar coordinate system.

[0050] Among them, the actual parameter coordinates are involved. The actual parameter coordinates refer to the numerical coordinates corresponding to each actual eigenvalue in the unified coordinate system.

[0051] Through this step, the various actual eigenvalues obtained from the power equipment are converted into a unified coordinate system (such as a polar coordinate system) to determine the specific position of each eigenvalue in this coordinate system, that is, the actual parameter coordinate. This process is a key step in standardizing and uniformly representing different types of signal characteristic parameters, laying the foundation for subsequent comprehensive analysis. By unifying multiple types of signal characteristic parameters into the same coordinate system, the obstacles to representation and comparison caused by different data types are eliminated, allowing different eigenvalues to be directly compared and analyzed, and can intuitively display the distribution and change trends of the eigenvalues, improving data comparability and analysis efficiency.

[0052] Step S110 : determining insulation impact parameters corresponding to the target device according to a plurality of actual parameter coordinates.

[0053] In step S110 provided in the present application, the insulation impact parameter corresponding to the target device is determined.

[0054] Among them, the insulation impact parameter is involved. The insulation impact parameter refers to a numerical value that can quantitatively describe the degree of degradation of the insulation performance of the equipment under the influence of factors such as thermal effects and mechanical stress.

[0055] In this step, the insulation hard parameters corresponding to the target equipment are determined by analyzing multiple actual parameter coordinates. The insulation influencing parameters include the influence of various factors on the insulation performance, such as the thermal effect caused by partial discharge and crack expansion caused by mechanical stress. They can quantify the insulation status of the target equipment, improve the accuracy of the insulation status of the power equipment determined subsequently, and avoid the misjudgment caused by traditional reliance on experience or a single indicator.

[0056] Step S112: determining the insulation status corresponding to the target device according to the insulation impact parameter.

[0057] In step S112 provided in the present application, the insulation status corresponding to the target device is determined.

[0058] In this step, the current insulation state of the target power equipment can be further determined based on the previously calculated insulation influencing parameters (such as thermal effect insulation parameters, stress insulation parameters, etc.).

[0059] Through the above steps S102-S112, an insulation state determination request can be obtained, wherein the insulation state determination request carries the device identification of the target device; in response to the insulation state determination request, power data corresponding to the target device within a predetermined time period is obtained based on the device identification; based on the power data, multiple actual characteristic values corresponding to multiple types of characteristic items of the target device within the predetermined time period are determined, wherein the characteristic indexes corresponding to the multiple types of characteristic items are greater than the index threshold, and the corresponding characteristic indexes are used to indicate the degree of influence of the corresponding characteristic items on the insulation state of the device; actual parameter coordinates corresponding to the multiple actual characteristic values in a unified coordinate system are determined; based on the multiple actual parameter coordinates, the insulation influence parameters corresponding to the target device are determined; based on the insulation influence parameters, the corresponding The insulation state method achieves the purpose of determining the insulation influencing parameters corresponding to the target device based on multiple actual parameter coordinates by determining the actual parameter coordinates corresponding to multiple actual characteristic values in a unified coordinate system. By converting the characteristic parameters corresponding to multiple types of signal characteristics of the target device into the target space coordinate system, the parameter coordinates corresponding to multiple characteristics are obtained, the characteristic parameter information of multiple dimensions is simplified, the dimension of the data is reduced, the relationship between the characteristic parameters is intuitively reflected, and the accuracy of the determined insulation state is improved, thereby achieving the technical effect of determining the insulation state corresponding to the target device based on the insulation influencing parameters, and thus solving the technical problem in the related technology that it is difficult to determine the correlation between the power data, resulting in low accuracy of the determined insulation state of the power equipment.

[0060] As an optional embodiment, the insulation influence parameter corresponding to the target device is determined based on multiple actual parameter coordinates, including: determining a target feature item from multiple categories of feature items based on the multiple actual parameter coordinates, wherein the target feature item is a feature item whose influence index on the insulation state of the target device is greater than a predetermined influence threshold; calling a target model corresponding to the target feature item, wherein the target model is determined based on multiple sample parameter coordinates corresponding to the target feature item and the device insulation parameters corresponding to the multiple sample parameter coordinates; determining the insulation influence parameter corresponding to the target device based on the actual parameter coordinates corresponding to the target feature item and the target model.

[0061] In this embodiment, specific steps of determining the insulation influencing parameters corresponding to the target device based on a plurality of actual parameter coordinates are described.

[0062] Among them, target feature items are involved. Target feature items refer to feature items that are screened out from multiple categories of feature items and have a significant impact on the insulation status of the target equipment.

[0063] Among them, the impact index is involved. The impact index refers to an indicator used to measure the degree of influence of a single feature item among multiple categories of feature items on the insulation status of the target equipment.

[0064] This involves a predetermined impact threshold, which serves as a standard for screening feature items. Only when the feature's impact index exceeds this threshold is it considered a target feature, meaning one with a significant impact on insulation condition. This threshold can be flexibly adjusted based on factors such as the type of power equipment, operating environment, and historical data.

[0065] Among them, the target model is involved. The target model refers to a model used to predict the insulation influencing parameters of the target equipment based on the actual parameter coordinates of the target characteristic items.

[0066] Among them, the sample parameter coordinates are involved, and the sample parameter coordinates refer to the parameter coordinates of the historical characteristic values corresponding to the target feature items in a unified coordinate system.

[0067] Among them, the equipment insulation parameters are involved, and the equipment insulation parameters refer to the equipment insulation parameters of the power equipment corresponding to the historical characteristic values.

[0068] In this step, by analyzing multiple actual parameter coordinates, the influence indexes corresponding to multiple types of feature items are determined, and the multiple influence indices are compared with the predetermined influence threshold value to screen out the feature items that have a significant impact on the insulation state of the target equipment, that is, the target feature items. For the determined target feature items, the corresponding target model is called. The target model is trained based on the sample parameter coordinates related to the target feature items in the historical data and the equipment insulation parameters corresponding to these coordinates. It can more accurately quantify the impact of the target feature items on the insulation state of the target equipment. By inputting the actual parameter coordinates corresponding to the target feature items into the target model, a quantitative indicator reflecting the degree of influence of the target feature items on the insulation state of the target equipment can be determined, that is, the insulation influence parameter. The insulation influence parameters include thermal effect insulation parameters, stress insulation parameters, etc., which can directly reflect the risk level of the insulation state of the equipment and provide a scientific basis for subsequent risk assessment and early warning.

[0069] Through this step, the characteristic items that have a significant impact on the insulation status of the power equipment, namely the target characteristic items, are determined. The target model is called to analyze the relationship between the target characteristic items and the insulation influencing parameters to obtain accurate insulation influencing parameter values, thereby improving the accuracy of the determined insulation status.

[0070] As an optional embodiment, determining the actual parameter coordinates corresponding to multiple actual eigenvalues in a unified coordinate system includes: when the unified coordinate system is a polar coordinate system and multiple categories of feature items respectively include corresponding first sub-feature items and corresponding second sub-feature items, determining the first sub-feature items corresponding to the multiple categories of feature items and the second sub-feature items corresponding to the multiple categories of feature items, wherein the corresponding first sub-feature item is a feature item for determining signal strength among multiple sub-feature items of the corresponding feature item, and the corresponding second sub-feature item is a feature item for determining signal phase among multiple sub-feature items of the corresponding feature item; determining the actual eigenvalue of the corresponding first sub-feature item in a predetermined time period to obtain a first eigenvalue, and determining the actual eigenvalue of the corresponding second sub-feature item in a predetermined time period to obtain a second eigenvalue; determining the polar diameter value of the corresponding first sub-feature item in the polar coordinate system based on the first eigenvalue, and determining the polar angle value of the corresponding first sub-feature item in the polar coordinate system based on the second eigenvalue; determining the corresponding actual parameter coordinates based on the polar diameter value and the polar angle value.

[0071] In this embodiment, specific steps of determining actual parameter coordinates corresponding to a plurality of actual eigenvalues when the unified coordinate system is a polar coordinate system are described.

[0072] Among them, the first sub-feature item is involved, and the first sub-feature item refers to a feature item used to determine signal strength, such as partial discharge amplitude, insulation resistance value, etc.

[0073] Among them, the second sub-feature item is involved, and the second sub-feature item refers to a feature item used to determine the signal phase, such as the partial discharge phase, partial discharge frequency, etc.

[0074] Among them, the first characteristic value is involved, and the first characteristic value refers to the actual characteristic value corresponding to the first sub-characteristic item in the power data of the target device.

[0075] Among them, the second characteristic value is involved, and the second characteristic value refers to the actual characteristic value corresponding to the second sub-characteristic item in the power data of the target device.

[0076] In this step, each characteristic item related to the insulation state is decomposed into a first sub-characteristic item and a second sub-characteristic item. The first sub-characteristic item represents signal strength, while the second sub-characteristic item represents signal phase. The actual characteristic values of the first and second sub-characteristic items within a predetermined time period are determined, namely the first and second characteristic values. These values directly reflect the insulation state and changes of the equipment during that period. Based on the first characteristic value, the polar diameter value in the polar coordinate system is determined. The polar diameter value reflects the signal strength. Based on the second characteristic value, the polar angle value in the polar coordinate system is determined. The polar angle value describes the signal phase. Finally, based on the polar diameter value and polar angle value, the actual parameter coordinates of each actual characteristic value are determined in the polar coordinate system.

[0077] Through this step, different types of eigenvalues are converted into a unified polar coordinate system. The polar coordinate system can represent the signal strength and phase information at the same coordinate point, so that the distribution and change trend of the eigenvalues can be observed intuitively, which is convenient for visual analysis and risk identification on the map, and facilitates the standardization and unified analysis of the data, eliminating the comparison barriers between eigenvalues due to different types.

[0078] As an optional embodiment, determining the actual characteristic value of the corresponding first sub-feature item in a predetermined time period to obtain the first characteristic value, and determining the actual characteristic value of the corresponding second sub-feature item in the predetermined time period to obtain the second characteristic value, includes: when the corresponding target sub-feature item includes multiple, determining the first weight values corresponding to the multiple target sub-feature items respectively, wherein the target sub-feature item includes at least one of the following: the first sub-feature item, the second sub-feature item; determining the target characteristic value based on the actual characteristic values corresponding to the multiple target sub-feature items in the predetermined time period and the first weight values corresponding to the target sub-feature items respectively, wherein the target characteristic value includes at least one of the following: the first characteristic value, the second characteristic value.

[0079] In this embodiment, specific steps of determining first weight values respectively corresponding to a plurality of target sub-feature items are described when the corresponding target sub-feature items include a plurality of target sub-feature items.

[0080] In this step, the specific measured value of the target sub-feature item during a predetermined time period, i.e., the actual feature value, is first determined. If the target sub-feature item includes multiple items, such as partial discharge features including discharge amplitude, discharge frequency, and discharge phase, the actual feature value of each target sub-feature item is determined separately. Next, a first weight value corresponding to each target sub-feature item is determined to reflect its relative importance in the insulation condition assessment. Finally, based on the multiple actual feature values and the multiple first weight values, the target feature value is comprehensively determined to ensure that the assessment results fully reflect the actual insulation condition of the equipment.

[0081] Through this step, different target sub-feature items are assigned different first weight values. The setting of the first weight value enables the evaluation process to finely consider the impact of each characteristic indicator on the insulation status, and to perform weighted comprehensive analysis, thereby avoiding the limitations of single characteristic item evaluation and improving the comprehensiveness and reliability of the evaluation results.

[0082] As an optional embodiment, based on the power data, multiple actual characteristic values corresponding to multiple types of characteristic items of the target device within a predetermined time period are determined, including: determining characteristic data based on the power data, wherein the amount of interference data corresponding to the characteristic data is lower than a predetermined threshold; determining multiple initial characteristic values corresponding to multiple types of characteristic items of the target device within a predetermined time period based on the characteristic data; determining an error correction coefficient corresponding to the power data; and determining multiple actual characteristic values corresponding to multiple types of characteristic items of the target device within a predetermined time period based on the error correction coefficient and the multiple initial characteristic values.

[0083] In this embodiment, specific steps of determining a plurality of actual characteristic values corresponding to a plurality of characteristic items of a target device within a predetermined time period according to power data are described.

[0084] Among them, feature data is involved. Feature data refers to data extracted from power data and closely related to feature items that affect the insulation status of the target equipment.

[0085] Among them, the amount of interference data is involved. The amount of interference data refers to the total amount of interference data such as environmental noise, electromagnetic interference, etc. mixed into the power data during the power data collection process.

[0086] Among them, a predetermined threshold is involved. The predetermined threshold refers to a set value used to determine whether the amount of interference data in the power data is within an acceptable range. Feature data with an amount of interference data lower than the predetermined threshold is considered to have less interference and can be further analyzed.

[0087] Among them, the initial eigenvalue is involved, and the initial eigenvalue refers to the numerical value of the feature item directly determined based on the feature data.

[0088] Among them, the error correction coefficient is involved. The error correction coefficient refers to a mathematical parameter used to correct errors in measurement data and improve data accuracy.

[0089] In this step, first, the characteristic data with low interference is identified and separated from the collected power data to ensure data quality for subsequent analysis. Next, based on this characteristic data, the initial characteristic values of multiple characteristic items of the target device within a predetermined time period are calculated. This is a preliminary assessment of the device status before error correction is performed. Next, the error correction coefficient is determined. This error correction coefficient is used to correct errors caused by the measurement environment and device characteristics, thereby improving the accuracy of the characteristic values. Finally, by applying the error correction coefficient to the initial characteristic values, the actual characteristic values of the multiple characteristic items of the target device within the predetermined time period are obtained. These serve as key input data for evaluating the insulation status of the power equipment and can more accurately reflect the insulation performance of the equipment.

[0090] This step filters out feature data with interference below a predetermined threshold, effectively improving data purity and reducing measurement errors caused by interference signals, ensuring the accuracy and reliability of the final analysis results. Determining an error correction coefficient and applying it to the initial feature value effectively corrects for systematic errors caused by varying measurement conditions, bringing the actual feature value closer to the device's true state and improving the accuracy of insulation condition assessment.

[0091] As an optional embodiment, determining the error correction coefficient corresponding to the power data includes: determining an initial correction coefficient, wherein the initial correction coefficient is determined based on sample data, and the sample data includes sample test data and sample actual data corresponding to the sample test data; determining predicted characteristic data based on the initial correction coefficient and the sample test data; determining the difference between the predicted characteristic data and the sample actual data to obtain a data difference; and when the data difference is less than a difference threshold, determining the initial correction coefficient as the error correction coefficient.

[0092] In this embodiment, the specific steps of determining the error correction coefficient corresponding to the power data are described.

[0093] Among them, the initial correction coefficient is involved. The initial correction coefficient refers to a parameter determined based on sample data and used to correct the error of the collected initial feature data.

[0094] Among them, sample test data is involved. Sample test data refers to historical feature data that is directly collected and has not been error-corrected.

[0095] Among them, the actual sample data is involved, and the actual sample data refers to the actual feature data corresponding to the sample test data and with the accuracy meeting the requirements.

[0096] Among them, predicted characteristic data is involved. Predicted characteristic data refers to the characteristic data obtained after error correction of sample test data using the initial correction coefficient.

[0097] The difference threshold is a preset value used to determine whether the correction result is acceptable when the data difference is less than the difference threshold.

[0098] In this step, an initial correction coefficient is first determined based on the sample data. Next, this initial correction coefficient is input into the model along with the sample test data to calculate the predicted characteristic data, i.e., the characteristic value predicted by the model. Subsequently, the model's prediction accuracy is evaluated by comparing the difference between the predicted characteristic data and the actual sample data (the data difference). If the data difference is less than a pre-set difference threshold, indicating that the difference between the model's prediction result and the actual data is within an acceptable range, the initial correction coefficient is then confirmed as the error correction coefficient and used for subsequent actual data correction or model prediction.

[0099] Through this step, the initial correction coefficient is trained and optimized to improve the accuracy of the determined error correction coefficient, so that the actual characteristic value subsequently determined by the error correction coefficient is more accurate, thereby improving the accuracy of the determined insulation state.

[0100] As an optional embodiment, determining the insulation state corresponding to the target device based on the insulation influencing parameters includes: when there are multiple insulation influencing parameters, determining the second weight values corresponding to the multiple insulation influencing parameters respectively; determining the insulation state corresponding to the target device based on the multiple insulation influencing parameters and the second weight values corresponding to the multiple insulation influencing parameters respectively.

[0101] In this embodiment, specific steps of determining the insulation status corresponding to the target device according to the insulation influencing parameters are described.

[0102] Among them, the second weight value is involved, and the second weight value refers to the relative proportion of multiple insulation influencing parameters in determining the insulation state.

[0103] In this step, when there are multiple insulation influencing parameters (such as thermal effect insulation parameters, stress insulation parameters, etc.), it is necessary to determine the second weight value of each insulation influencing parameter. Next, each insulation influencing parameter is weighted and summed with its corresponding second weight value to obtain a comprehensive insulation status score. Finally, the insulation status of the target power equipment is determined based on the insulation status score. Through this step, a weight is assigned to each insulation influencing parameter. The evaluation process takes into account the relative importance of different factors, avoiding the problem of a single factor dominating the evaluation result, making the insulation status evaluation more comprehensive and objective.

[0104] Based on the above embodiment and optional embodiment, an optional implementation manner is provided, which is described in detail below.

[0105] Traditional methods for assessing the insulation condition of power equipment rely on a single type of data or employ simple analytical methods, making it difficult to comprehensively and accurately assess insulation condition risks. Power equipment operates in complex environments, and the resulting data, such as partial discharge, insulation resistance, and dielectric loss factor, is often subject to noise, reducing data accuracy and reliability. Furthermore, existing methods lack comprehensive data analysis capabilities and are unable to fully explore potential connections between data. This makes it difficult to quantitatively assess insulation condition risks in power equipment, impacting the reliability and continuity of power supply.

[0106] In view of this, an optional embodiment of the present invention provides a method for evaluating the insulation status risk of power equipment based on polar coordinate vector domain analysis, which can comprehensively and accurately evaluate the insulation status risk of power equipment. Figure 2 Flowchart of the method for risk assessment of insulation status of power equipment provided by an optional embodiment of the present invention, such as Figure 2 As shown, it includes obtaining a variety of power data related to the insulation status during the operation of the power equipment, the power data including at least one of partial discharge data, insulation resistance data and dielectric loss factor; performing interference separation and feature extraction on the power data to obtain signal characteristic parameters related to the insulation status (the same as the above-mentioned actual characteristic values), the signal characteristic parameters including at least one of partial discharge characteristics, insulation resistance characteristics and dielectric loss factor characteristics; converting the obtained signal characteristic parameters into a polar coordinate vector domain (the same as the above-mentioned unified coordinate system) to construct a corresponding polar coordinate vector domain map; performing feature screening on the polar coordinate vector domain map and inputting it into a trained insulation impact assessment model (the same as the above-mentioned target model) to obtain insulation impact parameters; inputting the insulation impact parameters into a trained power risk assessment model to obtain a comprehensive risk score for the insulation status of the power equipment.

[0107] S1. Obtain an insulation status determination request.

[0108] S2. In response to the insulation status determination request, obtain power data corresponding to the target device within a predetermined time period based on the device identification.

[0109] Acquire various power data related to the insulation status of power equipment during operation; this power data includes at least one of partial discharge data, insulation resistance data, and dielectric loss factor. Partial discharge data can reflect the discharge conditions within the insulation, such as discharge amplitude, frequency, and phase distribution. Monitoring this data provides a direct understanding of the activity of insulation defects. Insulation resistance data is a key indicator for measuring insulation performance. Its value directly reflects the insulation material's ability to prevent current leakage, and the changing trend of the resistance value can effectively reflect insulation aging or moisture. The dielectric loss factor reflects the energy loss of the insulation material under an AC electric field. An abnormal increase in this value indicates internal insulation defects or increased aging.

[0110] The collected power data is often accompanied by various interference signals, which seriously affect the accurate assessment of the insulation status of power equipment. Therefore, professional data processing technology is needed to separate the interference of power data. For example, electromagnetic interference, power frequency noise, etc. in the data can be removed through bandpass filtering, wavelet denoising and other methods. After interference separation, feature extraction is performed. For partial discharge data, features such as discharge amplitude, discharge frequency, and discharge phase can be extracted. The larger the discharge amplitude and the higher the frequency, the more serious the insulation defect; from the insulation resistance data, features such as the resistance value and the rate of change of resistance over time can be obtained. A decrease in resistance value or an abnormal rate of change may indicate a decrease in insulation performance; for the dielectric loss factor, features such as its numerical value, change trend, and fluctuation under different voltages are extracted.

[0111] Interference suppression processing is performed on the partial discharge data based on the interference type to obtain partial discharge parameters after interference separation (the same as the above-mentioned characteristic data).

[0112] Figure 3 is a flow chart of a method for obtaining signal characteristic parameters provided by an optional embodiment of the present invention, such as Figure 3 As shown in the figure, spectral analysis is first used to identify partial discharge data within processed power data, thereby identifying corresponding interference types, such as periodic narrowband interference, pulse-shaped interference, and white noise interference. The interference signal sources, characteristics, and aliasing signal characteristics of partial discharge data during power equipment operation are analyzed, and interference suppression methods such as equivalent time-equivalent frequency are used to improve the anti-interference capability of partial discharge detection.

[0113] Then, according to the identified interference type, frequency domain filtering, wavelet transform and adaptive filtering methods are used to perform interference suppression on the partial discharge data, and then the partial discharge parameters after interference separation are obtained.

[0114] At the same time, electromagnetic shielding and differential processing are used to perform interference separation on the insulation resistance data and dielectric loss factor in the processed power data, thereby obtaining insulation resistance parameters and dielectric loss factor parameters that eliminate common-mode interference. Subsequently, feature extraction is performed on the partial discharge parameters after interference separation, the insulation resistance parameters after common-mode interference elimination, and the dielectric loss factor parameters, respectively, to obtain their respective characteristics. Partial discharge characteristics include discharge time domain characteristics, frequency domain characteristics, and phase characteristics; insulation resistance characteristics include insulation resistance value and insulation resistance change rate; and dielectric loss factor characteristics include dielectric loss factor and dielectric loss factor change trend. Finally, the partial discharge characteristics, insulation resistance characteristics, and dielectric loss factor characteristics are summarized and a data set is constructed to obtain signal characteristic parameters related to the insulation state.

[0115] This data is mined from various perspectives, encompassing key information such as discharge characteristics, resistance changes, and dielectric loss trends. This provides reliable data support for subsequent polar coordinate vector domain analysis, insulation impact assessment, and risk assessment, effectively ensuring the accuracy of insulation status assessments for power equipment and laying a solid foundation for the safe and stable operation of power systems.

[0116] Electromagnetic shielding and differential processing methods are used to separate the insulation resistance data and dielectric loss factor in the processed power data, and the insulation resistance parameters and dielectric loss factor parameters that eliminate common mode interference are obtained.

[0117] S3. Determine, based on the power data, a plurality of actual characteristic values corresponding to the plurality of characteristic items of the target device within a predetermined time period.

[0118] The separated partial discharge parameters are subjected to feature extraction to obtain signal feature parameters related to the insulation state (the same as the initial feature values mentioned above), wherein the signal feature parameters include at least one of partial discharge features, insulation resistance features, and dielectric loss factor features.

[0119] Feature quantities closely related to the insulation state are extracted from the collected data, such as the amplitude, frequency, and phase characteristics of partial discharge, and the changing trends of insulation resistance and dielectric loss factor. Feature extraction is performed on the insulation resistance parameters after common-mode interference is eliminated, resulting in insulation resistance characteristics; these characteristics include the insulation resistance value and the insulation resistance change rate. Feature extraction is performed on the dissipation factor parameters after common-mode interference is eliminated, resulting in dielectric loss factor characteristics; these characteristics include the dielectric loss factor and the dielectric loss factor changing trend. A data set is constructed based on the partial discharge characteristics, insulation resistance characteristics, and dielectric loss factor characteristics to obtain signal characteristic parameters related to the insulation state.

[0120] The signal characteristic parameters are subjected to error construction to obtain the corresponding parameter error model. Among them, for the partial discharge amplitude of the discharge time domain characteristic in the signal characteristic parameters, the measured partial discharge amplitude is:

[0121] A measured =K A *A ture +Offset A

[0122] Among them, A measured Indicates the measured partial discharge amplitude, A ture Indicates the actual partial discharge amplitude. The error correction coefficients include: K A , used to represent the sensitivity coefficient matrix related to the measurement system; Offset A , used to indicate zero point deviation.

[0123] The least squares method is used to estimate the parameters in the parameter error model and the reliability of the signal characteristic parameters is judged according to the preset error threshold to obtain updated signal characteristic parameters (the same as the actual characteristic values mentioned above). The above steps effectively eliminate or correct unreliable data, making the updated signal characteristic parameters closer to the true value, thereby enhancing the reliability and credibility of the data.

[0124] S4. Determine actual parameter coordinates corresponding to the multiple actual eigenvalues in a unified coordinate system.

[0125] Figure 4 is a flow chart of a method for constructing a polar coordinate vector domain atlas provided by an optional embodiment of the present invention, such as Figure 4 As shown, the updated signal characteristic parameters are converted into the polar coordinate vector domain to construct the corresponding polar coordinate vector domain map. The constructed polar coordinate vectors are classified using a machine learning algorithm combined with a threshold judgment method to obtain a vector classification result; the vector classification result includes a typical discharge signal and a noise signal; based on the mapping rule of the vector domain, each updated signal characteristic parameter is converted into a polar coordinate vector to construct a polar coordinate vector; according to the vector classification result, the typical discharge signal and the noise signal are plotted to obtain a polar coordinate vector domain map. According to the vector rule, each set of signal characteristic parameters is converted into a polar coordinate vector to form a polar coordinate vector domain map. In the map, different points represent the power data status at different times or under different measurement conditions. By analyzing the distribution, clustering, change trend and other characteristics of the vectors in the map, more comprehensive and in-depth information support is provided for the evaluation of the insulation status of power equipment.

[0126] S5. Determine the insulation impact parameters corresponding to the target device based on the multiple actual parameter coordinates.

[0127] The polar coordinate vector domain map is subjected to feature screening and input into the trained insulation impact assessment model (same as the target model mentioned above) to obtain the insulation impact parameters.

[0128] The filtered features are fed into a pre-trained insulation impact assessment model. This model simulates the complex relationships between factors like thermal effects and mechanical stress and insulation degradation. Once the filtered features are fed into the model, it performs calculations and analysis based on its internal algorithms and trained parameters, ultimately outputting a series of insulation impact parameters that quantify the impact of thermal effects and stress on insulation degradation.

[0129] Figure 5 is a flow chart of a method for obtaining insulation influencing parameters provided by an optional embodiment of the present invention, such as Figure 5 As shown, the polar coordinate vector domain map is subjected to feature screening and input into the trained insulation impact assessment model to obtain insulation impact parameters, which may include the following steps:

[0130] A1. Determine the target model.

[0131] Based on the insulation characteristics of power equipment and previous research experience, potential features closely related to insulation degradation were identified, such as the amplitude variation range of polar coordinate vectors, the fluctuation frequency of polar angles, and the density distribution of vectors within specific regions of the spectrum. Data mining algorithms, such as association rule mining and principal component analysis, were used to extract key features from the spectrum and remove redundant and irrelevant information. Specifically, thermal effect data and stress data were acquired separately and then processed using filtering and smoothing techniques to obtain processed thermal effect parameters and stress parameters.

[0132] Acquire thermal effect data, including partial discharge power and ambient temperature. Acquire stress data, including mechanical stress, crack length, and fatigue life. Process the thermal effect and stress data using filtering and smoothing techniques to obtain processed thermal effect and stress parameters.

[0133] The calculation formula of thermal effect insulation parameters is:

[0134]

[0135] Among them, RE is the thermal effect insulation parameter, W1 is the proportional constant related to the thermal effect, FP i represents the partial discharge power of the power equipment at the i-th moment, t represents the duration of partial discharge, M represents the quality of the insulation material of the power equipment, C q represents the specific heat capacity of the insulation material of the power equipment, e is an irrational constant, E1 represents the activation energy, Q represents the gas constant, T0 represents the initial temperature of the power equipment, ΔT represents the temperature rise, It represents the expansion trend of the insulation material of power equipment with temperature change, and τ represents the nonlinear index.

[0136] The thermal effect insulation parameter is calculated using a formula that comprehensively considers the insulation material's inherent characteristics and the impact of temperature changes on insulation performance. This formula covers the key factors influencing thermal effects and accurately quantifies their impact on the insulation condition of power equipment. This provides a precise quantitative indicator for evaluating the insulation performance of power equipment, helping operators more accurately assess the risk of insulation degradation due to thermal effects, allowing them to preemptively formulate effective maintenance measures to ensure stable operation of power equipment.

[0137] The calculation formula of stress insulation parameters is:

[0138]

[0139] Where JE represents the stress insulation parameter, W2 represents the proportional constant related to stress, and P iIt represents the stress borne by the insulation material of the power equipment during operation at the i-th moment, O n It indicates the yield strength at which the insulation material of power equipment begins to undergo plastic deformation under the action of external force. x represents the index of the influence of stress on the deterioration of insulation material. L i represents the crack length of the insulation material of the power equipment at the i-th moment, L0 represents the initial crack length in the insulation material of the power equipment, y represents the index of the impact of the crack on insulation degradation, S i Indicates the number of fatigue cycles endured by the insulation material of the power equipment at the i-th moment, S v Indicates the fatigue life of the insulation material.

[0140] Based on polar coordinate vector domain maps, the map features were screened to obtain key vector features related to thermal effects, stress, and insulation degradation. A model was constructed based on thermal effect parameters and stress parameters combined with key vector features to obtain an insulation impact assessment model that reflects the impact of thermal effects and stress on the degree of insulation degradation.

[0141] A2. Determine the target feature item from multiple categories of feature items.

[0142] Based on the polar coordinate vector domain map, the map feature screening is performed to obtain the key vector features of thermal effect, stress and insulation degradation (the same as the above target feature items).

[0143] A3. Retrieve the target model corresponding to the target feature item and determine the insulation impact parameters corresponding to the target device.

[0144] The polar coordinate vectors of the thermal effect parameters and stress parameters are input into the trained insulation impact assessment model to obtain the thermal effect impact degree and the stress impact degree. The thermal effect impact degree and the stress impact degree are calculated respectively using specific formulas to obtain the insulation impact parameters including the thermal effect insulation parameters and the stress insulation parameters.

[0145] Accurately locating the key factors that influence thermal effects and stress on insulation status and constructing an insulation impact assessment model based on this can effectively quantify the impact of thermal effects and stress on the degree of insulation degradation, providing an important basis for the insulation status assessment of power equipment, helping operation and maintenance personnel to predict equipment failure risks in advance and ensure the safe and stable operation of the power system.

[0146] S6. Determine the insulation status of the target device based on the insulation influencing parameters.

[0147] The insulation impact parameters are input into the trained power risk assessment model to obtain a comprehensive risk score for the insulation status of power equipment.

[0148] The power risk assessment model draws on vast amounts of historical data to establish a complex mapping between insulation-affecting parameters and the insulation risk of power equipment. These parameters are then fed into the model for feature extraction and conversion, adapting them to the model's operational logic. Ultimately, the model outputs a comprehensive risk score for the insulation condition of power equipment. This score comprehensively considers the impact of multiple factors, such as thermal effects and stress, on insulation condition, providing a quantitative representation of the degree of risk faced by the power equipment's insulation.

[0149] Figure 6 is a flow chart of a method for determining the insulation status of an electric power device provided by an optional embodiment of the present invention, such as Figure 6 As shown, inputting the insulation impact parameters into the trained power risk assessment model to obtain a comprehensive risk score for the insulation status of the power equipment may include the following steps:

[0150] First, the insulation impact parameters are calculated through the formula to obtain the weight values of different types of data causing risks caused by the insulation status of power equipment.

[0151] The formula for calculating the weight values of different types of data on risks caused by the insulation status of power equipment is:

[0152]

[0153] Among them, G represents the weight value of different types of data, n represents the type of risk assignment in the insulation impact parameter; R x Represents the weight of the data in category x, m f Represents R x The number of non-zero values.

[0154] Next, the weighted values of different data types are input into a trained power risk assessment model and calculated using a formula to obtain a comprehensive risk score for the insulation status of power equipment. The power risk assessment model includes at least one of a support vector machine, random forest, and neural network. The comprehensive risk score is quantitatively evaluated based on preset classification criteria to determine the risk level of the insulation status of the power equipment.

[0155] The calculation formula for the comprehensive risk score of power equipment in the insulation state is:

[0156]

[0157] Among them, C represents the comprehensive risk score, T represents the operating cycle of the power equipment, a represents the transformation coefficient, A represents the correction coefficient, b represents the stability coefficient of the data in the prediction data set, G represents the weight value corresponding to different types of data, f(kT) represents the adjustment function, and k represents a constant.

[0158] After determining the weights for different types of data, a specific formula can be used to calculate a comprehensive risk score for the insulation status of power equipment. This formula integrates multiple factors, including the power equipment's operating cycle, data stability, and the weighting of different data, comprehensively and systematically considering all factors affecting the insulation risk of power equipment. This provides a quantitative, comprehensive risk score for the insulation status of power equipment, making the assessment of insulation risk more intuitive and accurate, avoiding errors caused by relying solely on subjective judgment, and improving the adaptability and reliability of the assessment.

[0159] Finally, the resulting comprehensive risk score is quantitatively evaluated based on preset classification criteria to determine the risk level associated with the insulation status of power equipment. By calculating weights for different types of data, the importance of different insulation-influencing parameters to risk assessment is highlighted, making the assessment more targeted. Calculations are performed using a specific formula combined with multiple power risk assessment models, fully considering multiple factors such as the power equipment operating cycle and data stability, to comprehensively and accurately quantify the comprehensive risk score for the insulation status. Risk levels are divided according to preset criteria, and corresponding warning signals are issued based on the assessment results. A yellow warning is issued for medium risk, and a red warning is issued for high risk. This provides operations and maintenance personnel with an intuitive and clear basis for risk assessment, helping them to promptly and accurately understand the insulation status of power equipment, formulate appropriate maintenance strategies in advance, ensure the safe and stable operation of power equipment, and reduce the risk of failures caused by insulation problems.

[0160] Through the above optional implementation, at least the following beneficial effects can be achieved:

[0161] (1) Comprehensively utilize various power data related to insulation status, such as partial discharge data, insulation resistance data, and dielectric loss factor, to change the situation where traditional methods only rely on a single type of data and comprehensively obtain equipment insulation information;

[0162] (2) Convert signal characteristic parameters into polar coordinate vector domain to construct a spectrum, explore potential connections between data from a new perspective, and provide more in-depth information for insulation status assessment by analyzing the spectrum characteristics;

[0163] (3) Construct an insulation impact assessment model and a power risk assessment model, taking into account multiple factors such as thermal effects and stress, to achieve accurate quantitative assessment of the insulation status risk of power equipment, divide the risk level and issue early warnings, and provide strong support for operation and maintenance decisions.

[0164] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0165] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0166] Example 2

[0167] According to an embodiment of the present invention, there is also provided a device for implementing the above-mentioned method for determining the insulation status of the device. Figure 7 FIG. 1 is a structural block diagram of a device for determining the insulation status of a device according to an embodiment of the present invention. Figure 7 As shown, the apparatus includes: an acquisition module 702, a response module 704, a first determination module 706, a second determination module 708, a third determination module 710 and a fourth determination module 712. The apparatus will be described in detail below.

[0168] An acquisition module 702 is configured to acquire an insulation state determination request, wherein the insulation state determination request carries a device identifier of a target device. A response module 704 is connected to the acquisition module 702 and configured to respond to the insulation state determination request and, based on the device identifier, acquire power data corresponding to the target device within a predetermined time period. A first determination module 706 is connected to the response module 704 and configured to determine, based on the power data, a plurality of actual feature values corresponding to a plurality of feature items of the target device within a predetermined time period, wherein a feature index corresponding to each of the plurality of feature items is greater than an index threshold, and the corresponding feature index is used to indicate the degree of influence of the corresponding feature item on the insulation state of the device. A second determination module 708 is connected to the first determination module 706 and configured to determine actual parameter coordinates corresponding to the plurality of actual feature values in a unified coordinate system. A third determination module 710 is connected to the second determination module 708 and configured to determine, based on the plurality of actual parameter coordinates, an insulation influence parameter corresponding to the target device. A fourth determination module 712 is connected to the third determination module 710 and configured to determine the insulation state corresponding to the target device based on the insulation influence parameter.

[0169] It should be noted here that the above-mentioned acquisition module 702, response module 704, first determination module 706, second determination module 708, third determination module 710 and fourth determination module 712 correspond to steps S102 to S112 in the method for determining the insulation status of the equipment. The examples and application scenarios implemented by the multiple modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned embodiment 1.

[0170] Example 3

[0171] According to another aspect of an embodiment of the present invention, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions, wherein the processor is configured to execute the instructions to implement any of the above methods for determining the insulation status of a device.

[0172] Example 4

[0173] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can execute any of the above methods for determining the insulation status of a device.

[0174] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0175] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0176] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0177] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0178] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0179] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0180] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for determining the insulation status of a device, characterized in that: include: Obtaining an insulation status determination request, wherein the insulation status determination request carries a device identification of a target device; In response to the insulation state determination request, obtaining power data corresponding to the target device within a predetermined time period based on the device identification; Determining, based on the power data, a plurality of actual characteristic values corresponding to the plurality of characteristic items of the target device within the predetermined time period, wherein characteristic indexes corresponding to the plurality of characteristic items are greater than an index threshold, and the corresponding characteristic indexes are used to indicate the degree of influence of the corresponding characteristic items on the insulation state of the device; Determining actual parameter coordinates corresponding to the multiple actual eigenvalues in a unified coordinate system; Determining insulation impact parameters corresponding to the target device based on a plurality of actual parameter coordinates; An insulation state corresponding to the target device is determined according to the insulation influencing parameter.

2. The method according to claim 1, characterized in that Determining the insulation impact parameter corresponding to the target device based on the multiple actual parameter coordinates includes: Determining a target feature item from the multiple categories of feature items based on the multiple actual parameter coordinates, wherein the target feature item is a feature item whose influence index on the insulation state of the target device is greater than a predetermined influence threshold; Retrieving a target model corresponding to the target feature item, wherein the target model is determined based on a plurality of sample parameter coordinates corresponding to the target feature item and equipment insulation parameters respectively corresponding to the plurality of sample parameter coordinates; The insulation influence parameter corresponding to the target device is determined according to the actual parameter coordinates corresponding to the target feature item and the target model.

3. The method according to claim 1, characterized in that The determining of actual parameter coordinates corresponding to the plurality of actual eigenvalues in a unified coordinate system includes: In a case where the unified coordinate system is a polar coordinate system and the multiple categories of feature items respectively include corresponding first sub-feature items and corresponding second sub-feature items, determining the first sub-feature items respectively corresponding to the multiple categories of feature items and the second sub-feature items respectively corresponding to the multiple categories of feature items, wherein the corresponding first sub-feature item is a feature item for determining signal strength among multiple sub-feature items of the corresponding feature item, and the corresponding second sub-feature item is a feature item for determining signal phase among multiple sub-feature items of the corresponding feature item; Determine an actual characteristic value of the corresponding first sub-characteristic item in the predetermined time period to obtain a first characteristic value, and determine an actual characteristic value of the corresponding second sub-characteristic item in the predetermined time period to obtain a second characteristic value; Determining, based on the first eigenvalue, a polar diameter value of the corresponding first sub-feature item in the polar coordinate system, and determining, based on the second eigenvalue, a polar angle value of the corresponding first sub-feature item in the polar coordinate system; The corresponding actual parameter coordinates are determined according to the polar diameter value and the polar angle value.

4. The method according to claim 3, characterized in that The determining the actual characteristic value of the corresponding first sub-characteristic item in the predetermined time period to obtain the first characteristic value, and determining the actual characteristic value of the corresponding second sub-characteristic item in the predetermined time period to obtain the second characteristic value, includes: In the case where the corresponding target sub-feature items include multiple ones, determining first weight values respectively corresponding to the multiple target sub-feature items, wherein the target sub-feature items include at least one of the following: a first sub-feature item, a second sub-feature item; A target feature value is determined according to actual feature values corresponding to the multiple target sub-feature items within the predetermined time period and first weight values corresponding to the target sub-feature items, wherein the target feature value includes at least one of the following: a first feature value and a second feature value.

5. The method according to claim 1, characterized in that The determining, based on the power data, a plurality of actual characteristic values corresponding to the plurality of characteristic items of the target device within the predetermined time period, includes: determining characteristic data based on the power data, wherein an amount of interference data corresponding to the characteristic data is lower than a predetermined threshold; Determining, based on the characteristic data, a plurality of initial characteristic values corresponding to the plurality of characteristic items of the target device within the predetermined time period; determining an error correction coefficient corresponding to the power data; A plurality of actual characteristic values respectively corresponding to the plurality of characteristic items of the target device within the predetermined time period are determined according to the error correction coefficient and the plurality of initial characteristic values.

6. The method according to claim 5, characterized in that The determining of an error correction coefficient corresponding to the power data includes: Determining an initial correction coefficient, wherein the initial correction coefficient is determined based on sample data, the sample data including sample test data and sample actual data corresponding to the sample test data; Determining predicted characteristic data based on the initial correction coefficient and the sample test data; Determine the difference between the predicted feature data and the actual sample data to obtain a data difference; When the data difference is less than a difference threshold, the initial correction coefficient is determined to be an error correction coefficient.

7. The method according to any one of claims 1 to 6, characterized in that The determining, based on the insulation influencing parameter, the insulation state corresponding to the target device includes: In the case where the insulation influencing parameter includes multiple parameters, determining second weight values corresponding to the multiple insulation influencing parameters respectively; An insulation state corresponding to the target device is determined according to a plurality of insulation influencing parameters and second weight values respectively corresponding to the plurality of insulation influencing parameters.

8. A device for determining the insulation status of equipment, characterized in that: include: an acquisition module, configured to acquire an insulation status determination request, wherein the insulation status determination request carries a device identification of a target device; a response module, configured to, in response to the insulation status determination request, obtain power data corresponding to the target device within a predetermined time period according to the device identifier; a first determining module, configured to determine, based on the power data, a plurality of actual characteristic values corresponding to the plurality of characteristic items of the target device within the predetermined time period, wherein characteristic indices corresponding to the plurality of characteristic items are greater than an index threshold, and the corresponding characteristic indices are used to indicate a degree of influence of the corresponding characteristic items on the insulation state of the device; A second determining module is used to determine actual parameter coordinates corresponding to the multiple actual eigenvalues in a unified coordinate system; A third determination module is used to determine the insulation impact parameter corresponding to the target device based on a plurality of actual parameter coordinates; A fourth determining module is configured to determine an insulation state corresponding to the target device according to the insulation influencing parameter.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method for determining the insulation status of a device according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method for determining the insulation status of a device according to any one of claims 1 to 7.

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