Substation equipment test report auditing system and method

Abstract

The invention discloses a substation equipment test report auditing system and method, and relates to the technical field of report auditing, and the method comprises the following steps: carrying out the data extraction of a substation equipment test report, and obtaining preliminary test data; performing parameter consistency verification according to the preliminary test data to obtain a consistency result; performing abnormality diagnosis according to the consistency result to obtain an abnormality diagnosis result; performing integrity and rationality verification according to the abnormality diagnosis result to obtain a verification result; performing equipment health analysis according to the verification result to obtain a health evaluation result; and generating an auditing result according to the health evaluation result to obtain a final auditing conclusion. According to the method, abnormal parameter integration is realized, the comprehensiveness of health assessment is improved, the health index is weighted and calculated according to the influence of each abnormal parameter on equipment health, and the comparability and accuracy of health assessment are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of report auditing, in particular to a substation equipment test report auditing system and method. BACKGROUND

[0002] With the continuous expansion of the power system, the number of substation equipment is large and the types are complex, including main transformers, circuit breakers, busbars, protection and control units and other key equipment. The equipment test report is an important basis for ensuring the safe operation of the substation, and its content includes various equipment test parameters, measurement results and abnormal records.

[0003] In the prior art, the health assessment has the following problems: the existing health assessment method mostly only judges a single abnormal parameter, does not fully consider the correlation between abnormal parameters, and lacks continuous quantitative indexes, resulting in incomplete substation equipment operation risk assessment and easy misjudgment. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a substation equipment test report auditing system and method to solve the problems in the background art.

[0005] To achieve the above purpose, the present application provides the following technical solutions: In a first aspect, the present application provides a substation equipment test report auditing system and method, comprising the following steps: S1, data extraction is performed on the substation equipment test report to obtain preliminary test data; S2, parameter consistency verification is performed according to the preliminary test data to obtain consistency results; S3, abnormal diagnosis is performed according to the consistency results to obtain abnormal diagnosis results; S4, completeness and rationality verification is performed according to the abnormal diagnosis results to obtain verification results; S5, equipment health analysis is performed according to the verification results to obtain health evaluation results; S6, auditing result generation is performed according to the health evaluation results to obtain final auditing conclusions.

[0006] Further optimization of the technical solution, the data extraction in step S1 includes: The data in the substation equipment test report is uniformly processed, and the key parameter information is extracted and standardized to obtain preliminary test data.

[0007] Further optimization of the technical solution, the parameter consistency verification in step S2 includes: According to the obtained preliminary test data, the physical and logical rationality is verified, the data abnormality in the data record is identified, and the consistency results are obtained.

[0008] Further optimize the technical solution, the step S3 includes: According to the consistency of the results obtained, through multi-parameter anomaly analysis, abnormal diagnosis, in-depth analysis of the internal mechanism of abnormal formation, determine the true source of the anomaly, identify the influence path of the anomaly in the device, get the abnormal diagnosis result.

[0009] Further optimize the technical solution, the multi-parameter anomaly analysis includes: Based on the consistency of the results obtained, abnormal parameters are extracted, parameter correlation is established according to the device operation mechanism, and the abnormal parameters are analyzed by using the correlation relationship, the abnormal source is judged, the abnormal propagation path is identified, and the abnormal diagnosis result is generated.

[0010] Further optimize the technical solution, the step S4 includes: According to the abnormal diagnosis result obtained, the verification mechanism is established, the integrity and rationality are verified, and the verification result is obtained.

[0011] Further optimize the technical solution, the step S5 includes: According to the verification result obtained, the health quantification model is used for correlation analysis, the influence of the anomaly on the device performance is evaluated, the health level of the device is quantitatively reflected, and the health evaluation result is obtained.

[0012] Further optimize the technical solution, the health quantification model includes: Among them: : the health index of the device ; : the number of abnormal parameters of the device ; : the abnormal severity of the th abnormal parameter in the device ; : the abnormal weight of the th abnormal parameter in the device .

[0013] Further optimize the technical solution, the step S6 includes: According to the health evaluation result obtained, the influence of the device on the operation safety of the substation is identified through correlation integration, the health level is determined, the complete audit result is generated, and the final audit conclusion is obtained.

[0014] Further optimize the technical solutions, including the following functional modules: Data acquisition module, parameter verification module, abnormal diagnosis module, diagnosis verification module, health evaluation module, conclusion generation module.

[0015] In a second aspect, the present application provides a computer device comprising a memory and a processor, the memory storing a computer program, wherein: the computer program instructions are executed by the processor to realize the steps of the substation equipment test report auditing system and method according to the first aspect of the present application.

[0016] In a third aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein: the computer program instructions are executed by the processor to realize the steps of the substation equipment test report auditing system and method according to the first aspect of the present application.

[0017] Compared with the prior art, the present application provides a substation equipment test report auditing system and method, which has the following beneficial effects: The substation equipment test report auditing system and method realizes abnormal parameter integration through the health quantification model, improves the comprehensiveness of health evaluation, and improves the comparability and accuracy of health evaluation by weighting the health index according to the influence of each abnormal parameter on equipment health. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0019] Fig. 1 A flowchart of a substation equipment test report auditing method according to the present application is shown in the figure. Fig. 2 A flowchart of a health quantification model of a substation equipment test report auditing method according to the present application is shown in the figure. Fig. 3 A module diagram of a substation equipment test report auditing system according to the present application is shown in the figure. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0021] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be appreciated that the present application can be practiced in a variety of ways beyond the description and into combinations of these at least one implementation, and that the present application is not limited to the implementation described and shown, but can be practiced with alteration, modification, and variation of the devices, methods, and procedures such as those which would occur to those skilled in the art upon a reading of the detailed description.

[0022] Secondly, the "one embodiment" or "embodiment" referred to herein means a specific feature, structure, or characteristic that can be included in at least one implementation of the present application. "In one embodiment" appearing in various places in the specification does not all refer to the same embodiment, nor is it an embodiment that is separate or mutually exclusive with other embodiments.

[0023] Embodiment one: Referring to Figs. 1-2 For the first embodiment of the present application, the embodiment provides a substation equipment test report auditing method, comprising the following steps: S1, data extraction is performed on the substation equipment test report to obtain preliminary test data.

[0024] In this embodiment, the data extraction includes: The operational safety and reliability of substation equipment is highly dependent on the actual electrical performance and mechanical state of the equipment. Various types of equipment such as main transformers, circuit breakers, transformers, arresters, and protection and control devices need to be strictly tested during factory, installation, maintenance, and daily operation. The test report records the key parameters and performance indicators of these devices, such as voltage, current, insulation resistance, dielectric loss angle, winding ratio, mechanical action time, leakage current, etc. Test reports often come from various sources, with different formats, inconsistent unit markings, and field naming differences. If directly used for auditing or health evaluation, it may lead to data understanding bias or omission of key parameters.

[0025] By uniformly processing the scattered and non-uniform data in the test reports of various types of equipment in the substation, extracting and standardizing the key parameter information, a preliminary test data set that can be directly used for subsequent auditing and analysis is formed, thereby eliminating the understanding bias caused by report format differences or data entry errors and improving the data reliability.

[0026] The steps of data extraction include: Collecting equipment test reports: substation equipment types are complex, including main transformers, circuit breakers, transformers, arresters, and protection and control devices, etc. Collecting reports not only includes factory test reports of new equipment, but also includes installation handover test reports, test records after maintenance, and daily periodic detection reports, including paper reports, PDF electronic documents, electronic spreadsheets or database records, and various sources; Classification and organization reports: Classify by equipment type (main transformer, circuit breaker, instrument transformer, surge arrester, protection and control device, etc.), and organize by test stage (factory test, acceptance test, maintenance test, periodic preventive test), and use a unified naming rule (such as "equipment number, equipment type, test date") to ensure data traceability; Data extraction: Key parameters in the report are extracted into structured data, including electrical parameters (voltage, current, impedance, etc.), mechanical parameters (action time, mechanical action sequence, etc.), insulation performance (insulation resistance, dielectric loss angle, etc.), environmental parameters (temperature, humidity, etc.), and operating conditions. Field mapping and standardization: Unify the field naming of parameters with the same meaning in different reports, such as unifying "main transformer winding resistance" and "winding DC resistance" into "winding resistance", and uniformly convert data with different units, such as unifying voltage to kilovolts, current to amperes, and impedance to ohms, etc. Determine the standard unit and range of each parameter to form a standardized field list to ensure the consistency of subsequent data processing. Preliminary data integrity check: Check whether the extracted data covers all key parameters, mark and record missing data, abnormal units or invalid values, and ensure that each device contains at least the most basic electrical and mechanical parameters to form a preliminary test dataset that can be used for subsequent steps; Data recording and output: The processed preliminary test data are organized into a unified table or database record. Each data record includes the equipment number, equipment type, test date, parameter value, unit, and source report information.

[0027] S2. Verify the consistency of parameters based on the preliminary test data to obtain consistency results.

[0028] In this embodiment, the parameter consistency verification includes: Test data for substation equipment often exhibits inconsistencies across different devices, stages, or sources. For example, parameters recorded by different manufacturers or at different test stages may have logical deviations, unit conversion errors, or data entry errors. Failure to perform consistency verification may lead to misjudgments in subsequent anomaly analysis, health assessments, and final audit conclusions.

[0029] Based on the preliminary test data obtained, physical and logical rationality is verified, potential data anomalies in the data records are identified, and consistent results are obtained. This prevents data errors from affecting judgment, timely detection of potential test data anomalies, and improvement of data reliability, thereby ensuring the scientific nature of equipment safety assessment.

[0030] The implementation methods for parameter consistency verification include: Extract key parameters for comparison: Due to the wide variety of substation equipment and the complexity of test items, directly processing all data would increase the complexity of analysis and may lead to deviations in results. Therefore, based on the preliminary test data output from step S1, key parameters are selected, such as main transformer winding resistance, transformer ratio, circuit breaker opening and closing time, transformer ratio difference and phase angle error, etc. The same parameters in different test stages or different test reports of the same equipment are compared to ensure that the data can be directly compared and analyzed, so as to discover potential data anomalies, unit conversion errors or inconsistencies in records. Define parameter consistency rules: For different equipment types and the characteristics of key parameters, define rules that conform to equipment technical specifications and operating standards. For example, the resistance and ratio of the main transformer winding should meet the proportional relationship specified in the standard, the opening and closing time of the circuit breaker should be within the standard limit, and the opening time should be greater than the closing time. The ratio difference and phase angle error of the current transformer should be within the allowable range, and there should be no conflict of signs or quantities. This will enable the consistency verification to judge the physical and logical rationality of the parameters, thereby improving the scientificity and reliability of the verification results. Perform consistency verification: Compare the extracted key parameters one by one according to the consistency rules, check whether the parameters are within the standard allowable range, verify whether the logical relationship meets the physical laws and equipment technical specifications, and determine whether there are abnormal increases or decreases of the same type of parameters in different test stages, whether the comparison values ​​are reasonable, mark the data that does not conform to the rules, and record the anomaly type and degree; Generate a consistency result record: The verification results are formed into a structured record, including the device number, parameter name, parameter value, verification result (compliant or non-compliant), anomaly type description and rule basis, to obtain the consistency result; Verification of completeness: Check whether the consistency verification results cover all key parameters of the preliminary test data to ensure no omissions. For parameters that fail the consistency verification, indicate the potential impact range so that subsequent anomaly analysis can focus on them.

[0031] S3. Based on the consistency results, perform anomaly diagnosis to obtain the anomaly diagnosis results.

[0032] In this embodiment, the abnormal diagnosis includes: During the testing and auditing of substation equipment, simply relying on parameter consistency checks can only detect data discrepancies but cannot reveal the causes of the anomalies. For example, changes in the main transformer winding resistance may originate from differences in test temperature or insulation aging; excessive circuit breaker opening and closing times may stem from mechanical wear, control circuit delays, or data recording errors. Without systematic analysis of the abnormal data, its technical nature cannot be determined, rendering the audit conclusions lacking engineering guidance significance.

[0033] Based on the obtained consistency results, anomaly diagnosis is carried out through multi-parameter anomaly analysis, the internal mechanism of anomaly formation is analyzed in depth, the true source of the anomaly is determined, such as data error or equipment failure, the impact path of the anomaly in the equipment is identified, and the anomaly diagnosis results are obtained, thereby realizing a reasonable deduction from data anomaly to equipment status anomaly.

[0034] Furthermore, the multi-parameter anomaly analysis includes: Based on the consistency verification results, an abnormal parameter set is extracted, recording the device number, parameter name, and abnormality type. A parameter association set is established according to the device's operating mechanism, defining the logical and physical dependencies between parameters. The associations are then used to perform reasoning analysis on the abnormal parameters to determine the source of the abnormality, identify the propagation path, and generate an anomaly diagnosis result. The specific steps include: Extract inconsistent parameter set: Filter out the parameter records that are judged as "inconsistent" from the S2 output results, and attach the extracted device number, parameter name, parameter value, anomaly type and verification rule basis for each abnormal parameter; Establish parameter correlation relationships: Based on the equipment operation mechanism, test procedures and electrical physical characteristics, construct physical and logical correlation relationships between parameters. For example, the coupling relationship between the main transformer winding resistance, turns ratio and short-circuit impedance, the relationship between circuit breaker opening and closing time and mechanical wear, coil current, etc. Define these relationships as parameter correlation rules and form a set of parameter correlation rules as the basis for anomaly propagation judgment. Perform anomaly correlation reasoning analysis: For each anomalous parameter, search for other parameters that are logically coupled with it according to the association rule set. If multiple related parameters are anomalous at the same time, determine whether their anomalousness has a consistent trend (such as all exceeding limits, changing in the same direction, etc.) and whether it conforms to the energy loss law. If it is consistent and conforms to the energy loss law, it is judged to be a systemic anomaly caused by equipment performance degradation. If only individual parameters deviate and there is no trend consistency, it is initially judged to be an isolated anomaly caused by measurement or input error. In the judgment process, combine the historical change records of the parameters (from multiple reports) to identify whether the anomaly has continuous or phased characteristics. Continuous anomalies usually reflect performance degradation or aging, while phased anomalies may originate from environmental conditions or one-time errors. Identifying abnormal propagation paths: Based on the results of correlation reasoning, an abnormal propagation chain is constructed. If an abnormality in the main parameter causes a deviation in the subordinate parameter, the propagation relationship is recorded. For example, if the increase in the resistance of the main transformer winding leads to an increase in the short-circuit impedance, and the turns ratio changes slightly, it is judged to be a systemic abnormality caused by local heating or aging of the winding. Propagation chain analysis helps to distinguish between isolated abnormalities and systemic abnormalities. Isolated abnormalities usually do not have propagation characteristics and are mostly random measurement errors or data entry errors. Systemic abnormalities often reflect equipment performance degradation or component failure, thereby revealing the causal relationship inside the equipment and improving the technical credibility of the diagnostic conclusion. Generate abnormal diagnosis results: Summarize the abnormal diagnosis results of each device to form a diagnostic record that includes the abnormality type (systematic, isolated), the abnormality source (performance degradation, measurement error, external interference), the scope of impact, and the confidence level.

[0035] S4. Based on the abnormal diagnosis results, perform a completeness and rationality verification to obtain the verification results.

[0036] In this embodiment, the completeness and reasonableness verification includes: Substation equipment anomaly diagnoses often stem from multi-parameter logical analysis and reasoning, which may lead to issues such as insufficient diagnostic coverage, oversimplification of causal chains, or conflicting parameter interpretations. Without verification of completeness and rationality, diagnostic conclusions may be biased, affecting the accuracy of equipment health assessments and operational decisions.

[0037] Based on the obtained anomaly diagnosis results, this step establishes a verification mechanism to verify the completeness and rationality of the results, obtain verification results that truly reflect the equipment status, thereby checking the logical rationality of the causal chain in the anomaly diagnosis, confirming the completeness of the analytical conclusions in the anomaly diagnosis results, improving the credibility of the diagnosis, and ensuring the reliability of the audit.

[0038] The implementation steps include: Extracting the abnormal diagnosis result set: Extract the abnormal diagnosis record of each device from the S3 output results, including the abnormality type, abnormality source, impact range and confidence level. Store the diagnosis results of each device according to the device category to form a dataset to be verified, ensuring that the verification objects are complete and clearly classified. Establish a verification standard system: Based on the operating principles of electrical equipment, national standards, and historical operating data, establish completeness and rationality verification standards. The completeness standard is used to determine whether all key parameters have been analyzed and whether there are any omissions. For example, transformers should include key indicators such as winding temperature rise, electrical insulation, and oil chromatography. Any missing item indicates that the diagnostic results are incomplete. The rationality standard is used to determine whether the abnormal conclusions are consistent with the operating rules of the equipment and the physical logic between parameters. For example, if the operating current of a circuit breaker is abnormal, but the corresponding coil voltage is normal, the conclusion should be judged as unreasonable because abnormal current should be accompanied by voltage fluctuations. Perform integrity verification: By statistically analyzing the ratio of the number of abnormal parameters to the total number of parameters, check the coverage of the abnormal diagnosis results for each device to confirm that all key parameters have analysis results. Compare with the initial test parameter list to identify missing or unanalyzed parameters. If any undiagnosed key parameters are found, record the missing items and indicate the reasons, such as no abnormality, insufficient data, or logic not triggered. Integrity verification ensures that the diagnostic results are comprehensive and prevents partial judgments from leading to biased conclusions. Perform rationality verification: Based on the physical laws of the equipment and the dependencies between parameters, confirm the rationality of the anomaly diagnosis logic or re-verify the anomaly diagnosis logic according to the rationality index. If there is a logical conflict in the anomaly propagation chain (such as the lower-level parameter being abnormal but the upper-level parameter being normal), mark it as unreasonable reasoning. Based on the equipment operating mechanism, perform physical feasibility analysis on the diagnosis results (such as abnormal temperature rise but normal load, which may be a misjudgment). Combine with the historical operating data trend of the equipment to verify whether the anomaly direction conforms to the performance degradation or aging law. Perform consistency comparison on the judgment results to confirm the technical rationality of each anomaly conclusion. Generate verification results: Combine the results of integrity verification and rationality verification to form structured verification results, including verification objects, verification standards, verification conclusions, number of anomalies, and rationality analysis results. This serves as the technical basis for the final audit results, ensuring the credibility and verifiability of the diagnostic conclusions.

[0039] Furthermore, the calculation of the rationality index includes: in: Reasonableness index is used to measure the logical correctness of abnormal diagnosis results. For example, a reasonableness index greater than or equal to 0.9 indicates that the abnormal diagnosis logic is reasonable and the diagnosis is credible. A reasonableness index greater than or equal to 0.8 and less than 0.9 indicates that some parameters may have slight logical deviations and need to be reviewed. A reasonableness index less than 0.9 indicates that there are logical inconsistencies or misjudgments and that re-analysis is required. The threshold can be set according to historical diagnosis data and actual needs. The total number of comparisons between parameters, i.e., the number of pairwise comparisons of all parameters, is used to normalize the rationality index; :parameter and parameters The abnormal co-occurrence values ​​are reflected in the parameters of the abnormal diagnosis results. and parameters Whether anomalies are simultaneously observed can be determined based on the anomaly correlation reasoning in step S3. :parameter and parameters The logical association value represents the parameter. and parameters The theoretical logical dependency between them is used to determine whether the diagnostic results conform to physical laws or electrical logic, reflecting the theoretical parameter coupling structure. It can be determined based on the equipment design schematic diagram, operating procedures, and the logical relationship table provided by the manufacturer. For example, in a transformer, if there is a strong logical relationship between the winding temperature and the winding resistance, the value is 1; if there is no logical relationship, the value is 0. :parameter and parameters The sum of the differences between the abnormal co-occurrence values ​​and the logical association values ​​represents the total sum of the differences between the abnormal co-occurrence values ​​and the logical association values ​​for all pairwise parameter combinations. The rationality index is calculated by summing and averaging the differences between the abnormal co-occurrence values ​​and the logical correlation values ​​of all parameter pairs.

[0040] S5. Based on the verification results, perform equipment health analysis to obtain health evaluation results.

[0041] In this embodiment, the device health analysis includes: Substation equipment exhibits multi-parameter coupling characteristics, and an anomaly in a single parameter cannot fully reflect the overall health status of the equipment. Relying solely on the frequency or degree of exceedance of a single anomaly parameter can easily lead to deviations in the health status assessment.

[0042] The purpose of this step is to use a health quantification model to conduct correlation analysis based on the obtained verification results, assess the impact of anomalies on equipment performance, quantitatively reflect the equipment health level, obtain health evaluation results, thereby providing quantifiable health data, avoiding errors caused by isolated judgments, and providing a scientific basis for operation and maintenance decisions.

[0043] Furthermore, the health quantification model includes: in: :equipment The health index quantifies the overall health level of the equipment, ranging from 0 to 1, with a higher value indicating a higher level of equipment health. :equipment The number of abnormal parameters; :equipment The Middle The severity of an abnormal parameter is used to quantify the degree of harm of the abnormal parameter to the health of the equipment and reflect the impact of the abnormality on the performance of the equipment. The larger the value, the more serious the impact. It can be obtained by mapping the grade interval according to the national or enterprise-defined grading standards. For example, if the insulation resistance is divided into four levels, the severity of the abnormality corresponding to each level from low to high is 0, 0.33, 0.66, and 1. :equipment The Middle The anomaly weight of each anomaly parameter represents the importance of a single anomaly parameter in the equipment health assessment. It ranges from 0 to 1, and the sum of all weights is 1. It can be set according to the anomaly propagation chain, anomaly type, and scope of impact. For example, the weight of a systemic anomaly is greater than that of an isolated anomaly, and the weight of anomalies of critical components is greater than that of non-critical components.

[0044] This model describes how to quantify the health level of equipment through multi-parameter anomaly correlation analysis to obtain the equipment health index.

[0045] Traditional health assessment methods often rely on single-parameter statistics or cumulative number of exceedances, lacking correlation analysis between abnormal parameters and failing to reflect systemic anomalies or propagation effects between parameters. Furthermore, health results from different devices are difficult to compare directly. This model, however, integrates anomaly correlation analysis and propagation chains, considering systemic anomalies and multi-parameter coupling effects, and weights the calculation of the health index. This makes health assessment more accurate and allows for direct comparison of results from different devices, improving the comprehensiveness, comparability, and accuracy of health assessments. It also facilitates the analysis of anomaly sources and corresponding countermeasures.

[0046] The steps for using this model include: Data Acquisition: Obtain the number of abnormal parameters for each device from the test data, anomaly diagnosis results, and verification results in steps S1, S3, and S4. The severity of each abnormal parameter is determined based on its anomaly type and source. and abnormal weights ; Health index calculation: based on the severity of the abnormality of each obtained abnormal parameter. and abnormal weights The health index of the equipment was calculated. ; Health status assessment: The health level of the equipment is classified according to the health index. For example, a health index greater than 0.8 is considered healthy, greater than 0.6 and less than or equal to 0.8 is considered suboptimal health, greater than 0.4 and less than or equal to 0.6 is considered warning, and less than or equal to 0.4 is considered dangerous.

[0047] S6. Generate audit results based on health assessment results and obtain the final audit conclusion.

[0048] In this embodiment, the generation of the audit result includes: Because different equipment categories, operating conditions, and anomaly distribution characteristics vary, numerical indicators alone are insufficient to reflect the completeness, reliability, and risk level of equipment test reports. Furthermore, different equipment are coupled in terms of electrical topology, protection logic, and operating load, and a health anomaly of one piece of equipment may trigger a chain reaction.

[0049] Therefore, based on the health assessment results of each device, this step identifies the impact of key devices on the operational safety of the substation through correlation and integration, determines the overall health level, generates a complete audit result including equipment status, potential risks and audit opinions, and obtains the final audit conclusion, thereby supporting maintenance decisions and subsequent management.

[0050] The specific implementation steps include: Equipment results aggregation and hierarchical classification: Equipment is grouped according to functional level, such as main transformer, switchgear, busbar, protection and control unit, etc. The health evaluation results of equipment in each group are weighted and averaged to obtain the health score of each level. The weight is determined by the importance of the equipment to ensure that the impact of key equipment is accurately reflected. System health status reasoning and anomaly propagation analysis: Based on electrical topology, establish a health propagation chain between devices, determine the extended impact of local anomalies on the health of substation equipment, and trigger systemic risk assessment when the health levels of multiple key devices are below the threshold. For example, if the busbar and its connected switchgear are in a slightly abnormal state, the overall operational risk level will be increased by one level, thereby linking local anomalies with the overall health of the substation and realizing overall risk identification. Audit conclusion generation and multi-dimensional verification: Based on the health results at each level, the scope of impact, and the importance of the equipment, the audit conclusion for the entire station is generated, including the overall health level of the station (divided into Level I Normal, Level II Mild Abnormality, Level III Moderate Abnormality, and Level IV Severe Abnormality), a list of critical abnormal equipment and the impact path, and recommended measures, such as continued monitoring, planned maintenance, or emergency handling. By comparing with the standard operating indicator library, the consistency and traceability of the conclusion are ensured. Audit consistency verification: The generated audit conclusions are compared with historical audit data. If there is a significant difference between the current conclusions and historical trends, a review procedure is triggered to verify the rationality and stability of the health assessment results.

[0051] Example 2: Reference Fig. 3 This is the second embodiment of the present invention, which provides a substation equipment test report review system, including the following functional modules: Data acquisition module: Extracts raw test parameters from equipment test reports to obtain preliminary test data, providing basic data for subsequent steps; Parameter verification module: performs consistency checks on preliminary test data to determine whether the data conforms to equipment specifications and test procedures, ensuring the reliability of input data; Anomaly Diagnosis Module: Analyzes key equipment parameters, identifies the source of anomalies, confirms the anomaly type, analyzes the impact path, and obtains anomaly diagnosis results; Diagnostic verification module: Verifies abnormal diagnostic results to ensure data integrity, rationality, and traceability, thereby improving the credibility of the diagnosis and ensuring the reliability of the audit. Health assessment module: Performs correlation analysis on verified abnormal data, calculates the health index and health level of each device, and realizes comprehensive health assessment at the device level; Conclusion generation module: Integrates equipment health assessment results into a station-wide audit conclusion, taking into account equipment status, potential risks, and audit comments to generate the final audit conclusion.

[0052] Example 3: In practical applications, this method can be used to review test reports of high-voltage substation equipment. It is used to systematically analyze the operating status, test data accuracy, and potential risks of various key equipment in the substation. A typical scenario is illustrated using a 500kV substation as an example.

[0053] In practice, the first step is to extract raw data from the test reports of all equipment in the substation to form a complete preliminary test data set, ensuring that all parameter data are traceable and correspond one-to-one with the equipment number and functional level. Subsequently, the extracted test parameters are verified for consistency, including checking whether key indicators such as voltage, current, temperature, and insulation resistance meet equipment specifications and national and enterprise standards. Simultaneously, multiple measurement results are compared to eliminate measurement anomalies or recording errors, ensuring the reliability and accuracy of the data.

[0054] Anomaly diagnosis is performed on key equipment parameters. By quantifying the severity of anomalies, parameters deviating from rated values ​​or exhibiting abnormal historical trends are identified. Furthermore, the anomaly propagation path is analyzed in conjunction with the functional relationships between equipment and the electrical topology to identify potential systemic risks. Next, the system verifies the completeness and rationality of the anomaly diagnosis results, ensuring that the source of the anomaly data, logical relationships, and measurement methods all comply with procedural requirements, providing a reliable foundation for subsequent health assessments.

[0055] Using the validated anomaly results, further anomaly correlation analysis is conducted. Combining equipment functional level, criticality, and anomaly weight, the health index and health level of each piece of equipment are calculated to form a comprehensive equipment-level health evaluation result. This quantifies the impact of equipment anomalies on operational safety and provides comparable and interpretable health status indicators.

[0056] The results of equipment health assessments are integrated to generate a station-wide audit conclusion, which clarifies the health status, potential risks, and priority maintenance recommendations for each piece of equipment. At the same time, the conclusions are compared with historical audit data and standard operating indicators to ensure their reliability and traceability.

[0057] Example 4: This embodiment also provides a computer device applicable to a substation equipment test report review system and method, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the substation equipment test report review system and method proposed in the above embodiment.

[0058] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements a substation equipment test report review system and method as described in the above embodiments.

[0059] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0060] If a function is implemented as 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 this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0061] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0062] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0063] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0064] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for reviewing substation equipment test reports, characterized in that, Includes the following steps: S1. Extract data from the substation equipment test report to obtain preliminary test data; S2. Verify the consistency of parameters based on the preliminary experimental data to obtain consistency results; S3. Perform anomaly diagnosis based on the consistency results to obtain the anomaly diagnosis results; S4. Verify the completeness and rationality of the abnormal diagnosis results to obtain the verification results; S5. Based on the verification results, perform equipment health analysis to obtain health evaluation results; S6. Generate audit results based on health assessment results and obtain the final audit conclusion.

2. The method for reviewing substation equipment test reports according to claim 1, characterized in that, The data extraction in step S1 includes: The data in the substation equipment test report are processed in a unified manner, and key parameter information is extracted and standardized to obtain preliminary test data.

3. The method for reviewing substation equipment test reports according to claim 1, characterized in that, The parameter consistency verification in step S2 includes: Based on the preliminary experimental data obtained, physical and logical rationality is verified, data anomalies in the data records are identified, and consistency results are obtained.

4. The method for reviewing substation equipment test reports according to claim 1, characterized in that, The abnormal diagnosis in step S3 includes: Based on the obtained consistency results, anomaly diagnosis is performed through multi-parameter anomaly analysis. The underlying mechanism of anomaly formation is analyzed in depth to determine the true source of the anomaly, identify the impact path of the anomaly in the equipment, and obtain the anomaly diagnosis results.

5. The method for reviewing substation equipment test reports according to claim 4, characterized in that, The multi-parameter anomaly analysis includes: Based on the consistency verification results, abnormal parameters are extracted, and parameter correlations are established according to the equipment operation mechanism. The correlations are used to perform reasoning analysis on the abnormal parameters, determine the source of the abnormality, identify the abnormal propagation path, and generate abnormal diagnosis results.

6. The method for reviewing substation equipment test reports according to claim 1, characterized in that, The completeness and reasonableness verification in step S4 includes: Based on the obtained abnormal diagnosis results, a verification mechanism is established to verify the completeness and rationality, and the verification results are obtained.

7. The method for reviewing substation equipment test reports according to claim 1, characterized in that, The equipment health analysis in step S5 includes: Based on the obtained verification results, a health quantification model is used to conduct correlation analysis, assess the impact of anomalies on equipment performance, quantitatively reflect the equipment health level, and obtain health evaluation results.

8. The method for reviewing substation equipment test reports according to claim 7, characterized in that, The health quantification model includes: in: :equipment Health index; :equipment The number of abnormal parameters; :equipment The Middle The severity of the abnormality of each abnormal parameter; :equipment The Middle Abnormal weights of anomalous parameters.

9. The method for reviewing substation equipment test reports according to claim 1, characterized in that, The generation of the audit result in step S6 includes: Based on the obtained health assessment results, the impact of equipment on the safe operation of substations is identified through correlation and integration, the health level is determined, a complete audit result is generated, and the final audit conclusion is obtained.

10. The substation equipment test report review system according to claim 1, constructed based on the substation equipment test report review method according to any one of claims 1-9, characterized in that, Includes the following functional modules: The module includes a data acquisition module, a parameter verification module, an anomaly diagnosis module, a diagnosis verification module, a health evaluation module, and a conclusion generation module.

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