Distribution box operation fault diagnosis system and method

Abstract

The invention belongs to the field of distribution box fault diagnosis, relates to a data analysis technology, is used for solving the problem that in the prior art, after fault type diagnosis is completed, a processing decision cannot be accurately matched with a detection result, and particularly relates to a distribution box operation fault diagnosis system and method. The detection subsystem comprises an electrical detection module, an environment detection module and a mechanical detection module; the diagnosis subsystem comprises a fault analysis module and a fault diagnosis module; the detection subsystem is used for detecting and analyzing electrical parameters, environmental parameters and mechanical parameters of the distribution box; according to the method, the time sequence analysis model is established by dividing the detection time period, the aggregation characteristics of the abnormal parameters can be identified, and meanwhile, the matching mechanism of the characteristic parameter set breaks through the rule base limitation of a traditional expert system, so that the diagnosis result better fits the actual operation data; synchronous monitoring of mechanical parameters and environmental parameters makes up for the defect of missed judgment of mechanical faults caused by pure electrical detection.

Description

Technical Field

[0001] This invention belongs to the field of distribution box fault diagnosis and involves data analysis technology. Specifically, it is a distribution box operation fault diagnosis system and method. Background Technology

[0002] Distribution boxes are electrical equipment characterized by their small size, easy installation, special technical performance, fixed location, unique configuration functions, no site restrictions, wide application, stable and reliable operation, high space utilization, small footprint, and environmental benefits.

[0003] The invention patent with publication number CN119696157A discloses a fault diagnosis system based on cloud management of distribution boxes. This system collects operating status parameters and environmental parameters of the distribution boxes to continuously analyze the fault risks and fault tendencies of the distribution boxes. It then combines the results of the two analyses to perform continuous fault diagnosis of the distribution boxes and has a certain predictive effect in the diagnosis process, thus enabling the system to have a certain degree of foresight in fault diagnosis of distribution boxes. However, this system cannot independently detect and integrate the analysis of different types of parameters of the distribution boxes, resulting in the inability of the processing decisions after the fault type diagnosis is completed to accurately match the detection results, and the efficiency of fault risk containment is low. Summary of the Invention

[0004] The purpose of this invention is to provide a fault diagnosis system and method for distribution boxes, which solves the problem that the processing decisions after fault type diagnosis cannot be accurately matched with the detection results in the prior art; The technical problem to be solved by this invention is: how to provide a fault diagnosis system and method for power distribution boxes that can independently detect and integrate the analysis of different types of parameters of power distribution boxes.

[0005] The objective of this invention can be achieved through the following technical solutions: A fault diagnosis system for a distribution box includes a detection subsystem and a diagnosis subsystem. The detection subsystem includes an electrical detection module, an environmental detection module, and a mechanical detection module. The diagnosis subsystem includes a fault analysis module and a fault diagnosis module. The detection subsystem is used to detect and analyze the electrical, environmental, and mechanical parameters of the distribution box: it generates a detection cycle and divides the detection cycle into several detection periods, marks the abnormal electrical, environmental, and mechanical parameters at the end of each detection period, and sends the detection results of the electrical, environmental, and mechanical parameters to the fault analysis module. The fault analysis module is used to perform operational fault diagnosis and analysis on the distribution box: it calculates the risk coefficient by numerically calculating the number of times the environmental abnormal parameters, electrical abnormal parameters and mechanical abnormal parameters are marked, and determines whether the distribution box has a fault risk during the detection period by using the risk coefficient; The fault diagnosis module is used to perform fault type diagnosis and analysis on the distribution box.

[0006] Furthermore, the marking process for electrical abnormal parameters includes: electrical parameters such as the effective value of phase current, the effective value of phase voltage, power factor, and total harmonic distortion rate. At the end of the detection period, it is determined whether the values ​​of each electrical parameter meet the requirements. Electrical parameters that meet the requirements are marked as normal electrical parameters, and electrical parameters that do not meet the requirements are marked as abnormal electrical parameters.

[0007] Furthermore, the process of marking abnormal environmental parameters includes: environmental parameters such as the temperature value of the gas inside the chamber, the humidity value of the gas inside the chamber, the smoke concentration value inside the chamber, and the content of harmful gases; at the end of the detection period, it is determined whether the values ​​of each environmental parameter meet the requirements, environmental parameters that meet the requirements are marked as normal environmental parameters, and environmental parameters that do not meet the requirements are marked as abnormal environmental parameters.

[0008] Furthermore, the process of marking abnormal mechanical parameters includes: mechanical parameters such as circuit breaker tripping frequency, contactor engagement degree, contactor adhesion degree, and contactor contact erosion degree. At the end of the detection period, it is determined whether each mechanical parameter meets the requirements. Mechanical parameters that meet the requirements are marked as normal mechanical parameters, and environmental parameters that do not meet the requirements are marked as abnormal mechanical parameters.

[0009] Furthermore, the specific process for determining whether the distribution box has a fault risk during the detection period includes: comparing the risk coefficient with a preset risk threshold; if the risk coefficient is less than the risk threshold, it is determined that the distribution box does not have a fault risk during the detection period; if the risk coefficient is greater than or equal to the risk threshold, it is determined that the distribution box has a fault risk during the detection period, generating a fault diagnosis signal and sending the fault diagnosis signal to the fault diagnosis module.

[0010] Furthermore, the fault diagnosis module is used to perform fault type diagnosis analysis on the distribution box: it retrieves the feature parameter set corresponding to the fault type from the database. The feature parameter set includes environmental feature parameters, electrical feature parameters, and mechanical feature parameters corresponding to the fault type in the distribution box. An abnormal parameter set is formed by the abnormal environmental parameters, abnormal electrical parameters, and mechanical feature parameters during the detection period. The abnormal parameter set is compared with the feature parameter sets of all fault types to obtain the diagnosis type. The feature parameter set corresponding to the diagnosis type is marked as the diagnosis parameter set. The environmental overlap, electrical overlap, and mechanical overlap of the diagnosis parameter set and the abnormal parameter set are extracted. The environmental overlap, electrical overlap, and mechanical overlap are compared with preset overlap thresholds. The comparison results generate corresponding signals and send them to the mobile terminal of the management personnel.

[0011] Furthermore, the ratio of the number of identical elements in the abnormal parameter set and the characteristic parameter set to the total number of elements in the characteristic parameter set is marked as the overlap value of the characteristic parameter set, and the fault type corresponding to the characteristic parameter set with the largest overlap value is marked as the diagnosis type.

[0012] Furthermore, the environmental overlap is the number of overlaps between the environmental characteristic parameters in the diagnostic parameter set and the environmental abnormal parameters in the abnormal parameter set; the electrical overlap is the number of overlaps between the electrical characteristic parameters in the diagnostic parameter set and the electrical abnormal parameters in the abnormal parameter set; and the mechanical overlap is the number of overlaps between the mechanical characteristic parameters in the diagnostic parameter set and the mechanical abnormal parameters in the abnormal parameter set.

[0013] Furthermore, the specific process of comparing the environmental overlap, electrical overlap, and mechanical overlap with preset overlap thresholds includes: if the environmental overlap is greater than the overlap threshold, an environmental adjustment signal is generated and the environmental adjustment signal and diagnostic type are sent to the manager's mobile terminal; if the electrical overlap is greater than the overlap threshold, an emergency handling signal is generated and the emergency handling signal and diagnostic type are sent to the manager's mobile terminal; if the mechanical overlap is greater than or equal to the overlap threshold, a mechanical maintenance signal is generated and the mechanical maintenance signal and diagnostic type are sent to the manager's mobile terminal.

[0014] A method for diagnosing operational faults in a distribution box includes the following steps: Step 1: Simultaneously test and analyze the electrical, environmental, and mechanical parameters of the distribution cabinet, and mark any abnormal electrical, environmental, or mechanical parameters. Step 2: Perform operational fault diagnosis and analysis on the distribution box: Calculate the risk coefficient by numerically analyzing the number of times abnormal environmental parameters, electrical parameters, and mechanical parameters are marked during the detection period. Use the risk coefficient to determine whether the distribution box has a fault risk during the detection period. Step 3: Perform fault type diagnosis and analysis on the distribution box: retrieve the characteristic parameter set corresponding to the fault type from the database. The abnormal parameter set is composed of environmental abnormal parameters, electrical abnormal parameters and mechanical characteristic parameters during the detection period. The abnormal parameter set is compared with the characteristic parameter set of all fault types, and the diagnosis type is marked according to the comparison results.

[0015] The present invention has the following beneficial effects: 1. This application establishes a time series analysis model by dividing the detection period, which can identify the clustering characteristics of abnormal parameters. At the same time, the matching mechanism of the feature parameter set breaks through the rule base limitation of traditional expert systems, making the diagnostic results more consistent with actual operating data. In addition, the synchronous monitoring of mechanical parameters and environmental parameters makes up for the omission of mechanical faults by pure electrical testing. 2. This application can classify and determine the fault risk of the distribution box based on the cumulative characteristics of abnormal parameters, ensuring that the fault diagnosis module intervenes only when the risk is confirmed. This solves the problem of delayed processing decisions caused by the lack of independent parameter analysis in the prior art, and improves the accuracy of fault risk identification and the triggering efficiency of processing instructions. 3. This application can independently generate corresponding processing signals based on the overlap of different types of parameters. For example, when the mechanical overlap reaches the threshold, the mechanical maintenance process is triggered immediately to avoid mismatch of maintenance measures due to confusion of parameter types. At the same time, the independent calculation of the overlap of the three types of parameters can effectively distinguish the abnormal contribution of each dimension in the complex fault, thereby improving the accuracy of fault diagnosis and the pertinence of processing decisions. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a flowchart of the method in Embodiment 2 of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] In existing technologies, distribution boxes, as crucial power distribution nodes in power systems, directly impact power supply reliability due to their operational stability. Traditional fault diagnosis systems typically employ single-parameter monitoring, focusing only on changes in electrical parameters such as current and voltage, lacking comprehensive analysis of factors like mechanical component wear and environmental temperature and humidity fluctuations. When distribution boxes are in high-temperature, high-humidity, or dusty environments, mechanical problems such as contactor contact oxidation and circuit breaker mechanism jamming often correlate with abnormal electrical parameters. However, existing systems struggle to establish multi-dimensional parameter correlation models, leading to delayed fault warnings and a lack of specific diagnostic results.

[0020] To address the aforementioned issues, the inventors discovered limitations in existing diagnostic systems regarding parameter acquisition dimensions and data analysis methods. First, the gradual degradation of mechanical components cannot be promptly reflected through instantaneous changes in electrical parameters; second, deteriorating environmental conditions may accelerate equipment aging, but quantitative assessment methods are lacking; and finally, the characteristic parameter combinations of different types of faults are not systematically categorized and stored. Therefore, a multi-dimensional detection system needs to be constructed, establishing a dynamic fault risk assessment model through periodic parameter acquisition and anomaly labeling mechanisms, and achieving intelligent matching of abnormal parameter combinations with preset fault characteristics.

[0021] Example 1: As Figure 1 As shown, a fault diagnosis system for a distribution box includes a detection subsystem and a diagnosis subsystem. The detection subsystem includes an electrical detection module, an environmental detection module, and a mechanical detection module; the diagnosis subsystem includes a fault analysis module and a fault diagnosis module.

[0022] The electrical testing module is used to detect and analyze the electrical parameters of the distribution cabinet. The electrical parameters include the effective value of phase current, the effective value of phase voltage, the power factor, and the total harmonic distortion rate. It generates a testing cycle and divides the testing cycle into several testing periods. At the end of the testing period, it determines whether the values ​​of each electrical parameter meet the requirements. Electrical parameters that meet the requirements are marked as normal electrical parameters, and electrical parameters that do not meet the requirements are marked as abnormal electrical parameters. Among them, the effective value of phase current refers to the root mean square value of the current in the distribution line, which can be measured by using a current transformer and a digital signal processing unit to reflect whether the line load exceeds the safe range; the effective value of phase voltage refers to the root mean square value of the voltage between the phase line and the neutral line in the distribution line, which can be measured by using a voltage sensor and an analog-to-digital converter circuit to detect voltage fluctuations or undervoltage and overvoltage phenomena; the power factor refers to the ratio of active power to apparent power, which can be calculated by a power quality analyzer to determine the reactive power compensation status of the distribution system; the total harmonic distortion rate refers to the percentage of the effective value of harmonic current or voltage to the effective value of the fundamental component, which can be calculated by a spectrum analysis algorithm to assess the degree of harmonic pollution in the power grid; at the end of the detection period, the current transformer collects the three-phase current signal, calculates the effective value of the phase current after digital filtering; the voltage sensor synchronously collects the phase voltage waveform and obtains the effective value through integration; the power quality analyzer monitors the power factor and harmonic distortion rate in real time. When the effective value of the phase current exceeds 1.2 times the rated current of the circuit breaker, the parameter is marked as abnormal; when the effective value of the phase voltage deviates from the rated voltage by ±10%, an abnormality is triggered; when the power factor is below 0.9 or the total harmonic distortion exceeds 15%, the corresponding parameter is judged as abnormal. All abnormality marking results are transmitted to the fault analysis module to provide basic data for subsequent risk coefficient calculation.

[0023] The environmental monitoring module is used to detect and analyze the environmental parameters of the power distribution cabinet. These parameters include the temperature, humidity, smoke concentration, and harmful gas content inside the cabinet. At the end of the monitoring period, the module determines whether the values ​​of each environmental parameter meet the requirements. Parameters that meet the requirements are marked as normal environmental parameters, while those that do not meet the requirements are marked as abnormal environmental parameters. The internal gas temperature value refers to the thermodynamic state of the air inside the distribution box. This can be achieved by using temperature sensors to collect temperature data at different locations within the box in real time, reflecting the heat dissipation status of the equipment during operation. The internal gas humidity value refers to the percentage of water vapor in the air. This can be achieved by using humidity sensors to monitor changes in relative humidity inside the box, used to determine the risk of condensation. The internal smoke concentration value refers to the content of suspended particulate matter in the air. This can be achieved by using photoelectric smoke detectors for real-time monitoring, used for early warning of potential fire hazards. The harmful gas content refers to the concentration of potentially corrosive or flammable gases inside the box. This can be achieved by using electrochemical gas sensors to detect specific gas components, used to identify chemical corrosion or explosion risks.

[0024] Specifically, within the preset detection cycle, the environmental monitoring module periodically collects data according to the segmented detection time periods. At the end of each detection time period, the gas temperature value inside the chamber is compared with the preset safe temperature range; if it exceeds the threshold, it is marked as abnormal. The measured humidity value is compared with the anti-condensation humidity threshold; if it exceeds, it is marked as abnormal. Real-time data is obtained through the smoke concentration sensor and compared with the smoke warning threshold; if it exceeds the standard, an abnormality is triggered. The detection results of harmful gas content are matched with the corresponding safe concentration limit of the gas; if they do not meet the limit, abnormal parameters are generated. The status judgment results of all environmental parameters are synchronously transmitted to the fault analysis module as input data for subsequent risk coefficient calculation.

[0025] The mechanical testing module is used to detect and analyze the mechanical parameters of the distribution cabinet. The mechanical parameters include circuit breaker tripping frequency, contactor engagement degree, contactor adhesion degree, and contactor contact erosion degree. At the end of the testing period, it is determined whether each mechanical parameter meets the requirements. Mechanical parameters that meet the requirements are marked as normal mechanical parameters, and environmental parameters that do not meet the requirements are marked as abnormal mechanical parameters. Among these, circuit breaker tripping frequency refers to the number of times a circuit breaker automatically trips due to overload or short circuit per unit time. This can be achieved by linking a current monitoring module with a counter to reflect abnormal circuit load conditions. Contactor engagement degree refers to the ratio of the contact area between the moving and stationary contacts after the electromagnetic contactor is energized. This can be achieved by measuring the contact displacement using a displacement sensor to determine if the contactor's action is complete. Contactor adhesion degree refers to the degree of physical adhesion remaining between the contacts after the contactor is de-energized. This can be achieved by detecting the contact separation resistance using a pressure sensor to identify contact material aging or arc erosion. Contactor contact ablation degree refers to the thickness of the carbonized layer on the contact surface caused by the electric arc. This can be achieved by monitoring the contact temperature rise curve using an infrared thermal imager to assess the degree of degradation of the contact's conductivity.

[0026] Specifically, at the end of the testing period, the number of trips is recorded by the circuit breaker's built-in current sensor. If the number exceeds a preset threshold, it is marked as abnormal. The contactor's engagement degree is measured by a displacement sensor mounted on the operating mechanism; if the engagement degree does not reach the set proportion of the standard engagement degree, it is judged as abnormal. The contactor's adhesion degree is detected by a pressure sensor, measuring the resistance value of the moving contact when resetting after power failure; if the resistance exceeds the material's elastic limit, it is marked as abnormal. The contactor's contact erosion degree is measured by an infrared thermal imager, collecting temperature distribution data of the contacts during operation; if the area of ​​a local high-temperature region exceeds the safe range, it is judged as abnormal. All abnormal parameters are marked to form a set of mechanical abnormal parameters, which is then transmitted to the fault analysis module for subsequent risk calculation.

[0027] The test results of electrical parameters, environmental parameters, and mechanical parameters are sent to the fault analysis module.

[0028] The fault analysis module is used to perform operational fault diagnosis analysis on the distribution box. The number of times environmental, electrical, and mechanical abnormal parameters are marked are designated as environmental abnormality values ​​HY, electrical abnormality values ​​DY, and mechanical abnormality values ​​JY, respectively. Weighting coefficients a1, a2, and a3 are assigned to these values, where a1 + a2 + a3 = 1, and a3 > a2 > a1. The risk coefficient for the detection period is calculated by weighted summation of the environmental abnormality values ​​HY, DY, and JY according to the assigned weighting coefficients. This risk coefficient is then compared with a preset risk threshold. If the risk coefficient is less than the risk threshold, the distribution box is determined to have no fault risk during the detection period; if the risk coefficient is greater than or equal to the risk threshold, the distribution box is determined to have a fault risk during the detection period. A fault diagnosis signal is then generated and sent to the fault diagnosis module.

[0029] The risk coefficient is a quantitative indicator obtained by numerical calculation through the number of times environmental, electrical, and mechanical abnormal parameters are marked. Specifically, a weighted summation or statistical model can be used to integrate the cumulative number of abnormal parameters to reflect the possibility of the distribution box malfunctioning during the detection period.

[0030] Among them, the risk threshold refers to the preset critical judgment value, which can be set through historical fault data or experimental verification, and is used to distinguish whether there is a potential fault risk in the distribution box.

[0031] Among them, the fault diagnosis signal refers to the instruction that triggers the fault diagnosis module to start type analysis. Specifically, it can be generated through logic circuits or communication protocols and is used to transmit the risk assessment results to the subsequent processing stage.

[0032] Specifically, at the end of the detection period, the system inputs the number of times the environmental, electrical, and mechanical abnormal parameters are marked into the calculation model to obtain a risk coefficient. This coefficient is compared with a preset threshold in real time. If it is lower than the threshold, it is determined that there is no current fault risk, and the system maintains a normal monitoring state; if it reaches or exceeds the threshold, a fault diagnosis signal is generated and transmitted to the fault diagnosis module, triggering a deep analysis process of the fault type.

[0033] Compared to existing technologies, which rely solely on continuous analysis of operational and environmental parameters to determine fault tendencies without establishing a quantitative judgment mechanism based on the cumulative number of abnormal parameters, the accuracy of matching risk assessment results with subsequent processing decisions is insufficient. This solution, through dynamic comparison of risk coefficients and thresholds, achieves rapid integration of abnormal data and precise risk classification, ensuring that the fault diagnosis module only activates when a risk is confirmed, thus avoiding resource waste caused by ineffective analysis.

[0034] The fault diagnosis module is used to perform fault type diagnosis and analysis on the distribution box: it retrieves the characteristic parameter set corresponding to the fault type from the database. The characteristic parameter set includes environmental characteristic parameters, electrical characteristic parameters, and mechanical characteristic parameters corresponding to the fault type of the distribution box. The abnormal parameter set is composed of the environmental abnormal parameters, electrical abnormal parameters, and mechanical characteristic parameters during the detection period. The abnormal parameter set is compared with the characteristic parameter sets of all fault types: the ratio of the number of identical elements in the abnormal parameter set and the characteristic parameter set to the total number of elements in the characteristic parameter set is marked as the overlap value of the characteristic parameter set. The fault type corresponding to the characteristic parameter set with the largest overlap value is marked as the diagnosis type. The feature parameter set refers to the combination of parameters pre-stored in the database and associated with different fault types. It can be constructed using historical fault data statistics or expert experience summarization, providing a comparison benchmark for abnormal parameters. The abnormal parameter set refers to the combination of abnormal parameters actually detected during the current detection period. It can be formed by real-time collection of electrical, environmental, and mechanical parameters and filtering out abnormal values, reflecting the current operating status of the distribution box. Overlap comparison analysis determines the most likely fault type by calculating the degree of matching between the abnormal parameter set and the feature parameter sets of each fault type. This can be implemented using set intersection operations or similarity algorithms to improve the accuracy of fault type identification. Environmental overlap, electrical overlap, and mechanical overlap represent the number of matches between the abnormal parameter set and the diagnostic parameter set in the environmental, electrical, and mechanical parameter dimensions, respectively. These can be calculated by statistically analyzing the proportion of identical parameters, quantifying the degree of fault correlation in different dimensions.

[0035] Specifically, when the fault diagnosis module receives a fault diagnosis signal, it first retrieves the characteristic parameter sets corresponding to all known fault types from the database. For example, a short-circuit fault may correspond to specific current anomalies, temperature anomalies, and circuit breaker tripping characteristics. Then, it integrates the environmental, electrical, and mechanical anomaly parameters marked during the current detection period into an anomaly parameter set. By calculating the overlap value between the anomaly parameter set and each characteristic parameter set, the fault type corresponding to the characteristic parameter set with the highest overlap value is selected as the diagnosis type. Further, the characteristic parameter set corresponding to the diagnosis type is extracted, and the overlap rate between its environmental, electrical, and mechanical characteristic parameters and the corresponding category parameters in the anomaly parameter set is counted, forming environmental overlap, electrical overlap, and mechanical overlap. The three overlap rates are compared with preset thresholds. If the environmental overlap exceeds the threshold, an environmental adjustment signal is generated; if the electrical overlap exceeds the threshold, an emergency handling signal is generated; and if the mechanical overlap exceeds the threshold, a mechanical maintenance signal is generated. Finally, the signal and the diagnosis type are sent to the management terminal.

[0036] The set of characteristic parameters corresponding to the diagnostic type is labeled as the diagnostic parameter set. The environmental overlap, electrical overlap, and mechanical overlap are extracted between the diagnostic parameter set and the abnormal parameter set. Environmental overlap is the number of environmental characteristic parameters in the diagnostic parameter set that overlap with environmental abnormal parameters in the abnormal parameter set; electrical overlap is the number of electrical characteristic parameters in the diagnostic parameter set that overlap with electrical abnormal parameters in the abnormal parameter set; and mechanical overlap is the number of mechanical characteristic parameters in the diagnostic parameter set that overlap with mechanical abnormal parameters in the abnormal parameter set. The environmental overlap, electrical overlap, and mechanical overlap are compared with preset overlap thresholds: if the environmental overlap is greater than the overlap threshold, an environmental adjustment signal is generated and sent along with the diagnostic type to the administrator's mobile terminal; if the electrical overlap is greater than the overlap threshold, an emergency handling signal is generated and sent along with the diagnostic type to the administrator's mobile terminal; if the mechanical overlap is greater than or equal to the overlap threshold, a mechanical maintenance signal is generated and sent along with the diagnostic type to the administrator's mobile terminal.

[0037] The abnormal parameter set refers to the set of environmental, electrical, and mechanical abnormal parameters generated during the operation of the distribution box during the detection period. Specifically, it can be achieved by using sensors to collect data in real time and filtering abnormal parameters through threshold comparison. Its function is to dynamically reflect the abnormal indicators of the current operating status of the distribution box.

[0038] Among them, the overlap value refers to the proportion of the number of identical parameters between the abnormal parameter set and the characteristic parameter set of a certain fault type to the total number of parameters in the characteristic parameter set. It can be achieved through set intersection operation and division calculation, and its function is to quantify the degree of matching between abnormal parameters and fault types. The diagnosis type refers to selecting the fault type with the largest overlap value as the final diagnosis result after comparing the overlap values ​​of the characteristic parameter sets and the abnormal parameter sets of different fault types. It can be achieved by selecting the label corresponding to the maximum value through a sorting algorithm, and its function is to ensure that the fault diagnosis result has the strongest correlation with the current abnormal parameter.

[0039] Specifically, during fault diagnosis, the abnormal parameter set consists of environmental, electrical, and mechanical anomaly parameters collected in real time by the detection subsystem. The database pre-stores characteristic parameter sets corresponding to various fault types; for example, a short-circuit fault might correspond to a sudden current surge or circuit breaker tripping. The system sequentially compares the abnormal parameter set with the characteristic parameter sets of each fault type, calculating the ratio of the number of elements in each characteristic parameter set that are identical to those in the abnormal parameter set to the total number of elements in that set. This yields the overlap value for each fault type. For example, if a fault type's characteristic parameter set contains 10 parameters, and 6 of them overlap with the abnormal parameter set, its overlap value is 0.6. By iterating through the overlap values ​​of all fault types, the system selects the fault type with the highest value as the diagnostic type, thereby determining the most likely fault type currently affecting the distribution box.

[0040] Specifically, when multiple environmental characteristic parameters in the diagnostic parameter set and environmental anomaly parameters in the anomaly parameter set share the same parameters, the environmental overlap value increases. In this case, the system can determine that environmental factors have a high degree of influence on the current fault type. For example, if the diagnostic parameter set includes temperature anomaly as an environmental characteristic parameter, and the anomaly parameter set also contains a temperature anomaly marker, the environmental overlap increases. The calculation process for electrical and mechanical overlap is similar. By statistically analyzing the overlap of the three types of parameters, the correlation strength between anomaly parameters in each dimension and the fault type can be accurately quantified, thus providing data support for the generation of subsequent processing signals.

[0041] Example 2: Figure 2 As shown, a method for diagnosing operational faults in a distribution box includes the following steps: Step 1: Simultaneously test and analyze the electrical, environmental, and mechanical parameters of the distribution cabinet, and mark any abnormal electrical, environmental, or mechanical parameters. Step 2: Perform operational fault diagnosis and analysis on the distribution box: Calculate the risk coefficient by numerically analyzing the number of times abnormal environmental parameters, electrical parameters, and mechanical parameters are marked during the detection period. Use the risk coefficient to determine whether the distribution box has a fault risk during the detection period. Step 3: Perform fault type diagnosis and analysis on the distribution box: retrieve the characteristic parameter set corresponding to the fault type from the database. The abnormal parameter set is composed of environmental abnormal parameters, electrical abnormal parameters and mechanical characteristic parameters during the detection period. The abnormal parameter set is compared with the characteristic parameter set of all fault types, and the diagnosis type is marked according to the comparison results.

[0042] A fault diagnosis system and method for distribution boxes includes the following steps: During operation, the electrical, environmental, and mechanical parameters of the distribution box are simultaneously detected and analyzed, and abnormal electrical, environmental, and mechanical parameters are marked. A risk coefficient is calculated by numerically calculating the number of times these parameters are marked within the detection period. This risk coefficient is used to determine whether the distribution box has a fault risk during the detection period. The system retrieves a set of characteristic parameters corresponding to the fault type from a database. An abnormal parameter set is constructed from the environmental, electrical, and mechanical abnormal parameters within the detection period. This abnormal parameter set is then compared with the characteristic parameter sets of all fault types, and the diagnostic type is marked based on the comparison results.

[0043] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0044] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0045] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A fault diagnosis system for a distribution box, characterized in that, It includes a detection subsystem and a diagnostic subsystem. The detection subsystem includes an electrical detection module, an environmental detection module, and a mechanical detection module. The diagnostic subsystem includes a fault analysis module and a fault diagnosis module. The detection subsystem is used to detect and analyze the electrical, environmental, and mechanical parameters of the distribution box: it generates a detection cycle and divides the detection cycle into several detection periods, marks the abnormal electrical, environmental, and mechanical parameters at the end of each detection period, and sends the detection results of the electrical, environmental, and mechanical parameters to the fault analysis module. The fault analysis module is used to perform operational fault diagnosis and analysis on the distribution box: it calculates the risk coefficient by numerically calculating the number of times the environmental abnormal parameters, electrical abnormal parameters and mechanical abnormal parameters are marked, and determines whether the distribution box has a fault risk during the detection period by using the risk coefficient; The fault diagnosis module is used to perform fault type diagnosis and analysis on the distribution box.

2. The power distribution box operation fault diagnosis system according to claim 1, characterized in that, The process of marking abnormal electrical parameters includes: the electrical parameters include the effective value of phase current, the effective value of phase voltage, the power factor, and the total harmonic distortion rate. At the end of the detection period, it is determined whether the values ​​of each electrical parameter meet the requirements. Electrical parameters that meet the requirements are marked as normal electrical parameters, and electrical parameters that do not meet the requirements are marked as abnormal electrical parameters.

3. The power distribution box operation fault diagnosis system according to claim 2, characterized in that, The process of marking abnormal environmental parameters includes: environmental parameters such as the temperature value of the gas inside the chamber, the humidity value of the gas inside the chamber, the smoke concentration value inside the chamber, and the content of harmful gases; at the end of the detection period, it is determined whether the values ​​of each environmental parameter meet the requirements, environmental parameters that meet the requirements are marked as normal environmental parameters, and environmental parameters that do not meet the requirements are marked as abnormal environmental parameters.

4. The power distribution box operation fault diagnosis system according to claim 3, characterized in that, The process of marking abnormal mechanical parameters includes: mechanical parameters such as circuit breaker tripping frequency, contactor engagement degree, contactor adhesion degree, and contactor contact erosion degree. At the end of the detection period, it is determined whether each mechanical parameter meets the requirements. Mechanical parameters that meet the requirements are marked as normal mechanical parameters, and environmental parameters that do not meet the requirements are marked as abnormal mechanical parameters.

5. The power distribution box operation fault diagnosis system according to claim 4, characterized in that, The specific process for determining whether a distribution box has a fault risk during the detection period includes: comparing the risk coefficient with a preset risk threshold; if the risk coefficient is less than the risk threshold, the distribution box is determined not to have a fault risk during the detection period; if the risk coefficient is greater than or equal to the risk threshold, the distribution box is determined to have a fault risk during the detection period, a fault diagnosis signal is generated, and the fault diagnosis signal is sent to the fault diagnosis module.

6. The power distribution box operation fault diagnosis system according to claim 5, characterized in that, The fault diagnosis module is used to perform fault type diagnosis and analysis on the distribution box. It retrieves the characteristic parameter set corresponding to the fault type from the database. This set includes environmental, electrical, and mechanical characteristic parameters corresponding to the fault type in the distribution box. An abnormal parameter set is constructed from the abnormal environmental, electrical, and mechanical characteristic parameters during the detection period. The abnormal parameter set is compared with the characteristic parameter sets of all fault types to obtain the diagnostic type. The characteristic parameter set corresponding to the diagnostic type is marked as the diagnostic parameter set. The module extracts the environmental, electrical, and mechanical overlap between the diagnostic parameter set and the abnormal parameter set. These overlaps are then compared with preset overlap thresholds. The comparison results generate corresponding signals and are sent to the mobile terminal of the management personnel.

7. The distribution box operation fault diagnosis system according to claim 6, characterized in that, The ratio of the number of identical elements in the abnormal parameter set and the characteristic parameter set to the total number of elements in the characteristic parameter set is marked as the overlap value of the characteristic parameter set. The fault type corresponding to the characteristic parameter set with the largest overlap value is marked as the diagnosis type.

8. A fault diagnosis system for a distribution box according to claim 7, characterized in that, Environmental overlap is the number of overlaps between environmental characteristic parameters in the diagnostic parameter set and environmental abnormal parameters in the abnormal parameter set; electrical overlap is the number of overlaps between electrical characteristic parameters in the diagnostic parameter set and electrical abnormal parameters in the abnormal parameter set; and mechanical overlap is the number of overlaps between mechanical characteristic parameters in the diagnostic parameter set and mechanical abnormal parameters in the abnormal parameter set.

9. A power distribution box operation fault diagnosis system according to claim 8, characterized in that, The specific process of comparing the environmental overlap, electrical overlap, and mechanical overlap with preset overlap thresholds includes: if the environmental overlap is greater than the overlap threshold, an environmental adjustment signal is generated and the environmental adjustment signal and diagnostic type are sent to the mobile terminal of the management personnel; if the electrical overlap is greater than the overlap threshold, an emergency handling signal is generated and the emergency handling signal and diagnostic type are sent to the mobile terminal of the management personnel; if the mechanical overlap is greater than or equal to the overlap threshold, a mechanical maintenance signal is generated and the mechanical maintenance signal and diagnostic type are sent to the mobile terminal of the management personnel.

10. A method for diagnosing operational faults in a distribution box, characterized in that, Includes the following steps: Step 1: Simultaneously test and analyze the electrical, environmental, and mechanical parameters of the distribution cabinet, and mark any abnormal electrical, environmental, or mechanical parameters. Step 2: Perform operational fault diagnosis and analysis on the distribution box: Calculate the risk coefficient by numerically analyzing the number of times abnormal environmental parameters, electrical parameters, and mechanical parameters are marked during the detection period. Use the risk coefficient to determine whether the distribution box has a fault risk during the detection period. Step 3: Perform fault type diagnosis and analysis on the distribution box: retrieve the characteristic parameter set corresponding to the fault type from the database. The abnormal parameter set is composed of environmental abnormal parameters, electrical abnormal parameters and mechanical characteristic parameters during the detection period. The abnormal parameter set is compared with the characteristic parameter set of all fault types, and the diagnosis type is marked according to the comparison results.

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