Power transformation equipment power data fault defect acquisition and analysis system

Abstract

The invention discloses a power transformation equipment power data fault defect acquisition and analysis system, and relates to the technical field of power fault defect analysis. However, fault positioning in a module level for analyzing characteristics and laws of composite faults at present is often not rapid and accurate enough. Compared with the prior art, the fault early warning mechanisms of different modules may have differences, and the cooperative diagnosis capability among the modules is weak, so that the fault unit in the module can be more efficiently positioned by calculating the sum of the influences of the suspected unit on other units in the same module and sorting and grouping the other units in the same module according to the sum of the influences. According to the fault analysis method based on the unit influence, the internal relation between the units is fully utilized, blind troubleshooting is avoided, the accuracy and speed of fault positioning are greatly improved, the downtime of the equipment is shortened, and the operation reliability of the power transformation equipment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power fault defect analysis, and in particular to a power data fault defect collection and analysis system for substation equipment. Background Art

[0002] The power data fault defect collection and analysis system collects and stores power data in real time, and conducts in-depth data mining through advanced data analysis algorithms to accurately identify fault defects, provide a scientific basis for the maintenance, inspection and operation management of substation equipment, and ensure the safe and stable operation of the power system.

[0003] In this regard, publication number CN105631598A discloses a method for analyzing defects in power equipment based on cluster defects. The invention can fully cluster defect phenomena and defect analysis results of different equipment types from different manufacturers, and at the same time combine experts' handling and prevention measures for defects to form a complete set of substation equipment cluster defect libraries, which provides scientific and effective assistance for the analysis and handling of substation equipment defects, and lays the foundation for early warning of defect failures, which is conducive to supporting the safe and stable operation of the power grid.

[0004] Nowadays, when substation equipment fails, many fault analyses focus on a single fault type. However, in actual circuit or equipment operation, compound faults (multiple faults occurring simultaneously) are not uncommon. However, the fault location at the module level that currently analyzes the characteristics and rules of compound faults is often not fast and accurate enough. The fault warning mechanisms of different modules may be different, and the collaborative diagnosis capabilities between modules are weak.

[0005] In view of the above problems, a power data fault defect collection and analysis system for substation equipment is proposed. Summary of the invention

[0006] The purpose of the present invention is to provide a system for collecting and analyzing power data faults and defects of substation equipment, which solves the problem of slow fault and defect analysis in the background technology.

[0007] To achieve the above purpose, the present invention provides the following technical solutions: a power data fault defect collection and analysis system for substation equipment, including an electrical parameter collection module, an equipment status collection module and an environmental parameter collection module:

[0008] The electrical parameter acquisition module includes:

[0009] S11, voltage acquisition unit, current acquisition unit, power acquisition unit and frequency acquisition unit;

[0010] The device status acquisition module includes:

[0011] S22, temperature acquisition unit, pressure acquisition unit, mechanical state acquisition unit and insulation performance acquisition unit;

[0012] The environmental parameter acquisition module includes:

[0013] S33, ambient temperature collection unit, ambient humidity collection unit, meteorological parameter collection unit and electromagnetic environment collection unit;

[0014] A power data fault defect collection and analysis system for substation equipment, further comprising a fault warning submodule, the electrical parameter collection module, the equipment status collection module and the ambient temperature collection module. When a fault defect occurs in the above three modules, the fault warning submodule transmits the fault defect signal to the system to locate the location of the fault;

[0015] Step 1: Locate the faulty module according to the fault type;

[0016] Electrical parameter fault type: determine the suspected unit based on the abnormal conditions of voltage, current, power and frequency parameters;

[0017] Equipment status fault type, determine the suspected unit based on abnormal temperature, pressure, mechanical status, and insulation performance;

[0018] Environmental parameter fault type: determine the suspected unit based on abnormalities in ambient temperature, humidity, meteorological conditions, and electromagnetic environment;

[0019] Step 2: After analyzing the suspected unit for fault defects, calculate the influence between other units in the same module and the suspected unit, divide other units in the same module into unit groups in descending order of the total influence, and perform fault analysis on the unit groups in turn according to the comprehensive influence to locate the faulty unit;

[0020] Step 3: If the fault point cannot be located in step 2, select the divided unit groups in the faulty module, select the units in the normal module according to the influence of the unit groups, select the units in the normal module that are more affected, combine the units in the two modules to form a new cross-module group, and perform fault analysis on the units in the cross-module group to locate the fault point.

[0021] Preferably, when an abnormality occurs in the power data, the voltage and current in the circuit of the voltage acquisition unit and the current acquisition unit in the electrical parameter acquisition module are collected, and the power is calculated after the collection. The calculation formula is: voltage × current = power. After the power is calculated, it is compared with the data collected by the power acquisition unit, and the data coincidence is compared. The power calculated by the voltage acquisition unit and the current acquisition unit is P 1 , the data collected by the power acquisition unit is P 2 , the calculation formula of coincidence degree is:

[0022] Preferably, M i (1,2,3), where M 1 It is the electrical parameter acquisition module, M 2 It is the equipment status acquisition module, M 3 It is the environmental parameter acquisition module. It determines the module M where the fault defect is located according to the abnormal signal received by the fault warning submodule. i , when the fault warning submodule F i When an early warning signal is issued, F i =1, then determine M i A malfunction occurred.

[0023] Preferably, the order of unit sorting is determined according to the sum of the influence coefficients in the unit, the voltage acquisition value is U, the current acquisition value is I, the resistance is R, and the influence coefficient of voltage on current is: When R is determined, The larger the value is, the greater the influence of voltage change on current is, that is, the greater the influence of voltage acquisition unit on current acquisition unit is.

[0024] Preferably, in the equipment state fault type, the influence coefficient between temperature and mechanical state, wherein the mechanical state quantity is ΔM, the temperature change quantity is ΔT, the influence coefficient of the mechanical state on the temperature, and the functional relationship between the temperature change and the mechanical state change is T=f(M), The larger the absolute value of f′(M), the greater the influence of the mechanical state acquisition unit on the temperature acquisition unit.

[0025] Preferably, the influence coefficient of ambient temperature on humidity is , where the ambient temperature is T e The relative humidity of the environment is H. According to the relationship between humidity and temperature, H = g(T e ), g′(T e ) is larger, the greater the influence of the surface environment temperature acquisition unit on the environment humidity acquisition unit;

[0026] The influence coefficient of wind speed on ambient temperature is The wind speed is v and the ambient temperature change is ΔT e , the relationship between ambient temperature change and wind speed function is T e =h(v), the larger the absolute value of h′(v), the greater the influence of the collection part in the meteorological parameter collection unit on the ambient temperature collection unit.

[0027] Preferably, the total influence of the suspected unit on other units in the same module is calculated, and the unit groups are divided and sorted. The unit influence calculation formula is: Where j is the suspect unit number.

[0028] Preferably, cross-module fault defect analysis is performed by constructing a unit influence matrix, the unit influence matrix is ​​M, where M ij Indicates the influence of unit i on unit j. Fault module A divides unit groups into a k (k=1,2,…,n), n is the unit number in the unit group;

[0029] Unit b in normal module B l (l=1,2,…,m), m is the unit number in the normal module B, unit a k For unit b l The influence of M akbl ;

[0030] Determine the combined influence of the units in the faulty module A on the units in the normal module B. For each unit b in the normal module B l , calculate the unit group and comprehensive influence in fault module A Where W ak is the weight of unit ak in the group of other units;

[0031] According to the calculated comprehensive influence I bt Sort the units in the normal module B, select the units with greater comprehensive influence and combine them with the units in the faulty module A to form a new cross-module group, set the influence threshold T, and when I bt >T, combine unit bl with the related unit in faulty module A.

[0032] Preferably, in the cross-module unit selection step of cross-module fault analysis, an influence threshold is set, and units in the unit group within the fault module whose influence coefficient on other module units is greater than the influence threshold are selected as key units to narrow the screening range of cross-module related units.

[0033] Preferably, in the cross-module influence evaluation and ranking step, when determining the cross-module influence coefficient, if there are multiple physical connections or functional dependency paths that affect the cross-module unit relationship, the influence coefficient corresponding to each path is calculated separately, and the weighted sum is performed according to the importance weight of each path when the system is operating normally to obtain a comprehensive cross-module influence coefficient.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. The present invention provides a power data fault defect collection and analysis system for substation equipment. By calculating the total influence of the suspected unit on other units in the same module, and sorting and grouping other units in the same module according to the total influence, the faulty unit in the module can be located more efficiently. This fault analysis method based on unit influence makes full use of the internal connection between units, avoids blind investigation, greatly improves the accuracy and speed of fault location, reduces equipment downtime, and improves the operational reliability of substation equipment.

[0036] 2. The present invention provides a substation power data fault defect collection and analysis system, which constructs a cross-module fault analysis mechanism. During the cross-module fault analysis, the units in other modules that are more affected are selected based on the unit influence matrix, and the related units in the two modules are combined for fault analysis. This can accurately track and locate cross-module faults, and fully consider the correlation between the modules of the substation equipment. In this way, cross-module fault points can be quickly found, maintenance costs can be reduced, and the stability and maintainability of the entire substation equipment power data collection system can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of the fault defect analysis system of the present invention;

[0038] Figure 2 This is the overall fault location flow chart of the present invention;

[0039] Figure 3 This is a cross-module fault analysis flow chart of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings.

[0042] Combination Figure 1-Figure 3 , a power data fault defect collection and analysis system for substation equipment of the present invention comprises an electrical parameter collection module, an equipment status collection module and an environmental parameter collection module:

[0043] The electrical parameter acquisition module includes:

[0044] S11, voltage acquisition unit, current acquisition unit, power acquisition unit and frequency acquisition unit;

[0045] When the power data is abnormal, the voltage and current in the circuit of the voltage acquisition unit and the current acquisition unit in the electrical parameter acquisition module are collected, and the power is calculated after the collection. The calculation formula is: voltage × current = power. After the power is calculated, it is compared with the data collected by the power acquisition unit. The data coincidence is compared, and the power calculated by the voltage acquisition unit and the current acquisition unit is P 1 , the data collected by the power acquisition unit is P 2 , the calculation formula of coincidence degree is: By calculating power through voltage and current acquisition and comparing it with directly acquired power, the power acquisition unit can be monitored and calibrated in real time. If the overlap is low, it means that there is a fault in the power acquisition unit or there are errors in the voltage and current acquisition units. This can timely discover potential problems, improve the accuracy of electrical parameter acquisition, and provide a more reliable data basis for subsequent fault analysis.

[0046] The device status acquisition module includes:

[0047] S22, temperature acquisition unit, pressure acquisition unit, mechanical state acquisition unit and insulation performance acquisition unit, the influence coefficient between temperature and mechanical state in the equipment state fault type, where the mechanical state quantity is ΔM, the temperature change quantity is ΔT, the influence coefficient of the mechanical state on the temperature, the functional relationship between the temperature change and the mechanical state change T=f(M), The larger the absolute value of f′(M), the greater the influence of the mechanical state acquisition unit on the temperature acquisition unit.

[0048] The environmental parameter acquisition module includes:

[0049] S33, ambient temperature acquisition unit, ambient humidity acquisition unit, meteorological parameter acquisition unit and electromagnetic environment acquisition unit, influence coefficient of ambient temperature on humidity The ambient temperature is T e The relative humidity of the environment is H. According to the relationship between humidity and temperature, H = g(T e ), g′(T e ) is larger, the greater the influence of the surface environment temperature acquisition unit on the environment humidity acquisition unit;

[0050] The influence coefficient of wind speed on ambient temperature is The wind speed is v and the ambient temperature change is ΔT e , the relationship between ambient temperature change and wind speed function is T e =h(v), the larger the absolute value of h′(v), the greater the influence of the acquisition part in the meteorological parameter acquisition unit on the ambient temperature acquisition unit;

[0051] Calculate the total influence of the suspected unit on other units in the same module, divide and sort the units, and use the formula to calculate the unit influence: Where j is the suspect unit number, and the total influence of each suspect unit is calculated. Then, the other units in the same module are sorted in descending order of the total influence, and the units with similar total influence are grouped together to form unit groups. The unit groups are analyzed for faults in turn according to the comprehensive influence. When troubleshooting, targeted analysis can be performed based on the influence, avoiding blind inspection of all units. The unit group division further optimizes the troubleshooting order, giving priority to inspecting unit groups with large influence, which helps to find faulty units faster.

[0052] A power data fault defect collection and analysis system for substation equipment also includes a fault warning submodule, an electrical parameter collection module, an equipment status collection module and an ambient temperature collection module. When a fault defect occurs in the above three modules, the fault warning submodule transmits the fault defect signal to the system to locate the location of the fault. i (1,2,3), where M 1 It is the electrical parameter acquisition module, M 2 It is the equipment status acquisition module, M 3 It is the environmental parameter acquisition module. It determines the module M where the fault defect is located according to the abnormal signal received by the fault warning submodule. i , when the fault warning submodule F i When an early warning signal is issued, F i =1, then determine M i When a fault occurs, the order of the units is determined according to the sum of the influence coefficients in the units. The voltage acquisition value is U, the current acquisition value is I, the resistance is R, and the influence coefficient of voltage on current is: When R is determined, The larger the value is, the greater the influence of voltage change on current, that is, the greater the influence of voltage acquisition unit on current acquisition unit. The real-time monitoring and rapid response capabilities of fault warning submodule can send a signal at the first time a fault occurs, quickly identify the module where the fault is located, and avoid the long-term lurking and spreading of the fault in the system.

[0053] Step 1: Locate the faulty module according to the fault type;

[0054] Electrical parameter fault type: determine the suspected unit based on the abnormal conditions of voltage, current, power and frequency parameters;

[0055] Equipment status fault type, determine the suspected unit based on abnormal temperature, pressure, mechanical status, and insulation performance;

[0056] Environmental parameter fault type: determine the suspected unit based on abnormalities in ambient temperature, humidity, meteorological conditions, and electromagnetic environment;

[0057] Step 2: After analyzing the suspected unit for fault defects, calculate the influence between other units in the same module and the suspected unit, divide other units in the same module into unit groups in descending order of the total influence, and perform fault analysis on the unit groups in turn according to the comprehensive influence to locate the faulty unit;

[0058] Step 3: If the fault point cannot be located in step 2, select the divided unit groups in the faulty module, select the units in the normal module according to the influence of the unit groups, select the units in the normal module that are more affected, combine the units in the two modules to form a new cross-module group, and perform fault analysis on the units in the cross-module group to locate the fault point;

[0059] Cross-module fault defect analysis, through the constructed unit influence matrix, the unit influence matrix is ​​M, where M ij Indicates the influence of unit i on unit j. Fault module A divides unit groups into a k (k=1,2,…,n), n is the unit number in the unit group;

[0060] Unit b in normal module B l (l=1,2,…,m), m is the unit number in the normal module B, unit a k For unit b l The influence of

[0061] Determine the combined influence of the units in the faulty module A on the units in the normal module B. For each unit b in the normal module B l , calculate the unit group and comprehensive influence in fault module A Where W ak is the weight of unit ak in the group of other units;

[0062] According to the calculated comprehensive influence I bt Sort the units in the normal module B, select the units with greater comprehensive influence and combine them with the units in the faulty module A to form a new cross-module group, set the influence threshold T, and when I bt >T, combine unit bl with the related unit in faulty module A.

[0063] In the cross-module unit selection step of cross-module fault analysis, an influence threshold is set, and the unit whose influence coefficient on other module units is greater than the influence threshold in the unit group within the fault module is selected as the key unit to narrow the screening range of cross-module associated units. By setting the influence threshold, those units with weak influence on other modules can be quickly excluded, avoiding unnecessary association checks on a large number of irrelevant units in subsequent analysis, greatly narrowing the screening range of cross-module associated units, effectively reducing the consumption of computing resources and time, and improving the initial efficiency of fault analysis. At the same time, focusing on key units with greater influence can more accurately locate the source that may cause cross-module fault propagation, increasing the possibility of quickly finding the root cause of the fault, thereby reducing the downtime of equipment due to faults and improving the reliability and stability of the entire power system;

[0064] For example, in a power data acquisition system for a substation, the fault module is an electrical parameter acquisition module, and a certain unit group inside it contains multiple units related to voltage and current acquisition. For these units, the system will check their influence coefficients on the units in other modules such as the equipment status acquisition module and the environmental parameter acquisition module one by one. If the influence threshold is set to 0.5, when the influence coefficient of a voltage acquisition unit on the insulation monitoring unit in the equipment status acquisition module is greater than 0.5, the voltage acquisition unit will be regarded as a key unit.

[0065] In this way, by setting the influence threshold, those units with weak influence on other modules can be quickly excluded, avoiding unnecessary association checks on a large number of irrelevant units in subsequent analysis, greatly narrowing the screening scope of cross-module association units, which effectively reduces the consumption of computing resources and time, and improves the initial efficiency of fault analysis;

[0066] On the other hand, focusing on key units with greater influence can more accurately locate the source that may cause cross-module fault propagation, increasing the possibility of quickly finding the root cause of the fault, thereby reducing the downtime of equipment due to faults and improving the reliability and stability of the entire power system.

[0067] In the cross-module influence evaluation and ranking step, when determining the cross-module influence coefficient, if there are multiple physical connections or functional dependency paths that affect the cross-module unit relationship, the influence coefficient corresponding to each path is calculated separately, and the weighted sum is performed according to the importance weight of each path when the system is operating normally, so as to obtain a comprehensive cross-module influence coefficient;

[0068] The system will calculate the influence coefficient for each possible influence path. For the direct electrical signal influence path, if the functional relationship between current and device temperature is known to be T = f(I), where T is temperature and I is current, then the influence coefficient I of the temperature acquisition unit relative to the current acquisition unit on this path can be calculated based on this functional relationship. 1 ;

[0069] For the indirect control signal impact path, by analyzing the control logic of the protection device and the relationship between the equipment state change and the mechanical state acquisition unit, the influence coefficient I of the mechanical state acquisition unit relative to the voltage acquisition unit on this path is calculated. 2 .

[0070] Then, a weighted sum is calculated based on the importance weight of each path when the system is operating normally. For example, the importance weight of the electrical signal affecting the path when the system is operating normally is W. 1 =0.6, the importance weight of the indirect control signal impact path is W 2 =0.4, then the comprehensive cross-module influence coefficient I = W 1 I 1 +W 2 I 2 .

[0071] By calculating the influence coefficient of each path separately, no possible fault propagation path will be ignored, ensuring the integrity of the fault analysis. By weighted summation based on importance weights, the role of more critical influencing paths in normal operation can be highlighted, making the comprehensive influence coefficient more in line with the actual situation, thus providing a scientific and reasonable basis for the sorting of cross-module units. This helps to prioritize the combinations that are most likely to cause faults among many cross-module unit combinations, further improving the efficiency and accuracy of cross-module fault analysis, helping to quickly locate and solve cross-module fault problems, and ensuring the safe and stable operation of the power system.

[0072] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0073] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A power data fault defect collection and analysis system for substation equipment, comprising an electrical parameter collection module, an equipment status collection module and an environmental parameter collection module, characterized in that: The electrical parameter acquisition module includes: S11, voltage acquisition unit, current acquisition unit, power acquisition unit and frequency acquisition unit; The device status acquisition module includes: S22, temperature acquisition unit, pressure acquisition unit, mechanical state acquisition unit and insulation performance acquisition unit; The environmental parameter acquisition module includes: S33, ambient temperature collection unit, ambient humidity collection unit, meteorological parameter collection unit and electromagnetic environment collection unit; It also includes a fault warning submodule, the electrical parameter acquisition module, the equipment status acquisition module and the ambient temperature acquisition module. When a fault defect occurs in the above three modules, the fault warning submodule transmits the fault defect signal to the system to locate the location of the fault; Step 1: Locate the faulty module according to the fault type; Electrical parameter fault type: determine the suspected unit based on the abnormal conditions of voltage, current, power and frequency parameters; Equipment status fault type, determine the suspected unit based on abnormal temperature, pressure, mechanical status, and insulation performance; Environmental parameter fault type: determine the suspected unit based on abnormalities in ambient temperature, humidity, meteorological conditions, and electromagnetic environment; Step 2: After analyzing the suspected unit for fault defects, calculate the influence between other units in the same module and the suspected unit, divide other units in the same module into unit groups in descending order of the total influence, and perform fault analysis on the unit groups in turn according to the comprehensive influence to locate the faulty unit; Step 3: If the fault point cannot be located in step 2, select the divided unit groups in the faulty module, select the units in the normal module according to the influence of the unit groups, select the units in the normal module that are more affected, combine the units in the two modules to form a new cross-module group, and perform fault analysis on the units in the cross-module group to locate the fault point.

2. A power data fault defect collection and analysis system for substation equipment according to claim 1, characterized in that: When the power data is abnormal, the voltage and current in the circuit of the voltage acquisition unit and the current acquisition unit in the electrical parameter acquisition module are collected, and the power is calculated after the collection. The calculation formula is: voltage × current = power. After the power is calculated, it is compared with the data collected by the power acquisition unit to compare the data coincidence. The power calculated by the voltage acquisition unit and the current acquisition unit is P1, and the data collected by the power acquisition unit is P2. The coincidence calculation formula is:

3. The power data fault defect collection and analysis system for substation equipment according to claim 1 is characterized by: M i (1,2,3), where M1 is the electrical parameter acquisition module, M2 is the equipment status acquisition module, and M3 is the environmental parameter acquisition module. According to the abnormal signal received by the fault warning submodule, the fault defect module M is determined. i , when the fault warning submodule F i When an early warning signal is issued, F i =1, then determine M i A malfunction occurred.

4. A power data fault defect collection and analysis system for substation equipment according to claim 3, characterized in that: The order of the units is determined according to the sum of the influence coefficients within the units. The voltage acquisition value is U, the current acquisition value is I, the resistance is R, and the influence coefficient of voltage on current is: When R is determined, The larger the value is, the greater the influence of voltage change on current is, that is, the greater the influence of voltage acquisition unit on current acquisition unit is.

5. The power data fault defect collection and analysis system for substation equipment according to claim 1 is characterized by: The influence coefficient between temperature and mechanical state in the equipment state fault type, where the mechanical state quantity is ΔM, the temperature change quantity is ΔT, the influence coefficient of mechanical state on temperature, and the functional relationship between temperature change and mechanical state change T=f(M), The larger the absolute value of f′(M), the greater the influence of the mechanical state acquisition unit on the temperature acquisition unit.

6. A power data fault defect collection and analysis system for substation equipment according to claim 1, characterized in that: The influence coefficient of ambient temperature on humidity The ambient temperature is T e The relative humidity of the environment is H. According to the relationship between humidity and temperature, H = g(T e ), g′(T e ) is larger, the greater the influence of the surface environment temperature acquisition unit on the environment humidity acquisition unit; The influence coefficient of wind speed on ambient temperature is The wind speed is v and the ambient temperature change is ΔT e , the relationship between ambient temperature change and wind speed function is T e =h(v), the larger the absolute value of h′(v), the greater the influence of the collection part in the meteorological parameter collection unit on the ambient temperature collection unit.

7. The power data fault defect collection and analysis system for substation equipment according to claim 1 is characterized by: Calculate the total influence of the suspected unit on other units in the same module, divide and sort the units, and use the formula to calculate the unit influence: Where j is the suspect unit number.

8. The power data fault defect collection and analysis system for substation equipment according to claim 1 is characterized by: Cross-module fault defect analysis, through the constructed unit influence matrix, the unit influence matrix is ​​M, where M ij Indicates the influence of unit i on unit j. Fault module A divides unit groups into a k (k=1,2,…,n), n is the unit number in the unit group; Unit b in normal module B l (l=1,2,…,m), m is the unit number in the normal module B, unit a k For unit b l The influence of Determine the combined influence of the units in the faulty module A on the units in the normal module B. For each unit b in the normal module B l , calculate the unit group and comprehensive influence in fault module A Where W ak is the weight of unit ak in the group of other units; According to the calculated comprehensive influence I bt Sort the units in the normal module B, select the units with greater comprehensive influence and combine them with the units in the faulty module A to form a new cross-module group, set the influence threshold T, and when I bt >T, combine unit bl with the related unit in faulty module A.

9. The power data fault defect collection and analysis system for substation equipment according to claim 1, characterized in that: In the cross-module unit selection step of cross-module fault analysis, an influence threshold is set, and units in the unit group within the fault module whose influence coefficient on other module units is greater than the influence threshold are selected as key units to narrow the screening range of cross-module related units.

10. The power data fault defect collection and analysis system for substation equipment according to claim 1, characterized in that: In the cross-module influence evaluation and ranking step, when determining the cross-module influence coefficient, if there are multiple physical connections or functional dependency paths that affect the cross-module unit relationship, the influence coefficient corresponding to each path is calculated separately, and the weighted sum is performed according to the importance weight of each path when the system is operating normally to obtain a comprehensive cross-module influence coefficient.

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