A diagnostic method and system for a self-diagnostic switch device for distribution network faults

Abstract

The invention relates to the technical field of power grid fault diagnosis, and specifically discloses a method and system for diagnosing a self-judgment switch device for a distribution network fault, which monitors the operating current of the distribution network in real time, and obtains a current characterization value according to the operating current analysis; obtains the distribution network operating risk value according to the operating current of the distribution network, and generates a large distribution network operating risk signal; diagnoses the disconnection time of the self-judgment switch of the distribution network, obtains the switch diagnosis total value according to the disconnection time analysis of the self-judgment switch, and generates an insensitive signal of the self-judgment switch; obtains the switch diagnosis total value, obtains the self-judgment switch adjustment coefficient through a formula calculation, and completes the optimization adjustment work of the self-judgment switch; the invention diagnoses and analyzes the sensitivity of the self-judgment switch, determines whether it is caused by the insufficient sensitivity of the self-judgment switch, and then completes the monitoring frequency of the self-judgment switch in the distribution network based on the generated factors, so as to facilitate timely diagnosis and maintenance of the self-judgment switch.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid fault diagnosis, and in particular to a method and system for diagnosing a power distribution network fault self-diagnosis switch device. Background Art

[0002] Distribution network fault diagnosis is an important means to improve the safe and reliable operation of distribution networks from a technical perspective. Accurate fault location, fault cause analysis, and fault solution proposals can reduce power outage time, adjust switch sensitivity, reduce the impact of faults on surrounding equipment and lines, and reduce the difficulty and time of fault repair.

[0003] A Chinese invention patent with publication number CN117872037A discloses a fault diagnosis method, device and distribution network system based on a smart switch. The distribution network includes distributed power sources, each of which has multiple lines. The smart switch is located on the line. The method includes: obtaining line information; determining the fault type based on the line information; constructing a Petri net model based on the line information, and performing fault diagnosis on the distribution network based on the Petri net model to obtain the fault location; and obtaining fault information based on the fault type and the fault location.

[0004] Although the above can determine the fault information, in the specific implementation process, the information on distribution network fault diagnosis is relatively one-sided. It can only obtain the type of fault, and cannot effectively and accurately diagnose the degree of the fault, and cannot diagnose the impact relationship between the faults caused by the self-diagnostic switches based on the fault degree generated by the distribution network diagnosis. Summary of the invention

[0005] The object of the present invention is to provide a method and system for diagnosing a power distribution network fault self-diagnosis switch device to solve the technical problems in the above background.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A method for diagnosing a power distribution network fault self-diagnosis switch device, comprising the following steps:

[0008] Step 1: monitor the operating current of the distribution network in real time, and obtain a current characterization value based on the operating current analysis. If the current characterization value is greater than or equal to the current characterization threshold, generate a strong distribution network operation fluctuation signal;

[0009] The current characterization value is calculated based on the high peak current amplitude value and the low peak current amplitude value;

[0010] Step 2: When a strong signal of distribution network operation fluctuation is obtained, the distribution network operation risk value is analyzed according to the operation current of the distribution network. If the distribution network operation risk value is greater than or equal to the distribution network operation risk threshold, a large signal of distribution network operation risk is generated;

[0011] The distribution network operation risk value is calculated based on the peak fluctuation risk value and the low peak fluctuation risk value;

[0012] Step 3: Based on the large signal of the distribution network operation risk, diagnose the disconnection time of the distribution network self-judgment switch, and obtain the switch diagnosis total value according to the disconnection time analysis of the self-judgment switch. If the switch diagnosis total value is greater than or equal to the switch diagnosis total threshold, generate a self-judgment switch insensitivity signal;

[0013] The total switch diagnosis value is calculated based on the high peak switch diagnosis value and the low peak switch diagnosis value;

[0014] Step 4: Based on the insensitive signal of the self-judgment switch, the total switch diagnosis value is obtained, and the self-judgment switch adjustment coefficient is calculated by the formula to complete the optimization adjustment of the self-judgment switch;

[0015] The formula is: , the adjusted monitoring frequency of the self-judgment switch is calculated, T is represented by the current monitoring frequency of the self-judgment switch, and K is the self-judgment switch adjustment coefficient.

[0016] As a further solution of the present invention: the monitoring period includes a peak period and a low-peak period, and the division standard between the peak period and the low-peak period is: obtain the power load of the distribution network within the monitoring period, compare the power load with the preset standard power load, if the power load is greater than or equal to the preset standard power load, the corresponding time is marked as the peak period, if the power load is less than the preset standard power load, the corresponding time is marked as the low-peak period.

[0017] As a further solution of the present invention: the peak current amplitude value is obtained in the following manner:

[0018] The peak cycle is divided into several peak time periods at equal time intervals. The current value at the start time node of the peak time period is subtracted from the current value at the end time node to obtain the peak current change value. The standard deviation of all the peak current change values ​​in the peak cycle is calculated to obtain the peak current amplitude value.

[0019] As a further solution of the present invention: the low peak current amplitude value is obtained in the following manner:

[0020] The low-peak cycle is divided into several low-peak periods according to equal time intervals. The current value at the start time node of the low-peak period is subtracted from the current value at the end time node to obtain the low-peak current change value. The standard deviation of all low-peak current change values ​​in the low-peak cycle is calculated to obtain the low-peak current amplitude value.

[0021] As a further solution of the present invention: the analysis process of the peak fluctuation risk value is:

[0022] Obtain all peak current change values ​​within the peak period, and compare the peak current change value with a peak current change threshold;

[0023] If the peak current change value is greater than or equal to the peak current change threshold, a high-risk signal for peak current change is generated, and the peak period corresponding to the high-risk signal for peak current change is marked as a high-risk period of the peak cycle;

[0024] The number of high-risk periods that generate the peak cycle within the monitoring period is obtained, marked as the number of peak high-risk periods, and the ratio of the number of peak high-risk periods to the number of peak periods divided by the peak cycle is calculated to obtain the peak fluctuation risk value.

[0025] As a further solution of the present invention: the analysis process of the low peak fluctuation risk value is:

[0026] Obtain all low-peak current change values ​​within the low-peak period, and compare the low-peak current change value with a low-peak current change threshold;

[0027] If the low peak current change value is greater than or equal to the low peak current change threshold, a low peak current change high risk signal is generated, and the low peak period corresponding to the low peak current change high risk signal is marked as a high risk period of the low peak cycle;

[0028] Obtain the number of high-risk time periods that generate low-peak cycles within the monitoring period, mark them as the number of low-peak high-risk time periods, calculate the ratio of the number of low-peak high-risk time periods to the number of low-peak time periods divided by the low-peak cycle, and obtain the low-peak fluctuation risk value.

[0029] As a further solution of the present invention: the peak switch diagnostic value is obtained in the following manner:

[0030] In the peak cycle, the switch disconnection time in each peak period in the peak cycle is detected and marked as the peak switch disconnection time;

[0031] Subtract the peak switch disconnection time from the peak switch disconnection time standard value to obtain the peak switch disconnection time deviation value;

[0032] All peak switch disconnection time deviation values ​​are added and averaged to obtain the peak switch diagnostic value.

[0033] As a further solution of the present invention: the low peak switch diagnostic value is obtained in the following manner:

[0034] In the low-peak cycle, the switch disconnection time in each low-peak period in the low-peak cycle is detected and marked as the low-peak switch disconnection time;

[0035] Subtract the low-peak switch disconnection time from the low-peak switch disconnection time threshold to obtain a low-peak switch disconnection time deviation value;

[0036] All low-peak switch disconnection time deviation values ​​are added and averaged to obtain the low-peak switch diagnosis value.

[0037] As a further solution of the present invention: the process of obtaining the self-judgment switch adjustment coefficient K is:

[0038] formula: , the self-judgment switch adjustment coefficient K is calculated, and ZD is expressed as the total switch diagnosis value of the current monitoring cycle. Expressed as the overall switch diagnosis threshold.

[0039] A diagnostic system for a power distribution network fault self-diagnosis switch device, the system comprising:

[0040] Operation diagnosis module: monitors the operation current of the distribution network in real time, and obtains the current characterization value based on the operation current analysis. If the current characterization value is greater than or equal to the current characterization threshold, a strong signal of distribution network operation fluctuation is generated;

[0041] The current characterization value is calculated based on the high peak current amplitude value and the low peak current amplitude value;

[0042] Risk diagnosis module: When a strong signal of distribution network operation fluctuation is obtained, the distribution network operation risk value is analyzed according to the operating current of the distribution network. If the distribution network operation risk value is greater than or equal to the distribution network operation risk threshold, a large distribution network operation risk signal is generated;

[0043] The distribution network operation risk value is calculated based on the peak fluctuation risk value and the low peak fluctuation risk value;

[0044] Factor diagnosis module: Based on the large signal of distribution network operation risk, the disconnection time of the self-judgment switch of the distribution network is diagnosed, and the total switch diagnosis value is obtained according to the disconnection time analysis of the self-judgment switch. If the total switch diagnosis value is greater than or equal to the total switch diagnosis threshold, an insensitive signal of the self-judgment switch is generated;

[0045] The total switch diagnosis value is calculated based on the high peak switch diagnosis value and the low peak switch diagnosis value;

[0046] Monitoring and control module: Based on the insensitive signal of the self-judgment switch, the total switch diagnosis value is obtained, and the self-judgment switch adjustment coefficient is calculated through the formula to complete the optimization adjustment of the self-judgment switch;

[0047] The formula is: , calculate the monitoring frequency of the adjusted self-judgment switch , T represents the monitoring frequency of the current self-judgment switch, and K is the self-judgment switch adjustment coefficient.

[0048] Beneficial effects of the present invention:

[0049] The present invention arranges a current sensor on the distribution network to monitor the operation current of the distribution network in a monitoring period in real time, first analyzes the operation current to obtain a high peak current amplitude value and a low peak current amplitude value, then adds the high peak current amplitude value and the low peak current amplitude value to obtain a current characterization value, compares the current characterization value with a current characterization threshold value, generates a distribution network operation fluctuation strong signal if the current characterization value is greater than or equal to the current characterization threshold value, and analyzes the operation current according to the distribution network operation fluctuation strong signal to obtain a high peak fluctuation risk value and a low peak fluctuation risk value, calculates the high peak fluctuation risk value and the low peak fluctuation risk value to obtain a distribution network operation risk value, compares the distribution network operation risk value with the distribution network operation risk threshold value, generates a distribution network operation risk large signal, and compares the diagnosis current characterization value with the current characterization threshold value, and the distribution network operation risk value with the distribution network operation risk threshold value, thereby realizing step-by-step diagnosis of the distribution network operation from fluctuation to risk of different degrees, thereby more accurately diagnosing the operation state of the distribution network;

[0050] The present invention diagnoses the disconnection time of the self-judgment switch of the distribution network based on the large signal of the risk of the operation of the distribution network, obtains the total switch diagnosis value according to the disconnection time analysis of the self-judgment switch, and compares it with the threshold value to generate a signal whether the self-judgment switch is sensitive; based on the insensitive signal of the self-judgment switch, the total switch diagnosis value is obtained, and the self-judgment switch adjustment coefficient is calculated by a formula to complete the optimization adjustment of the self-judgment switch; the present invention diagnoses and analyzes the sensitivity of the self-judgment switch to determine whether it is caused by the insufficient sensitivity of the self-judgment switch, and then adjusts the monitoring frequency of the self-judgment switch in the distribution network based on the generated factors, so as to facilitate timely diagnosis and maintenance of the self-judgment switch. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The present invention will be further described below in conjunction with the accompanying drawings.

[0052] Figure 1 is a flow chart of a method according to Embodiment 1 of the present invention;

[0053] Figure 2is a method flow chart of Embodiment 2 of the present invention;

[0054] Figure 3 It is a module schematic diagram of the system of the present invention. DETAILED DESCRIPTION

[0055] 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. Embodiment 1

[0056] See also Figure 1 As shown, a method for diagnosing a power distribution network fault self-diagnosis switch device according to an embodiment of the present invention comprises the following steps:

[0057] Step 1: monitor the operating current of the distribution network in real time, obtain a current characterization value based on the operating current analysis, compare the current characterization value with the current characterization threshold, and generate a distribution network operation fluctuation signal;

[0058] The distribution network operation fluctuation signal includes a distribution network operation fluctuation strong signal or a distribution network operation fluctuation weak signal;

[0059] In some embodiments, a current sensor is provided on the distribution network to monitor the operating current of the distribution network in a monitoring period in real time, and the operating current is first analyzed to obtain a high peak current amplitude value and a low peak current amplitude value, and then the high peak current amplitude value and the low peak current amplitude value are added to obtain a current characterization value;

[0060] The current characterization value is compared with the current characterization threshold value. The comparison process is as follows:

[0061] If the current characterization value is greater than or equal to the current characterization threshold, it means that during the operation of the distribution network, the fluctuation degree of the distribution network operation current during the peak period and the off-peak period does not meet the standard requirements of the distribution network, and a strong fluctuation signal of the distribution network operation is generated;

[0062] If the current characterization value is less than the current characterization threshold, it means that during the operation of the distribution network, the fluctuation degree of the distribution network operation current during the peak period and the off-peak period meets the standard requirements of the distribution network, and a weak signal of the operation fluctuation of the distribution network is generated;

[0063] The monitoring period includes a peak period and a low-peak period. The division criteria of the peak period and the low-peak period are as follows: the power load of the distribution network within the monitoring period is obtained, and the power load is compared with the standard power load preset by the technicians. If the power load is greater than or equal to the preset standard power load, the corresponding time mark is the peak period. On the contrary, if the power load is less than the preset standard power load, the corresponding time mark is the low-peak period.

[0064] First, the peak current amplitude value is obtained in the following way:

[0065] Divide the peak cycle into several peak time periods at equal time intervals, obtain the current values ​​at the start time nodes and the end time nodes of all peak time periods, subtract the current values ​​at the start time nodes and the end time nodes of the peak period to obtain the peak current change value, calculate the standard deviation of all the peak current change values ​​in the peak cycle to obtain the peak current amplitude value;

[0066] The second specific method for obtaining the low peak current amplitude value is:

[0067] Divide the low-peak cycle into several low-peak time periods at equal time intervals, obtain the current values ​​at the start time nodes and the end time nodes of all low-peak time periods, subtract the current value at the start time node from the current value at the end time node of the low-peak period to obtain the low-peak current change value, calculate the standard deviation of all low-peak current change values ​​in the low-peak cycle, and obtain the low-peak current amplitude value;

[0068] Step 2: When a strong signal of distribution network operation fluctuation is obtained, the distribution network operation risk value is analyzed according to the operation current of the distribution network, and the distribution network operation risk value is compared with the distribution network operation risk threshold to generate a distribution network operation risk signal;

[0069] Among them, the distribution network operation risk signal includes a distribution network operation risk large signal or a distribution network operation risk small signal;

[0070] In some embodiments, when a strong signal of distribution network operation fluctuation is generated, the operation current of the monitored distribution network within the monitoring period is obtained, the operation current is first analyzed to obtain a high peak fluctuation risk value and a low peak fluctuation risk value, and the high peak fluctuation risk value and the low peak fluctuation risk value are summed to obtain the distribution network operation risk value;

[0071] First, the specific analysis process of the peak fluctuation risk value is as follows:

[0072] Obtain all peak current change values ​​within the peak period, and compare the peak current change value with a peak current change threshold;

[0073] If the peak current change value is greater than or equal to the peak current change threshold, it means that the peak current change range is large, reflecting that its change degree exceeds the normal fluctuation degree of power distribution. During this time, there is a high risk in the distribution network, and a high-risk signal of peak current change is generated. The peak period corresponding to the high-risk signal of peak current change is marked as a high-risk period of the peak cycle.

[0074] If the peak current change value is less than the peak current change threshold, it means that the peak current change interval is small, reflecting that its change degree does not exceed the normal fluctuation degree of power distribution. During this time, the distribution network has a low risk, and a peak current change low risk signal is generated. The peak period corresponding to the peak current change low risk signal is marked as a low risk period of the peak cycle.

[0075] Obtain the number of high-risk periods that generate a peak cycle within the monitoring period, mark it as the number of peak high-risk periods, calculate the ratio of the number of peak high-risk periods to the number of peak periods divided by the peak cycle, and obtain the peak fluctuation risk value;

[0076] The second specific analysis process of low peak volatility risk value is as follows:

[0077] Obtain all low-peak current change values ​​within the low-peak period, and compare the low-peak current change value with a low-peak current change threshold;

[0078] If the low-peak current change value is greater than or equal to the low-peak current change threshold, it means that the low-peak current change range is large, reflecting that its change degree exceeds the normal fluctuation degree of power distribution, and there is a high risk in the distribution network during this time, then a low-peak current change high-risk signal is generated, and the low-peak period corresponding to the low-peak current change high-risk signal is marked as a high-risk period of the low-peak cycle;

[0079] If the low-peak current change value is less than the low-peak current change threshold, it means that the low-peak current change interval is small, reflecting that its change degree does not exceed the normal fluctuation degree of power distribution, and the distribution network has a low risk during this time, then a low-peak current change low-risk signal is generated, and the low-peak period corresponding to the low-peak current change low-risk signal is marked as a low-risk period of the low-peak cycle;

[0080] Obtain the number of high-risk periods that generate low-peak cycles within the monitoring period, mark them as the number of low-peak high-risk periods, calculate the ratio of the number of low-peak high-risk periods to the number of low-peak periods divided by the low-peak cycle, and obtain the low-peak fluctuation risk value;

[0081] It needs to be further explained that the meaning of the distribution network operation risk value is: after the preliminary analysis of the fluctuation intensity of the distribution network operation current, further analysis is performed on the situation where the current changes greatly in the peak period and the low peak period to judge the risk level of the distribution network operation. The larger the distribution network operation risk value, the greater the risk of the distribution network operation; the smaller the distribution network operation risk value, the smaller the risk of the distribution network operation.

[0082] The distribution network operation risk value is compared with the distribution network operation risk threshold. The comparison process is as follows:

[0083] If the distribution network operation risk value is greater than or equal to the distribution network operation risk threshold, it means that the distribution network operation current has a serious change in the peak cycle and the low-peak cycle, and the distribution network has a high risk in both the peak cycle and the low-peak cycle, generating a large distribution network operation risk signal;

[0084] If the distribution network operation risk value is less than the distribution network operation risk threshold, it means that the current change of the distribution network operation current in the peak period and the low peak period is not serious, and the distribution network has a low risk in both the peak period and the low peak period, and a small signal of distribution network operation risk is generated;

[0085] Specific implementation scheme of the embodiment of the present invention: by arranging a current sensor on the distribution network, the operating current of the distribution network within the monitoring period is monitored in real time, the operating current is first analyzed to obtain a high peak current amplitude value and a low peak current amplitude value, and then the high peak current amplitude value and the low peak current amplitude value are added to obtain a current characterization value, and the current characterization value is compared with the current characterization threshold value. If the current characterization value is greater than or equal to the current characterization threshold value, a strong distribution network operation fluctuation signal is generated, and according to the strong distribution network operation fluctuation signal, the operating current is analyzed to obtain a high peak fluctuation risk value and a low peak fluctuation risk value, and the high peak fluctuation risk value and the low peak fluctuation risk value are summed to obtain a distribution network operation risk value, and the distribution network operation risk value is compared with the distribution network operation risk threshold value. If the distribution network operation risk value is greater than or equal to the distribution network operation risk threshold value, a large distribution network operation risk signal is generated, and by comparing the diagnosis current characterization value with the current characterization threshold value and the distribution network operation risk value with the distribution network operation risk threshold value, a step-by-step diagnosis of the distribution network operation from fluctuation to risk is realized, thereby more accurately diagnosing the operation status of the distribution network. Embodiment 2

[0086] Based on Example 1, please refer to Figure 2 As shown, the diagnosis method of a power distribution network fault self-diagnosis switch device according to an embodiment of the present invention also includes the following method:

[0087] Step 3: Based on the large signal of the distribution network operation risk, diagnose the disconnection time of the distribution network self-judgment switch, analyze the disconnection time of the self-judgment switch to obtain the total switch diagnosis value, and compare it with the threshold to generate a signal whether the self-judgment switch is sensitive;

[0088] Wherein, the self-judgment switch sensitivity signal includes a self-judgment switch sensitive signal or a self-judgment switch insensitive signal;

[0089] In some embodiments, when a large signal of distribution network operation risk is generated, the disconnection time of the distribution network self-judgment switch in the peak period and the low peak period is diagnosed respectively to obtain a peak switch diagnostic value and a low peak switch diagnostic value, the peak switch diagnostic value and the low peak switch diagnostic value are added and summed to obtain a switch diagnostic total value, and the switch diagnostic total value is compared with the switch diagnostic total threshold, and the comparison process is as follows:

[0090] If the switch diagnosis total value is greater than or equal to the switch diagnosis total threshold value, it means that the sensitivity of the self-judgment switch is unstable in both the peak period and the low peak period, and a self-judgment switch insensitivity signal is generated;

[0091] If the total switch diagnosis value is less than the total switch diagnosis threshold, it means that the sensitivity of the self-judgment switch is stable in both the peak period and the low peak period, and a self-judgment switch sensitivity signal is generated;

[0092] First, the peak switch diagnostic value is obtained in the following way:

[0093] In the peak cycle, the switch disconnection time in each peak period in the peak cycle is detected and marked as the peak switch disconnection time;

[0094] Subtract the peak switch disconnection time from the peak switch disconnection time standard value (preset by technical personnel based on historical experience data) to obtain the peak switch disconnection time deviation value;

[0095] All peak switch disconnection time deviation values ​​are added and averaged to obtain the peak switch diagnostic value;

[0096] It can be understood that: the peak switch diagnostic value indicates: the degree of deviation between all peak switch disconnection times and the peak switch disconnection time standard value in the peak cycle. If the peak switch diagnostic value is small, it means that the switch disconnection time in the peak cycle is less different from the switch disconnection time threshold, and the self-judgment switch sensitivity is relatively stable. If the peak switch diagnostic value is large, it means that the switch disconnection time in the peak cycle is more different from the switch disconnection time standard value, and the self-judgment switch sensitivity is relatively unstable.

[0097] Second, the method for obtaining the low peak switch diagnostic value is as follows:

[0098] In the low-peak cycle, the switch disconnection time in each low-peak period in the low-peak cycle is detected and marked as the low-peak switch disconnection time;

[0099] Subtract the low-peak switch disconnection time from the standard value of the low-peak switch disconnection time (preset by the technicians based on historical experience data) to obtain the low-peak switch disconnection time deviation value;

[0100] All the low-peak switch disconnection time deviation values ​​are added and averaged to obtain the low-peak switch diagnosis value;

[0101] It can be understood that: the low-peak switch diagnostic value indicates: in the low-peak cycle, the degree of deviation between all low-peak switch disconnection times and the standard value of the low-peak switch disconnection time. If the low-peak switch diagnostic value is small, it means that the switch disconnection time in the low-peak cycle is less different from the switch disconnection time threshold, and the sensitivity of the self-judgment switch is relatively stable. If the low-peak switch diagnostic value is large, it means that in the low-peak cycle, the switch disconnection time is more different from the standard value of the switch disconnection time, and the sensitivity of the self-judgment switch is relatively unstable.

[0102] Step 4: Based on the insensitive signal of the self-judgment switch, the total switch diagnosis value is obtained, and the self-judgment switch adjustment coefficient is calculated by the formula to complete the optimization adjustment of the self-judgment switch;

[0103] In some embodiments, when a switch insensitivity signal is generated, a switch diagnosis total value is obtained, the switch diagnosis total value is substituted into a formula, and a self-judgment switch adjustment coefficient is calculated to complete the optimization adjustment of the switch sensitivity;

[0104] Among them, the formula: , the self-judgment switch adjustment coefficient K is calculated, and ZD is expressed as the total switch diagnosis value of the current monitoring cycle. It is expressed as the total threshold of switch diagnosis;

[0105] Specifically, the optimization and adjustment process of the self-judgment switch is as follows:

[0106] By formula: , calculate the monitoring frequency of the adjusted self-judgment switch , T represents the monitoring frequency of the current self-judgment switch.

[0107] The specific implementation scheme of the embodiment of the present invention is as follows: based on the large signal of the risk of the operation of the distribution network, the disconnection time of the self-judgment switch of the distribution network is diagnosed, and the total switch diagnosis value is obtained according to the disconnection time analysis of the self-judgment switch, and compared with the threshold value to generate a signal whether the self-judgment switch is sensitive; based on the insensitive signal of the self-judgment switch, the total switch diagnosis value is obtained, and the self-judgment switch adjustment coefficient is calculated by a formula to complete the optimization adjustment of the self-judgment switch; the present invention diagnoses and analyzes the sensitivity of the self-judgment switch to determine whether it is caused by the insufficient sensitivity of the self-judgment switch, and then based on the generated factors, the monitoring frequency of the self-judgment switch in the distribution network is adjusted to facilitate timely diagnosis and maintenance of the self-judgment switch. Embodiment 3

[0108] Based on the first and second embodiments, please refer to Figure 3 As shown, a diagnostic system for a power grid fault self-diagnosis switch device according to an embodiment of the present invention includes the following system modules:

[0109] Operation diagnosis module: monitors the operation current of the distribution network in real time, and obtains the current characterization value based on the operation current analysis. If the current characterization value is greater than or equal to the current characterization threshold, a strong signal of distribution network operation fluctuation is generated;

[0110] The current characterization value is calculated based on the high peak current amplitude value and the low peak current amplitude value;

[0111] Risk diagnosis module: When a strong signal of distribution network operation fluctuation is obtained, the distribution network operation risk value is analyzed according to the operating current of the distribution network. If the distribution network operation risk value is greater than or equal to the distribution network operation risk threshold, a large distribution network operation risk signal is generated;

[0112] The distribution network operation risk value is calculated based on the peak fluctuation risk value and the low peak fluctuation risk value;

[0113] Factor diagnosis module: Based on the large signal of distribution network operation risk, the disconnection time of the self-judgment switch of the distribution network is diagnosed, and the total switch diagnosis value is obtained according to the disconnection time analysis of the self-judgment switch. If the total switch diagnosis value is greater than or equal to the total switch diagnosis threshold, an insensitive signal of the self-judgment switch is generated;

[0114] The total switch diagnosis value is calculated based on the high peak switch diagnosis value and the low peak switch diagnosis value;

[0115] Monitoring and control module: Based on the insensitive signal of the self-judgment switch, the total switch diagnosis value is obtained, and the self-judgment switch adjustment coefficient is calculated through the formula to complete the optimization adjustment of the self-judgment switch;

[0116] The formula is: , calculate the monitoring frequency of the adjusted self-judgment switch , T represents the monitoring frequency of the current self-judgment switch, and K is the self-judgment switch adjustment coefficient.

[0117] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for diagnosing a fault self-diagnosis switch device in a distribution network, characterized in that: The following steps are involved: Step 1: monitor the operating current of the distribution network in real time, and obtain a current characterization value based on the operating current analysis. If the current characterization value is greater than or equal to the current characterization threshold, generate a strong distribution network operation fluctuation signal; The current characterization value is calculated based on the high peak current amplitude value and the low peak current amplitude value; Step 2: When a strong signal of distribution network operation fluctuation is obtained, the distribution network operation risk value is analyzed according to the operation current of the distribution network. If the distribution network operation risk value is greater than or equal to the distribution network operation risk threshold, a large signal of distribution network operation risk is generated; The distribution network operation risk value is calculated based on the peak fluctuation risk value and the low peak fluctuation risk value; The analysis process of peak volatility risk value is as follows: Obtain all peak current change values ​​within the peak period, and compare the peak current change value with a peak current change threshold; If the peak current change value is greater than or equal to the peak current change threshold, a high-risk signal for peak current change is generated, and the peak period corresponding to the high-risk signal for peak current change is marked as a high-risk period of the peak cycle; Obtain the number of high-risk periods that generate a peak cycle within the monitoring period, mark it as the number of peak high-risk periods, calculate the ratio of the number of peak high-risk periods to the number of peak periods divided by the peak cycle, and obtain the peak fluctuation risk value; The analysis process of low peak volatility risk value is as follows: Obtain all low-peak current change values ​​within the low-peak period, and compare the low-peak current change value with a low-peak current change threshold; If the low peak current change value is greater than or equal to the low peak current change threshold, a low peak current change high risk signal is generated, and the low peak period corresponding to the low peak current change high risk signal is marked as a high risk period of the low peak cycle; Obtain the number of high-risk periods that generate low-peak cycles within the monitoring period, mark them as the number of low-peak high-risk periods, calculate the ratio of the number of low-peak high-risk periods to the number of low-peak periods divided by the low-peak cycle, and obtain the low-peak fluctuation risk value; Step 3: Based on the large signal of the distribution network operation risk, diagnose the disconnection time of the distribution network self-judgment switch, and obtain the switch diagnosis total value according to the disconnection time analysis of the self-judgment switch. If the switch diagnosis total value is greater than or equal to the switch diagnosis total threshold, generate a self-judgment switch insensitivity signal; The total switch diagnosis value is calculated based on the high peak switch diagnosis value and the low peak switch diagnosis value; The peak switch diagnostic value is obtained as follows: In the peak cycle, the switch disconnection time in each peak period in the peak cycle is detected and marked as the peak switch disconnection time; Subtract the peak switch disconnection time from the peak switch disconnection time standard value to obtain the peak switch disconnection time deviation value; All peak switch disconnection time deviation values ​​are added and averaged to obtain the peak switch diagnostic value; The low peak switch diagnostic value is obtained as follows: In the low-peak cycle, the switch disconnection time in each low-peak period in the low-peak cycle is detected and marked as the low-peak switch disconnection time; Subtract the low-peak switch disconnection time from the low-peak switch disconnection time threshold to obtain a low-peak switch disconnection time deviation value; All the low-peak switch disconnection time deviation values ​​are added and averaged to obtain the low-peak switch diagnosis value; Step 4: Based on the insensitive signal of the self-judgment switch, the total switch diagnosis value is obtained, and the self-judgment switch adjustment coefficient is calculated by the formula to complete the optimization adjustment of the self-judgment switch; The process of obtaining the self-judgment switch adjustment coefficient K is: formula: , the self-judgment switch adjustment coefficient K is calculated, and ZD is expressed as the total switch diagnosis value of the current monitoring cycle. It is expressed as the total threshold of switch diagnosis; The formula is: , calculate the monitoring frequency of the adjusted self-judgment switch , T represents the monitoring frequency of the current self-judgment switch, and K is the self-judgment switch adjustment coefficient.

2. The method for diagnosing a power distribution network fault self-diagnosis switch device according to claim 1, characterized in that: The monitoring period includes peak period and off-peak period. The criteria for dividing peak period and off-peak period are as follows: obtain the power load of the distribution network during the monitoring period, compare the power load with the preset standard power load, if the power load is greater than or equal to the preset standard power load, the corresponding time mark is the peak period, if the power load is less than the preset standard power load, the corresponding time mark is the off-peak period.

3. The method for diagnosing a power distribution network fault self-diagnosis switch device according to claim 2, characterized in that: The peak current amplitude is obtained as follows: The peak cycle is divided into several peak time periods at equal time intervals. The current value at the start time node of the peak time period is subtracted from the current value at the end time node to obtain the peak current change value. The standard deviation of all the peak current change values ​​in the peak cycle is calculated to obtain the peak current amplitude value.

4. The method for diagnosing a power distribution network fault self-diagnosis switch device according to claim 3, characterized in that: The low peak current amplitude value is obtained as follows: The low-peak cycle is divided into several low-peak periods according to equal time intervals. The current value at the start time node of the low-peak period is subtracted from the current value at the end time node to obtain the low-peak current change value. The standard deviation of all low-peak current change values ​​in the low-peak cycle is calculated to obtain the low-peak current amplitude value.

5. A diagnostic system for a power distribution network fault self-diagnosis switch device, characterized in that: The system is used to execute the method described in any one of claims 1 to 4 above, and the system comprises: Operation diagnosis module: monitors the operation current of the distribution network in real time, and obtains the current characterization value based on the operation current analysis. If the current characterization value is greater than or equal to the current characterization threshold, a strong signal of distribution network operation fluctuation is generated; The current characterization value is calculated based on the high peak current amplitude value and the low peak current amplitude value; Risk diagnosis module: When a strong signal of distribution network operation fluctuation is obtained, the distribution network operation risk value is analyzed according to the operating current of the distribution network. If the distribution network operation risk value is greater than or equal to the distribution network operation risk threshold, a large distribution network operation risk signal is generated; The distribution network operation risk value is calculated based on the peak fluctuation risk value and the low peak fluctuation risk value; The analysis process of peak volatility risk value is as follows: Obtain all peak current change values ​​within the peak period, and compare the peak current change value with a peak current change threshold; If the peak current change value is greater than or equal to the peak current change threshold, a high-risk signal for peak current change is generated, and the peak period corresponding to the high-risk signal for peak current change is marked as a high-risk period of the peak cycle; Obtain the number of high-risk periods that generate a peak cycle within the monitoring period, mark it as the number of peak high-risk periods, calculate the ratio of the number of peak high-risk periods to the number of peak periods divided by the peak cycle, and obtain the peak fluctuation risk value; The analysis process of low peak volatility risk value is as follows: Obtain all low-peak current change values ​​within the low-peak period, and compare the low-peak current change value with a low-peak current change threshold; If the low peak current change value is greater than or equal to the low peak current change threshold, a low peak current change high risk signal is generated, and the low peak period corresponding to the low peak current change high risk signal is marked as a high risk period of the low peak cycle; Obtain the number of high-risk periods that generate low-peak cycles within the monitoring period, mark them as the number of low-peak high-risk periods, calculate the ratio of the number of low-peak high-risk periods to the number of low-peak periods divided by the low-peak cycle, and obtain the low-peak fluctuation risk value; Factor diagnosis module: Based on the large signal of distribution network operation risk, the disconnection time of the self-judgment switch of the distribution network is diagnosed, and the total switch diagnosis value is obtained according to the disconnection time analysis of the self-judgment switch. If the total switch diagnosis value is greater than or equal to the total switch diagnosis threshold, an insensitive signal of the self-judgment switch is generated; The total switch diagnosis value is calculated based on the high peak switch diagnosis value and the low peak switch diagnosis value; The peak switch diagnostic value is obtained as follows: In the peak cycle, the switch disconnection time in each peak period in the peak cycle is detected and marked as the peak switch disconnection time; Subtract the peak switch disconnection time from the peak switch disconnection time standard value to obtain the peak switch disconnection time deviation value; All peak switch disconnection time deviation values ​​are added and averaged to obtain the peak switch diagnostic value; The low peak switch diagnostic value is obtained as follows: In the low-peak cycle, the switch disconnection time in each low-peak period in the low-peak cycle is detected and marked as the low-peak switch disconnection time; Subtract the low-peak switch disconnection time from the low-peak switch disconnection time threshold to obtain a low-peak switch disconnection time deviation value; All the low-peak switch disconnection time deviation values ​​are added and averaged to obtain the low-peak switch diagnosis value; Monitoring and control module: Based on the insensitive signal of the self-judgment switch, the total switch diagnosis value is obtained, and the self-judgment switch adjustment coefficient is calculated through the formula to complete the optimization adjustment of the self-judgment switch; The process of obtaining the self-judgment switch adjustment coefficient K is: formula: , the self-judgment switch adjustment coefficient K is calculated, and ZD is expressed as the total switch diagnosis value of the current monitoring cycle. It is expressed as the total threshold of switch diagnosis; The formula is: , calculate the monitoring frequency of the adjusted self-judgment switch , T represents the monitoring frequency of the current self-judgment switch, and K is the self-judgment switch adjustment coefficient.

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