Wind power plant current collection line fault section and fault branch identification method

Abstract

The invention belongs to the technical field of line fault identification, and particularly discloses a wind power plant current collection line fault section and fault branch identification method, which comprises the following steps: segmenting a wind power plant current collection line, and screening out easily faulty sections by using fault records of each line section; then, fault evolution trend analysis is carried out on the historical fault data of the fault-prone sections, on this basis, in operation monitoring of the current fault-prone sections, even if no abnormity is found, the occurrence time of potential faults can be predicted through the fault evolution trend, and therefore prospective recognition of the hidden faults of the wind power plant current collection line is achieved; meanwhile, the operation monitoring frequency is adjusted according to the predicted fault occurrence time of the current normal line section, prevention measures before the fault occurs are embodied, it is ensured that high-frequency monitoring is carried out in a key time period, and the fault identification accuracy is improved. Therefore, the probability of monitoring omission during the period that the fault does not occur can be reduced to a certain extent.

Description

Technical Field

[0001] The invention belongs to the technical field of line fault identification, and specifically discloses a method for identifying a fault section and a fault branch of a wind farm collector line. Background Art

[0002] The collector line of a wind farm is an important component that collects the electricity generated by each wind turbine and transmits it to a booster station or substation. Since wind power generation systems are usually located in remote areas and wind farms are large in scale, the collector lines are long and complex and prone to failure. In order to ensure the reliable operation of wind farms, it is necessary to effectively identify the faulty sections and faulty branches in the collector line.

[0003] Generally speaking, faults in wind farm collector lines can be divided into two categories: significant faults and latent faults. Significant faults can be directly identified through physical observation or simple electrical measurements. When such faults occur, they usually cause obvious damage or interruption to the system, such as open circuits and short circuits. Hidden faults are not easy to be directly observed in the early stages, and gradually deteriorate over time, eventually leading to system performance degradation or failure. Common hidden faults include insulation aging, overload, and poor contact. Compared with significant faults, latent faults are more likely to occur in the harsh environment of wind farm collector lines, and the identification cycle is longer. Therefore, more stringent monitoring methods are required.

[0004] At present, the identification of hidden faults in wind farm collector lines mainly relies on real-time monitoring technology. However, real-time monitoring technology cannot predict the time of fault occurrence when no fault has occurred at present, resulting in a lack of foresight in fault identification and an inability to take preventive measures in advance, such as adjusting the monitoring frequency. In addition, the real-time monitoring process may result in the loss or omission of monitoring data due to equipment failure, network interruption, data transmission error, etc., which will reduce the accuracy and timeliness of fault identification and increase the risk of potential faults not being discovered in time. Summary of the invention

[0005] To this end, one purpose of an embodiment of the present application is to provide a method for identifying fault sections and fault branches of wind farm collector lines for hidden faults in wind farm collector lines. By using historical fault data to increase the prediction of the time of occurrence of hidden faults, the optimization of hidden fault identification is achieved, effectively solving the problems mentioned in the background technology.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A method for identifying fault sections and fault branches of a wind farm collector line, comprising the following steps: constructing a power grid topology model based on a layout diagram of the wind farm collector line, and on this basis capturing key nodes in the line, and then dividing the line segments according to the connection relationship between adjacent nodes.

[0007] Retrieve the fault records of each line section, identify the fault-prone sections, and analyze the fault characteristics of the fault-prone sections, specifically the fault types and fault evolution trends.

[0008] The fault-prone segments are monitored according to their fault-prone types, and whether there are faults is identified based on the monitoring data. The fault-prone segments with faults are extracted and marked as current fault segments, and the location of the current fault segments is located.

[0009] Based on the current monitoring data of the fault-prone segments, the fault-prone segments that do not currently have faults are screened out and marked as the current normal segments, and the fault evolution trend of the current normal segments and the current monitoring data are used to predict the time of fault occurrence.

[0010] Adjust the operation monitoring frequency according to the predicted fault occurrence time of the current normal segment.

[0011] In combination with all the above-mentioned technical solutions, the positive effects of the present invention are as follows: 1. The present invention divides the wind farm collection line into sections, and uses the fault records of each line section to screen out the fault-prone sections, and then performs fault evolution trend analysis on the historical fault data of these fault-prone sections. On this basis, in the operation monitoring of the current fault-prone sections, even if no abnormality is found, the fault evolution trend can be used to predict the occurrence time of potential faults, thereby realizing forward-looking identification of hidden faults in the wind farm collection line, and significantly improving the timeliness of fault identification.

[0012] 2. The present invention adjusts the operating monitoring frequency based on the predicted fault occurrence time of the current normal line section, which reflects the preventive measures before the fault occurs and ensures high-frequency monitoring during the critical time period, thereby reducing the probability of missing monitoring during the period when the fault does not occur to a certain extent and enhancing the timeliness of fault identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0014] Figure 1 It is a diagram of the steps for implementing the method of the present invention.

[0015] Figure 2 It is a schematic diagram of locating the location of the current fault section as the fault section in the present invention.

[0016] Figure 3 It is a schematic diagram of locating the location of the current fault segment as the fault branch in the present invention. DETAILED DESCRIPTION

[0017] 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.

[0018] See also Figure 1 As shown, the present invention proposes a method for identifying fault sections and fault branches of a wind farm collector line, comprising the following steps: constructing a power grid topology model according to a layout diagram of the wind farm collector line, and on this basis capturing nodes in the line, and then dividing the line segments according to the connection relationship between adjacent nodes.

[0019] It is important to know that the wind farm collection line topology diagram is a graphical representation used to show the connection relationship and layout between various wind turbines, transformers, switch stations, cable lines and other equipment within the wind farm.

[0020] It is further important to know that the nodes in the line can include various types of support structures such as poles and suspension points. The main function of these nodes is to support and fix the wires to ensure the safety and reliability of power transmission. By dividing the line segments according to adjacent nodes, physical isolation can be provided between nodes to reduce electromagnetic interference. On long lines, electromagnetic interference may propagate along the entire line, and in segmented monitoring, the electromagnetic interference of each section is limited to a local range, reducing the impact on other sections. Therefore, by dividing the wind farm collector line into line segments using adjacent nodes, it is more convenient to monitor the line operation status.

[0021] Retrieve the fault records of each line section, identify the fault-prone sections, and analyze the fault characteristics of the fault-prone sections, specifically the fault types and fault evolution trends.

[0022] Applied to the above scheme, the following process is used to identify the fault-prone sections: a specified historical period is selected, and the proportion of fault records corresponding to each line section within this period is counted.

[0023] It should be made clear that selecting a specified historical period can ensure that the fault data analyzed is from the most recent period, which can better reflect the current system operation status and fault trends. At the same time, selecting an appropriate historical period can ensure that there are enough fault record samples. Too short a time period may result in insufficient samples, and the results are easily affected by accidental factors. An appropriate historical period can reduce contingency and improve the reliability of the analysis results. For example, fault records from the past 12 months can be selected.

[0024] The occurrence time of each fault is extracted from the fault records of the same line section, and the time interval between adjacent fault records is calculated, thereby obtaining the average fault interval duration of each line section.

[0025] Substitute the average fault interval time of each line section and the fault record ratio into the expression Calculate the fault susceptibility index of each line section , where , Respectively represent The proportion of fault records and the average fault interval time of the line section, Indicates the line segment number. , Indicates the duration of the specified historical period. Represents a natural constant.

[0026] From the above formula, it can be seen that the greater the proportion of fault records in the line section, the shorter the average fault interval, and the greater the fault susceptibility index.

[0027] The fault-prone index of each line segment is compared with a preset warning value. For example, the warning value is 0.7. Line segments whose fault-prone index reaches or exceeds the warning value are screened out and marked as fault-prone segments.

[0028] The purpose of the present invention to identify the fault-prone sections by using the historical fault records of the line sections is to be able to further use the fault characteristics of the fault-prone sections to perform targeted fault processing. As for the non-fault-prone sections, due to their lack of fault tendency, regular routine inspections and maintenance are performed on the non-fault-prone sections to ensure that the basic operating status of these sections is good, and more maintenance resources and energy are concentrated on the fault-prone sections. The non-fault-prone sections can appropriately reduce the investment in maintenance resources and improve resource utilization efficiency.

[0029] Further applied to the above scheme, the prone fault types of the prone fault section refer to the following analysis process: the fault type of each fault is extracted from the fault record of the prone line section, and then the occurrence frequency of the same fault type is summarized.

[0030] Calculate the standard deviation of the occurrence frequency of each fault type corresponding to the fault-prone section, and compare it with the preset allowable standard deviation, where the allowable standard deviation is preset and is set to determine whether the difference in the occurrence frequency of fault types is within an acceptable range. If the standard deviation is greater than the allowable standard deviation, it indicates that the occurrence frequency of the fault types is quite different. In this case, the fault type with the highest occurrence frequency is taken as the prone fault type of the fault-prone section. This is because the fault type with the highest occurrence frequency can best represent the main fault mode on the line. Otherwise, it indicates that the frequency of the fault types is relatively uniform, which means that each fault type has a certain occurrence frequency, but there is no obvious dominant fault type. Then calculate the occurrence ratio of each fault type and compare it with the median occurrence ratio, where the median occurrence ratio is 0.5. The fault type with an occurrence ratio higher than the median occurrence ratio is taken as the prone fault type.

[0031] It should be understood that when the occurrence frequencies of fault types are relatively uniform, selecting a fault type with an occurrence ratio higher than the median occurrence ratio can ensure that the selected fault type has a high representativeness among all fault types.

[0032] Further applied to the above scheme, the fault evolution trend of the fault-prone segment corresponding to the fault-prone type can be seen in the following analysis process: based on the fault-prone type, the fault records that meet the fault-prone type are extracted from the fault records retrieved from the fault-prone segment and recorded as valid fault records.

[0033] The operation monitoring records of the fault-prone section within the specified historical period are retrieved, and the monitoring time is extracted from the operation monitoring records, which are then associated and mapped with the valid fault records to construct an operation monitoring record set associated with the valid fault records.

[0034] It is important to understand that when identifying a line segment with an operational fault, it is achieved by monitoring its operational status in real time. When the operational status monitoring data shows an abnormality, the line segment is identified as having an operational fault. Therefore, the front end of each fault record corresponds to a series of operational monitoring records.

[0035] In the specific implementation of the above scheme, when the association mapping of valid fault records to operation monitoring records is performed, by comparing the monitoring time of the retrieved operation monitoring record with the fault occurrence time of the valid fault record, the operation monitoring records with the monitoring time before the fault occurrence time are screened out as the operation monitoring records associated with the valid fault record.

[0036] As an example of the above implementation, assume that at 11:00 on March 1, 2020, the system detects that the insulation resistance of line section A is abnormal and marks it as an insulation aging fault. At this time, a fault record is generated to record the time when the fault occurred, fault type, fault location and other information. For example, the fault record is as follows: Fault ID: 001 Fault time: 2020-03-01 11:00:00, Fault type: Insulation aging, Fault location: Line section A, and retrieve a series of operation monitoring records of the line section before the fault occurrence time, and compare them with the fault occurrence time of the valid fault record, and extract the operation monitoring records with the monitoring time before the fault occurrence time as the operation monitoring records associated with the valid fault record.

[0037] Monitoring data are extracted from the operation monitoring records, and a coordinate system is constructed with monitoring time as the horizontal axis and monitoring data as the vertical axis. For each valid fault record in the fault-prone segment, the monitoring time and monitoring data are extracted from its associated set of operation monitoring records, and a fault evolution trend curve corresponding to each valid fault record is generated in the constructed coordinate system.

[0038] It should be noted that the monitoring data extracted from the operation monitoring records are demand monitoring parameters, and when the fault type is insulation aging, the monitoring data extracted from the operation monitoring records are insulation resistance.

[0039] The fault-prone segments are monitored according to their fault-prone types, and whether there are faults is identified based on the monitoring data. The fault-prone segments with faults are extracted and marked as current fault segments, and the location of the current fault segments is located.

[0040] Preferably, operation monitoring of the fault-prone segments according to their fault-prone types is implemented as follows: summarize the number of fault-prone types obtained from the analysis of the fault-prone segments; if there is only one fault-prone type, then operation monitoring of the fault-prone segments is performed based on the demand monitoring parameters corresponding to the fault-prone type to obtain real-time monitoring data; if there is more than one fault-prone type, then each fault-prone type is arranged from high to low according to the frequency of occurrence in the fault record to obtain a monitoring order for the fault-prone types; and the fault-prone segments are monitored in turn according to the demand monitoring parameters corresponding to each fault-prone type in this order.

[0041] In the above-mentioned preferred operation examples, different fault types require monitoring of different data parameters. For example, if the fault type is poor contact, the required monitoring parameter is contact resistance. In another example, if the fault type is insulation aging, the required monitoring parameter is insulation resistance. In another example, if the fault type is overload, the required monitoring parameters are conductor temperature and current.

[0042] It should be pointed out that if the demand monitoring parameters of all prone fault types are monitored simultaneously when there is more than one prone fault type, this means that the system collects and processes monitoring data of multiple fault types at the same time, which may lead to resource shortages and increase the complexity of the system and the difficulty of data processing. By monitoring the demand monitoring parameters of prone fault types in sequence according to the frequency of occurrence of the fault types, this means that the system monitors the fault types one by one in a certain order, giving priority to monitoring the fault types with high frequency, which can make more efficient use of resources, give priority to monitoring high-risk fault types, and respond to potential problems faster. In addition, monitoring one by one reduces the pressure of processing a large amount of data at the same time and simplifies the data processing process.

[0043] It should be further pointed out that if resources are sufficient, a simultaneous monitoring strategy can be considered, because this can ensure that all fault types can be monitored in a timely manner and provide more comprehensive fault information.

[0044] Further preferably, the following operation is performed to identify whether a fault exists: compare the monitoring data of the fault-prone segment corresponding to the fault-prone type with the normal operating data. If the monitoring data of a certain fault-prone type does not match the normal operating data, the presence of a fault is identified and the fault-prone type is used as the current fault type.

[0045] In the above operation example, when the prone fault type is insulation aging, the insulation resistance of the prone fault section corresponding to the insulation aging is compared with the normal insulation resistance. The normal insulation resistance corresponding to the prone fault section can be obtained by referring to the product manual provided by the manufacturer of the cable or equipment used in the line section, which usually contains the recommended normal insulation resistance value.

[0046] Further preferably, the location of the current fault segment is located as follows: locate the two end nodes corresponding to the current fault segment from the power grid topology model. If the two end nodes are located on the main line, refer to Figure 2 As shown, the current fault segment is identified as a fault segment. If the nodes at both ends are located on the branch line, the current fault segment is identified as a fault branch. Figure 3 shown.

[0047] It should be added that when conducting operation monitoring of fault-prone sections, the focus is on monitoring the demand monitoring parameters of fault-prone types. This does not mean that other parameters are not monitored. By focusing on the demand monitoring parameters of fault-prone types, monitoring resources can be concentrated on the key parameters that are most likely to cause faults, which can make more efficient use of limited monitoring resources. At the same time, by monitoring key parameters, precursors of faults can be discovered earlier, and preventive measures can be taken in time to avoid the expansion of faults.

[0048] Based on the current monitoring data of the fault-prone segments, the fault-prone segments that do not currently have faults are screened out and marked as the current normal segments, and the fault evolution trend of the current normal segments and the current monitoring data are used to predict the time of fault occurrence.

[0049] Adjust the operation monitoring frequency according to the predicted fault occurrence time of the current normal segment.

[0050] In the above, the specific operation of predicting the fault occurrence time is as follows: extract the fault evolution trend curve of the prone fault type corresponding to the current normal segment in each valid fault record, and then capture the current monitoring data of the prone fault type corresponding to the current normal segment from the fault evolution trend curve to the point where the current monitoring data is located on the curve as the current state point.

[0051] The time of the current state point on the horizontal axis of the fault evolution trend curve of each valid fault record of the prone fault type is obtained as the historical time of the current state in each valid fault record, and the month in which the historical time is located is obtained.

[0052] In the above operation example, assuming that the time of the current state point on the horizontal axis is 14:00 on October 4, 2019, the month of the historical time is October.

[0053] The historical month of the current status point in each valid fault record is compared with the current month, and the valid fault record whose historical month is consistent with the current month is selected as the available fault record.

[0054] The time length between the current state point captured by the fault evolution trend curve of the available fault record corresponding to the prone fault type in the current normal segment and the end point on the curve on the horizontal axis is obtained as the fault hiding time length.

[0055] Add the current time and the fault hiding time to get the predicted fault occurrence time.

[0056] It is important to understand that the occurrence of hidden faults is closely related to the environmental conditions (such as temperature and humidity) of the line. For example, insulation aging faults will accelerate in high temperature and high humidity environments. Therefore, when the current insulation resistance is 200MΩ and decays to 100MΩ, the high temperature and high humidity environment will shorten the decay time of the insulation resistance. Therefore, when predicting the time of fault occurrence based on the current monitoring data, the impact of the environmental conditions of the area where the line is located on the prediction must be considered.

[0057] Based on this consideration, the month of the monitoring time of the current monitoring data is included in the prediction model because the environmental conditions of a region are related to the seasonal month. The environmental conditions of the same month in different years are mostly similar. By selecting the valid fault records whose historical month is consistent with the current month from the fault evolution trend curve of each valid fault record as the available fault records based on the month of the monitoring time of the current monitoring data, it can be ensured to the greatest extent that the prediction of the fault occurrence time is carried out under the same environmental conditions, thereby improving the accuracy and reliability of the prediction results.

[0058] In the innovation of the above scheme, the prediction of the fault occurrence time using the fault evolution trend of the current normal segment and the current monitoring data also includes the following process: when comparing the historical month of the current state in each valid fault record with the current month, if there is no valid fault record in which the historical month is consistent with the current month, the area spanned by the current normal segment is located, thereby obtaining the meteorological type of the area spanned by the current normal segment.

[0059] Based on the meteorological type of the area that the current normal segment crosses, the seasonal distribution months corresponding to the area that the current normal segment crosses are obtained.

[0060] Exemplarily, climate types include tropical rainforest climate, tropical savanna climate, tropical monsoon climate, subtropical monsoon climate, etc. Since different climate types correspond to different climatic characteristics, the resulting climate distribution is different, which makes the seasonal distribution months corresponding to different climate types different. For example, the spring distribution months corresponding to the subtropical monsoon climate are from March to May, the summer distribution months are from June to August, the autumn distribution months are from September to November, and the winter distribution months are from December to February of the following year.

[0061] The historical month of the current status in each valid fault record and the current month are converted to the season of the historical time, and then a seasonal consistency comparison is performed, and the valid fault records whose historical season is consistent with the current season are selected as available fault records.

[0062] In the further implementation of the above scheme, the operation monitoring frequency is adjusted according to the predicted fault occurrence time of the current normal segment as follows: the fault hiding time of the current normal segment corresponding to the prone fault type is compared with the set reference fault hiding time to calculate the fault urgency of the prone fault type. The specific calculation formula is , where Indicates the fault hiding time. Indicates the reference fault hiding time.

[0063] It should be added that the purpose of setting the reference fault hiding time is to eliminate the dimension of the fault hiding time when calculating the fault urgency, and the setting of the reference fault hiding time must satisfy the condition that when the fault hiding time reaches the reference fault hiding time, the fault is not urgent. Monitoring can be performed according to the current monitoring frequency. When the fault hiding time is less than the reference fault hiding time, the fault is urgent. The shorter the fault hiding time, the greater the fault urgency. For the specific reference fault hiding time, you can consult an experienced electrical engineer. By way of example, the reference fault hiding time is 10 days.

[0064] Get the current monitoring frequency of the required monitoring parameters corresponding to the prone fault type , and substitute it into the expression with the fault urgency Get the adjustment monitoring frequency of the required monitoring parameters corresponding to the prone fault type .

[0065] It should be noted that the monitoring frequency mentioned above is the monitoring frequency within a unit time period. For example, the unit time period is one month. When the fault urgency is 0, monitoring can be performed according to the current monitoring frequency. When the fault urgency is greater, the required monitoring frequency is greater than the current monitoring frequency, that is, the monitoring frequency within the unit time period is greater.

[0066] Perform operational monitoring of the required monitoring parameters for the fault-prone types according to the adjusted monitoring frequency.

[0067] The present invention adjusts the operation monitoring frequency based on the predicted fault occurrence time of the current normal line section, embodying preventive measures before the fault occurs, ensuring high-frequency monitoring during the critical time period, thereby reducing the probability of missing monitoring during the period when the fault does not occur to a certain extent and enhancing the timeliness of fault identification.

[0068] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A method for identifying faulty sections and faulty branches of a wind farm collector line, characterized in that: The following steps are involved: The grid topology model is constructed based on the layout diagram of the wind farm collection line, and on this basis, the nodes in the line are captured, and then the line segments are divided according to the connection relationship between adjacent nodes; Retrieve the fault records of each line section, identify the fault-prone sections, and analyze the fault characteristics of the fault-prone sections, specifically the fault types and fault evolution trends; Perform operation monitoring on the fault-prone segments according to the fault-prone types of the fault-prone segments, identify whether there are faults based on the monitoring data, extract the fault-prone segments with faults and mark them as current fault segments, and locate the current fault segments; Based on the current monitoring data of the fault-prone segments, the fault-prone segments that do not currently have faults are screened out and marked as the current normal segments. The fault evolution trend of the current normal segments and the current monitoring data are used to predict the time of fault occurrence. Adjust the operation monitoring frequency according to the predicted fault occurrence time of the current normal segment.

2. A method for identifying faulty sections and faulty branches of a wind farm collector line according to claim 1, characterized in that: The process of identifying the fault-prone segment is as follows: Select a specified historical period and count the proportion of fault records corresponding to each line segment within this period; Extract the occurrence time of each fault from the fault records of the same line section, calculate the time interval between adjacent fault records, and thus obtain the average fault interval time of each line section; Substitute the average fault interval time of each line section and the fault record ratio into the expression Calculate the fault susceptibility index of each line section , where , Respectively represent The proportion of fault records and the average fault interval time of the line section, Indicates the line segment number. , Indicates the duration of the specified historical period. represents a natural constant; The fault-prone index of each line section is compared with the preset warning value, and the line sections whose fault-prone index reaches or exceeds the warning value are screened out and marked as fault-prone sections.

3. A method for identifying faulty sections and faulty branches of a wind farm collector line according to claim 1, characterized in that: The types of faults that are prone to occur refer to the following analysis process: Extract the fault type of each fault from the fault records of the prone line section, and then summarize the occurrence frequency of the same fault type; The standard deviation of the occurrence frequency of each fault type corresponding to the fault-prone segment is calculated and compared with the preset allowable standard deviation. If the standard deviation is greater than the allowable standard deviation, the fault type with the highest occurrence frequency is taken as the prone fault type of the fault-prone segment; otherwise, the occurrence ratio of each fault type is calculated and compared with the median occurrence ratio, and the fault type with an occurrence ratio higher than the median occurrence ratio is taken as the prone fault type.

4. A method for identifying faulty sections and faulty branches of a wind farm collector line according to claim 1, characterized in that: The fault evolution trend is shown in the following analysis process: Based on the prone fault type, the fault records retrieved from the prone fault segment are extracted to match the prone fault type and recorded as valid fault records; Retrieve the operation monitoring records of the fault-prone section within the specified historical period, extract the monitoring time from the operation monitoring records, and then associate and map with the valid fault records to build a set of operation monitoring records associated with the valid fault records; Monitoring data are extracted from the operation monitoring records, and a coordinate system is constructed with monitoring time as the horizontal axis and monitoring data as the vertical axis. For each valid fault record in the fault-prone segment, the monitoring time and monitoring data are extracted from its associated set of operation monitoring records, and a fault evolution trend curve corresponding to each valid fault record is generated in the constructed coordinate system.

5. A method for identifying faulty sections and faulty branches of a wind farm collector line according to claim 1, characterized in that: The operation monitoring of the fault-prone section according to the fault-prone section's fault type is implemented as follows: Summarize the number of fault-prone types obtained from the analysis of the fault-prone segments. If there is only one fault-prone type, perform operation monitoring on the fault-prone segment based on the demand monitoring parameters corresponding to the fault-prone type to obtain real-time monitoring data. If there is more than one fault-prone type, arrange each fault-prone type from high to low according to the frequency of occurrence in the fault record to obtain the monitoring order of the fault-prone types, and monitor the demand monitoring parameters corresponding to each fault-prone type in the fault-prone segment in this order.

6. A method for identifying faulty sections and faulty branches of a wind farm collector line according to claim 1, characterized in that: The identification of whether there is a fault is performed as follows: The monitoring data of the prone fault type corresponding to the prone fault segment is compared with the normal operation data. If the monitoring data of a prone fault type does not match the normal operation data, the fault is identified and the prone fault type is used as the current fault type.

7. A method for identifying faulty sections and faulty branches of a wind farm collector line according to claim 1, characterized in that: The location of the current fault segment is as follows: The two end nodes corresponding to the current fault segment are located from the power grid topology model. If the two end nodes are located on the main line, the current fault segment is identified as a fault segment. If the two end nodes are located on the branch line, the current fault segment is identified as a fault branch.

8. A method for identifying faulty sections and faulty branches of a wind farm collector line according to claim 4, characterized in that: The prediction of the fault occurrence time using the fault evolution trend of the current normal segment and the current monitoring data is performed as follows: Extract the fault evolution trend curve of the prone fault type corresponding to the current normal segment in each valid fault record, and then capture the current monitoring data of the prone fault type corresponding to the current normal segment from the fault evolution trend curve to the point where the current monitoring data is located on the curve as the current state point; The time of the current state point on the horizontal axis of the fault evolution trend curve of each valid fault record of the prone fault type is obtained respectively as the historical time of the current state in each valid fault record, and the month in which the historical time is located is obtained; Compare the historical month of the current state point in each valid fault record with the current month, and select the valid fault record whose historical month is consistent with the current month as the available fault record; Obtain the time between the current state point captured by the fault evolution trend curve of the available fault record corresponding to the prone fault type in the current normal segment and the end point on the curve on the horizontal axis as the fault hiding time; Add the current time and the fault hiding time to get the predicted fault occurrence time.

9. A method for identifying faulty sections and faulty branches of a wind farm collector line according to claim 8, characterized in that: The prediction of the fault occurrence time by using the fault evolution trend of the current normal segment and the current monitoring data also includes the following process: When comparing the historical month of the current state in each valid fault record with the current month, if there is no valid fault record whose historical month is consistent with the current month, the area spanned by the current normal section is located, thereby obtaining the meteorological type of the area spanned by the current normal section; Based on the meteorological type of the area that the current normal segment crosses, obtain the seasonal distribution month corresponding to the area that the current normal segment crosses; The historical month of the current status in each valid fault record and the current month are converted to the season of the historical time, and then a seasonal comparison is performed, and the valid fault records whose historical season is consistent with the current season are selected as available fault records.

10. A method for identifying faulty sections and faulty branches of a wind farm collector line according to claim 8, characterized in that: The operation monitoring frequency of the normal segment is adjusted according to the predicted fault occurrence time as follows: Compare the fault hiding time of the prone fault type in the current normal segment with the set reference fault hiding time to calculate the fault urgency of the prone fault type. ; Get the current monitoring frequency of the required monitoring parameters corresponding to the prone fault type , and substitute it into the expression with the fault urgency Get the adjustment monitoring frequency of the required monitoring parameters corresponding to the prone fault type ; Perform operational monitoring of the required monitoring parameters for the fault-prone types according to the adjusted monitoring frequency.

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