Transformer Area Abnormality Detection Method, Device, Terminal and Storage Medium

Abstract

The present invention provides a method, device, terminal and storage medium for detecting abnormal conditions in a power distribution area. The method includes: obtaining a line loss data set of a target power distribution area collected in consecutive M collection periods; M≥2; judging whether the line loss of the target power distribution area is abnormal according to the line loss data set of the target power distribution area; if it is determined that the line loss of the target power distribution area is abnormal, obtaining power consumption data sets respectively corresponding to each electric energy meter under the target power distribution area collected in consecutive M collection periods, and obtaining the electric energy meters with abnormal power consumption data under the target power distribution area according to the power consumption data sets respectively corresponding to each electric energy meter; sending all the electric energy meters with abnormal power consumption data under the target power distribution area to a preset terminal, so that the staff corresponding to the preset terminal can process the electric energy meters with abnormal power consumption data. The present invention enables the staff to directly process the received electric energy meter to complete the task of reducing losses and eliminating defects, without relying on the experience of the staff, and can improve the efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of substation area detection, and particularly to a method, device, terminal and storage medium for detecting substation area anomalies. Background Art

[0002] The Internet of Things technology has been widely applied in the power industry. By using a large number of nodes deployed in the target area to sense and collect various monitoring object information, it can meet the requirements of real-time, accurate and comprehensive information acquisition in important links such as power generation, power transmission, power transformation, power distribution and power consumption of an intelligent power grid in various scenarios, and comprehensively improve the sensing capabilities of power grid production, power grid services and power grid management. The application of the Internet of Things technology in the power industry can connect people, machines and things in all links of energy and power production and consumption in real time online, comprehensively carry and penetrate power grid transmission, transformation, distribution, enterprise operation management, external customer service and other services, and comprehensively improve the level of power grid construction, management and service.

[0003] Based on the Internet of Things technology, it is possible to detect substation area anomalies. However, the existing technology can only roughly detect whether the line loss of the substation area is abnormal. It is necessary for the staff to arrive at the scene and judge whether there is a real line loss anomaly in the substation area according to experience. After discovering a line loss anomaly in the substation area, the loss reduction and defect elimination work is carried out according to experience, resulting in the substation area anomaly detection being overly dependent on the work experience of the staff and having low efficiency. Summary of the Invention

[0004] Embodiments of the present invention provide a method, device, terminal and storage medium for detecting substation area anomalies, so as to solve the problems that the existing technology is overly dependent on the work experience of the staff and has low efficiency when detecting substation area anomalies.

[0005] In a first aspect, an embodiment of the present invention provides a method for detecting substation area anomalies, including:

[0006] Obtain a line loss data set of a target substation area collected in M consecutive collection cycles; M≥2;

[0007] Judge whether the line loss of the target substation area is abnormal according to the line loss data set of the target substation area;

[0008] If it is determined that the line loss of the target substation area is abnormal, obtain an electricity consumption data set corresponding to each electric energy meter under the target substation area collected in M consecutive collection cycles, and obtain the electric energy meters with abnormal electricity consumption data under the target substation area according to the electricity consumption data set corresponding to each electric energy meter;

[0009] Send all the electric energy meters with abnormal electricity consumption data under the target substation area to a preset terminal, so that the staff corresponding to the preset terminal processes the electric energy meters with abnormal electricity consumption data.

[0010] In a possible implementation, the line loss data set of the target substation area includes the line loss data of the target substation area collected in the current collection period;

[0011] Judging whether the line loss of the target substation area is abnormal according to the line loss data set of the target substation area includes:

[0012] If the line loss data of the target substation area collected in the current collection period exceeds the target line loss threshold range of the target substation area, extract the characteristic values of the line loss data set of the target substation area;

[0013] Input the characteristic values of the line loss data set of the target substation area into the pre-trained line loss classification model to obtain the line loss status of the target substation area;

[0014] If the line loss status of the target substation area is an abnormal status, determine that the line loss of the target substation area is abnormal.

[0015] In a possible implementation, the determination process of the target line loss threshold range of the target substation area includes:

[0016] Obtain the first set of nearby substation areas of the target substation area, and the first set of nearby substation areas includes all substation areas whose distance from the target substation area is less than the preset distance threshold;

[0017] If there is a target nearby substation area in the first set of nearby substation areas, remove all target nearby substation areas from the set of nearby substation areas to obtain the second set of nearby substation areas; the target nearby substation area is a nearby substation area that was determined to have an abnormal line loss in at least one collection period within N consecutive collection periods before M consecutive collection periods; N≥2;

[0018] Obtain the historical line loss data set of the target substation area and the historical line loss data sets of each nearby substation area in the second set of nearby substation areas; the historical line loss data set of each substation area includes a set composed of the line loss data of this substation area collected in N consecutive collection periods;

[0019] Determine the target line loss threshold range of the target substation area according to the historical line loss data set of the target substation area and the historical line loss data sets of each nearby substation area in the second set of nearby substation areas.

[0020] In a possible implementation, determining the target line loss threshold range of the target substation area according to the historical line loss data set of the target substation area and the historical line loss data sets of each nearby substation area in the second set of nearby substation areas includes:

[0021] Calculate the average value of the historical line loss data set of the target substation area to obtain the historical average line loss data of the target substation area;

[0022] Calculate the average values of the historical line loss data sets of each nearby substation area in the second set of nearby substation areas respectively to obtain the historical average line loss data corresponding to each nearby substation area;

[0023] Based on the historical average line loss data corresponding to each nearby substation area and the weight coefficients corresponding to each nearby substation area, the weighted average line loss data of the second set of nearby substation areas is obtained; wherein, the weight coefficients corresponding to each nearby substation area are inversely correlated with the distance between the nearby substation area and the target substation area.

[0024] Calculate the average value of the historical average line loss data of the target substation area and the weighted average line loss data of the second set of nearby substation areas to obtain the reference line loss data of the target line loss threshold range of the target substation area.

[0025] Subtract the preset line loss data threshold from the reference line loss data to obtain the lower limit value of the target line loss threshold range of the target substation area.

[0026] Add the preset line loss data threshold to the reference line loss data to obtain the upper limit value of the target line loss threshold range of the target substation area.

[0027] In a possible implementation, the characteristic values of the line loss data set of the target substation area include at least one of a trend characteristic, a difference degree characteristic, and a fluctuation characteristic.

[0028] In a possible implementation, the power consumption data sets corresponding to each watt-hour meter include the power consumption data of the watt-hour meter collected in the current collection period.

[0029] Based on the power consumption data sets corresponding to each watt-hour meter, obtain the watt-hour meters with abnormal power consumption data in the target substation area, including:

[0030] For each watt-hour meter in the target substation area, if the power consumption data of the watt-hour meter collected in the current collection period exceeds the target power consumption data range of the watt-hour meter, extract the characteristic values of the power consumption data set of the watt-hour meter; input the characteristic values of the power consumption data set of the watt-hour meter into a pre-trained power consumption data classification model to obtain the power consumption data status of the watt-hour meter; if the power consumption data status of the watt-hour meter is an abnormal status, determine that the watt-hour meter has abnormal power consumption data.

[0031] In a possible implementation, after sending all the watt-hour meters with abnormal power consumption data in the target substation area to the preset terminal, the substation area abnormal detection method further includes:

[0032] Obtain the number of times of line loss abnormality of the target substation area within a preset duration.

[0033] If the number of times is greater than the preset number threshold, shorten the duration of the collection period to obtain a new collection period, and collect the line loss data of the target substation area and the power consumption data of each watt-hour meter in the target substation area with the new collection period.

[0034] Second aspect, an embodiment of the present invention provides a device for detecting abnormalities in a power distribution area, including:

[0035] An acquisition module, configured to acquire a line loss data set of a target power distribution area collected in consecutive M acquisition cycles; M≥2;

[0036] A line loss abnormality determination module, configured to determine whether the line loss of the target power distribution area is abnormal according to the line loss data set of the target power distribution area;

[0037] An electric energy meter abnormality determination module, configured to, if it is determined that the line loss of the target power distribution area is abnormal, acquire an electricity consumption data set corresponding to each electric energy meter under the target power distribution area collected in consecutive M acquisition cycles, and acquire, according to the electricity consumption data set corresponding to each electric energy meter, the electric energy meters with abnormal electricity consumption data under the target power distribution area;

[0038] A sending module, configured to send all the electric energy meters with abnormal electricity consumption data under the target power distribution area to a preset terminal, so that the staff corresponding to the preset terminal processes the electric energy meters with abnormal electricity consumption data.

[0039] Third aspect, an embodiment of the present invention provides a terminal, including a processor and a memory, where the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the power distribution area abnormality detection method described in the first aspect or any possible implementation manner of the first aspect above.

[0040] Fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the power distribution area abnormality detection method described in the first aspect or any possible implementation manner of the first aspect above are implemented.

[0041] An embodiment of the present invention provides a method, a device, a terminal, and a storage medium for detecting abnormalities in a power distribution area. Through the line loss data set of the target power distribution area, it is possible to determine whether the line loss of the target power distribution area is abnormal; if it is determined that the line loss of the target power distribution area is abnormal, then according to the electricity consumption data set corresponding to each electric energy meter, it is possible to acquire the electric energy meters with abnormal electricity consumption data under the target power distribution area, and send all the electric energy meters with abnormal electricity consumption data under the target power distribution area to a preset terminal, so that the staff corresponding to the preset terminal processes the electric energy meters with abnormal electricity consumption data, thereby eliminating the need for staff to arrive at the scene to judge whether there are abnormalities in the power distribution area based on work experience. The staff can directly process the electric energy meter according to the received electric energy meter with abnormal electricity consumption data to complete the task of reducing losses and eliminating defects, no longer relying on the experience of the staff, and improving efficiency. Description of the Drawings

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0043] Figure 1 is a schematic diagram of the substation area anomaly detection method provided by the embodiment of the present invention;

[0044] Figure 2 is a schematic structural diagram of the substation area anomaly detection device provided by the embodiment of the present invention;

[0045] Figure 3 is a schematic diagram of the terminal provided by the embodiment of the present invention. Detailed implementation manners

[0046] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0047] To make the purpose, technical solutions, and advantages of the present invention clearer, the following will be described through specific embodiments in conjunction with the drawings.

[0048] Refer to Figure 1 , which shows the implementation flowchart of the substation area anomaly detection method provided by the embodiment of the present invention. Among them, the execution subject of the substation area anomaly detection method can be a terminal.

[0049] Refer to Figure 1 , the above-mentioned substation area anomaly detection method includes:

[0050] In S101, obtain the line loss data set of the target substation area collected in M consecutive collection periods; M≥2.

[0051] Among them, the line loss data set of the target substation area includes the line loss data of the target substation area collected in M consecutive collection periods. This line loss data can include the line loss rate or the line loss power consumption, etc. The target substation area is the substation area to be detected. The above-mentioned M consecutive collection periods can be M consecutive collection periods starting from the current collection period and going in the direction of the historical collection period. M is a positive integer greater than or equal to 2.

[0052] In this embodiment, the line loss data of the target substation area can be collected according to the collection period by existing methods. There is no limitation on the specific means of collecting line loss data. For example, it can be collected through Internet of Things technology, etc.

[0053] In S102, according to the line loss data set of the target substation area, it is determined whether the line loss of the target substation area is abnormal.

[0054] In this embodiment, according to the line loss data set of the target substation area, it can be determined whether the line loss of the target substation area is abnormal. Compared with the solution of detecting line loss anomalies only based on the line loss data of a single period, the solution provided in this embodiment has significantly improved accuracy.

[0055] If it is determined that the line loss of the target substation area is normal, the steps of S103 - S104 do not need to be executed, and the line loss data of the target substation area can be continuously collected to continue detecting whether the line loss of the target substation area is abnormal.

[0056] In S103, if it is determined that the line loss of the target substation area is abnormal, the power consumption data sets respectively corresponding to each electric energy meter under the target substation area collected in consecutive M collection periods are obtained, and according to the power consumption data sets respectively corresponding to each electric energy meter, the electric energy meters with abnormal power consumption data under the target substation area are obtained.

[0057] Among them, the power consumption data sets respectively corresponding to each electric energy meter may include the power consumption data of this electric energy meter collected in consecutive M collection periods. The power consumption data may include data such as power consumption.

[0058] In this embodiment, the power consumption data of each electric energy meter under the target substation area can be collected according to the collection period by existing methods. There is no limitation on the specific means of collecting power consumption data. For example, it can be collected through Internet of Things technology, etc.

[0059] In this embodiment, when it is determined that the line loss of the target substation area is abnormal, the power consumption data sets respectively corresponding to each electric energy meter under the target substation area can be obtained, and according to the power consumption data sets respectively corresponding to each electric energy meter, all the electric energy meters with abnormal power consumption data under the target substation area are determined.

[0060] In S104, all the electric energy meters with abnormal power consumption data under the target substation area are sent to a preset terminal so that the staff corresponding to the preset terminal can process the electric energy meters with abnormal power consumption data.

[0061] All the electric energy meters with abnormal power consumption data under the target substation area are sent to a preset terminal, so that the staff corresponding to the preset terminal can perform defect elimination processing on the electric energy meters with abnormal power consumption data, so as to complete the current loss reduction and defect elimination task as soon as possible.

[0062] Among them, the preset terminal can be the mobile terminal of the staff handling the defect elimination task, such as a mobile phone, etc., or can also be a desktop computer, a notebook, or other terminals.

[0063] In a possible implementation manner, the nameplate information of the electricity meter with abnormal power consumption data can be obtained, and the nameplate information and power consumption data sets of all electricity meters with data anomalies are sent to the preset terminal.

[0064] In a possible implementation manner, the nameplate information and line loss data set of the target power grid area can be sent to the preset terminal simultaneously.

[0065] In this embodiment, through the line loss data set of the target power grid area, it can be determined whether the line loss of the target power grid area is abnormal; if it is determined that the line loss of the target power grid area is abnormal, then according to the power consumption data sets corresponding to each electricity meter, the electricity meters with abnormal power consumption data under the target power grid area can be obtained, and all the electricity meters with abnormal power consumption data under the target power grid area are sent to the preset terminal, so that the staff corresponding to the preset terminal can process the electricity meters with abnormal power consumption data, thereby eliminating the need for the staff to arrive at the scene and judge whether there is an abnormality in the power grid area based on work experience. The staff can directly process the electricity meter according to the received electricity meter with abnormal power consumption data to complete the task of reducing losses and eliminating defects, no longer relying on the experience of the staff, and improving efficiency.

[0066] In some embodiments, the line loss data set of the target power grid area includes the line loss data of the target power grid area collected in the current collection period;

[0067] Judging whether the line loss of the target power grid area is abnormal according to the line loss data set of the target power grid area includes:

[0068] If the line loss data of the target power grid area collected in the current collection period exceeds the target line loss threshold range of the target power grid area, then extract the characteristic value of the line loss data set of the target power grid area;

[0069] Input the characteristic value of the line loss data set of the target power grid area into the pre-trained line loss classification model to obtain the line loss status of the target power grid area;

[0070] If the line loss status of the target power grid area is an abnormal status, then determine that the line loss of the target power grid area is abnormal.

[0071] Among them, the line loss classification model can be a logistic regression model, a decision tree model, a random forest model, a neural network model, etc., and no specific limitation is made here.

[0072] Before using the line loss classification model for classification, it is necessary to use a training line loss data set pre-calibrated to a normal state or an abnormal state for training, so as to obtain a pre-trained line loss classification model. The input of this model is the feature value of the line loss data set, and the output is the line loss state. The line loss state is a normal state or an abnormal state.

[0073] In this embodiment, first, it is judged whether the line loss data of the target substation area collected in the current collection period exceeds the target line loss threshold range of the target substation area; if it does not exceed, it is determined that the line loss of the target substation area is normal; if it exceeds, the feature value of the line loss data set of the target substation area is extracted, and this feature value is input into the pre-trained line loss classification model to obtain the line loss state of the target substation area; if this state is an abnormal state, it is determined that the line loss of the target substation area is abnormal; if this state is a normal state, it is determined that the line loss of the target substation area is normal.

[0074] Among them, the target line loss threshold range of the target substation area can be a preset line loss range or a line loss range determined according to subsequent steps. When the collected line loss data is the line loss rate, the target line loss threshold range is the target line loss rate threshold range; when the collected line loss data is the line loss amount, the target line loss threshold range is the target line loss amount threshold range.

[0075] The feature value of the line loss data set can be a numerical value reflecting the volatility or difference and other characteristics of the line loss data set.

[0076] In this embodiment, through double determination, that is, whether the line loss data of the target substation area collected in the current collection period exceeds the target line loss threshold range of the target substation area, and whether the line loss state of the target substation area output by the line loss classification model is an abnormal state, to determine whether the line loss of the target substation area is abnormal, which can improve the accuracy of the determination of the line loss abnormality of the target substation area, and further can provide more accurate information for the staff.

[0077] In some embodiments, the determination process of the target line loss threshold range of the target substation area includes:

[0078] Obtain a first set of nearby substations of the target substation area, and the first set of nearby substations includes all substations whose distance from the target substation area is less than a preset distance threshold;

[0079] If there is a target nearby substation in the first set of nearby substations, then all target nearby substations are removed from the set of nearby substations to obtain a second set of nearby substations; the target nearby substation is a nearby substation that was determined to have an abnormal line loss in at least one collection period within N consecutive collection periods before M consecutive collection periods; N≥2;

[0080] Obtain the historical line loss data set of the target substation area and the historical line loss data sets of each nearby substation area in the second nearby substation area set; the historical line loss data set of each substation area includes a set composed of the line loss data of this substation area collected in N consecutive collection periods.

[0081] Determine the target line loss threshold range of the target substation area according to the historical line loss data set of the target substation area and the historical line loss data sets of each nearby substation area in the second nearby substation area set.

[0082] Considering that the differences between the power users and power supply equipment of the substations with similar geographical locations are small, in this embodiment, when determining the target line loss threshold range of the target substation area, the line loss data of its nearby substations is considered at the same time.

[0083] Among them, the preset distance threshold can be set according to actual needs and will not be specifically limited here.

[0084] In order to improve the accuracy of the target line loss threshold range of the target substation area, the next period of the last collection period of the above-mentioned N consecutive collection periods is the first collection period of the above-mentioned M consecutive collection periods, that is to say, the above-mentioned N consecutive collection periods are connected to the above-mentioned M collection periods. N is an integer greater than or equal to 2. N and M can be equal or not equal.

[0085] In this embodiment, first obtain the first nearby substation area set of the target substation area, and then, since the line loss data of the nearby substations with abnormal line losses has no reference value, all target nearby substations are removed from the first nearby substation area set to obtain the second nearby substation area set. Finally, determine the target line loss threshold range of the target substation area according to the historical line loss data set of the target substation area and the historical line loss data sets of each nearby substation area in the second nearby substation area set.

[0086] In some embodiments, determining the target line loss threshold range of the target substation area according to the historical line loss data set of the target substation area and the historical line loss data sets of each nearby substation area in the second nearby substation area set includes:

[0087] Calculate the average value of the historical line loss data set of the target substation area to obtain the historical average line loss data of the target substation area;

[0088] Calculate the average value of the historical line loss data set of each nearby substation area in the second nearby substation area set respectively to obtain the historical average line loss data corresponding to each nearby substation area respectively;

[0089] Obtain the weighted average line loss data of the second nearby substation area set according to the historical average line loss data corresponding to each nearby substation area respectively and the weight coefficient corresponding to each nearby substation area respectively; among them, the weight coefficient corresponding to each nearby substation area is inversely correlated with the distance between this nearby substation area and the target substation area;

[0090] Obtain the average value of the historical average line loss data of the target substation area and the weighted average line loss data of the second nearby substation area set to obtain the reference line loss data of the target line loss threshold range of the target substation area;

[0091] Subtract the preset line loss data threshold from the reference line loss data to obtain the lower limit value of the target line loss threshold range of the target substation area;

[0092] Add the preset line loss data threshold to the reference line loss data to obtain the upper limit value of the target line loss threshold range of the target substation area.

[0093] In this embodiment, calculate the average value of each historical line loss data in the historical line loss data set of the target substation area to obtain the historical average line loss data of the target substation area; and for each nearby substation area in the second nearby substation area set, calculate the average value of each historical line loss data in the historical line loss data set of the nearby substation area to obtain the historical average line loss data of the nearby substation area.

[0094] Each nearby substation area can determine a weight coefficient according to the distance between it and the target substation area. Among them, the weight coefficients corresponding to each nearby substation area are inversely correlated with the distance between the nearby substation area and the target substation area, that is, the larger the distance value, the smaller the weight coefficient. For example, they can be inversely proportional. Specifically how to set it can be set according to actual needs and will not be specifically limited here.

[0095] The above-mentioned obtaining the weighted average line loss data of the second nearby substation area set according to the historical average line loss data corresponding to each nearby substation area and the weight coefficients corresponding to each nearby substation area may include:

[0096] Sum the products of the historical average line loss data corresponding to each nearby substation area and their respective corresponding weight coefficients to obtain the first total;

[0097] Sum the weight coefficients corresponding to each nearby substation area to obtain the second total;

[0098] Divide the first total by the second total to obtain the weighted average line loss data of the second nearby substation area set.

[0099] Next, calculate the average value of the historical average line loss data of the target power distribution area and the weighted average line loss data of the second nearby power distribution area set, and the baseline line loss data of the target line loss threshold range of the target power distribution area can be obtained; subtract the preset line loss data threshold from the baseline line loss data to obtain the lower limit value of the target line loss threshold range of the target power distribution area; add the preset line loss data threshold to the baseline line loss data to obtain the upper limit value of the target line loss threshold range of the target power distribution area. The range between the lower limit value and the upper limit value is the target line loss threshold range of the target power distribution area. The preset line loss data threshold can be set according to actual needs. For example, if the line loss data is the line loss rate, this threshold can be 2.5% or 5%, etc.

[0100] In this embodiment, the target line loss threshold range of the target power distribution area is dynamically determined, and different target line loss threshold ranges can be determined according to different times and different positions. By judging whether the line loss of the target power distribution area is abnormal through the target line loss threshold range of the target power distribution area, the accuracy of the line loss abnormality judgment of the power distribution area can be improved, and thus more accurate abnormal power distribution area information can be provided for the staff.

[0101] In some embodiments, the eigenvalue of the line loss data set of the target power distribution area includes at least one of a trend feature, a difference degree feature, and a fluctuation feature.

[0102] The trend feature can represent the rising trend or falling trend of the line loss data in the line loss data set, and can include at least one of features such as a rising trend feature, a falling trend feature, and a change rate.

[0103] The difference degree feature can represent the difference degree of the line loss data in the line loss data set, and can include at least one of features such as the range, the interquartile range, the mean difference between the first half of the data and the second half of the data, and the difference between the mean of the segmented data and the total mean.

[0104] The fluctuation feature can represent the volatility of the line loss data in the line loss data set, and can include at least one of features such as the standard deviation and the ratio of the standard deviation to the mean.

[0105] Optionally, the eigenvalue may further include a ratio feature. The ratio feature may include at least one of features such as the ratio of the mean of the first half of the data to the total mean, the ratio of the mean of the second half of the data to the total mean, and the correlation coefficient between the total mean and the average speed limit data of the nearby power distribution areas.

[0106] In some embodiments, the power consumption data sets respectively corresponding to each electric energy meter include the power consumption data of this electric energy meter collected in the current collection period;

[0107] According to the power consumption data sets respectively corresponding to each electric energy meter, obtain the electric energy meters with abnormal power consumption data in the target power distribution area, including:

[0108] For each electricity meter in the target substation area, if the electricity consumption data of the electricity meter collected in the current collection period exceeds the target electricity consumption data range of the electricity meter, the eigenvalue of the electricity consumption data set of the electricity meter is extracted; the eigenvalue of the electricity consumption data set of the electricity meter is input into a pre-trained electricity consumption data classification model to obtain the electricity consumption data status of the electricity meter; if the electricity consumption data status of the electricity meter is an abnormal status, it is determined that there is abnormal electricity consumption data for the electricity meter.

[0109] Among them, the method for determining whether there is abnormal electricity consumption data for an electricity meter is the same as the method for determining whether there is abnormal line loss in the target substation area described above, except that the processed data is different. For specific reference, please refer to the above description and will not be elaborated here.

[0110] In some embodiments, after sending all the electricity meters with abnormal electricity consumption data in the target substation area to a preset terminal, the substation area abnormal detection method further includes:

[0111] Obtain the number of times of abnormal line loss in the target substation area within a preset duration;

[0112] If the number of times is greater than a preset number threshold, shorten the duration of the collection period to obtain a new collection period, and collect the line loss data of the target substation area and the electricity consumption data of each electricity meter in the target substation area with the new collection period.

[0113] In this embodiment, if the number of times of abnormal line loss in the target substation area within a preset duration is too large, that is, greater than the preset number threshold, the target substation area can be marked as a sensitive substation area, and the collection period of the sensitive substation area is shortened, that is, targeted monitoring measures are taken to timely detect the abnormalities in the sensitive substation area and shorten the time required to completely solve the problem of abnormal line loss in the substation area.

[0114] Among them, the preset duration can be the duration from the current time to the historical time direction, and its specific duration can be set according to actual needs. For example, it can be KN collection periods, where K is a positive integer greater than or equal to 2. The preset number threshold can also be set according to actual needs. For example, it can be 3 times, 2 times, etc.

[0115] The duration of the new collection period and the duration of the original collection period can be set according to actual needs. For example, the duration of the new collection period can be half of the duration of the original collection period, and so on.

[0116] In a possible implementation manner, after collecting the line loss data of the target substation area and the electricity consumption data of each electricity meter in the target substation area with the new collection period, it further includes:

[0117] Re-obtain the number of times of abnormal line loss in the target substation area within a preset duration;

[0118] If the number of abnormal line losses in the target power distribution area within the preset time period obtained again is not greater than the preset number threshold, the new collection period is extended to the original collection period, and the line loss data of the target power distribution area and the power consumption data of each electric energy meter under the target power distribution area are collected with the original collection period.

[0119] That is to say, after shortening the duration of the collection period, if the number of abnormal line losses in the target power distribution area within a period of time is not greater than the preset number threshold, the collection period is restored to the original collection period, and the target power distribution area is restored and marked as a normal power distribution area.

[0120] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0121] The following is an apparatus embodiment of the present invention. For details not described in detail, reference may be made to the corresponding method embodiments above.

[0122] Figure 2 The structural schematic diagram of the power distribution area abnormality detection device provided by the embodiment of the present invention is shown. For the sake of convenience of description, only the parts related to the embodiment of the present invention are shown and are described in detail as follows:

[0123] As Figure 2 shown, the power distribution area abnormality detection device 30 includes: an acquisition module 31, a line loss abnormality determination module 32, an electric energy meter abnormality determination module 33, and a sending module 34.

[0124] The acquisition module 31 is used to acquire the line loss data set of the target power distribution area collected in M consecutive collection periods; M≥2;

[0125] The line loss abnormality determination module 32 is used to determine whether the line loss of the target power distribution area is abnormal according to the line loss data set of the target power distribution area;

[0126] The electric energy meter abnormality determination module 33 is used to, if it is determined that the line loss of the target power distribution area is abnormal, acquire the power consumption data sets respectively corresponding to each electric energy meter under the target power distribution area collected in M consecutive collection periods, and acquire the electric energy meters with abnormal power consumption data under the target power distribution area according to the power consumption data sets respectively corresponding to each electric energy meter;

[0127] The sending module 34 is used to send all the electric energy meters with abnormal power consumption data under the target power distribution area to a preset terminal, so that the staff corresponding to the preset terminal processes the electric energy meters with abnormal power consumption data.

[0128] In a possible implementation manner, the line loss data set of the target power distribution area includes the line loss data of the target power distribution area collected in the current collection period;

[0129] The line loss anomaly determination module 32 is specifically configured to:

[0130] If the line loss data of the target substation area collected in the current collection period exceeds the target line loss threshold range of the target substation area, extract the characteristic values of the line loss data set of the target substation area;

[0131] Input the characteristic values of the line loss data set of the target substation area into a pre-trained line loss classification model to obtain the line loss status of the target substation area;

[0132] If the line loss status of the target substation area is an abnormal status, determine that the line loss of the target substation area is abnormal.

[0133] In a possible implementation manner, the line loss anomaly determination module 32 is further configured to:

[0134] Obtain a first set of nearby substation areas of the target substation area, where the first set of nearby substation areas includes all substation areas whose distance from the target substation area is less than a preset distance threshold;

[0135] If there is a target nearby substation area in the first set of nearby substation areas, remove all target nearby substation areas from the set of nearby substation areas to obtain a second set of nearby substation areas; the target nearby substation area is a nearby substation area that was determined to have an abnormal line loss in at least one collection period within N consecutive collection periods before M consecutive collection periods; N≥2;

[0136] Obtain the historical line loss data set of the target substation area and the historical line loss data sets of each nearby substation area in the second set of nearby substation areas; the historical line loss data set of each substation area includes a set composed of the line loss data of this substation area collected in N consecutive collection periods;

[0137] Determine the target line loss threshold range of the target substation area according to the historical line loss data set of the target substation area and the historical line loss data sets of each nearby substation area in the second set of nearby substation areas.

[0138] In a possible implementation manner, the line loss anomaly determination module 32 is further configured to:

[0139] Calculate the average value of the historical line loss data set of the target substation area to obtain the historical average line loss data of the target substation area;

[0140] Calculate the average value of the historical line loss data set of each nearby substation area in the second set of nearby substation areas respectively to obtain the historical average line loss data corresponding to each nearby substation area;

[0141] Obtain the weighted average line loss data of the second set of nearby substation areas according to the historical average line loss data corresponding to each nearby substation area and the weight coefficient corresponding to each nearby substation area; wherein, the weight coefficient corresponding to each nearby substation area is inversely correlated with the distance between this nearby substation area and the target substation area;

[0142] Obtain the average value of the historical average line loss data of the target substation area and the weighted average line loss data of the second nearby substation area set, to obtain the reference line loss data of the target line loss threshold range of the target substation area;

[0143] Subtract the preset line loss data threshold from the reference line loss data to obtain the lower limit value of the target line loss threshold range of the target substation area;

[0144] Add the preset line loss data threshold to the reference line loss data to obtain the upper limit value of the target line loss threshold range of the target substation area.

[0145] In a possible implementation manner, the eigenvalue of the line loss data set of the target substation area includes at least one of a trend feature, a difference degree feature, and a fluctuation feature.

[0146] In a possible implementation manner, the power consumption data sets respectively corresponding to each electric energy meter include the power consumption data of this electric energy meter collected in the current collection period;

[0147] The electric energy meter anomaly determination module 33 is specifically configured to:

[0148] For each electric energy meter under the target substation area, if the power consumption data of this electric energy meter collected in the current collection period exceeds the target power consumption data range of this electric energy meter, then extract the eigenvalue of the power consumption data set of this electric energy meter; input the eigenvalue of the power consumption data set of this electric energy meter into a pre-trained power consumption data classification model to obtain the power consumption data status of this electric energy meter; if the power consumption data status of this electric energy meter is an abnormal status, then determine that this electric energy meter has abnormal power consumption data.

[0149] Figure 3 It is a schematic diagram of the terminal provided by an embodiment of the present invention. As Figure 3 shown, the terminal 4 of this embodiment includes: a processor 40 and a memory 41. The memory 41 is used to store a computer program 42, and the processor 40 is used to call and run the computer program 42 stored in the memory 41, and execute the steps in the above-mentioned embodiments of the abnormal detection method for each substation area, such as Figure 1 shown S101 to S104. Or, the processor 40 is used to call and run the computer program 42 stored in the memory 41 to implement the functions of each module / unit in the above-mentioned device embodiments, such as Figure 2 the functions of the modules / units 31 to 34 shown.

[0150] Exemplarily, the computer program 42 may be divided into one or more modules / units, which are stored in the memory 41 and executed by the processor 40 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program 42 in the terminal 4. For example, the computer program 42 may be divided into Figure 2 the module / units 31 to 34 as shown.

[0151] The terminal 4 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal 4 may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art can understand that Figure 3 these are merely examples of the terminal 4 and do not constitute a limitation on the terminal 4. It may include more or fewer components than shown in the figure, or combine certain components, or have different components. For example, the terminal may further include input / output devices, network access devices, a bus, etc.

[0152] The so-called processor 40 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0153] The memory 41 may be an internal storage unit of the terminal 4, such as the hard disk or memory of the terminal 4. The memory 41 may also be an external storage device of the terminal 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal 4. Further, the memory 41 may also include both the internal storage unit and the external storage device of the terminal 4. The memory 41 is used to store the computer program and other programs and data required by the terminal 4. The memory 41 may also be used to temporarily store the data that has been output or is to be output.

[0154] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example for illustration. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiment and will not be repeated here.

[0155] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0156] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in the form of hardware or software depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0157] In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal and method can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For example, the division of the module or unit is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.

[0158] The unit described as a separated component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0159] In addition, in each embodiment of the present invention, each functional unit may be integrated into a processing unit, may exist separately as individual physical units, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0160] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described embodiment of the abnormal detection method for each substation area can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0161] The above-described embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention and should all be included in the protection scope of the present invention.

Claims

1. A method for detecting abnormal conditions in a power distribution area, characterized in that, Including: Obtaining a line loss data set of a target power distribution area collected in continuous M collection cycles; M≥2； Judging whether the line loss of the target power distribution area is abnormal according to the line loss data set of the target power distribution area; If it is determined that the line loss of the target power distribution area is abnormal, obtaining an electricity consumption data set corresponding to each electricity meter under the target power distribution area collected in the continuous M collection cycles, and obtaining the electricity meters with abnormal electricity consumption data under the target power distribution area according to the electricity consumption data sets corresponding to the respective electricity meters; Sending all the electricity meters with abnormal electricity consumption data under the target power distribution area to a preset terminal, so that the staff corresponding to the preset terminal processes the electricity meters with abnormal electricity consumption data; The line loss data set of the target power distribution area includes the line loss data of the target power distribution area collected in the current collection cycle; The judging whether the line loss of the target power distribution area is abnormal according to the line loss data set of the target power distribution area includes: If the line loss data of the target power distribution area collected in the current collection cycle exceeds the target line loss threshold range of the target power distribution area, extracting the characteristic value of the line loss data set of the target power distribution area; Inputting the characteristic value of the line loss data set of the target power distribution area into a pre-trained line loss classification model to obtain the line loss state of the target power distribution area; If the line loss state of the target power distribution area is an abnormal state, determining that the line loss of the target power distribution area is abnormal; The determination process of the target line loss threshold range of the target power distribution area includes: Obtaining a first set of nearby power distribution areas of the target power distribution area, where the first set of nearby power distribution areas includes all power distribution areas whose distance from the target power distribution area is less than a preset distance threshold; If there is a target nearby power distribution area in the first set of nearby power distribution areas, removing all the target nearby power distribution areas from the set of nearby power distribution areas to obtain a second set of nearby power distribution areas; the target nearby power distribution area is a nearby power distribution area determined to have abnormal line loss in at least one collection cycle within the continuous N collection cycles before the continuous M collection cycles; N≥2; Obtaining the historical line loss data set of the target power distribution area and the historical line loss data sets of the respective nearby power distribution areas in the second set of nearby power distribution areas; the historical line loss data set of each power distribution area includes a set composed of the line loss data of this power distribution area collected in the continuous N collection cycles; Determining the target line loss threshold range of the target power distribution area according to the historical line loss data set of the target power distribution area and the historical line loss data sets of the respective nearby power distribution areas in the second set of nearby power distribution areas.

2. The abnormal detection method for the power distribution area according to claim 1, wherein The determining the target line loss threshold range of the target power distribution area according to the historical line loss data set of the target power distribution area and the historical line loss data sets of the respective nearby power distribution areas in the second set of nearby power distribution areas includes: Calculating the average value of the historical line loss data set of the target power distribution area to obtain the historical average line loss data of the target power distribution area; Calculating the average values of the historical line loss data sets of the respective nearby power distribution areas in the second set of nearby power distribution areas to obtain the historical average line loss data corresponding to the respective nearby power distribution areas; Obtain the weighted average line loss data of the second set of nearby substations according to the historical average line loss data corresponding to each nearby substation and the weight coefficients corresponding to each nearby substation, where the weight coefficients corresponding to each nearby substation are inversely correlated with the distance between the nearby substation and the target substation; Calculate the average value of the historical average line loss data of the target substation and the weighted average line loss data of the second set of nearby substations to obtain the reference line loss data of the target line loss threshold range of the target substation; Subtract the preset line loss data threshold from the reference line loss data to obtain the lower limit value of the target line loss threshold range of the target substation; Add the preset line loss data threshold to the reference line loss data to obtain the upper limit value of the target line loss threshold range of the target substation.

3. The abnormal detection method for the power distribution area according to claim 1, wherein The eigenvalue of the line loss data set of the target substation includes at least one of a trend feature, a difference feature, and a fluctuation feature.

4. The area abnormality detection method according to any one of claims 1 to 3, characterized in that The power consumption data set corresponding to each watt-hour meter includes the power consumption data of the watt-hour meter collected in the current collection period; The method for obtaining the watt-hour meters with abnormal power consumption data under the target substation according to the power consumption data sets corresponding to each watt-hour meter includes: For each watt-hour meter under the target substation, if the power consumption data of the watt-hour meter collected in the current collection period exceeds the target power consumption data range of the watt-hour meter, extract the eigenvalue of the power consumption data set of the watt-hour meter; Input the eigenvalue of the power consumption data set of the watt-hour meter into a pre-trained power consumption data classification model to obtain the power consumption data status of the watt-hour meter; if the power consumption data status of the watt-hour meter is an abnormal status, it is determined that the watt-hour meter has abnormal power consumption data.

5. The method for detecting abnormalities in a transformer substation area according to any one of claims 1 to 3, characterized in that After sending all the watt-hour meters with abnormal power consumption data under the target substation to a preset terminal, the substation abnormality detection method further includes: Obtain the number of times of line loss abnormality of the target substation within a preset time period; If the number is greater than a preset number threshold, shorten the duration of the collection period to obtain a new collection period, and collect the line loss data of the target substation and the power consumption data of each watt-hour meter under the target substation with the new collection period.

6. A substation area anomaly detection device, characterized in that, It includes: An acquisition module, configured to acquire a line loss data set of a target substation collected in M consecutive collection periods; M≥2； A line loss abnormality determination module, configured to determine whether the line loss of the target substation is abnormal according to the line loss data set of the target substation; A watt-hour meter abnormality determination module, configured to, if it is determined that the line loss of the target substation is abnormal, acquire the power consumption data sets corresponding to each watt-hour meter under the target substation collected in the M consecutive collection periods, and obtain the watt-hour meters with abnormal power consumption data under the target substation according to the power consumption data sets corresponding to each watt-hour meter; A sending module, configured to send all the watt-hour meters with abnormal power consumption data under the target substation to a preset terminal, so that the staff corresponding to the preset terminal processes the watt-hour meters with abnormal power consumption data; The line loss data set of the target substation includes the line loss data of the target substation collected in the current collection period; The line loss anomaly determination module is specifically configured to: if the line loss data of the target substation area collected in the current collection period exceeds the target line loss threshold range of the target substation area, extract the characteristic values of the line loss data set of the target substation area; Input the characteristic values of the line loss data set of the target substation area into a pre-trained line loss classification model to obtain the line loss status of the target substation area; if the line loss status of the target substation area is an abnormal status, determine that the line loss of the target substation area is abnormal; The line loss anomaly determination module is further configured to: Obtain a first set of nearby substations of the target substation area, where the first set of nearby substations includes all substations whose distance from the target substation area is less than a preset distance threshold; If there is a target nearby substation in the first set of nearby substations, remove all the target nearby substations from the set of nearby substations to obtain a second set of nearby substations; The target nearby substation is a nearby substation that was determined to have an abnormal line loss in at least one collection period within the consecutive N collection periods before the consecutive M collection periods; N≥2； Obtain the historical line loss data set of the target substation area and the historical line loss data sets of each nearby substation in the second set of nearby substations; the historical line loss data set of each substation area includes a set composed of the line loss data of this substation area collected in the consecutive N collection periods; Determine the target line loss threshold range of the target substation area according to the historical line loss data set of the target substation area and the historical line loss data sets of each nearby substation in the second set of nearby substations.

7. A terminal, characterized in that, It includes a processor and a memory. The memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the substation area anomaly detection method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it realizes the steps of the substation area anomaly detection method according to any one of claims 1 to 5 as above.

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