Abnormity early warning method, device and equipment of transformer and storage medium

Abstract

The invention discloses an abnormity early warning method and device for a transformer, equipment and a storage medium. The method comprises the steps that m temperature data sets of m positions in the transformer in a preset time period and an electrical data set in the preset time period are acquired; determining a temperature abrupt change data set in the m temperature data sets to obtain n temperature abrupt change data sets; determining a temperature abrupt change data set meeting a first preset condition in the n temperature abrupt change data sets as an abnormal temperature data set, and obtaining i abnormal temperature data sets; first early warning information for the i abnormal temperature data sets is sent to monitoring equipment; determining k sections of electrical abrupt change data of the transformer in the electrical data set; determining the electrical abrupt change data meeting a second preset condition in the k sections of electrical abrupt change data as abnormal electrical data, and obtaining p sections of abnormal electrical data; and sending second early warning information for the p-section abnormal electrical data to the monitoring equipment. According to the invention, the accuracy of transformer abnormity early warning can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of transformer monitoring, and in particular to a transformer abnormality early warning method, device, equipment and storage medium. Background Art

[0002] Transformers are key components of power system equipment. With the rapid development of modern power systems, the safe operation of transformers has received more attention, and timely diagnosis of abnormal transformer failures has become particularly important. Modern intelligent transformers are equipped with intelligent diagnostic systems, which can achieve early warning and rapid diagnosis of transformer failures by analyzing monitoring data and operating status, thereby improving the reliability and safety of the power grid.

[0003] In the existing fault diagnosis technology, the transformer electrical data is usually monitored to determine whether the transformer electrical data is within the reference variation range, so as to determine whether the transformer is faulty or has an abnormal variation trend. However, the abnormal variation of the transformer electrical data is only one of the factors leading to transformer failure. The accuracy of the abnormal warning of the transformer is poor only through the electrical data. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the embodiments of the present application provide a transformer abnormal warning method, device, equipment and storage medium. By analyzing m temperature data sets at m positions in the transformer within a preset time period, n temperature mutation data sets are determined, and when the temperature mutation data set meets the first preset condition, the temperature mutation data set is used as an abnormal temperature data set to send a first warning information for the abnormal temperature data set to the monitoring device. In addition, based on the electrical data set of the transformer within the preset time period, k segments of electrical mutation data are determined, so that the electrical mutation data that meets the second preset condition is used as abnormal electrical data, and the second warning information for the abnormal electrical data is sent to the monitoring device. The accuracy of abnormal warning for the transformer is improved.

[0005] In a first aspect, an embodiment of the present application provides a transformer abnormality warning method, comprising:

[0006] Obtain m temperature data sets at m positions in the transformer within a preset time period, and an electrical data set of the transformer within the preset time period; each position corresponds to a temperature data set; m is an integer greater than 1;

[0007] Determine that a temperature data set having temperature mutation data among the m temperature data sets is a temperature mutation data set, and obtain n temperature mutation data sets; the temperature mutation data is temperature data whose temperature change value of a first preset time period is greater than or equal to a first threshold; n is a positive integer less than or equal to m;

[0008] Determine that a temperature mutation data set that meets a first preset condition among the n temperature mutation data sets is an abnormal temperature data set, and obtain i abnormal temperature data sets; i is a positive integer less than or equal to n;

[0009] Sending first warning information for the i abnormal temperature data sets to a monitoring device; the first warning information includes i positions corresponding to the i abnormal temperature data sets among the m positions; each abnormal temperature data set corresponds to one position;

[0010] Determining k segments of electrical mutation data of the transformer in the electrical data set;

[0011] Determine that the electrical mutation data that meets the second preset condition among the k pieces of electrical mutation data is abnormal electrical data, and obtain p pieces of abnormal electrical data;

[0012] Sending second warning information for the p-segment abnormal electrical data to the monitoring device.

[0013] In a second aspect, an embodiment of the present application provides an abnormal warning device for a transformer, the device comprising a data acquisition module, a data processing module and a warning module;

[0014] The data acquisition module is used to acquire m temperature data sets at m positions in the transformer within a preset time period, and an electrical data set of the transformer within the preset time period; each position corresponds to a temperature data set; m is an integer greater than 1;

[0015] The data processing module is used to determine that a temperature data set having temperature mutation data among the m temperature data sets is a temperature mutation data set, and obtain n temperature mutation data sets; the temperature mutation data is temperature data whose temperature change value of a first preset time period is greater than or equal to a first threshold; n is a positive integer less than or equal to m;

[0016] Determine that a temperature mutation data set that meets a first preset condition among the n temperature mutation data sets is an abnormal temperature data set, and obtain i abnormal temperature data sets; i is a positive integer less than or equal to n;

[0017] The early warning module is used to send first early warning information for the i abnormal temperature data sets to the monitoring device; the first early warning information includes i positions corresponding to the i abnormal temperature data sets in the m positions; each abnormal temperature data set corresponds to one position;

[0018] The data processing module is used to determine k segments of electrical mutation data of the transformer in the electrical data set;

[0019] Determine that the electrical mutation data that meets the second preset condition among the k pieces of electrical mutation data is abnormal electrical data, and obtain p pieces of abnormal electrical data;

[0020] The early warning module is used to send second early warning information for the p-segment abnormal electrical data to the monitoring device.

[0021] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor and a memory, the processor being connected to the memory, the memory being used to store a computer program, and the processor being used to execute the computer program stored in the memory, so that the electronic device performs the method described in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method described in the first aspect.

[0023] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program product is operable to cause a computer to execute the method described in the first aspect.

[0024] Implementing the embodiments of the present application has the following beneficial effects:

[0025] In an embodiment of the present application, firstly, m temperature data sets of m positions in the transformer within a preset time period and electrical data sets of the transformer within a preset time period are obtained, wherein each position corresponds to a temperature data set. Then, the temperature data set with temperature mutation data in the m temperature data sets is determined to be a temperature mutation data set, and n temperature mutation data sets are obtained, wherein the temperature mutation data set is the temperature data set with a temperature change value greater than or equal to a first threshold value for a first preset time period. Next, the temperature mutation data set that meets the first preset condition in the n temperature mutation data sets is determined to be an abnormal temperature data set, and i abnormal temperature data sets are obtained. Further, a first warning information for i abnormal temperature data sets is sent to a monitoring device, and the first warning information includes i positions corresponding to the i abnormal temperature data sets in the m positions, and each abnormal temperature data set corresponds to one position. Thus, the abnormal temperature data set can be determined according to the m temperature data sets in the m positions in the transformer within the preset time period, and the first warning information is sent to the monitoring device to improve the safety and stability of the transformer during operation. The first warning information also includes the position corresponding to each abnormal temperature data set, which can improve the accuracy of the abnormal warning of the transformer. Furthermore, the present application can determine k segments of electrical mutation data of the transformer in the electrical data set, and then determine the electrical mutation data that meets the second preset condition in the k segments of electrical mutation data as abnormal electrical data, and obtain p segments of abnormal electrical data. Finally, a second warning message for the p segments of abnormal electrical data is sent to the monitoring device. Thus, the abnormal electrical data of the transformer can be determined based on the electrical data set of the transformer within a preset time period, and a second warning message can be sent to the monitoring device to improve the safety and stability of the transformer during operation. Compared with determining whether an abnormality occurs during the operation of the transformer only by electrical data, the present application can combine temperature data and electrical data to more accurately determine whether the transformer is abnormal, thereby improving the accuracy of the warning of transformer abnormalities. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 A schematic diagram of a transformer abnormality warning system provided in an embodiment of the present application;

[0028] Figure 2 A schematic diagram of a flow chart of a transformer abnormality warning method provided in an embodiment of the present application;

[0029] Figure 3A schematic diagram of a transformer abnormality warning device provided in an embodiment of the present application;

[0030] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0032] The terms "first", "second", "third" and "fourth" etc. in the specification and claims of the present application and the drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0033] Reference to "embodiments" herein means that a particular feature, result, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0034] First, see Figure 1 , Figure 1 A schematic diagram of a transformer abnormality warning system provided in an embodiment of the present application. Figure 1 As shown, the abnormal early warning system of the transformer includes: a transformer, a database, a processing device and a monitoring device.

[0035] Among them, the transformer is an electrical device that uses the principle of electromagnetic induction to change the AC voltage, and is mainly composed of an iron core and a winding. The iron core can provide a magnetic circuit for the transformer, concentrate and guide the magnetic lines of force. There can be two or more windings, including a primary winding and a secondary winding. The primary winding inputs the voltage, and the secondary winding outputs the transformed voltage. Setting a transformer in the circuit can play the role of changing the AC voltage, circuit isolation, impedance matching, etc. It should be noted that the transformer in the embodiment of the present application can be a digital transformer. The digital transformer is a new type of transformer that integrates digital technology and intelligent technology on the basis of a traditional transformer, and has the characteristics of high intelligence, digital control, high efficiency and energy saving. In the power system, the digital transformer, as one of the key equipment, can realize the intelligent monitoring, control and management of the power system, and improve the reliability and stability of the power grid. A variety of sensors are provided on the transformer, which can monitor the electrical data, temperature data, etc. of the transformer during operation.

[0036] Among them, the database is used to store and manage data, including electrical data, temperature data, etc. during the operation of the transformer. The database can be a server with data storage function, including: cloud server (Elastic Compute Service, ECS), embedded server (Embedded Service Processor, ESP), database server, etc. The database can also be a memory with data storage function, including: random access memory (Random Access Memory, RAM), read-only memory (Read-Only Memory, ROM), non-volatile flash memory (Flash Memory), hard disk, optical disk, etc. The embodiment of the present application is not limited to this. The database can obtain the electrical data, temperature data, etc. of the transformer at the current moment from the sensor of the transformer, and correspond the collected electrical data and temperature data to the acquisition time. Therefore, the electrical data and temperature data of the transformer at each moment are stored in the database.

[0037] Among them, the processing device can be, for example, a server for data processing and communication functions, including: a rack server, an application server, a cloud server, a virtual private server (Virtual Private Server, VPS), etc. The processing device can also be a processor chip for data processing and communication functions, including: a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU), a digital signal processor (Digital Signal Processing, DSP), etc. The embodiment of the present application is not limited to this. In the embodiment of the present application, the processing device can communicate with a database, obtain electrical data and temperature data during the operation of the transformer from the database, and determine whether the transformer is abnormal based on the electrical data and temperature data.

[0038] The monitoring device may be an electronic device with data display function, including: mobile phone, computer, etc. The monitoring device can communicate with the processing device. When the processing device determines that the transformer is abnormal, it will send an early warning message to the monitoring device, which will be displayed on the monitoring device. Users can determine that the transformer is abnormal and perform maintenance through the early warning information displayed on the monitoring device, thereby improving the safety and stability of the transformer operation.

[0039] It should be noted that in the traditional transformer abnormality warning method, only by monitoring the electrical data of the transformer can it be judged whether the transformer is in an abnormal state. However, the abnormality that occurs during the operation of the transformer is determined by many factors. Therefore, only by monitoring the electrical data of the transformer, the accuracy of the transformer abnormality warning will be poor.

[0040] To this end, in the transformer abnormality warning method of the present application, the processing device obtains m temperature data sets at m positions in the transformer within a preset time period, and an electrical data set of the transformer within the preset time period; the processing device determines that the temperature data set with temperature mutation data in the m temperature data sets is a temperature mutation data set, and obtains n temperature mutation data sets; the processing device determines that the temperature mutation data set that meets the first preset condition in the n temperature mutation data sets is an abnormal temperature data set, and obtains i abnormal temperature data sets; the processing device sends a first warning message for the i abnormal temperature data sets to the monitoring device; the processing device determines k segments of electrical mutation data of the transformer in the electrical data set; the processing device determines that the electrical mutation data that meets the second preset condition in the k segments of electrical mutation data is abnormal electrical data, and obtains p segments of abnormal electrical data; the processing device sends a second warning message for the p segments of abnormal electrical data to the monitoring device.

[0041] It can be seen that in the abnormal warning system applied to the above-mentioned transformer, the processing device can determine the abnormal temperature data set based on the m temperature data sets at the m positions in the transformer within the preset time period, so as to send the first warning information for the abnormal temperature data set to the monitoring device, thereby improving the safety and stability of the transformer during operation. The first warning information also includes the position corresponding to the abnormal temperature data set, which can improve the accuracy of the abnormal warning of the transformer. In addition, the processing device can determine the abnormal electrical data based on the electrical data set of the transformer within the preset time period, so as to send the second warning information for the abnormal electrical data to the monitoring device, thereby improving the safety and stability of the transformer during operation. By monitoring the temperature data and electrical data of the transformer, the accuracy of the abnormal warning of the transformer can be improved.

[0042] See also Figure 2 , Figure 2 A schematic flow chart of a transformer abnormality warning method provided in an embodiment of the present application. The method is applied to a processing device in the above transformer abnormality warning system. The method includes but is not limited to the following steps:

[0043] 201: Obtain m temperature data sets at m locations in a transformer within a preset time period, and an electrical data set of the transformer within the preset time period.

[0044] In the embodiment of the present application, each position corresponds to a temperature data set, and m is an integer greater than 1. The electrical data set includes a current data set and a voltage data set. The preset time period is a time period including the current moment. The processing device can obtain from the database m temperature data sets of m positions in the transformer within the preset time period, and the electrical data set of the transformer within the preset time period. Optionally, the database can be divided into a temperature database and an electrical database, and the database can also include a total database of temperature data and electrical data of the transformer.

[0045] It should be noted that the temperature sensor inside the transformer can collect temperature data at m positions in the transformer, and within a preset time period, the temperature sensor can collect multiple temperature data according to the temperature collection cycle, thereby forming m temperature data sets at m positions in the transformer within the preset time period. Similarly, the current sensor in the transformer can collect multiple current data within the preset time period according to the current collection cycle to form a current data set. The voltage sensor can collect multiple voltage data within the preset time period according to the voltage collection cycle to form a voltage data set. Thus, an electrical data set is obtained.

[0046] 202: Determine a temperature data set having temperature mutation data among the m temperature data sets as a temperature mutation data set, and obtain n temperature mutation data sets.

[0047] In the embodiment of the present application, the temperature mutation data is temperature data whose temperature change value is greater than or equal to the first threshold value during the first preset time period, and n is a positive integer less than or equal to m. The processing device can analyze the m temperature data sets respectively, determine the temperature data sets that have the temperature mutation data among the m temperature data sets, and obtain n temperature mutation data sets.

[0048] It should be noted that when there is temperature mutation data in the temperature data set, it means that the temperature change in the temperature data set may be abnormal, and the temperature data set needs to be extracted to obtain the temperature mutation data set. The temperature mutation data set can be analyzed to determine whether the temperature of the transformer during operation is abnormal.

[0049] Exemplarily, determining a temperature data set having temperature mutation data among m temperature data sets as a temperature mutation data set, and obtaining n temperature mutation data sets, for example, may include the following steps:

[0050] Get the target temperature data set;

[0051] Dividing the preset time period into multiple sections according to the first preset duration to obtain multiple first time periods;

[0052] Determine temperature data corresponding to a plurality of first time periods in the target temperature data set to obtain a plurality of temperature data segments;

[0053] Determine a temperature change value corresponding to each temperature data segment in the multiple temperature data segments to obtain multiple temperature change values;

[0054] If at least one temperature change value among the multiple temperature change values ​​is greater than or equal to the first threshold, it is determined that the target temperature data set is one of the n temperature mutation data sets.

[0055] In the embodiment of the present application, the target temperature data set includes multiple temperature data, each temperature data corresponds to a collection time. The target temperature data set is any one of the m temperature data sets. The temperature change value is the difference between the temperature data corresponding to the start time of the first time period and the temperature data corresponding to the end time.

[0056] Specifically, the processing device obtains a target temperature data set from the m temperature data sets. The preset time period is divided into multiple segments according to the first preset time length to obtain multiple first time periods. The first preset time length can be pre-set based on the collection time corresponding to each temperature data and the determination accuracy of the temperature mutation data set. The shorter the first preset time length, the more accurate the determined temperature mutation data set.

[0057] Then, the processing device determines the temperature data corresponding to the multiple first time periods in the target temperature data set, and obtains multiple temperature data. By analyzing each temperature data segment, it can be determined whether there is temperature mutation data in the target temperature data set. Specifically, the processing device can determine the difference between the temperature data at the start time of the first time period and the temperature data at the end time, and obtain the temperature change value corresponding to each temperature data segment, thereby obtaining multiple temperature change values ​​corresponding to the multiple temperature data segments. It should be noted that the temperature change value is the difference between the temperature data corresponding to the start time of the first time period and the temperature data corresponding to the end time, and can only reflect the overall temperature change of each temperature data segment in the first time period. The processing first determines whether the overall temperature change of each temperature data segment in the first time period is within the normal range to determine whether the target temperature data set is a temperature mutation data set.

[0058] If at least one of the multiple temperature change values ​​is greater than or equal to the first threshold, it means that there is temperature mutation data in the target temperature data set, and the target temperature data set is determined to be one of the n temperature mutation data sets. The first threshold can be pre-set according to the determination accuracy of the temperature mutation data set.

[0059] If the multiple temperature change values ​​are all smaller than the first threshold, it means that there is no temperature mutation data in the target temperature data set, and it is determined that the target temperature data set is not a temperature mutation data set.

[0060] In this way, the processing device can process the m temperature data sets according to the processing method of the target temperature data set, determine that the temperature data set with temperature mutation data in the m temperature data sets is the temperature mutation data set, and obtain n temperature mutation data sets. Therefore, the temperature mutation data set can be extracted from the m temperature data sets, and the temperature mutation data set can be analyzed to determine whether the temperature data is abnormal, thereby improving the accuracy of abnormal warning for the transformer.

[0061] 203: Determine that a temperature mutation data set that meets a first preset condition among the n temperature mutation data sets is an abnormal temperature data set, and obtain i abnormal temperature data sets.

[0062] In the embodiment of the present application, i is a positive integer less than or equal to n. The processing device can analyze each of the n temperature mutation data sets to determine whether each temperature mutation data set meets the first preset condition, thereby determining i abnormal temperature data sets from the n temperature mutation data sets.

[0063] Exemplarily, determining a temperature mutation data set that meets a first preset condition among n temperature mutation data sets as an abnormal temperature data set, and obtaining i abnormal temperature data sets, for example, may include:

[0064] Acquire multiple temperature data in the target temperature mutation data set to obtain multiple target temperature data;

[0065] Fit multiple target temperature data to obtain a temperature curve corresponding to the target temperature mutation data set;

[0066] Determine the slopes corresponding to the multiple target temperature data in the temperature curve to obtain multiple slopes;

[0067] Using the multiple slopes as multiple temperature change rates corresponding to the multiple target temperature data;

[0068] If there is temperature mutation data that does not fall within the temperature change reference rate range among the multiple temperature change rates, it is determined that the target temperature mutation data set is one of the i abnormal temperature data sets.

[0069] In the embodiment of the present application, the target temperature mutation data set is any one of the n temperature mutation data sets. Each target temperature data corresponds to a slope. Each target temperature data corresponds to a temperature change rate.

[0070] Specifically, the processing device obtains a target temperature mutation data set from n temperature mutation data sets, and obtains multiple temperature data corresponding to the target temperature mutation data set to obtain multiple target temperature data. Then, curve fitting is performed on the multiple target temperature data to obtain a temperature curve corresponding to the target temperature mutation data set. The horizontal coordinate of the temperature curve is the collection time corresponding to each target temperature data, and the vertical coordinate of the temperature curve is the target temperature data. The temperature curve is used to reflect the change of temperature data in each temperature mutation data set over time.

[0071] Then, the processing device determines the slope of the temperature curve at the intersection point of each target temperature data to obtain the slope corresponding to each target temperature data, thereby obtaining multiple slopes. The slope corresponding to each target temperature data is used to represent the temperature change rate at the target temperature data. The processing device uses the multiple slopes as multiple temperature change rates corresponding to multiple target temperature data.

[0072] It should be noted that in the embodiment of the present application, whether the target temperature data is abnormal temperature data is determined by determining whether the temperature change rate corresponding to the target temperature data is within the temperature change reference rate range.

[0073] If there is a temperature mutation data that does not fall within the temperature change reference rate range among the multiple temperature change rates, the target temperature mutation data set is determined to be one of the i abnormal temperature data sets. The temperature change reference rate range is determined according to a preset temperature rise relationship of the transformer.

[0074] If multiple temperature change rates all fall within the temperature change reference rate range, the target temperature mutation data set is determined to be a normal temperature data set. At this time, the processing device can mark the position corresponding to the target temperature mutation data set as a focus position to continuously monitor the focus position.

[0075] In this way, the processing device can analyze and process n temperature mutation data sets according to the processing method of the target temperature mutation data set, and determine the abnormal temperature data set that meets the first preset condition from the n temperature mutation data sets. In this way, the abnormal temperature data of the transformer when it is running within the preset time period can be determined to warn the abnormal temperature data, improve the safety and stability of the transformer during operation, and by analyzing each target temperature data in each temperature mutation data set, the accuracy of abnormal warning for the transformer can be improved.

[0076] Exemplarily, the method may further include:

[0077] Determine the highest temperature value of the target temperature mutation data set according to the temperature curve;

[0078] If the highest temperature value is higher than the second threshold, the target temperature mutation data set is determined to be one of the i abnormal temperature data sets.

[0079] In the embodiment of the present application, the processing device also determines the maximum temperature value of the position corresponding to the target temperature mutation data set of the transformer in the preset time period according to the temperature curve corresponding to the target temperature mutation data set. If the maximum temperature value is higher than the second threshold, it means that the temperature of the position in the preset time period has abnormally increased, and the target temperature mutation data set is determined to be one of the i abnormal temperature data sets.

[0080] In this way, the processing equipment can analyze the n temperature curves corresponding to the n temperature mutation data sets according to the processing method of the target temperature mutation data set, determine the abnormal temperature data set from the n temperature mutation data sets, and use the temperature mutation data set with too high temperature during the operation of the transformer as the abnormal temperature data set, and issue an early warning for the abnormal temperature data set to improve the safety and stability of the transformer during operation. The n temperature curves corresponding to the n temperature mutation data sets are analyzed in turn, which can improve the accuracy of the abnormal early warning for the transformer.

[0081] It should be noted that in the embodiment of the present application, the temperature change reference rate range and the second threshold value can be obtained by temperature evolution according to a preset temperature rise relationship of the transformer.

[0082] Exemplarily, the method may further include the following steps:

[0083] Obtain the temperature rise parameters of the transformer;

[0084] Determine the load power loss of the transformer in a preset time period based on the winding resistance and electrical data of the transformer;

[0085] Obtaining the no-load power loss of the transformer within a preset time period;

[0086] Determine the power loss of the transformer within a preset time period based on the load power loss and the no-load power loss;

[0087] Based on the heat dissipation surface area, heat dissipation coefficient, power loss and a preset temperature rise relationship of the transformer, a temperature change reference rate range and a second threshold value are determined.

[0088] In the embodiment of the present application, the temperature rise parameters include the heat dissipation surface area and the heat dissipation coefficient of the transformer winding.

[0089] Specifically, the processing device first obtains the heat dissipation surface area, heat dissipation coefficient, and winding resistance of the transformer winding. Based on the winding resistance and electrical data of the transformer, the load power loss of the transformer in a preset time period is determined. For example, the heating power of the transformer can be determined based on the current data of the transformer in a preset time period and the winding resistance of the transformer, and the heating power is used as the load power loss of the transformer in the preset time period.

[0090] Then, the processing device will obtain the no-load power loss of the transformer in the preset time period, wherein the no-load power loss of the transformer is related to the shape and quality of the transformer core. The no-load power loss of each transformer has been determined during the production process, and the processing device can directly obtain the no-load power loss of the transformer. Then, the sum of the load power loss and the no-load power loss of the transformer in the preset time period is taken as the power loss of the transformer in the preset time period.

[0091] Based on the heat dissipation surface area, heat dissipation coefficient, power loss and the preset temperature rise relationship of the transformer, the temperature evolution is performed to determine the temperature change reference rate range and the second threshold. The preset temperature rise relationship can be expressed by the following formula (1):

[0092] ΔT=P L / (S×α) formula (1)

[0093] Among them, ΔT represents the temperature rise value of the transformer, that is, the temperature change value of the transformer, P L represents the power loss of the transformer in a preset time period, S represents the heat dissipation surface area of ​​the transformer winding, and α represents the heat dissipation coefficient of the transformer winding. The processing device can perform temperature evolution on the temperature change of the transformer according to the temperature data of the transformer at the start time of the preset time period through the above preset temperature rise relationship to determine the temperature change reference rate range and the second threshold.

[0094] It can be seen that in the embodiment of the present application, the processing device can determine the load power loss of the transformer in a preset time period according to the winding resistance and electrical data of the transformer, and obtain the no-load power loss of the transformer in the preset time period. Based on the load power loss and no-load power loss of the transformer in the preset time period, the power loss of the transformer in the preset time period can be determined. Based on the heat dissipation surface area, heat dissipation coefficient, power loss and the preset temperature rise relationship of the transformer, the temperature evolution of the transformer can be performed to determine the temperature change reference rate range and the second threshold. Therefore, it is possible to determine whether the temperature mutation data set is an abnormal temperature data set based on the temperature change reference rate range and the second threshold, thereby improving the accuracy of abnormal warning for the transformer.

[0095] 204: Sending first warning information for i abnormal temperature data sets to the monitoring device.

[0096] In an embodiment of the present application, after determining i abnormal temperature data sets, the processing device may generate first warning information of the i abnormal temperature data sets, and the first warning information includes i positions corresponding to the i abnormal temperature data sets in m positions. Each abnormal temperature data set corresponds to one position. The first warning information also includes a temperature curve corresponding to the abnormal temperature data set, as well as the above-mentioned temperature change reference rate range and the second threshold value.

[0097] Thus, the user can obtain the first warning information for i abnormal temperature data sets from the monitoring device. Thus, the abnormal temperature data sets that appear in the transformer within the preset time period and the corresponding position of each abnormal temperature data set are obtained, so as to perform abnormal location and abnormal maintenance according to the abnormal temperature data sets and the corresponding position of each abnormal temperature data set, thereby improving the safety and stability of the transformer during operation.

[0098] 205: Determine k segments of electrical mutation data of the transformer in the electrical data set.

[0099] In an embodiment of the present application, the processing device may divide the electrical data set into multiple segments of electrical data, and analyze each segment of electrical data to determine k segments of electrical mutation data in the electrical data set.

[0100] Exemplarily, the electrical data set may include a current data set and a voltage data set. Determining k segments of electrical mutation data of the transformer in the electrical data set may include the following steps:

[0101] Divide the preset time period into multiple sections according to the second preset duration to obtain multiple second time periods;

[0102] Determine current data corresponding to a plurality of second time periods in the current data set to obtain a plurality of segments of current data;

[0103] Determine a current change value corresponding to each segment of current data in the multiple segments of current data to obtain multiple current change values;

[0104] Determine the current data whose current change values ​​in the multiple current data are greater than or equal to the third threshold as the current mutation data, and obtain the multiple current mutation data;

[0105] Determine voltage data corresponding to a plurality of second time periods in the voltage data set to obtain a plurality of sections of voltage data;

[0106] Determine a current change value corresponding to each section of voltage data in the multiple sections of voltage data to obtain multiple voltage change values;

[0107] Determine that voltage data whose voltage change values ​​in the multiple sections of voltage data are greater than or equal to a fourth threshold value is voltage mutation data, and obtain multiple sections of voltage mutation data;

[0108] The multiple segments of current mutation data and the multiple segments of voltage mutation data are taken as k segments of electrical mutation data.

[0109] In the embodiment of the present application, the current change value is the difference between the current data corresponding to the start time of the second time period and the current data corresponding to the end time. The voltage change value is the difference between the voltage data corresponding to the start time of the second time period and the voltage data corresponding to the end time.

[0110] Specifically, the processing device can divide the preset time period into multiple segments according to the second preset time length to obtain multiple second time periods. The second preset time length can be preset according to the collection time of each electrical data in the electrical data set and the accuracy of the electrical mutation data.

[0111] Then, the processing device determines the current data corresponding to multiple second time periods in the current data set, and obtains multiple sections of current data, and multiple second time periods correspond to the multiple sections of current data one by one. Next, the difference between the current data corresponding to the starting moment of each second time period and the current data corresponding to the end moment is determined to obtain the current change value of each second time period, thereby obtaining multiple current change values. The current data with a current change value greater than or equal to the third threshold in the multiple sections of current data is used as current mutation data, thereby obtaining multiple sections of current mutation data. If multiple current change values ​​are all less than the third threshold, it means that the current data of the transformer within the preset time period is abnormal. Among them, the third threshold can be pre-set according to the actual working state of the transformer.

[0112] Similarly, the processing device determines the voltage data corresponding to multiple second time periods in the voltage data set to obtain multiple sections of voltage data, and multiple second time periods correspond to the multiple sections of voltage data one by one. Next, the difference between the voltage data corresponding to the start time of each second time period and the voltage data corresponding to the end time is determined to obtain the voltage change value of each second time period, thereby obtaining multiple voltage change values. The voltage data in the multiple sections of voltage data whose voltage change value is greater than or equal to the fourth threshold is used as voltage mutation data, thereby obtaining multiple sections of voltage mutation data. If the multiple voltage change values ​​are all less than the fourth threshold, it means that there is no abnormality in the voltage data of the transformer within the preset time period. Among them, the fourth threshold can be pre-set according to the actual working state of the transformer.

[0113] Finally, the processing device uses the multiple segments of current mutation data and the multiple segments of voltage mutation data as k segments of electrical mutation data.

[0114] Therefore, the processing device can divide the current data set and the voltage data set into multiple segments of current data and multiple segments of voltage data, and analyze each segment of current data and each segment of voltage data respectively to determine multiple segments of current mutation data and multiple segments of voltage mutation data, thereby obtaining k segments of electrical mutation data. By analyzing the k segments of electrical mutation data, it can be determined whether the electrical data of the transformer within a preset time period is abnormal, so as to improve the accuracy of abnormal warning for the transformer.

[0115] 206: Determine that the electrical mutation data satisfying the second preset condition among the k pieces of electrical mutation data is abnormal electrical data, and obtain p pieces of abnormal electrical data.

[0116] In the embodiment of the present application, the processing device can respectively determine whether each of the k pieces of electrical mutation data meets the second preset condition to determine p pieces of abnormal electrical data in the k pieces of electrical mutation data.

[0117] Exemplarily, determining that the electrical mutation data satisfying the second preset condition among the k pieces of electrical mutation data is abnormal electrical data and obtaining p pieces of abnormal electrical data may include, for example:

[0118] Determine the current variation interval corresponding to the target current mutation data;

[0119] Determine a target voltage variation interval corresponding to the current variation interval;

[0120] Determine a first target time period corresponding to the target current mutation data;

[0121] If the voltage data in the first target time period does not fall within the target voltage change interval, the target current mutation data is determined to be abnormal current data;

[0122] Determine the voltage variation interval corresponding to the target voltage mutation data;

[0123] Determine the target current variation interval corresponding to the target voltage mutation data;

[0124] Determine a second target time period corresponding to the target voltage mutation data;

[0125] If the current data in the second target time period does not fall within the target current change interval, the target voltage mutation data is determined to be abnormal voltage data;

[0126] Abnormal current data in the multiple sections of current mutation data and abnormal voltage data in the multiple sections of voltage mutation data are taken as abnormal electrical data to obtain p sections of abnormal electrical data.

[0127] In the embodiment of the present application, the target current mutation data is any one of the multiple current mutation data. The target voltage mutation data is any one of the multiple voltage mutation data.

[0128] Specifically, the processing device first obtains the target current mutation data from the k segments of electrical mutation data, and obtains the maximum current and the minimum current in the current data corresponding to the target current mutation data, so as to obtain the current change interval corresponding to the target current mutation data. According to the current change interval, the target voltage change interval corresponding to the current change interval can be determined, wherein the correspondence between the current change interval and the target voltage change interval can be pre-set based on the actual working state of the transformer.

[0129] Then, the processing device determines the first target time period corresponding to the target current mutation data. If the voltage data in the first target time period does not fall within the target voltage change interval, it means that the change trend of the current data and the voltage data in the first target time period is not within the normal change range, and the target current mutation data is determined to be abnormal current data. Thus, by analyzing and processing multiple current mutation data according to the target current mutation data, abnormal current data in the multiple current mutation data can be obtained.

[0130] Similarly, the processing device first obtains the target voltage mutation data from the k-segment electrical mutation data, and obtains the maximum voltage and the minimum voltage in the voltage data corresponding to the target voltage mutation data, so as to obtain the voltage variation interval corresponding to the target voltage mutation data. According to the voltage variation interval, the target current variation interval corresponding to the voltage variation interval can be determined, wherein the correspondence between the voltage variation interval and the target current variation interval can be pre-set based on the actual working state of the transformer.

[0131] Then, the processing device determines the second target time period corresponding to the target voltage mutation data. If the current data in the second target time period does not fall into the target current change interval, it means that the change trend of the voltage data and the current data in the second target time period is not within the normal change range, and the target voltage mutation data is determined to be abnormal voltage data. Thus, by analyzing and processing multiple sections of voltage mutation data according to the target voltage mutation data, abnormal voltage data in the multiple sections of voltage mutation data can be obtained.

[0132] Finally, the processing device uses the abnormal current data in the multiple segments of current mutation data and the abnormal voltage data in the multiple segments of voltage mutation data as abnormal electrical data, and can obtain p segments of abnormal electrical data.

[0133] It can be seen that the processing equipment can analyze the current mutation data and the voltage mutation data in the k-segments of electrical mutation data respectively, so as to respectively determine the abnormal current data in the multiple-segments of current mutation data and the abnormal voltage data in the multiple-segments of voltage mutation data, so that the abnormal current data and the abnormal voltage data can be used together as abnormal electrical data to issue an early warning for the abnormal electrical data, so as to improve the accuracy of the abnormal early warning for the transformer.

[0134] 207: Sending a second warning message for the abnormal electrical data of segment p to the monitoring device.

[0135] In an embodiment of the present application, after determining the p-segment abnormal electrical data, the processing device may generate a second warning information for the p-segment abnormal electrical data, and send the second warning information to the monitoring device. The second warning information includes the current data and voltage data corresponding to the p-segment abnormal electrical data. The user can obtain the second warning information from the monitoring device to perform abnormal processing and maintenance on the transformer.

[0136] In summary, in the embodiment of the present application, firstly, m temperature data sets of m positions in the transformer within a preset time period and electrical data sets of the transformer within a preset time period are obtained, wherein each position corresponds to a temperature data set. Then, the temperature data set with temperature mutation data in the m temperature data sets is determined to be a temperature mutation data set, and n temperature mutation data sets are obtained, wherein the temperature mutation data set is the temperature data set with a temperature change value greater than or equal to the first threshold value for the first preset time length. Next, the temperature mutation data set that meets the first preset condition in the n temperature mutation data sets is determined to be an abnormal temperature data set, and i abnormal temperature data sets are obtained. Further, the first warning information for the i abnormal temperature data sets is sent to the monitoring device, and the first warning information includes i positions corresponding to the i abnormal temperature data sets in the m positions, and each abnormal temperature data set corresponds to one position. Thus, the abnormal temperature data set can be determined according to the m temperature data sets in the m positions in the transformer within the preset time period, and the first warning information is sent to the monitoring device to improve the safety and stability of the transformer during operation. The first warning information also includes the position corresponding to each abnormal temperature data set, which can improve the accuracy of the abnormal warning of the transformer. Furthermore, the present application can determine k segments of electrical mutation data of the transformer in the electrical data set, and then determine the electrical mutation data that meets the second preset condition in the k segments of electrical mutation data as abnormal electrical data, and obtain p segments of abnormal electrical data. Finally, a second warning message for the p segments of abnormal electrical data is sent to the monitoring device. Thus, the abnormal electrical data of the transformer can be determined based on the electrical data set of the transformer within a preset time period, and a second warning message can be sent to the monitoring device to improve the safety and stability of the transformer during operation. Compared with determining whether an abnormality occurs during the operation of the transformer only by electrical data, the present application can combine temperature data and electrical data to more accurately determine whether the transformer is abnormal, thereby improving the accuracy of the warning of transformer abnormalities.

[0137] See also Figure 3 , Figure 3 A schematic diagram of a transformer abnormality warning device provided in an embodiment of the present application. The transformer abnormality warning device 300 includes an acquisition module 301, a data processing module 302 and a warning module 303;

[0138] The data acquisition module 301 is used to acquire m temperature data sets at m positions in the transformer within a preset time period, and an electrical data set of the transformer within the preset time period; each position corresponds to a temperature data set; m is an integer greater than 1;

[0139] The data processing module 302 is used to determine that the temperature data set with temperature mutation data in the m temperature data sets is a temperature mutation data set, and obtain n temperature mutation data sets; the temperature mutation data is the temperature data whose temperature change value of the first preset time period is greater than or equal to the first threshold value; n is a positive integer less than or equal to m;

[0140] Determine that a temperature mutation data set that meets a first preset condition among the n temperature mutation data sets is an abnormal temperature data set, and obtain i abnormal temperature data sets; i is a positive integer less than or equal to n;

[0141] The warning module 303 is used to send a first warning message for i abnormal temperature data sets to the monitoring device; the first warning message includes i positions corresponding to the i abnormal temperature data sets in the m positions; each abnormal temperature data set corresponds to one position;

[0142] The data processing module 302 is used to determine k segments of electrical mutation data of the transformer in the electrical data set;

[0143] Determine that the electrical mutation data that meets the second preset condition among the k segments of electrical mutation data is abnormal electrical data, and obtain p segments of abnormal electrical data;

[0144] The warning module 303 is used to send a second warning message for the p-segment abnormal electrical data to the monitoring device.

[0145] In a possible embodiment, in determining that a temperature data set having temperature mutation data among m temperature data sets is a temperature mutation data set, and obtaining n temperature mutation data sets, the data processing module 302 is specifically used to:

[0146] Obtain a target temperature data set; the target temperature data set includes multiple temperature data, each temperature data corresponds to a collection time; the target temperature data set is any temperature data set among the m temperature data sets;

[0147] Dividing the preset time period into multiple sections according to the first preset duration to obtain multiple first time periods;

[0148] Determine temperature data corresponding to a plurality of first time periods in the target temperature data set to obtain a plurality of temperature data segments;

[0149] Determine the temperature change value corresponding to each temperature data segment in the multiple temperature data segments to obtain multiple temperature change values; the temperature change value is the difference between the temperature data corresponding to the start time of the first time segment and the temperature data corresponding to the end time;

[0150] If at least one temperature change value among the multiple temperature change values ​​is greater than or equal to the first threshold, it is determined that the target temperature data set is one of the n temperature mutation data sets.

[0151] In a possible embodiment, in determining that a temperature mutation data set that meets a first preset condition among n temperature mutation data sets is an abnormal temperature data set, and obtaining i abnormal temperature data sets, the data processing module 302 is specifically used to:

[0152] Acquire multiple temperature data in the target temperature mutation data set to obtain multiple target temperature data; the target temperature mutation data set is any one of the n temperature mutation data sets;

[0153] Fit multiple target temperature data to obtain a temperature curve corresponding to the target temperature mutation data set;

[0154] Determine the slopes corresponding to the multiple target temperature data in the temperature curve to obtain multiple slopes; each target temperature data corresponds to one slope;

[0155] The multiple slopes are used as multiple temperature change rates corresponding to multiple target temperature data; each target temperature data corresponds to a temperature change rate;

[0156] If there is temperature mutation data that does not fall within the temperature change reference rate range among the multiple temperature change rates, it is determined that the target temperature mutation data set is one of the i abnormal temperature data sets.

[0157] In a possible embodiment, the data processing module 302 is further configured to:

[0158] Determine the highest temperature value of the target temperature mutation data set according to the temperature curve;

[0159] If the highest temperature value is higher than the second threshold, the target temperature mutation data set is determined to be one of the i abnormal temperature data sets.

[0160] In a possible embodiment, the data processing module 302 is further configured to:

[0161] Obtaining temperature rise parameters of the transformer, the temperature rise parameters including the heat dissipation surface area and heat dissipation coefficient of the transformer winding;

[0162] Determine the load power loss of the transformer in a preset time period based on the winding resistance and electrical data of the transformer;

[0163] Obtaining the no-load power loss of the transformer within a preset time period;

[0164] Determine the power loss of the transformer within a preset time period based on the load power loss and the no-load power loss;

[0165] Based on the heat dissipation surface area, heat dissipation coefficient, power loss and a preset temperature rise relationship of the transformer, a temperature change reference rate range and a second threshold value are determined.

[0166] In a possible embodiment, the electrical data set includes a current data set and a voltage data set; in terms of determining k-segment electrical mutation data of the transformer in the electrical data set, the data processing module 302 is specifically used to:

[0167] Divide the preset time period into multiple sections according to the second preset duration to obtain multiple second time periods;

[0168] Determine current data corresponding to a plurality of second time periods in the current data set to obtain a plurality of segments of current data;

[0169] Determine the current change value corresponding to each current data segment in the multiple current data segments to obtain multiple current change values; the current change value is the difference between the current data corresponding to the start time of the second time segment and the current data corresponding to the end time;

[0170] Determine the current data whose current change values ​​in the multiple current data are greater than or equal to the third threshold as the current mutation data, and obtain the multiple current mutation data;

[0171] Determine voltage data corresponding to a plurality of second time periods in the voltage data set to obtain a plurality of sections of voltage data;

[0172] Determine the current change value corresponding to each section of voltage data in the multiple sections of voltage data to obtain multiple voltage change values; the voltage change value is the difference between the voltage data corresponding to the start time of the second time period and the voltage data corresponding to the end time;

[0173] Determine that voltage data whose voltage change values ​​in the multiple sections of voltage data are greater than or equal to a fourth threshold value is voltage mutation data, and obtain multiple sections of voltage mutation data;

[0174] The multiple segments of current mutation data and the multiple segments of voltage mutation data are taken as k segments of electrical mutation data.

[0175] In a possible embodiment, in determining that the electrical mutation data satisfying the second preset condition in the k-segments of electrical mutation data is abnormal electrical data, and obtaining p-segments of abnormal electrical data, the data processing module 302 is specifically used to:

[0176] Determine a current variation interval corresponding to target current mutation data; the target current mutation data is any current mutation data among multiple current mutation data;

[0177] Determine a target voltage variation interval corresponding to the current variation interval;

[0178] Determine a first target time period corresponding to the target current mutation data;

[0179] If the voltage data in the first target time period does not fall within the target voltage change interval, the target current mutation data is determined to be abnormal current data;

[0180] Determine a voltage variation interval corresponding to target voltage mutation data; the target voltage mutation data is any voltage mutation data among multiple voltage mutation data;

[0181] Determine the target current variation interval corresponding to the target voltage mutation data;

[0182] Determine a second target time period corresponding to the target voltage mutation data;

[0183] If the current data in the second target time period does not fall within the target current change interval, the target voltage mutation data is determined to be abnormal voltage data;

[0184] Abnormal current data in the multiple sections of current mutation data and abnormal voltage data in the multiple sections of voltage mutation data are taken as abnormal electrical data to obtain p sections of abnormal electrical data.

[0185] See also Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 4 As shown, the electronic device 400 includes a transceiver 401, a processor 402 and a memory 403. They are connected via a bus 404. The memory 403 is used to store computer programs and data, and can transmit the data stored in the memory 403 to the processor 402. The electronic device 400 can be the abnormal warning device 300 for the transformer. The electronic device 400 can also be a processing device of any of the above embodiments.

[0186] The processor 402 is used to read the computer program in the memory 403 and perform the following operations:

[0187] Obtain m temperature data sets at m positions in the transformer within a preset time period, and an electrical data set of the transformer within the preset time period; each position corresponds to a temperature data set; m is an integer greater than 1;

[0188] Determine that a temperature data set having temperature mutation data among the m temperature data sets is a temperature mutation data set, and obtain n temperature mutation data sets; the temperature mutation data is temperature data whose temperature change value of a first preset time period is greater than or equal to a first threshold; n is a positive integer less than or equal to m;

[0189] Determine that a temperature mutation data set that meets a first preset condition among the n temperature mutation data sets is an abnormal temperature data set, and obtain i abnormal temperature data sets; i is a positive integer less than or equal to n;

[0190] Sending first warning information for i abnormal temperature data sets to the monitoring device; the first warning information includes i positions corresponding to the i abnormal temperature data sets among the m positions; each abnormal temperature data set corresponds to one position;

[0191] Determine k segments of electrical mutation data of the transformer in the electrical data set;

[0192] Determine that the electrical mutation data that meets the second preset condition among the k segments of electrical mutation data is abnormal electrical data, and obtain p segments of abnormal electrical data;

[0193] A second warning message for the abnormal electrical data of segment p is sent to the monitoring device.

[0194] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process of the method side. It is understandable that the electronic device 400 includes a hardware structure and / or software module corresponding to each function in order to realize the above functions. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiment provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0195] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement part or all of the steps of any one of the methods described in the above method embodiments.

[0196] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of any one of the methods recorded in the above method embodiments.

[0197] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0198] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0199] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be electrical or other forms.

[0200] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0201] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, 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 program module.

[0202] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or CD-ROM and other media that can store program codes.

[0203] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0204] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A transformer abnormality early warning method, characterized in that: include: Acquire m temperature data sets at m locations in the transformer within a preset time period, and an electrical data set of the transformer within the preset time period; Each position corresponds to a temperature data set; m is an integer greater than 1; Determine that a temperature data set having temperature mutation data among the m temperature data sets is a temperature mutation data set, and obtain n temperature mutation data sets; the temperature mutation data is temperature data whose temperature change value of a first preset time period is greater than or equal to a first threshold; n is a positive integer less than or equal to m; Determine that a temperature mutation data set that meets a first preset condition among the n temperature mutation data sets is an abnormal temperature data set, and obtain i abnormal temperature data sets; i is a positive integer less than or equal to n; Sending first warning information for the i abnormal temperature data sets to a monitoring device; The first warning information includes i positions corresponding to the i abnormal temperature data sets in the m positions; each abnormal temperature data set corresponds to one position; Determining k segments of electrical mutation data of the transformer in the electrical data set; Determine that the electrical mutation data that meets the second preset condition among the k pieces of electrical mutation data is abnormal electrical data, and obtain p pieces of abnormal electrical data; Sending second warning information for the p-segment abnormal electrical data to the monitoring device.

2. The method according to claim 1, characterized in that The step of determining that a temperature data set having temperature mutation data among the m temperature data sets is a temperature mutation data set, and obtaining n temperature mutation data sets, comprises: Acquire a target temperature data set; the target temperature data set includes a plurality of temperature data, each temperature data corresponds to a collection time; the target temperature data set is any one of the m temperature data sets; Dividing the preset time period into multiple sections according to the first preset duration to obtain multiple first time periods; Determine the temperature data corresponding to the plurality of first time periods in the target temperature data set to obtain a plurality of sections of temperature data; Determine a temperature change value corresponding to each of the multiple temperature data segments to obtain multiple temperature change values; the temperature change value is the difference between the temperature data corresponding to the start time of the first time period and the temperature data corresponding to the end time; If at least one temperature change value among the multiple temperature change values ​​is greater than or equal to the first threshold, the target temperature data set is determined to be one of the n temperature mutation data sets.

3. The method according to claim 1 or 2, characterized in that: The step of determining that a temperature mutation data set that meets a first preset condition among the n temperature mutation data sets is an abnormal temperature data set, and obtaining i abnormal temperature data sets, comprises: Acquire multiple temperature data in a target temperature mutation data set to obtain multiple target temperature data; the target temperature mutation data set is any one of the n temperature mutation data sets; Fitting the multiple target temperature data to obtain a temperature curve corresponding to the target temperature mutation data set; Determine the slopes corresponding to the plurality of target temperature data in the temperature curve to obtain a plurality of slopes; each target temperature data corresponds to a slope; The plurality of slopes are used as a plurality of temperature change rates corresponding to the plurality of target temperature data; each target temperature data corresponds to a temperature change rate; If there is temperature mutation data that does not fall within the temperature change reference rate range among the multiple temperature change rates, it is determined that the target temperature mutation data set is one of the i abnormal temperature data sets.

4. The method according to claim 3, characterized in that The method further comprises: Determine the highest temperature value of the target temperature mutation data set according to the temperature curve; If the highest temperature value is higher than a second threshold, the target temperature mutation data set is determined to be one of the i abnormal temperature data sets.

5. The method according to claim 4, characterized in that The method further comprises: Acquiring temperature rise parameters of the transformer, wherein the temperature rise parameters include a heat dissipation surface area and a heat dissipation coefficient of a winding of the transformer; Determining the load power loss of the transformer in the preset time period based on the winding resistance of the transformer and the electrical data; Obtaining the no-load power loss of the transformer within the preset time period; Determining the power loss of the transformer within the preset time period based on the load power loss and the no-load power loss; The temperature change reference rate range and the second threshold are determined based on the heat dissipation surface area, the heat dissipation coefficient, the power loss and a preset temperature rise relationship of the transformer.

6. The method according to claim 1, characterized in that The electrical data set includes a current data set and a voltage data set; and determining k segments of electrical mutation data of the transformer in the electrical data set includes: Dividing the preset time period into multiple sections according to the second preset duration to obtain multiple second time periods; Determine the current data corresponding to the plurality of second time periods in the current data set to obtain a plurality of segments of current data; Determine a current change value corresponding to each current data segment in the multiple current data segments to obtain multiple current change values; the current change value is the difference between the current data corresponding to the start time of the second time period and the current data corresponding to the end time; Determine the current data whose current change values ​​in the multiple current data are greater than or equal to the third threshold as the current mutation data, and obtain the multiple current mutation data; Determine the voltage data corresponding to the plurality of second time periods in the voltage data set to obtain a plurality of sections of voltage data; Determine a current change value corresponding to each section of the multiple sections of voltage data to obtain multiple voltage change values; the voltage change value is the difference between the voltage data corresponding to the start time of the second time period and the voltage data corresponding to the end time; Determine that voltage data whose voltage change values ​​in the multiple sections of voltage data are greater than or equal to a fourth threshold value is voltage mutation data, and obtain multiple sections of voltage mutation data; The multiple segments of current mutation data and the multiple segments of voltage mutation data are used as the k segments of electrical mutation data.

7. The method according to claim 6, characterized in that The step of determining that the electrical mutation data satisfying the second preset condition among the k segments of electrical mutation data is abnormal electrical data, and obtaining p segments of abnormal electrical data, comprises: Determine a current change interval corresponding to target current mutation data; the target current mutation data is any one of the multiple current mutation data; Determine a target voltage variation interval corresponding to the current variation interval; Determine a first target time period corresponding to the target current mutation data; If the voltage data in the first target time period does not fall within the target voltage change interval, determining that the target current mutation data is abnormal current data; Determine a voltage variation interval corresponding to target voltage mutation data; the target voltage mutation data is any one of the multiple voltage mutation data; Determine a target current variation interval corresponding to the target voltage mutation data; Determine a second target time period corresponding to the target voltage mutation data; If the current data in the second target time period does not fall within the target current change interval, determining that the target voltage mutation data is abnormal voltage data; The abnormal current data in the multiple sections of current mutation data and the abnormal voltage data in the multiple sections of voltage mutation data are used as the abnormal electrical data to obtain the p sections of abnormal electrical data.

8. A transformer abnormality warning device, characterized in that: The device includes a data acquisition module, a data processing module and an early warning module; The data acquisition module is used to acquire m temperature data sets at m positions in the transformer within a preset time period, and an electrical data set of the transformer within the preset time period; each position corresponds to a temperature data set; m is an integer greater than 1; The data processing module is used to determine that a temperature data set having temperature mutation data among the m temperature data sets is a temperature mutation data set, and obtain n temperature mutation data sets; the temperature mutation data is temperature data whose temperature change value of a first preset time period is greater than or equal to a first threshold; n is a positive integer less than or equal to m; Determine that a temperature mutation data set that meets a first preset condition among the n temperature mutation data sets is an abnormal temperature data set, and obtain i abnormal temperature data sets; i is a positive integer less than or equal to n; The early warning module is used to send first early warning information for the i abnormal temperature data sets to the monitoring device; The first warning information includes i positions corresponding to the i abnormal temperature data sets in the m positions; each abnormal temperature data set corresponds to one position; The data processing module is used to determine k segments of electrical mutation data of the transformer in the electrical data set; Determine that the electrical mutation data that meets the second preset condition among the k pieces of electrical mutation data is abnormal electrical data, and obtain p pieces of abnormal electrical data; The early warning module is used to send second early warning information for the p-segment abnormal electrical data to the monitoring device.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the processor is connected to the memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device executes the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 7.

Images