Transformer operation monitoring method, device and system and storage medium

Abstract

The invention relates to the technical field of power equipment monitoring, and discloses a transformer operation monitoring method, device and system and a storage medium, which are used for improving the operation stability and reliability of a transformer. The transformer operation monitoring method comprises the following steps: acquiring transformer operation monitoring data, wherein the transformer operation monitoring data comprises current data, winding temperature rise data and insulating oil gas data; determining predicted alarm temperature rise at the target moment according to the current data and a preset temperature rise alarm limit value function; generating a temperature rise abnormal alarm according to the winding temperature rise data, the predicted alarm temperature rise and a preset temperature rise alarm rule; and in response to the temperature rise abnormal alarm, a predictive maintenance strategy is generated according to the insulating oil gas data, abnormal temperature rise data and the transformer operation monitoring data, and the abnormal temperature rise data is the transformer temperature rise corresponding to the triggered temperature rise abnormal alarm.

Description

Technical Field

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

[0002] In order to ensure the continuous and stable operation of the power grid, real-time monitoring of power equipment is necessary. Among them, the transformer is an important component of power equipment. When the transformer operates abnormally, such as abnormal temperature rise, it will cause aging and damage to the transformer insulation, affecting the transformer's service life. In severe cases, it may even cause electrical breakdown, fire and other accidents, threatening the safety of the power grid.

[0003] In the related technology, the transformer operation monitoring method mainly relies on static threshold alarm and regular manual inspection. However, the traditional monitoring method often only relies on static threshold alarm when generating alarm, lacks comprehensive analysis of multi-dimensional data, and the temperature rise prediction by constructing BP neural network needs to achieve the global optimal solution. It is sensitive to input data. Under extreme operating environment and complex operation of operators, it is impossible to predict transformer abnormality in time to realize real-time intelligent diagnosis. The generation of fault diagnosis and maintenance strategy is usually ex post facto. When inspection and maintenance are carried out, the fault has often occurred, affecting the reliability and service life of the transformer. Summary of the invention

[0004] The present application provides a transformer operation monitoring method, device, system and storage medium for solving the hysteresis problem of fault diagnosis and maintenance strategy.

[0005] A first aspect of the present application provides a transformer operation monitoring method, comprising: obtaining transformer operation monitoring data, wherein the transformer operation monitoring data includes current data, winding temperature rise data, and insulating oil gas data;

[0006] Determining a predicted alarm temperature rise at a target time according to the current data and a preset temperature rise alarm limit function;

[0007] generating a temperature rise abnormality alarm according to the winding temperature rise data, the predicted alarm temperature rise and a preset temperature rise alarm rule;

[0008] In response to the abnormal temperature rise alarm, a predictive maintenance strategy is generated according to the insulating oil gas data, the abnormal temperature rise data and the transformer operation monitoring data, wherein the abnormal temperature rise data is the transformer temperature rise corresponding to the triggering abnormal temperature rise alarm.

[0009] A second aspect of the present application provides a transformer operation monitoring device, comprising: an acquisition module, for acquiring transformer operation monitoring data, wherein the transformer operation monitoring data includes current data, winding temperature rise data, and insulating oil gas data;

[0010] A prediction module, used to determine the predicted alarm temperature rise at a target time according to the current data and a preset temperature rise alarm limit function;

[0011] An alarm module, used for generating a temperature rise abnormality alarm according to the winding temperature rise data, the predicted alarm temperature rise and a preset temperature rise alarm rule;

[0012] A generation module is used to generate a predictive maintenance strategy in response to the abnormal temperature rise alarm according to the insulating oil gas data, abnormal temperature rise data and the transformer operation monitoring data, wherein the abnormal temperature rise data is the transformer temperature rise corresponding to the triggering abnormal temperature rise alarm.

[0013] The third aspect of the present application provides a transformer operation monitoring system, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the transformer operation monitoring system executes the above-mentioned transformer operation monitoring method.

[0014] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned transformer operation monitoring method.

[0015] In the technical solution provided by the present application, the operating status of the transformer is monitored in real time, and a temperature rise abnormality alarm is generated according to the preset temperature rise alarm limit function and temperature rise alarm rule. This enables the operator to detect temperature rise abnormalities in a timely manner and take necessary measures to avoid equipment failure. In response to the temperature rise abnormality alarm, a predictive maintenance strategy is generated using insulating oil gas data and abnormal temperature rise data, which can effectively prevent transformer failures, improve the reliability and stability of the power system, and extend the service life of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of an embodiment of a transformer operation monitoring method in the present application;

[0017] Figure 2 A schematic diagram of another embodiment of the transformer operation monitoring method in the present application;

[0018] Figure 3 A schematic diagram of an embodiment of a transformer operation monitoring device in the present application;

[0019] Figure 4 This is a schematic diagram of another embodiment of the transformer operation monitoring device in the present application;

[0020] Figure 5 This is a schematic diagram of an embodiment of a transformer operation monitoring system in the present application. DETAILED DESCRIPTION

[0021] The present application provides a transformer operation monitoring method, device, system and storage medium for implementing predictive maintenance of transformers and improving the stability and reliability of transformer operation.

[0022] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "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 necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0024] A new round of scientific and technological revolution and industrial transformation is emerging. The gathering of big data, the innovation of theoretical algorithms, the improvement of computing power and the evolution of network facilities are driving the development of artificial intelligence into a new stage. Artificial intelligence is accelerating its penetration and integration in the industrial field, driving the progress of industrial enterprises and promoting industrial upgrading. Predictive maintenance is an important application scenario of industrial big data and artificial intelligence. It targets the failure and failure of equipment and facilities, and is constantly improving and developing in the evolution from post-maintenance to regular maintenance (manual inspection) to active prevention and finally to advance prediction and comprehensive planning management.

[0025] For ease of understanding, the specific process of this application is described below. Figure 1 , an embodiment of the transformer operation monitoring method in the present application includes:

[0026] 101. Obtain transformer operation monitoring data.

[0027] It is understandable that the execution subject of the present application may be a transformer operation monitoring device, or a transformer operation monitoring system, a terminal or a server, which is not limited here. This embodiment is described by taking the transformer operation monitoring system as the execution subject.

[0028] The transformer operation monitoring data of this embodiment is used to indicate the parameters of the transformer itself and the relevant data during its operation. The transformer operation monitoring data includes but is not limited to current data, winding temperature rise data and insulating oil gas data.

[0029] The current data of the above transformer is obtained by installing a current sensor on the high voltage side or the low voltage side of the transformer to measure the current of the transformer in real time.

[0030] The above winding temperature rise data is used to indicate the difference between the ambient temperature and the winding temperature. The transformer winding temperature can be measured by an indirect calculation method based on the hot spot calculation formula of the load guideline, which can ensure extremely high accuracy. This method is mainly based on the assumption of the transformer thermal model, and then combined with the experience of various countries and the standards specified in the relevant oil-immersed transformer load guideline to derive the hot spot temperature rise calculation formula, thereby realizing the calculation of the transformer winding temperature.

[0031] The insulating oil gas data can be collected by a gas chromatograph or a gas sensor.

[0032] It is understandable that after the transformer is put into operation, the electric field and magnetic field work together, the temperature rises, and the load increases. The higher the ambient temperature, the greater the impact on the current. Therefore, it is possible to choose to analyze the transformer operation monitoring data during the target period, such as performing fault diagnosis at 2 pm every day.

[0033] 102. Determine the predicted alarm temperature rise at the target time according to the current data and a preset temperature rise alarm limit function.

[0034] In this embodiment, the temperature rise alarm limit function is used to indicate the relationship between the current and the temperature rise alarm. The temperature rise threshold for triggering an abnormal alarm under the current current value can be determined through the temperature rise alarm limit function.

[0035] Substitute the measured current value at the target time into the temperature rise alarm limit function to obtain the predicted alarm temperature rise. This embodiment can accurately identify the data and issue an alarm even if the terminal or operator performs complex operations through the temperature rise alarm limit function. Damage to the transformer or personnel accidents will be a major loss.

[0036] 103. Generate a temperature rise abnormality alarm according to the winding temperature rise data, the predicted alarm temperature rise and the preset temperature rise alarm rule.

[0037] In this embodiment, the temperature rise alarm rule is used to indicate the conditions for triggering the temperature rise alarm, which may include determining whether the alarm condition is met by comparing the relative size between the actual temperature rise and the predicted alarm temperature rise, or determining whether the alarm condition is met by comparing whether the temperature rise change rate is too large. It may also include other conditions and any combination of conditions, which are not specifically limited in this embodiment.

[0038] Optionally, based on whether the currently corresponding measured temperature rise in the winding temperature rise data is greater than the predicted alarm temperature rise, if so, a temperature rise abnormality alarm is generated; if not, monitoring continues.

[0039] 104. In response to the abnormal temperature rise alarm, a predictive maintenance strategy is generated according to the insulating oil gas data, the abnormal temperature rise data and the transformer operation monitoring data, wherein the abnormal temperature rise data is the transformer temperature rise corresponding to the triggering of the abnormal temperature rise alarm.

[0040] In this embodiment, the predictive maintenance strategy is used to indicate a series of measures to predict and prevent potential equipment failures based on the transformer's operating life assessment, by dynamically adjusting the inspection cycle and selecting the corresponding maintenance strategy, thereby ensuring the stable operation of the equipment and extending its service life.

[0041] In response to the abnormal temperature rise alarm, data mining is performed on the fault-related factors in the insulating oil gas data, abnormal temperature rise data and transformer operation monitoring data, the correlation factor set of various types of data is explored, and the influence rules between various types of abnormal data are analyzed to form an intelligent diagnosis knowledge base. Based on the abnormal temperature rise data and insulating oil gas data of the transformer, the impact of the equipment operating life and equipment status is evaluated, and the corresponding operation and maintenance strategy is generated.

[0042] In this embodiment, the operating status of the transformer is monitored in real time, and a temperature rise abnormality alarm is generated according to the preset temperature rise alarm limit function and temperature rise alarm rule. This enables the operator to detect temperature rise abnormalities in a timely manner and take necessary measures to avoid equipment failure. In response to the temperature rise abnormality alarm, a predictive maintenance strategy is generated using insulating oil gas data and abnormal temperature rise data, which can effectively prevent transformer failures, improve the reliability and stability of the power system, and extend the service life of the equipment.

[0043] See also Figure 2 Another embodiment of the transformer operation monitoring method in the present application includes:

[0044] 201. The target operation monitoring data is linearly fitted by the least square method to obtain the current-winding temperature rise function.

[0045] Specifically, the historical operation monitoring data of the transformer is obtained, and the historical operation monitoring data is preprocessed to obtain the target operation monitoring data; a current-winding temperature rise scatter plot is constructed through the target operation monitoring data, and a linear regression is performed on the current-winding temperature rise scatter plot through the least squares method to obtain a current-winding temperature rise function. This embodiment linearly fits the target operation monitoring data through the least squares method to obtain a current-winding temperature rise function, and determines a temperature rise alarm limit function based on the temperature rise anomaly coefficient and the current-winding temperature rise function, thereby improving the accuracy and reliability of the alarm.

[0046] Among them, the scatter plot refers to the distribution diagram of data points on the rectangular coordinate plane in the regression analysis. The scatter plot of temperature rise represents the general trend of the dependent variable temperature rise changing with the independent variable current. Here we select the processed data and use the function to fit the points of current and temperature rise.

[0047] Linear fitting is to try to find an optimal linear equation model, which can be defined as:

[0048] f(x)=kx+b

[0049] Among them, f(x) is the winding temperature rise, x is the measured current, k is the slope, and b is the intercept.

[0050] It is understandable that some data may have errors due to unsuccessful transmission. To improve data quality and better use it in subsequent analysis processes, filter out incomplete erroneous data due to errors in the transmission process. For example, some data with a value of 0 may be interrupted during the transmission process, or it may not be successfully transmitted for other reasons. Remove values ​​with missing data and remove abnormal values ​​caused by operational errors. Ensure data integrity by removing the maximum and minimum values ​​of current and winding temperature rise, and record the data in the database. Ensure data uniqueness by removing duplicate data at the same time. For example, if the same data appears at two points on the same day, delete it.

[0051] 202. Perform temperature rise anomaly analysis based on the current-winding temperature rise function and target operation monitoring data to obtain a temperature rise alarm limit function.

[0052] Specifically, the temperature rise is predicted based on the measured current data and the current-winding temperature rise function in the target operation monitoring data to obtain the predicted temperature rise data; the temperature rise anomaly coefficient is determined based on the predicted temperature rise data and the target operation monitoring data; the temperature rise alarm limit function is determined based on the temperature rise anomaly coefficient and the current-winding temperature rise function. This embodiment dynamically adjusts the temperature rise alarm limit based on the temperature rise anomaly coefficient and the current-winding temperature rise function, making the alarm more flexible and accurate and reducing false alarms and missed alarms.

[0053] The above-mentioned determination of the temperature rise anomaly coefficient based on the predicted temperature rise data and the target operation monitoring data includes: determining the maximum value of the winding temperature rise deviation value based on the measured temperature rise data, the predicted temperature rise data and the ambient temperature in the target operation monitoring data; and determining the sum of the maximum value of the winding temperature rise deviation value and the preset offset coefficient as the temperature rise anomaly coefficient. In this embodiment, the degree of deviation is determined by calculating the temperature rise anomaly coefficient. This method can issue an early warning at the early stage of the temperature rise anomaly, take measures in advance, and avoid the occurrence of faults.

[0054] Among them, the temperature rise anomaly coefficient is used to indicate the offset of the current-winding temperature rise function. The offset coefficient can be adjusted according to the actual situation. Specifically, the data at 2 o'clock in the afternoon every day is filtered out, and the current-winding temperature rise function is used to calculate the calculated value of the winding temperature rise under each current value, that is, the predicted value, and the difference between the measured value and the calculated value of the winding temperature rise is calculated to obtain the winding temperature rise deviation value corresponding to each current value; the maximum value of the winding temperature rise deviation value is determined according to the winding temperature rise deviation value corresponding to each current value.

[0055] For example: when the current is 41A, the winding temperature is 30℃, the ambient temperature is 7℃, and the actual temperature rise is 23℃. The predicted winding temperature rise is 20℃ calculated by the least squares method. At this time, the difference between the measured value and the calculated value is 3℃, and the percentage of the difference is 10.36%.

[0056] Assuming that the maximum difference after all power values ​​are predicted is 11.54%, the offset coefficient is 1.1, the slope of the k value is 0.034, and the intercept of the b value is 20.253, therefore, the temperature rise anomaly coefficient c = 1.22, and the temperature rise alarm limit equation y = (k*x+b)*c = (0.034x+20.253)*1.22.

[0057] 203. Obtain transformer operation monitoring data.

[0058] 204. Determine the predicted alarm temperature rise at the target time according to the current data and a preset temperature rise alarm limit function.

[0059] Steps 203 and 204 may be performed with reference to steps 101 and 102 and will not be described in detail here.

[0060] 205. Obtain a first judgment result according to whether the predicted alarm temperature rise is greater than the actual measured temperature rise at the target time in the winding temperature rise data.

[0061] Compare the measured temperature rise with the predicted alarm temperature rise; if the measured temperature rise is greater than the predicted alarm temperature rise, the first judgment result is that the alarm condition is met; if the measured temperature rise is less than or equal to the predicted alarm temperature rise, the first judgment result is that the alarm condition is not met.

[0062] 206. Obtain a second judgment result according to whether the measured temperature rise change rate between the target moment and the previous moment is greater than the historical maximum temperature rise change rate.

[0063] Determine the measured temperature rise change rate between the target moment and the previous moment; compare the measured temperature rise change rate with the historical maximum temperature rise change rate; if the measured temperature rise change rate is greater than the historical maximum temperature rise change rate, the second judgment result is that the alarm condition is met; if the measured temperature rise change rate is less than or equal to the historical maximum temperature rise change rate, the second judgment result is that the alarm condition is not met.

[0064] The above-mentioned historical maximum temperature rise change rate is determined as the historical maximum temperature rise change rate by calculating the temperature rise change rate at two adjacent moments in the target operation monitoring data, and the maximum value of the temperature rise change rate in the target operation monitoring data is determined as the historical maximum temperature rise change rate.

[0065] 207. If at least one of the first judgment result and the second judgment result is satisfied, a temperature rise abnormality alarm is generated.

[0066] Exemplarily, if the first judgment result is satisfied and the second judgment result is not satisfied, a temperature rise abnormality alarm is generated, that is, the measured temperature rise is greater than the predicted alarm temperature rise, and the measured temperature rise change rate is less than or equal to the historical maximum temperature rise change rate.

[0067] Exemplarily, if the first judgment result is not satisfied and the second judgment result is satisfied, a temperature rise abnormality alarm is generated, that is, the measured temperature rise is less than or equal to the predicted alarm temperature rise, and the measured temperature rise change rate is greater than the historical maximum temperature rise change rate.

[0068] Exemplarily, if the first judgment result is satisfied and the second judgment result is satisfied, a temperature rise abnormality alarm is generated, that is, the measured temperature rise is greater than the predicted alarm temperature rise, and the measured temperature rise change rate is greater than the historical maximum temperature rise change rate.

[0069] It is understandable that if both the first judgment result and the second judgment result are not satisfied, no abnormal temperature rise alarm is generated.

[0070] 208. In response to the abnormal temperature rise alarm, a predictive maintenance strategy is generated according to the insulating oil gas data, the abnormal temperature rise data and the transformer operation monitoring data, wherein the abnormal temperature rise data is the transformer temperature rise corresponding to the triggering of the abnormal temperature rise alarm.

[0071] Specifically, potential fault analysis is performed based on insulating oil gas data and abnormal temperature rise data to obtain potential fault analysis results; inspection and operation and maintenance strategies are dynamically adjusted based on potential fault analysis results and transformer operation monitoring data to obtain predictive maintenance strategies. This embodiment can determine a reasonable inspection cycle, avoid excessive or insufficient inspections, and improve the efficiency and effectiveness of inspection work.

[0072] The above-mentioned potential fault analysis is performed based on the insulating oil gas data and the abnormal temperature rise data to obtain the potential fault analysis result, including: determining the target gas and gas production rate in the insulating oil gas data, and determining the fault type and fault location based on the abnormal temperature rise data to obtain the potential fault analysis result. In this embodiment, by determining the target gas and gas production rate in the insulating oil gas data, and combining the abnormal temperature rise data, the method can more accurately diagnose the fault type and fault location, provide a scientific basis for subsequent maintenance, and fine fault diagnosis helps operators quickly locate problems, reduce troubleshooting time, and improve maintenance efficiency.

[0073] It can be understood that different faults will produce different main characteristic gases and secondary characteristic gases. The composition, content, and gas production rate of these fault gases are closely related to the type and severity of the fault. By analyzing the gas dissolved in the oil, the latent faults inside the equipment can be discovered as early as possible, and the development of the fault can be monitored at any time. The cycle of gas generation and detection design is relatively long. This embodiment confirms through temperature that before the transformer fails, under the action of electrical and thermal effects, methane, ethane, ethylene, acetylene, hydrogen, carbon monoxide, carbon dioxide, etc. will be precipitated inside.

[0074] The following describes the relationship between gas and fault type, fault location, and fault degree. The gas generated during the aging process of normal operation is mainly carbon monoxide and carbon dioxide; if the gas includes hydrogen and methane, there is partial discharge in the oil-paper insulation; if the gas is mainly methane, the fault temperature is not much higher than the normal operating temperature; if the gas contains more acetylene, it means that there is an arc temperature (above 3000C; if more carbon monoxide and carbon dioxide are generated when the temperature is higher than 1000C, it means that the fault involves solid insulating materials. This embodiment determines the potential fault of the transformer through dual analysis of temperature and insulating oil.

[0075] The above-mentioned inspection and maintenance strategy is dynamically adjusted according to the potential fault analysis results and the transformer operation monitoring data to obtain a predictive maintenance strategy, including: determining a target inspection cycle according to the potential fault analysis results and the transformer operation monitoring data; determining target inspection measures according to the potential fault analysis results; and determining the target inspection cycle and target inspection measures as a predictive maintenance strategy. This embodiment dynamically adjusts the inspection and maintenance strategy through the potential fault analysis results and the transformer operation monitoring data, improves the pertinence and effectiveness of maintenance, and reduces unnecessary maintenance work.

[0076] The transformer operation monitoring data of this embodiment also includes the type of transformer, importance, operating environment, historical fault records and manufacturer's recommendations, etc. This embodiment dynamically adjusts the maintenance strategy based on the potential fault analysis results and the transformer operation monitoring data to obtain a predictive maintenance strategy.

[0077] The above inspection cycle is used to indicate different inspection frequencies, including daily inspection, weekly inspection, monthly inspection, quarterly inspection, etc., among which daily inspection is usually at least once a day, and the inspection measures corresponding to daily inspection are usually to check whether there is any abnormality in the appearance of the transformer, and to repair the transformer that has been warned during the inspection; and weekly inspection is usually at least once a week, and the operating status of the transformer is further checked on the basis of daily inspection, including cooling system, temperature indicator, oil level, etc.; monthly inspection is usually at least once a month, and all transformers, especially transformers in key positions, are comprehensively inspected on the basis of the above inspection, including electrical testing (such as insulation resistance measurement), oil sample analysis, etc.; and quarterly inspection is usually at least once a quarter, so as to conduct more in-depth inspections on important or high-risk transformers, such as partial discharge test, winding deformation test, etc. By implementing these measures, this embodiment can effectively extend the service life of the transformer, reduce the possibility of failure, and also significantly reduce maintenance costs and improve overall operating efficiency.

[0078] In this embodiment, the temperature rise alarm limit function is continuously optimized using data through the least squares method, the operating status of the transformer is monitored in real time, and the alarm temperature rise is predicted according to the preset temperature rise alarm limit function. By setting a comparison between the predicted alarm temperature rise and the measured temperature rise, and a dual judgment mechanism for monitoring the temperature rise change rate, the temperature rise abnormality alarm is improved to accurately identify the temperature rise abnormality, reduce false alarms and missed alarms, and improve the reliability of the alarm, so that the operator can find the temperature rise abnormality in time and take necessary measures to avoid equipment failure. In response to the temperature rise abnormality alarm, the insulating oil gas data and the abnormal temperature rise data are used to generate a predictive maintenance strategy, which can effectively prevent transformer failures, improve the reliability and stability of the power system, and extend the service life of the equipment.

[0079] The transformer operation monitoring method in this application is described above. The transformer operation monitoring device in this application is described below. Figure 3 , an embodiment of the transformer operation monitoring device in the present application includes:

[0080] An acquisition module 301 is used to acquire transformer operation monitoring data, where the transformer operation monitoring data includes current data, winding temperature rise data, and insulating oil gas data;

[0081] A prediction module 302, used to determine the predicted alarm temperature rise at the target time according to the current data and a preset temperature rise alarm limit function;

[0082] The alarm module 303 is used to generate a temperature rise abnormality alarm according to the winding temperature rise data, the predicted alarm temperature rise and the preset temperature rise alarm rules;

[0083] The generating module 304 is used to generate a predictive maintenance strategy in response to the abnormal temperature rise alarm according to the insulating oil gas data, the abnormal temperature rise data and the transformer operation monitoring data, wherein the abnormal temperature rise data is the transformer temperature rise corresponding to the triggering abnormal temperature rise alarm.

[0084] In this embodiment, the operating status of the transformer is monitored in real time, and a temperature rise abnormality alarm is generated according to the preset temperature rise alarm limit function and temperature rise alarm rule. This enables the operator to detect temperature rise abnormalities in a timely manner and take necessary measures to avoid equipment failure. In response to the temperature rise abnormality alarm, a predictive maintenance strategy is generated using insulating oil gas data and abnormal temperature rise data, which can effectively prevent transformer failures, improve the reliability and stability of the power system, and extend the service life of the equipment.

[0085] See also Figure 4 Another embodiment of the transformer operation monitoring device in the present application includes:

[0086] An acquisition module 301 is used to acquire transformer operation monitoring data, where the transformer operation monitoring data includes current data, winding temperature rise data, and insulating oil gas data;

[0087] A prediction module 302, used to determine the predicted alarm temperature rise at the target time according to the current data and a preset temperature rise alarm limit function;

[0088] The alarm module 303 is used to generate a temperature rise abnormality alarm according to the winding temperature rise data, the predicted alarm temperature rise and the preset temperature rise alarm rules;

[0089] The generating module 304 is used to generate a predictive maintenance strategy in response to the abnormal temperature rise alarm according to the insulating oil gas data, the abnormal temperature rise data and the transformer operation monitoring data, wherein the abnormal temperature rise data is the transformer temperature rise corresponding to the triggering abnormal temperature rise alarm.

[0090] Optionally, the transformer operation monitoring device also includes: a fitting module 305, which is used to perform linear fitting on the target operation monitoring data through the least squares method to obtain a current-winding temperature rise function; a construction module 306, which is used to perform temperature rise anomaly analysis based on the current-winding temperature rise function and the target operation monitoring data to obtain a temperature rise alarm limit function.

[0091] Optionally, construct module 306, specifically for performing temperature rise prediction according to the measured current data and the current-winding temperature rise function in the target operation monitoring data to obtain predicted temperature rise data;

[0092] Determine the temperature rise anomaly coefficient based on the predicted temperature rise data and target operation monitoring data;

[0093] The temperature rise alarm limit function is determined based on the temperature rise anomaly coefficient and the current-winding temperature rise function.

[0094] Optionally, the alarm module 303 is specifically used to: obtain a first judgment result according to whether the predicted alarm temperature rise is greater than the measured temperature rise at the target time in the winding temperature rise data;

[0095] A second judgment result is obtained according to whether the measured temperature rise change rate between the target moment and the previous moment is greater than the historical maximum temperature rise change rate;

[0096] If at least one of the first judgment result and the second judgment result is satisfied, an abnormal temperature rise alarm is generated.

[0097] Optionally, the generating module 304 includes:

[0098] The analysis unit 3041 is used to perform potential fault analysis based on the insulating oil gas data and the abnormal temperature rise data to obtain a potential fault analysis result;

[0099] The adjustment unit 3042 is used to dynamically adjust the inspection and operation and maintenance strategy according to the potential fault analysis results and the transformer operation monitoring data to obtain a predictive maintenance strategy.

[0100] Optionally, the analysis unit 3041 is specifically used to: determine the target gas and gas production rate in the insulating oil gas data, and determine the fault type and fault location according to the abnormal temperature rise data to obtain a potential fault analysis result.

[0101] Optionally, the adjustment unit 3042 is specifically used to: determine a target inspection cycle based on potential fault analysis results and transformer operation monitoring data; determine target inspection measures based on potential fault analysis results; and determine the target inspection cycle and target inspection measures as a predictive maintenance strategy.

[0102] In this embodiment, the temperature rise alarm limit function is continuously optimized using data through the least squares method, the operating status of the transformer is monitored in real time, and the alarm temperature rise is predicted according to the preset temperature rise alarm limit function. By setting a comparison between the predicted alarm temperature rise and the measured temperature rise, and a dual judgment mechanism for monitoring the temperature rise change rate, the temperature rise abnormality alarm is improved to accurately identify the temperature rise abnormality, reduce false alarms and missed alarms, and improve the reliability of the alarm, so that the operator can find the temperature rise abnormality in time and take necessary measures to avoid equipment failure. In response to the temperature rise abnormality alarm, the insulating oil gas data and the abnormal temperature rise data are used to generate a predictive maintenance strategy, which can effectively prevent transformer failures, improve the reliability and stability of the power system, and extend the service life of the equipment.

[0103] above Figure 3 and Figure 4 The transformer operation monitoring device in the present application is described in detail from the perspective of modular functional entities, and the transformer operation monitoring system in the present application is described in detail from the perspective of hardware processing.

[0104] See also Figure 5 As shown, the transformer operation monitoring system includes a processor 500 and a memory 501 . The memory 501 stores machine executable instructions that can be executed by the processor 500 . The processor 500 executes the machine executable instructions to implement the above transformer operation monitoring system.

[0105] further, Figure 5 The transformer operation monitoring system shown further includes a bus 502 and a communication interface 503 , and the processor 500 , the communication interface 503 and the memory 501 are connected via the bus 502 .

[0106] The memory 501 may include a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), for example, at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 503 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 502 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0107] The processor 500 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 500. The above processor 500 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present disclosure can be directly embodied as a hardware decoding processor to be executed, or a combination of hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 501 , and the processor 500 reads the information in the memory 501 and completes the method steps of the above-mentioned embodiment in combination with its hardware.

[0108] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the transformer operation monitoring method. The steps specifically include:

[0109] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0110] If the integrated 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 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 storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0111] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A transformer operation monitoring method, characterized in that: The transformer operation monitoring method comprises: Acquiring transformer operation monitoring data, wherein the transformer operation monitoring data includes current data, winding temperature rise data, and insulating oil gas data; Determining a predicted alarm temperature rise at a target time according to the current data and a preset temperature rise alarm limit function; generating a temperature rise abnormality alarm according to the winding temperature rise data, the predicted alarm temperature rise and a preset temperature rise alarm rule; In response to the abnormal temperature rise alarm, a predictive maintenance strategy is generated according to the insulating oil gas data, the abnormal temperature rise data and the transformer operation monitoring data, wherein the abnormal temperature rise data is the transformer temperature rise corresponding to the triggering abnormal temperature rise alarm.

2. The transformer operation monitoring method according to claim 1, characterized in that: The generating of the abnormal temperature rise alarm according to the winding temperature rise data, the predicted alarm temperature rise and the preset temperature rise alarm rule comprises: Obtaining a first judgment result according to whether the predicted alarm temperature rise is greater than the measured temperature rise at the target time in the winding temperature rise data; Obtaining a second judgment result according to whether the measured temperature rise change rate between the target moment and the previous moment is greater than the historical maximum temperature rise change rate; If at least one of the first judgment result and the second judgment result is satisfied, an abnormal temperature rise alarm is generated.

3. The transformer operation monitoring method according to claim 1, characterized in that: The generating of a predictive maintenance strategy according to the insulating oil gas data, the abnormal temperature rise data and the transformer operation monitoring data comprises: Performing potential fault analysis based on the insulating oil gas data and the abnormal temperature rise data to obtain a potential fault analysis result; The inspection and operation and maintenance strategy is dynamically adjusted according to the potential fault analysis results and the transformer operation monitoring data to obtain a predictive maintenance strategy.

4. The transformer operation monitoring method according to claim 3, characterized in that: The performing of potential fault analysis according to the insulating oil gas data and the abnormal temperature rise data to obtain a potential fault analysis result includes: The target gas and the gas production rate in the insulating oil gas data are determined, and the fault type and the fault location are determined according to the abnormal temperature rise data to obtain a potential fault analysis result.

5. The transformer operation monitoring method according to claim 3, characterized in that: The dynamically adjusting the inspection and operation and maintenance strategy according to the potential fault analysis result and the transformer operation monitoring data to obtain a predictive maintenance strategy includes: Determine a target inspection cycle according to the potential fault analysis results and the transformer operation monitoring data; Determine target inspection measures according to the potential fault analysis results; The target inspection cycle and the target inspection measures are determined as a predictive maintenance strategy.

6. The transformer operation monitoring method according to any one of claims 1 to 5, characterized in that: Before obtaining the transformer operation monitoring data, the method further includes: Performing linear fitting on the target operation monitoring data by the least square method to obtain a current-winding temperature rise function; A temperature rise abnormality analysis is performed based on the current-winding temperature rise function and the target operation monitoring data to obtain a temperature rise alarm limit function.

7. The transformer operation monitoring method according to claim 6, characterized in that: The performing temperature rise abnormality analysis according to the current-winding temperature rise function and the target operation monitoring data to obtain a temperature rise alarm limit function includes: Perform temperature rise prediction based on the measured current data in the target operation monitoring data and the current-winding temperature rise function to obtain predicted temperature rise data; Determine a temperature rise anomaly coefficient according to the predicted temperature rise data and the target operation monitoring data; The temperature rise alarm limit function is determined according to the temperature rise abnormality coefficient and the current-winding temperature rise function.

8. A transformer operation monitoring device, characterized in that: The transformer operation monitoring device comprises: An acquisition module is used to acquire transformer operation monitoring data, wherein the transformer operation monitoring data includes current data, winding temperature rise data and insulating oil gas data; A prediction module, used to determine the predicted alarm temperature rise at a target time according to the current data and a preset temperature rise alarm limit function; An alarm module, used for generating a temperature rise abnormality alarm according to the winding temperature rise data, the predicted alarm temperature rise and a preset temperature rise alarm rule; A generation module is used to generate a predictive maintenance strategy in response to the abnormal temperature rise alarm according to the insulating oil gas data, abnormal temperature rise data and the transformer operation monitoring data, wherein the abnormal temperature rise data is the transformer temperature rise corresponding to the triggering abnormal temperature rise alarm.

9. A transformer operation monitoring system, characterized in that: The transformer operation monitoring system comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instruction in the memory so that the transformer operation monitoring system executes the transformer operation monitoring method according to any one of claims 1 to 7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instruction is read and executed, the transformer operation monitoring method according to any one of claims 1 to 7 is executed.

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