A power distribution transformer key equipment state multi-parameter sensing method and system

Abstract

The application relates to the technical field of power distribution transformer state detection, in particular to a power distribution transformer key equipment state multi-parameter sensing method and system, which specifically comprises the following steps: constructing a load degree at each moment based on the data size and change of the current and voltage of the power distribution transformer at each moment; calculating a thermal overload probability at each moment based on the load degree at each moment and the moment before the same, in combination with the oil tank temperature difference between each moment and the previous moment; calculating a partial discharge probability at each moment in combination with the distribution feature difference between the peak point amplitudes of acoustic emission in the historical time of adjacent moments; adjusting the data uploading frequency at each moment based on the load degree, the thermal overload probability and the partial discharge probability at each moment; and reducing the data uploading frequency when the power distribution transformer is normal, so that the uplink communication pressure is reduced, a large amount of redundant data accumulation is avoided, the data uploading fluency when the transformer is in an abnormal state is improved, and the system operation efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of distribution transformer condition detection technology, specifically to a method and system for sensing the condition of key equipment in a distribution transformer using multiple parameters. Background Technology

[0002] Oil-immersed transformers are critical power conversion and transmission equipment in power systems, widely used in industrial and mining enterprises, residential communities, and urban power distribution networks. Their internal windings and core are immersed in transformer oil, which provides insulation and cooling. Due to long-term operation in outdoor environments, oil-immersed transformers are susceptible to load fluctuations, temperature rises and aging, oil deterioration, and loose contacts, which can lead to serious faults such as overheating, breakdown, discharge, or explosion. Therefore, real-time monitoring of their operating status using multiple parameters is of great significance, as it can provide timely warnings of potential hazards, extend equipment lifespan, and improve the safety and reliability of the power supply system.

[0003] When monitoring the condition of distribution oil-immersed transformers, multiple key operating parameters need to be collected to comprehensively assess their health status and operational safety. In existing distribution oil-immersed transformer monitoring systems, a large amount of routine operating data collected by sensors is uploaded to the platform without filtering, resulting in a large amount of redundant data and heavy uplink communication pressure. This not only consumes limited communication bandwidth but also exacerbates the data processing and storage burden on the platform. Because the condition assessment algorithm is deployed on a cloud platform, data transmission and processing are delayed, making it difficult to achieve real-time response and accurate early warning for critical faults. The long-accumulated redundant data also occupies significant storage resources, reducing system operating efficiency. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a method and system for sensing the status of key equipment in distribution transformers using multiple parameters. The specific technical solution adopted is as follows:

[0005] In a first aspect, embodiments of this application provide a method for sensing the status of key equipment in a distribution transformer using multiple parameters, the method comprising the following steps:

[0006] Acquire acoustic emission signals, tank temperature, high-voltage side incoming line voltage, and low-voltage side outgoing line current of the distribution transformer at various times;

[0007] Based on the difference between the moving average current and the rated current at each moment, and the difference between the voltage and the overall voltage distribution at all moments, the load level of the distribution transformer at each moment is constructed; based on the difference between the load level at each moment and the previous moments, and the load level at each moment and the previous moments, the high load duration of the distribution transformer at each moment is calculated.

[0008] Based on the difference in tank temperature between each time point and the previous time point, and the tank temperature at each time point, combined with the degree of high load duration at each time point and the time points before it, the probability of thermal overload at each time point is calculated.

[0009] For any time before each time, based on the difference in distribution characteristics between the peak amplitude of acoustic emission at any time and the historical time before that time, and combined with the thermal overload probability at any time, the partial discharge probability at each time is calculated.

[0010] The data upload frequency is adjusted based on the load level, thermal overload probability, and partial discharge probability at each time point.

[0011] In one embodiment, the process of obtaining the load level is as follows:

[0012]

[0013] In the formula, For the first The load level of the distribution transformer at any given time; For the first The moving average of the current at each moment; This refers to the rated current of the distribution transformer; This represents the average voltage of the distribution transformer at all times. For the first The voltage of the distribution transformer at a given moment; This is the normalization function.

[0014] In one embodiment, the process of obtaining the high load duration is as follows:

[0015] Calculate the first Before the [time]th moment The relative load attention at each moment is denoted as . , The expression is:

[0016] In the formula, b is the first... Before the [time]th moment The order corresponding to each moment; For the first The load level at any given moment; For the first Before the [time]th moment The load level at any given moment; This is a function to find the maximum value. This is the normalization function;

[0017] Based on the load level at each moment, as well as the load level and relative load attention at each moment within the preset time period before each moment, the high load duration of the distribution transformer at each moment is calculated.

[0018] In one embodiment, the duration of high load on the distribution transformer at each time point is specifically defined as follows:

[0019] Calculate the first Before the [time]th moment The product of the load level and the relative load attention at time point 1 is denoted as the first product; the product of the second product and the third product is calculated. The average of the first product of all times within the preset number of days prior to the nth time point, and the nth time point... The product of the load levels at time t is used as the product of the load levels at time t. The degree of high load duration of the distribution transformer at any given time.

[0020] In one embodiment, the process of obtaining the thermal overload probability is as follows:

[0021] The rate of change of the tank temperature at each moment is determined based on the difference between the tank temperature at each moment and the previous moment; the degree of thermal overload performance at each moment is calculated based on the tank temperature at each moment and the corresponding rate of change.

[0022] Obtain the minimum value of the high load duration of all times between each time point and any time point before it; calculate the product of the minimum value and the thermal overload performance at any time point, and record it as the second product between each time point and any time point before it; calculate the mean of the second product between each time point and all times within a preset time period before it, and use it as the thermal overload probability at each time point.

[0023] In one embodiment, the rate of change of the temperature value at each time moment is specifically:

[0024] If the temperature difference between each time point and the previous time point is greater than or equal to 0, the temperature difference is taken as the rate of change of the temperature value at each time point; otherwise, the rate of change of the temperature value at each time point is set to 0.

[0025] In one embodiment, the expression for the degree of thermal overload at each moment is:

[0026] In the formula, For the first The degree of thermal overload performance at each moment; For the first The normalized value of the rate of change of temperature at each time point; For the first The fuel tank temperature at that moment.

[0027] In one embodiment, the process of obtaining the partial discharge probability is as follows:

[0028] The peak points of the acoustic emission signals at all times are obtained, and the partial discharge performance at each time is calculated based on the amplitude of all the peak points within a preset time period before each time.

[0029] The degree of partial discharge increase at each time step is set based on the difference between the partial discharge performance at each time step and the previous time step.

[0030] Calculate the product of the thermal overload probability and the degree of partial discharge increase at each time point, and denote it as the third product; take the normalized value of the sum of the third products at all times before each time point as the partial discharge probability at each time point.

[0031] In one embodiment, the degree of partial discharge performance at each moment is the sum of the amplitudes of all the peak points within a preset time period prior to each moment.

[0032] In one embodiment, the process of obtaining the degree of increase in partial discharge is as follows:

[0033] If the difference between the partial discharge performance at each time point and the previous time point is greater than 0, then the increase in partial discharge at each time point is set to 1; otherwise, the increase in partial discharge at each time point is set to 0.

[0034] In one embodiment, the process of obtaining the data upload frequency is as follows:

[0035] Set the load level threshold, thermal overload probability threshold, and partial discharge probability threshold;

[0036] If the normalized values ​​of the load level, thermal overload probability, and partial discharge probability at each moment are all greater than the corresponding threshold, then the operating condition at each moment is determined to be a normal operating condition.

[0037] If the normalized values ​​of the thermal overload probability and partial discharge probability at each moment are greater than the corresponding threshold, while the normalized value of the load level is less than or equal to the load level threshold, then the operating condition at that moment is determined to be a heavy load condition.

[0038] If the normalized value of the partial discharge probability at each moment is greater than the partial discharge probability threshold, while the normalized values ​​of the load level and thermal overload probability are both less than or equal to the corresponding threshold, then the operating condition at that moment is determined to be a thermal overload operating condition.

[0039] If the normalized values ​​of the load level, thermal overload probability, and partial discharge probability at each moment are all less than or equal to the corresponding threshold, then the operating condition at that moment is determined to be a partial discharge condition.

[0040] Different upload frequencies are used for different operating conditions. The upload frequency for normal operating conditions is... Upload frequency under heavy load conditions Upload frequency under thermal overload conditions The upload frequency of local discharge conditions.

[0041] Secondly, embodiments of this application also provide a multi-parameter sensing system for the status of key equipment of a distribution transformer, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0042] The embodiments of this application have at least the following beneficial effects:

[0043] This application improves the accuracy of distribution transformer operation status assessment by collecting and analyzing multidimensional data. Based on the difference between the moving average current and the rated current at each moment, and the difference between the voltage and the overall voltage distribution at all moments, the load level of the distribution transformer at each moment is constructed, facilitating timely detection of normal and overload conditions and timely adjustment of data transmission frequency. Based on the load level differences between each moment and previous moments, and the load levels at each moment and previous moments, the high load duration of the distribution transformer at each moment is calculated, taking into account the operating characteristics of the distribution transformer and avoiding the problem of misclassifying normal conditions such as short-term heavy load operation as abnormal conditions, thus improving the accuracy of determining the actual operating status of the distribution transformer. Based on the temperature differences between each moment and the previous moment, and the temperature at each moment, combined with the high load at each moment and previous moments, the application further improves the accuracy of determining the actual operating status of the distribution transformer. The system calculates the thermal overload probability at each moment, taking into account the lag in temperature rise caused by load, and assesses the likelihood of the distribution transformer being in a thermal overload state. For any moment prior to each moment, based on the difference in the distribution characteristics of the acoustic emission peak amplitude between that moment and the previous moment in historical time, and combined with the thermal overload probability at that moment, the system calculates the partial discharge probability at each moment, considering that prolonged thermal overload can lead to partial discharge, and assesses the probability of the distribution transformer being in a partial discharge condition. Based on the current load level, thermal overload probability, and partial discharge probability, the system adjusts the data upload frequency at the current moment. This allows for more precise adjustment of the data transmission frequency according to the transformer status. When the distribution transformer is normal, the data upload frequency is reduced, thereby reducing uplink communication pressure, avoiding the accumulation of a large amount of redundant data, improving the smoothness of data upload when the transformer is in an abnormal state, and improving the system operating efficiency. Attached Figure Description

[0044] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 A flowchart illustrating the steps of a multi-parameter sensing method for the status of key equipment in a distribution transformer, as provided in one embodiment of this application;

[0046] Figure 2 A flowchart illustrating the steps of a multi-parameter sensing method for the status of key equipment in a distribution transformer;

[0047] Figure 3 This diagram illustrates the data transmission between the fusion terminal, the wireless multi-parameter acquisition device, and the wireless sensor. Detailed Implementation

[0048] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a multi-parameter sensing method and system for the status of key equipment in a distribution transformer proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0050] The following description, in conjunction with the accompanying drawings, details the specific scheme of the multi-parameter sensing method and system for the key equipment status of a distribution transformer provided in this application.

[0051] Please see Figure 1 The diagram illustrates a flowchart of a multi-parameter sensing method for the status of key equipment in a distribution transformer, according to an embodiment of this application. The method includes the following steps:

[0052] Step S1: Collect the acoustic emission signal, oil tank temperature, high-voltage side incoming line voltage, and low-voltage side outgoing line current of the distribution transformer at each time.

[0053] Multiple status data of the distribution oil-immersed transformer are monitored by a variety of wireless sensors; the data monitored by the multiple wireless sensors are collected and analyzed by a wireless multi-parameter acquisition device, and the data and some status information are transmitted to the fusion terminal.

[0054] The wireless multi-parameter acquisition device includes a wireless communication module, a logic control module, and a data processing module. The wireless communication module is responsible for communicating with wireless sensors using various protocols. The logic control and data processing module is responsible for acquiring and processing sensor data from the wireless communication module and transmitting it to the fusion terminal.

[0055] When monitoring the condition of a distribution oil-immersed transformer, it is necessary to collect multiple key operating parameters to comprehensively assess its health status and operational safety. The main monitoring data include: collecting partial discharge signals and tank temperature in the winding area by placing electromagnetic saw sensors near the windings on the tank casing to identify minor abnormal signals of early discharge and reflect the operating thermal state; collecting high-voltage side incoming line voltage data by installing voltage transformers at the high-voltage side incoming line; and collecting low-voltage side outgoing line current data by installing current transformers at the low-voltage side outgoing line.

[0056] Each data point is collected at a fixed sampling frequency. Preferably, in this embodiment, the sampling frequency for the partial discharge signal is set to 1MHz, the sampling frequency for the current and voltage data is set to 2kHz, and the sampling frequency for the temperature data is set to 1Hz. In other embodiments of this application, the implementer can set the sampling frequency for each data point according to actual conditions.

[0057] Acoustic emission signal sequences and temperature sequences were constructed using the acquired partial discharge signals and tank temperature, respectively; a voltage sequence was constructed using the acquired voltage data; and a current sequence was constructed using the acquired current data. This yielded a multi-dimensional data sequence for the distribution transformer.

[0058] Multi-parameter collaborative monitoring facilitates intelligent diagnosis and operation and maintenance management of transformers. However, during the transmission to the fusion terminal, a large amount of routine operating data collected by sensors is uploaded to the platform without filtering, resulting in a large amount of redundant data and heavy uplink communication pressure. Therefore, standardizing all sequence values ​​of each data sequence in the logic control and data processing modules can eliminate the influence of units and dimensions, facilitating subsequent calculations.

[0059] Step S2: Based on the difference between the moving average current and the rated current at each time point, and the difference between the voltage and the overall voltage distribution at all times, construct the load level of the distribution transformer at each time point; based on the difference in load level between each time point and previous times, and the load level between each time point and previous times, calculate the high load duration of the distribution transformer at each time point.

[0060] (1) A large amount of normal operating data collected by the sensors is uploaded to the platform without being filtered, resulting in a large amount of redundant data and heavy uplink communication pressure. Therefore, it is necessary to identify the operating conditions of the distribution transformer and collect multiple parameters of the distribution transformer as needed based on the operating conditions. Therefore, the load level of the distribution transformer should be judged first.

[0061] During the operation of a distribution transformer, a continuous increase in current reflects an actual increase in the transformer's output load, while a synchronized drop in voltage reveals the voltage drop effect caused by the impedance of the distribution system. The simultaneous occurrence of both often indicates that the transformer is under high load or even overload. Therefore, based on a significant increase in current as the primary criterion for identifying heavy load, and combined with a synchronized drop in low-voltage side voltage as an auxiliary criterion, the load level of the distribution transformer is quantified, specifically as follows:

[0062] First, a simple moving average (SMA) is applied to the current series to obtain the moving average of the current at each time point, which facilitates subsequent analysis of the current's changing trend. The simple moving average (SMA) is a well-known technique, and its specific process will not be elaborated upon here.

[0063] Then, the load level of the distribution transformer at each time point is calculated, expressed as:

[0064]

[0065] In the formula, For the first The load level of the distribution transformer at any given time; For the first The moving average of the current at each moment; This refers to the rated current of the distribution transformer; This represents the average voltage of the distribution transformer at all times. For the first The voltage of the distribution transformer at a given moment; This is the normalization function.

[0066] Furthermore, the load level of the distribution transformer at all times is normalized.

[0067] (2) During the operation of a distribution transformer, short-term heavy-load operation is considered normal operating condition, and the equipment can withstand high loads for short periods without damage. However, if the heavy-load condition lasts for too long, the copper and iron losses inside the transformer accumulate due to the continuously large load current, and the heat generated cannot be dissipated in time, causing the winding temperature and oil temperature to rise continuously, eventually exceeding the heat resistance limit of the equipment, thus entering a thermal overload condition. At this time, it is necessary to further assess the operating condition of the transformer.

[0068] During the operation of distribution transformers, the temperature typically rises with a lag when the load increases. When a distribution transformer is under thermal overload, its temperature is high, and the load remains high for an extended period, leading to heat accumulation. Therefore, it is necessary to calculate the duration of this high load on the distribution transformer.

[0069] Because electricity consumption exhibits a clear peak-valley trend, when calculating the duration of high load on a distribution transformer at any given moment, only the most recent period of relatively high continuous load is considered. Therefore, the expression for calculating the load attention level at each moment is:

[0070]

[0071] In the formula, For the first Before the [time]th moment The relative load attention at time b; b is the relative load attention at time t; Before the [time]th moment The order corresponding to each moment; For the first The load level at any given moment; For the first Before the [time]th moment The load level at any given moment; This is a function to find the maximum value. This is the normalization function.

[0072] The larger the value, the higher the value. The moment before The time relative to the first The lower the load at any given moment, the better. The larger the value, the more likely it is to be the first. The moment before it During a given period, there are more periods with lower loads and less continuity with high loads, thus reducing the load's focus.

[0073] Furthermore, upon obtaining the relative to the first After determining the relative load attention at each time point, the relative load attention can be used as a weight to obtain the distribution transformer at the [number]th [time point]. The duration of high load at any given moment is expressed as:

[0074]

[0075] In the formula, For the first The degree of high load duration of the distribution transformer at any given time; For the first The load level of the distribution transformer at any given time; For the first The number of all data collection times within the preceding t days of a given time; For the first Before the [time]th moment Relative load attention at any given moment; For the first Before the [time]th moment The load level at a given time. Preferably, in this embodiment, the value of t is set to 1. As other embodiments of this application, the implementer can set the value of t according to actual conditions. This is the first product.

[0076] When constructing the above formula The purpose is to ensure that the equipment is in the first stage At any given moment, it is under high load. The larger the value, the stronger the persistence.

[0077] Step S3: Based on the difference in tank temperature between each time point and the previous time point, and the tank temperature at each time point, combined with the degree of high load duration at each time point and the time points before it, calculate the probability of thermal overload at each time point.

[0078] Thermal overload conditions are characterized by a continuous rise in the temperature of the distribution transformer, and due to the lag in temperature rise caused by the load, the high load duration is relatively high during the temperature rise process.

[0079] When a distribution transformer is under thermal overload, the transformer temperature is rising rapidly or is already at a high temperature.

[0080] Therefore, for the temperature sequence of the distribution transformer, the rate of change of each temperature value is obtained. Taking the a-th temperature value as an example, the rate of change of the a-th temperature value is the rate of change of the a-th temperature value. The temperature value and the first The difference between the a-th temperature values ​​is taken as the rate of change of the a-th temperature value. If the difference is less than 0, it indicates that the temperature is decreasing, so only the magnitude of the temperature value is considered. The rate of change of all temperature values ​​is then normalized.

[0081] Furthermore, the calculation of the distribution transformer in the first... The degree of thermal overload at each moment is expressed as:

[0082]

[0083] In the formula, For the distribution transformer in the first The degree of thermal overload performance at each moment; For the distribution transformer in the first The normalized value of the rate of change of temperature at each time point; For the distribution transformer in the first The fuel tank temperature at that moment.

[0084] The logic reflected in the above formula is that, The larger the value, the more attention is paid to the rate of change itself, and The smaller the value, the more attention is paid to the magnitude of the temperature reading.

[0085] Since the heat generated during a thermal overload is due to a sustained high load, the duration of the high load on the distribution transformer is used as the confidence level for the degree of thermal overload performance. The probability of thermal overload at each moment is then calculated using the following expression:

[0086]

[0087] In the formula, For the first The probability of thermal overload at any given moment; For the first The number of all data collection times within the preceding t days of a given time; For the first The moment before it The minimum duration of high load across all time points; For the first Before the [time]th moment The degree of thermal overload performance at each moment. Among them, This is the second product.

[0088] The larger the value, the more likely it is to be the first. The moment before it The high load duration at each moment is relatively large, because the first The higher the load at a given moment, the greater the probability of thermal overload at that moment, so it is used as the confidence level.

[0089] Step S4: For any time before each time point, based on the difference in distribution characteristics between the peak amplitude of acoustic emission at any time point and the historical time before the previous time point, and combined with the thermal overload probability at any time point, calculate the partial discharge probability at each time point.

[0090] When distribution oil-immersed transformers are subjected to prolonged thermal overload, the internal windings, insulating oil, and solid insulation materials gradually deteriorate, leading to an increase in local electric field strength and a decrease in insulation strength. This significantly increases the likelihood and intensity of partial discharge. Therefore, historical data shows that the longer the thermal overload duration, the greater the degree of thermal aging and stress accumulation in the insulation materials, making it easier to form discharge channels or tip discharge points. This results in more pronounced, longer-lasting, and more severe partial discharge signals, and a higher probability of partial discharge.

[0091] Obtain the peak points of the acoustic emission signals at all times, and calculate the th peak. The degree of partial discharge performance at each moment is expressed as:

[0092]

[0093] In the formula, For the first The degree of localized discharge performance at a given moment; For the first A moment ago The number of peak points in all acoustic emission signals within a second; For the first A moment ago Within seconds The amplitude of each of the aforementioned peak points. Preferably, in this embodiment of the application, the amplitude of each peak point is... The value is set to 1. As another embodiment of this application, the implementer may set it according to the actual situation.

[0094] When the degree of partial discharge increases with repeated occurrences of thermal overload conditions in history, it indicates the presence of partial discharge, and thus the probability of the distribution transformer being in a partial discharge condition can be judged.

[0095] For any given moment, the difference between the degree of partial discharge performance at that moment and the degree of partial discharge performance at the previous moment is recorded as the change in partial discharge performance at that moment. If the change in partial discharge performance is greater than 0, the increase in partial discharge performance at that moment is set to 1; if the change in partial discharge performance is less than or equal to 0, the increase in partial discharge performance at that moment is set to 0.

[0096] Historical data shows that when thermal overload occurs, if the degree of partial discharge gradually increases synchronously, it indicates that thermal overload has caused partial discharge.

[0097] Calculate the first The partial discharge probability at time t is expressed as:

[0098]

[0099] In the formula, For the first The probability of partial discharge at a given moment; For the first The number of historical moments before a given moment; For the first Before the [time]th moment The probability of thermal overload at any given moment; For the first Before the [time]th moment The degree of increase in local discharge at each moment; This is the normalization function.

[0100] when The larger the value, the higher the value in the historical data. The more likely a thermal overload occurs at a given moment, the more likely partial discharge will occur. If the degree of partial discharge also shows an upward trend, it indicates that the partial discharge becomes more pronounced as the thermal overload continues to occur, and the greater the probability of partial discharge.

[0101] Step S5: Adjust the data upload frequency at each time point based on the load level, thermal overload probability, and partial discharge probability at each time point.

[0102] Based on the above method, the current operating condition of the distribution transformer is determined. Different operating conditions require different data upload frequencies. Specifically:

[0103] The load level threshold, thermal overload probability threshold, and partial discharge probability threshold are set separately. Preferably, in this embodiment, the load level threshold, thermal overload probability threshold, and partial discharge probability threshold are set to 0.9, 0.7, and 0.7, respectively. As other embodiments of this application, implementers can set the load level threshold, thermal overload probability threshold, and partial discharge probability threshold according to actual conditions.

[0104] The load level, thermal overload probability, and partial discharge probability at all times are normalized. If the normalized values ​​of the load level, thermal overload probability, and partial discharge probability at each time are all greater than the corresponding threshold, then the working condition at that time is judged as a normal working condition, and the corresponding upload frequency is set to upload once every 5 minutes.

[0105] If the normalized values ​​of thermal overload probability and partial discharge probability at each moment are both greater than the corresponding threshold, while the normalized value of load level is less than or equal to the load level threshold, then the working condition at that moment is determined to be a heavy load working condition, and the corresponding upload frequency is set to upload once every 1 minute.

[0106] If the normalized value of the partial discharge probability at each moment is greater than the partial discharge probability threshold, while the normalized value of the load level and the normalized value of the thermal overload probability are both less than or equal to the corresponding threshold, then the working condition at that moment is determined to be a thermal overload working condition, and the corresponding upload frequency is set to upload once every 30 seconds.

[0107] If the normalized values ​​of load level, thermal overload probability, and partial discharge probability at each time point are all less than or equal to the corresponding thresholds, then the operating condition at that time point is determined to be a partial discharge condition, and the corresponding upload frequency is set to upload once every 100ms. As shown in Table 1.

[0108] Table 1

[0109] Load level greater than 0.9 The probability of thermal overload is greater than 0.7. The probability of partial discharge is greater than 0.7. Working condition judgment results Corresponding upload frequency no no no Normal operating conditions 5min yes no no Heavy load condition (normal) 1min yes yes no Thermal overload conditions 30 seconds yes yes yes Local discharge conditions 100ms

[0110] Under normal operating conditions, data changes are stable and fluctuations are small, so high-frequency uploading is not very meaningful and can easily lead to redundancy and storage waste. Therefore, low-frequency uploading is chosen.

[0111] Although the transformer did not overheat under heavy load conditions, it was under high load, which increased the risk. The real-time requirements were slightly higher than under normal conditions, and the platform needed to grasp the heavy load trend.

[0112] Thermal overload conditions can affect insulation life and further induce serious faults such as partial discharge. Key data such as temperature, current, and load rate should be uploaded quickly for real-time evaluation by the platform.

[0113] Partial discharge is a precursor to major faults and can easily escalate into accidents such as breakdown and burnout. Real-time monitoring and response are crucial, so high-frequency uploading is required for fault analysis.

[0114] Therefore, different upload frequencies are used for different operating conditions, including the upload frequency under normal operating conditions. Upload frequency under heavy load conditions Upload frequency under thermal overload conditions Upload frequency for partial discharge (PD) conditions. It should be noted that this application only provides one method for setting the upload frequency for each condition. Implementers can set the upload frequency for each condition according to their actual situation; this application does not impose specific restrictions.

[0115] The flowchart of the above method is as follows Figure 2 As shown; a schematic diagram of data transmission between the fusion terminal, the wireless multi-parameter acquisition device, and the wireless sensor is shown below. Figure 3 As shown.

[0116] Based on the same inventive concept as the above method, this application embodiment also provides a multi-parameter sensing system for the status of key equipment of distribution transformers, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described methods for multi-parameter sensing of the status of key equipment of distribution transformers.

[0117] In summary, this application provides a multi-parameter sensing method for the status of key equipment in a distribution transformer. By collecting and analyzing multi-dimensional data of the distribution transformer, it improves the accuracy of evaluating the operating status of the distribution transformer. Based on the difference between the moving average current and the rated current at each moment, and the difference between the voltage and the overall voltage distribution at all moments, the load level of the distribution transformer at each moment is constructed, which helps to promptly determine and detect the normal and overload states of the load and adjust the data transmission frequency accordingly. Based on the difference in load level between each moment and previous moments, and the load level between each moment and previous moments, the high load duration of the distribution transformer at each moment is calculated, taking into account the operating characteristics of the distribution transformer and avoiding the problem of judging normal operating conditions such as short-term heavy load operation as abnormal operating conditions, thus improving the accuracy of determining the actual operating status of the distribution transformer. Based on the temperature difference between each moment and the previous moment, and the temperature at each moment... By combining the high load duration at each moment and the moments before, the thermal overload probability at each moment is calculated, taking into account the lag in temperature rise caused by load, and assessing the possibility of the distribution transformer being in a thermal overload state. For any moment before each moment, based on the difference in the distribution characteristics of the acoustic emission peak amplitude between that moment and the previous moment in the historical time, combined with the thermal overload probability at that moment, the partial discharge probability at each moment is calculated, taking into account the fact that prolonged thermal overload can lead to partial discharge, and assessing the probability of the distribution transformer being in a partial discharge condition. Based on the load level, thermal overload probability, and partial discharge probability at the current moment, the data upload frequency at the current moment is adjusted. This allows for more precise adjustment of the data transmission frequency according to the transformer status. When the distribution transformer is normal, the data upload frequency is reduced, thereby reducing uplink communication pressure, avoiding the accumulation of a large amount of redundant data, improving the smoothness of data upload when the transformer is in an abnormal state, and improving the system operating efficiency.

[0118] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this application. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0119] The various embodiments in this application are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0120] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A power distribution transformer key equipment state multi-parameter sensing method, characterized in that, The method comprises the following steps: Collecting acoustic emission signals, oil tank temperatures, high-voltage side incoming line voltages and low-voltage side outgoing line currents of the distribution transformer at each time point; Based on the difference between the moving average of the current at each time point and the rated current, and the difference between the voltage and the overall distribution of the voltage at all time points, the load degree of the distribution transformer at each time point is constructed; based on the difference between the load degree at each time point and the load degree at the previous time point, and the load degree at each time point and the previous time point, the high load duration of the distribution transformer at each time point is calculated; Based on the difference between the oil tank temperature at each time point and the previous time point, and the oil tank temperature at each time point, combined with the high load duration at each time point and the previous time point, the thermal overload probability at each time point is calculated; For any time point before each time point, based on the difference between the distribution characteristics of the amplitude of the acoustic emission peak point in the historical time of the any time point and the previous time point, combined with the thermal overload probability of the any time point, the partial discharge probability of each time point is calculated; Based on the load degree, thermal overload probability and partial discharge probability at each time point, the data upload frequency of each time point is adjusted; The acquisition process of the load degree is: wherein A a is the load degree of the distribution transformer at the a-th time instant; B a is the moving average of the current at the a-th time instant; B0is the rated current of the distribution transformer; C0is the average of the voltage of the distribution transformer at all time instants; C a is the voltage of the distribution transformer at the a-th time instant; norm() is a normalization function; The acquisition process of the high load duration is: The relative load attention degree of the bth moment before the ath moment is calculated, denoted as D a,b , D a,b The expression is: where b is the order corresponding to the bth moment before the ath moment; A a is the load degree at the ath moment; A a,c is the load degree at the cth moment before the ath moment; MAX() is a maximum function; norm() is a normalization function; Based on the load degree at each time point, and the load degree and relative load attention degree of each time point within a preset time period before each time point, the high load duration of the distribution transformer at each time point is calculated; The high load duration of the distribution transformer at each time point is specifically: The product of the load degree and the relative load attention degree of the bth time point before the a th time point is calculated, which is recorded as the first product; the product of the average value of the first product of all time points within the previous preset days of the a th time point and the load degree of the a th time point is calculated as the high load duration of the distribution transformer at the a th time point.

2. The power distribution transformer critical equipment condition multi-parameter sensing method of claim 1, wherein, The acquisition process of the thermal overload probability is: Based on the difference between the oil tank temperature at each time point and the previous time point, the change rate of the oil tank temperature value at each time point is determined; based on the oil tank temperature at each time point and the corresponding change rate, the thermal overload performance degree at each time point is calculated; The minimum value of the high load duration of all time points between each time point and any time point before it is obtained; The product of the minimum value and the thermal overload performance degree of the any time point is calculated, which is recorded as the second product between each time point and the any time point before it; the average value of the second product between each time point and all time points within a preset time period before it is calculated as the thermal overload probability of each time point.

3. The power distribution transformer critical equipment condition multi-parameter sensing method of claim 2, wherein, The change rate of the temperature value at each time point is specifically: If the temperature difference between each time point and the previous time point is greater than or equal to 0, the temperature difference is taken as the change rate of the temperature value at each time point; otherwise, the change rate of the temperature value at each time point is set to 0.

4. The power distribution transformer critical equipment condition multi-parameter sensing method of claim 2, wherein, The expression of the thermal overload performance degree at each time point is: F a = K a × K a + (1 - K a ) × H a , where F a is the thermal overload performance degree at the a-th moment; K a is the normalized value of the rate of change of the temperature value at the a-th moment; and H a is the oil tank temperature at the a-th moment.

5. The power distribution transformer critical equipment condition multi-parameter sensing method of claim 1, wherein, The acquisition process of the partial discharge probability is: Each peak point in the acoustic emission signal of all time points is obtained, and based on the amplitude of all the peak points within a preset time period before each time point, the partial discharge performance degree at each time point is calculated; The partial discharge increase degree at each time point is set based on the difference between the partial discharge performance degree at each time point and the previous time point. The product of the thermal overload probability and the PD increase degree at each time point is calculated, and is recorded as a third product; and the normalized value of the sum of the third products at all time points before each time point is taken as the PD probability at each time point.

6. The power distribution transformer critical equipment condition multi-parameter sensing method of claim 5, wherein, The PD performance degree at each time point is the sum of the amplitudes of all the peak points in a preset time period before each time point.

7. The power distribution transformer critical equipment condition multi-parameter sensing method of claim 5, wherein, The PD increase degree is obtained by: If the difference between the PD performance degree at each time point and that at the previous time point is greater than 0, the PD increase degree at each time point is set to 1; otherwise, the PD increase degree at each time point is set to 0.

8. The power distribution transformer critical equipment condition multi-parameter sensing method of claim 1, wherein, The data upload frequency is obtained by: A load degree threshold, a thermal overload probability threshold and a PD probability threshold are set; If the normalized values of the load degree, the thermal overload probability and the PD probability at each time point are all greater than the corresponding thresholds, the working condition at each time point is determined as a normal working condition; If the normalized values of the thermal overload probability and the PD probability at each time point are both greater than the corresponding thresholds, and the normalized value of the load degree is less than or equal to the load degree threshold, the working condition at each time point is determined as a heavy load working condition; If the normalized value of the PD probability at each time point is greater than the PD probability threshold, and the normalized values of the load degree and the thermal overload probability are both less than or equal to the corresponding thresholds, the working condition at each time point is determined as a thermal overload working condition; If the normalized values of the load degree, the thermal overload probability and the PD probability at each time point are all less than or equal to the corresponding thresholds, the working condition at each time point is determined as a PD working condition; Different upload frequencies are adopted for different working conditions, and the upload frequency of the normal working condition < the upload frequency of the heavy load working condition < the upload frequency of the thermal overload working condition < the upload frequency of the PD working condition.

9. A power distribution transformer critical equipment condition multi-parameter perception system comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor implements the steps of the method of any one of claims 1-8 when executing the computer program.

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