High-precision transformer fault detection method and system

Abstract

The invention discloses a high-precision transformer fault detection method and system, relates to the technical field of transformer detection, is used for solving the problems of insensitive fault characterization and delayed diagnosis under a light load condition, and judges whether to enter a load judgment mechanism or not by acquiring input side access voltage information and counting low-load accumulated duration. When entering a load discrimination mechanism, detecting the start-stop behavior of a load branch and the temperature rise trend of a winding, calculating a light load index, identifying a light load state, carrying out region division on an iron core, collecting magnetic flux density data, and calculating iron loss balance characteristics aiming at iron loss deviation which is easy to occur under a light load condition; the insulation degradation coefficient is calculated by fusing the oil temperature peak value and the dielectric loss factor, and the abnormal index is generated with the iron loss balance characteristic, so that the early warning of the early hidden fault of the transformer can be realized in a light load scene in which the traditional temperature rise and current indexes are insensitive, and the operation reliability and maintenance efficiency of the transformer in a power distribution system are improved.

Description

Technical Field

[0001] This invention relates to the field of transformer testing technology, and more specifically, to a high-precision transformer fault detection method and system. Background Technology

[0002] As a core piece of equipment in power distribution networks and industrial power supply systems, the long-term continuous operation of transformers has a significant impact on the stability of the power system, power supply quality, and equipment lifespan. Traditional transformer health status assessment mainly relies on macroscopic parameters such as current, voltage, temperature rise, and oil chromatography, and analyzes the operating status by monitoring indicators such as winding temperature, oil temperature changes, current fluctuations, and partial discharge signals.

[0003] The existing technology has the following shortcomings: Currently, existing technologies lack spatial detection methods for iron loss changes under light load conditions and cannot perform refined quantitative analysis of magnetic flux density distribution characteristics. Under light load scenarios, the proportion of copper loss in transformers is relatively low, while iron loss becomes the main type of loss. Local magnetic flux density shifts, iron loss imbalances, and local overmagnetization phenomena in the iron core are more likely to occur. It is impossible to identify the co-evolution trend of iron loss shifts and insulation degradation in the early stages, resulting in insensitive fault characterization and delayed diagnosis under light load conditions, making it difficult to detect latent fault risks in a timely manner. Therefore, a high-precision transformer fault detection method and system are proposed.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a high-precision transformer fault detection method and system, which solves the problems mentioned in the background art by employing a light load identification mechanism based on input voltage behavior, a magnetic flux density analysis model for iron loss regionalization, and an insulation degradation coefficient fusion discrimination algorithm.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a high-precision transformer fault detection method, comprising the following steps: Step S1: Obtain the input voltage information of the transformer, set the analysis time, access the running database to obtain the input voltage reference, combine the input voltage information to calculate the low load cumulative duration and determine whether to enter the load discrimination mechanism. Step S2: When entering the load discrimination mechanism, the start-stop status of the transformer connected to each load branch is detected and the number of load jumps is evaluated. The winding temperature of the transformer is collected and the winding temperature rise trend is calculated. The number of load jumps is combined to determine whether the transformer is in a light load state. Step S3: When the transformer is under light load, the iron loss region of the transformer is divided into regions, magnetic flux density data of each region is collected, and the iron loss balance characteristics are evaluated using the magnetic flux density data. Step S4: Detect the peak oil temperature of the transformer and the dielectric loss factor of the winding insulation. Evaluate the insulation degradation coefficient of the transformer based on the detection results. Determine whether an abnormal signal is generated by combining the insulation degradation coefficient with the iron loss balance characteristics.

[0007] In a preferred embodiment, in step S1, the access voltage information of the transformer input terminal, including the access voltage, is obtained through the electrical acquisition unit; Set the preset analysis time and access the running database to obtain the access voltage reference; The access voltage is compared with the access voltage reference, and the duration during which the access voltage is lower than the access voltage reference is recorded as the low load period. Within the preset analysis time, the low-load periods are accumulated to obtain the low-load cumulative duration, and the ratio of the low-load cumulative duration to the preset analysis time is used as the low-load distribution ratio. If the proportion of low-load distribution is greater than the preset low-load distribution threshold, then the load discrimination mechanism is activated. Conversely, if the load is not detected, the load assessment mechanism will not be initiated, and monitoring will continue.

[0008] In a preferred embodiment, in step S2, when entering the load discrimination mechanism, a preset statistical period is set, and the opening and closing status signals of the transformer connected to each load branch are detected by the load status acquisition unit, including closing indication and opening indication. If the opening / closing status signal is a tripping indication, then the start / stop status of the load branch is set to 0; If the opening / closing status signal is a closing indication, then the start / stop status of the load branch is set to 1; The start / stop status of the load branches is sorted in chronological order, and the start / stop statuses of adjacent time periods are compared: If adjacent start / stop status values ​​are different, it is determined that there is a jump event in the load branch; Conversely, continue the comparison; After the preset statistical period ends, the cumulative result of all switching events is divided by the number of load branches to obtain the number of load switching events of the transformer.

[0009] In a preferred embodiment, in step S2, the winding temperature of the transformer is collected by a temperature sensor, and the difference between the winding temperature at the end of the preset statistical period and the start of the period is used to obtain the temperature rise change value. The ratio of the temperature rise change value to the preset statistical period is used as the winding temperature rise trend; After standardizing the winding temperature rise trend and the number of load jumps respectively, the load jump coefficient and the temperature rise trend coefficient are obtained. The light load index is calculated based on the load jump coefficient and the temperature rise trend coefficient. If the light load index is greater than the preset light load threshold, the transformer is determined to be in a light load state. Conversely, if the transformer is not under light load, it is determined that the transformer is not in a light load state.

[0010] In a preferred embodiment, in step S3, when the transformer is under light load, the iron loss region of the transformer is divided based on the structural composition of the transformer core. A preset sampling period is used to collect magnetic flux response signals from each divided region using a magnetic flux sensor. Within the preset sampling period, the peak amplitude of the magnetic flux response signal is extracted, and the peak amplitude is the maximum instantaneous amplitude reached by the magnetic flux response waveform.

[0011] In a preferred embodiment, in step S3, the magnetic flux density data of each divided region are summed to obtain the total magnetic flux density; Divide the magnetic flux density data by the sum of magnetic flux densities to obtain the magnetic flux distribution ratio of the divided region, and calculate the magnetic flux distribution entropy value based on the magnetic flux distribution ratio. Using the entropy value of magnetic flux distribution to evaluate the iron loss balance characteristics of a transformer: ,in, The total number of regions. For iron loss balance characteristics, This represents the magnetic flux distribution entropy value.

[0012] In a preferred embodiment, in step S4, the insulating oil temperature of the transformer within a preset sampling period is detected by a platinum resistance thermometer, and the maximum detected insulating oil temperature is taken as the oil temperature peak value. The dielectric loss factor of the winding insulation is obtained through the insulation monitoring database; The product of the peak oil temperature and the dielectric loss factor after standardization is used as the insulation degradation coefficient of the transformer.

[0013] In a preferred embodiment, in step S4, the anomaly index is calculated by combining the insulation degradation coefficient and the iron loss balance characteristics. An abnormality index and a preset abnormality threshold are used to determine whether an abnormal signal should be generated. If the anomaly index is greater than the preset anomaly threshold, then an anomaly signal is generated. Conversely, if the condition is not met, it is determined that no abnormal signal is generated.

[0014] A high-precision transformer fault detection system includes a voltage acquisition module, a load discrimination module, an iron loss analysis module, and an insulation assessment module. The functions of each module are as follows: The voltage acquisition module is used to obtain the access voltage information at the transformer input terminal, set the analysis time, access the running database to obtain the access voltage reference, combine the access voltage information to calculate the low load cumulative duration and determine whether to enter the load discrimination module. The load discrimination module is used to detect the start-stop status of the transformer connected to each load branch and evaluate the number of load jumps, collect the winding temperature of the transformer and calculate the winding temperature rise trend, and combine the number of load jumps to determine whether the transformer is in a light load state. When the transformer is under light load, the iron loss analysis module divides the iron loss area of ​​the transformer into regions, collects magnetic flux density data of each region, and uses the magnetic flux density data to evaluate the iron loss balance characteristics. The insulation assessment module is used to detect the peak oil temperature of the transformer and the dielectric loss factor of the winding insulation. Based on the detection results, it assesses the insulation degradation coefficient of the transformer and judges whether an abnormal signal is generated by combining the insulation degradation coefficient with the iron loss balance characteristics.

[0015] The technical effects and advantages of this invention are as follows: This invention obtains input voltage information and calculates the cumulative duration of low load to determine whether to enter the load discrimination mechanism. When the load discrimination mechanism is entered, it detects the start-stop behavior of the load branch and the winding temperature rise trend, calculates the light load index and identifies the light load state. For iron loss deviation that is prone to occur under light load conditions, the iron core is divided into regions and magnetic flux density data is collected to calculate iron loss balance characteristics. The insulation degradation coefficient is calculated by integrating the oil temperature peak and the dielectric loss factor. An abnormal index is generated by combining the iron loss balance characteristics. This invention can provide early warning of early hidden faults in transformers in light load scenarios where traditional temperature rise and current indicators are not sensitive, thereby improving the reliability and maintenance efficiency of transformer operation in the power distribution system. Attached Figure Description

[0016] Figure 1 This is a flowchart of a high-precision transformer fault detection method according to the present invention.

[0017] Figure 2 This is a schematic diagram of a high-precision transformer fault detection system according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] This invention obtains input voltage information and calculates the cumulative duration of low load to determine whether to enter the load discrimination mechanism. When the load discrimination mechanism is entered, it detects the start-stop behavior of the load branch and the winding temperature rise trend, calculates the light load index and identifies the light load state. For iron loss deviation that is prone to occur under light load conditions, the iron core is divided into regions and magnetic flux density data is collected to calculate iron loss balance characteristics. The insulation degradation coefficient is calculated by integrating the oil temperature peak and the dielectric loss factor. An abnormal index is generated by combining the iron loss balance characteristics. This invention can realize early warning of early hidden faults of transformers in light load scenarios where traditional temperature rise and current indicators are not sensitive.

[0020] Example 1, as Figure 1 As shown, a high-precision transformer fault detection method includes the following steps: Step S1: Obtain the input voltage information of the transformer, set the analysis time, access the running database to obtain the input voltage reference, combine the input voltage information to calculate the low load cumulative duration and determine whether to enter the load discrimination mechanism. Step S2: When entering the load discrimination mechanism, the start-stop status of the transformer connected to each load branch is detected and the number of load jumps is evaluated. The winding temperature of the transformer is collected and the winding temperature rise trend is calculated. The number of load jumps is combined to determine whether the transformer is in a light load state. Step S3: When the transformer is under light load, the iron loss region of the transformer is divided into regions, magnetic flux density data of each region is collected, and the iron loss balance characteristics are evaluated using the magnetic flux density data. Step S4: Detect the peak oil temperature of the transformer and the dielectric loss factor of the winding insulation. Evaluate the insulation degradation coefficient of the transformer based on the detection results. Determine whether an abnormal signal is generated by combining the insulation degradation coefficient with the iron loss balance characteristics.

[0021] The specific implementation is as follows: In step S1, under the operation scenario of small distributed power supply access, the transformer is often in a long-term light load state. Long-term light load operation will cause the heat dissipation and iron loss distribution of certain parts inside the transformer to become unbalanced, thereby generating the risk of local overheating or increased iron loss. In order to identify the internal thermal imbalance characteristics that may occur under light load conditions in a timely manner, the electrical characteristics of the transformer input side are continuously monitored to build an early discrimination capability between load change trend and light load operation mode, thereby providing the prerequisite for iron loss analysis and insulation degradation detection.

[0022] The electrical acquisition unit obtains the input voltage information of the transformer. The input voltage information is used to reflect the operating voltage status of the primary side of the transformer, including the input voltage. The analysis time is preset, and the running database is accessed to obtain the access voltage reference. The access voltage reference refers to the historical access voltage reference value formed by the transformer under normal operating conditions. The access voltage is compared with the access voltage reference, and the duration when the access voltage is lower than the access voltage reference is recorded as the low load period. Within the preset analysis time, the low load periods are accumulated to obtain the low load cumulative duration. The ratio of the low load cumulative duration to the preset analysis time is used as the low load distribution ratio. The low-load distribution ratio is compared with the preset low-load distribution threshold to determine whether to enter the load discrimination mechanism: If the proportion of low-load distribution is greater than the preset low-load distribution threshold, then the load discrimination mechanism is activated. Conversely, if the load is not detected, the load assessment mechanism will not be initiated, and monitoring will continue. When the proportion of low load distribution is greater than the preset low load distribution threshold, it indicates that the transformer has been in the range of low access voltage for a long time within the set analysis time. There may be potential risks such as local heat accumulation, uneven heat dissipation, or iron loss distribution deviation. Further refinement of the light load state is carried out by combining load jump behavior and temperature rise trend.

[0023] It should be noted that the preset analysis time can be set according to the transformer's operating characteristics and monitoring strategy; the operating database is a background data resource library used to store the transformer's historical operating data; the electrical acquisition unit is a primary side electrical parameter acquisition device deployed at the transformer's input end, used to acquire electrical quantities such as the connected phase voltage in real time.

[0024] In step S2, when entering the load discrimination mechanism, a preset statistical period is set, and the opening and closing status signals of the transformer connected to each load branch are detected by the load status acquisition unit, including closing indication and opening indication. When the opening / closing status signal is a closing indication, it means that the corresponding load branch is in the connected state; when the opening / closing status signal is a opening indication, it means that the corresponding load branch is in the disconnected state. If the opening / closing status signal is a tripping indication, then the start / stop status of the load branch is set to 0; If the opening / closing status signal is a closing indication, then the start / stop status of the load branch is set to 1; The start / stop status of the load branches is sorted in chronological order, and the start / stop statuses of adjacent time periods are compared: If adjacent start / stop status values ​​are different, it is determined that there is a jump event in the load branch; Conversely, continue the comparison; After the preset statistical period ends, the cumulative result of all switching events is divided by the number of load branches to obtain the number of load switching events of the transformer. The number of load transitions reflects the frequency of start-stop behavior of each load branch and the overall load fluctuation characteristics. The higher the number of load transitions, the more frequently the load branches start and stop switching occurs, and the load carried by the transformer exhibits obvious dynamic fluctuation characteristics. The lower the number of load transitions, the more stable each load branch remains for a long time, with fewer start-stop switching events, leading to risks such as a single internal heat dissipation path and iron loss distribution deviation. The temperature of the transformer windings is collected by temperature sensors installed on the transformer windings. The temperature rise change value is obtained by subtracting the winding temperature at the end and start of the preset statistical period. The ratio of the temperature rise change value to the preset statistical period is used as the winding temperature rise trend; After standardizing the winding temperature rise trend and the number of load jumps respectively, the load jump coefficient and the temperature rise trend coefficient are obtained. Calculate the light load index based on the load jump coefficient and temperature rise trend coefficient: ,in, For light load index, and To preset the adjustment weight, This is the load jump factor. This is the temperature rise trend coefficient; The larger the light load index, the more stable the transformer load change and the more stable the winding temperature rise, indicating that the transformer is in a light load state. Further subsequent evaluation should be carried out to identify local anomaly risks under light load conditions, and to avoid missing potential fault hazards by monitoring strategies based on temperature rise or load. The light load index is compared with a preset light load threshold to determine whether the transformer is in a light load state. If the light load index is greater than the preset light load threshold, the transformer is determined to be in a light load state. Conversely, if the transformer is not under light load, the testing should continue. Further quantitative assessment of transformers under light load conditions is needed to identify potential hidden risks such as unbalanced iron loss distribution, local heat retention, slow accumulation of local hot spots, and early insulation degradation. It should be noted that the preset statistical period can be set according to the transformer's operating characteristics, load change patterns, and monitoring sensitivity requirements; the load status acquisition unit is a monitoring device used to collect the opening and closing status of each load branch connected to the transformer in real time; the temperature sensor is a temperature measurement device installed at the transformer winding location; the preset adjustment weight can be set according to the degree of attention paid to the load jump characteristics and temperature rise change characteristics of different monitoring targets. For example, when focusing on the impact of load-side fluctuations on the light load judgment, the preset adjustment weight corresponding to the load jump coefficient can be increased.

[0025] This step enables accurate identification of long-term light-load operation status, even when it is difficult to identify hidden risks under light load. It provides reliable prerequisites for subsequent iron loss area analysis and insulation degradation assessment, and improves the early fault identification capability of transformers under light load conditions.

[0026] In step S3, when the transformer is under light load, the iron loss region of the transformer is divided based on the structural composition of the transformer core. Among them, the iron loss area refers to the physical parts in the transformer core where hysteresis loss and eddy current loss are generated, including the core column, yoke, lamination contact strip, magnetic flux concentration area and the part adjacent to the winding. For example, based on the three-column structure of the transformer core, each core column can be divided into three regions—upper, middle, and lower—in the height direction to distinguish the changes in magnetic flux density at different heights.

[0027] With a preset sampling period, the magnetic flux response signal of each divided area is collected by the magnetic flux sensor. The magnetic flux response signal refers to the time-varying magnetic flux response waveform output by the magnetic flux sensor under the action of magnetic circuit change, reflecting the magnetic flux intensity of the divided area during operation. Within a preset sampling period, the peak amplitude of the magnetic flux response signal is extracted. The peak amplitude is the maximum instantaneous amplitude reached by the magnetic flux response waveform. The larger the peak amplitude, the more concentrated the magnetic flux or the higher the magnetic flux density in the divided region. The peak amplitude is used as the magnetic flux density data of the divided region. The total magnetic flux density is obtained by summing the magnetic flux density data of each divided region. Divide the magnetic flux density data by the sum of magnetic flux densities to obtain the magnetic flux distribution ratio of the divided regions, and calculate the magnetic flux distribution entropy value based on the magnetic flux distribution ratio: ,in, The total number of regions. The values ​​are 1, 2, 3... , For the first The proportion of magnetic flux distribution in each divided region The magnetic flux distribution entropy value; Using the entropy value of magnetic flux distribution to evaluate the iron loss balance characteristics of a transformer: ,in, The total number of regions. This is a characteristic of iron loss balance; The magnetic flux distribution entropy value is used to reflect the uniformity of magnetic flux inside the iron core. The smaller the magnetic flux distribution entropy value, the more concentrated the magnetic flux is in a few areas, the higher the degree of uneven distribution of iron loss, and the greater the iron loss imbalance characteristic. The larger the magnetic flux distribution entropy value, the more uniform the magnetic flux is among the various areas, the iron loss distribution tends to be in normal operating condition, and the smaller the iron loss imbalance characteristic.

[0028] This step involves dividing the transformer core into structured regions under light load conditions and collecting magnetic flux density data for each region. This quantifies whether the magnetic flux distribution inside the transformer is uniform, amplifies the hidden iron loss anomalies that are difficult to detect through overall temperature rise or load current under light load conditions, and identifies potential risks of localized iron loss concentration, accumulation of potential hot spots, and slow insulation degradation under light load conditions in advance, thereby improving the accuracy of early fault diagnosis under light load operating conditions of the transformer.

[0029] It should be noted that the preset sampling period can be set according to the magnetic flux change distribution of the transformer core and the monitoring accuracy requirements. The magnetic flux sensor is a magnetic flux density detection element used to detect the local magnetic flux or magnetic flux density change of the core during operation. In this embodiment, it is used to collect the magnetic flux response signal of each divided area.

[0030] In step S4, the temperature of the insulating oil of the transformer within a preset sampling period is detected by a platinum resistance thermometer. The maximum value of the detected insulating oil temperature is taken as the oil temperature peak value. The larger the oil temperature peak value, the higher the amount of heat caused by the load and losses such as iron loss and copper loss accumulated in the oil. The dielectric loss factor of the winding insulation is obtained through the insulation monitoring database. The dielectric loss factor is the ratio of active power loss generated by the insulation medium to capacitive reactive current. The higher the dielectric loss factor, the greater the polarization loss and leakage current inside the insulating material, the more serious the moisture content, aging degree or dielectric defects of the insulation, and the weaker the insulation performance. The product of the peak oil temperature and the dielectric loss factor after standardization is used as the insulation degradation coefficient of the transformer. Anomaly index calculated by combining insulation degradation coefficient and iron loss balance characteristics: ,in, and To preset the weighting coefficients, Here, e is the insulation degradation coefficient, and e is the natural constant. For iron loss balance characteristics, An abnormal index; An abnormality index and a preset abnormality threshold are used to determine whether an abnormal signal should be generated. If the anomaly index is greater than the preset anomaly threshold, then an anomaly signal is generated. Conversely, if the condition is not met, it is determined that no abnormal signal is generated.

[0031] When the abnormal index is greater than the preset abnormal threshold, it indicates that the transformer exhibits characteristics of insulation degradation and iron loss distribution imbalance under light load operation conditions, and there may be potential abnormal risks such as insulation aging due to moisture, local magnetic flux concentration or slow accumulation of hot spots. It should be noted that the platinum resistance thermometer is a temperature detection element installed on the wall or oil passage of the transformer tank, which can obtain the temperature of the insulating oil; the insulation monitoring database is an operation management library that records the dielectric loss factor and related historical data of the transformer during long-term operation; the preset weighting coefficient can be set according to the transformer's historical fault samples and the degree of attention paid to the insulation degradation coefficient and iron loss balance characteristics under different operating scenarios; the preset abnormal threshold can be set according to the statistical distribution of historical fault data or power industry standards.

[0032] When a transformer is under light load, some areas inside the transformer may be in a state of low heat dissipation and weak circulation for a long time. This step constructs an insulation degradation coefficient by integrating the peak oil temperature and the dielectric loss factor, and generates an anomaly index by combining the iron loss balance characteristics. It can identify early insulation degradation and iron loss deviation risks when the temperature rise is not significant and the load fluctuation is limited. When the anomaly index exceeds the preset anomaly threshold, insulation decay, local hot spot development and potential core anomalies can be detected in advance, realizing a highly sensitive early warning of transformer faults in light load operation scenarios.

[0033] Example 2, as Figure 2 As shown, a high-precision transformer fault detection system includes a voltage acquisition module, a load discrimination module, an iron loss analysis module, and an insulation assessment module. The functions of each module are as follows: The voltage acquisition module is used to obtain the access voltage information at the transformer input terminal, set the analysis time, access the running database to obtain the access voltage reference, combine the access voltage information to calculate the low load cumulative duration and determine whether to enter the load discrimination module. The load discrimination module is used to detect the start-stop status of the transformer connected to each load branch and evaluate the number of load jumps, collect the winding temperature of the transformer and calculate the winding temperature rise trend, and combine the number of load jumps to determine whether the transformer is in a light load state. When the transformer is under light load, the iron loss analysis module divides the iron loss area of ​​the transformer into regions, collects magnetic flux density data of each region, and uses the magnetic flux density data to evaluate the iron loss balance characteristics. The insulation assessment module is used to detect the peak oil temperature of the transformer and the dielectric loss factor of the winding insulation. Based on the detection results, it assesses the insulation degradation coefficient of the transformer and judges whether an abnormal signal is generated by combining the insulation degradation coefficient with the iron loss balance characteristics.

[0034] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0035] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0036] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0037] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0038] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0039] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0040] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0041] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0042] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0043] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0044] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0045] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the above specification.

Claims

1. A high-precision transformer fault detection method, characterized in that: The method comprises the following steps: Step S1: obtaining access voltage information of the transformer input end, setting an analysis time, accessing an operation database to obtain an access voltage reference, combining the access voltage information to statistically obtain a low-load cumulative duration and judging whether to enter a load discrimination mechanism; Step S2: when entering the load discrimination mechanism, detecting the start-stop state of each load branch connected to the transformer and evaluating the load jump number, collecting the winding temperature of the transformer and calculating the winding temperature rise trend, and combining the load jump number to judge whether the transformer is in a light load state; Step S3: when the transformer is in a light load state, dividing the transformer iron loss area into regions, collecting the magnetic flux density data of each divided region, and evaluating the iron loss imbalance feature by using the magnetic flux density data; Step S4: detecting the oil temperature peak value of the transformer and the dielectric loss factor of the winding insulation, evaluating the insulation degradation coefficient of the transformer according to the detection result, and comprehensively judging whether to generate an abnormal signal according to the insulation degradation coefficient and the iron loss imbalance feature.

2. The high-precision transformer fault detection method according to claim 1, wherein in step S1, the access voltage information of the transformer input end is obtained by an electrical acquisition unit, including the access voltage; a preset analysis time is set, and an operation database is accessed to obtain an access voltage reference; the access voltage is compared with the access voltage reference, and the duration when the access voltage is less than the access voltage reference is taken as a low-load period; in the preset analysis time, the low-load period is accumulated to obtain a low-load cumulative duration, and the ratio of the low-load cumulative duration to the preset analysis time is taken as a low-load distribution ratio; if the low-load distribution ratio is greater than a preset low-load distribution threshold, it is judged that the load discrimination mechanism is entered; otherwise, it is judged that the load discrimination mechanism is not entered, and the monitoring is continued.

3. The high-precision transformer fault detection method according to claim 1, wherein in step S2, when the load discrimination mechanism is entered, a preset statistical period is set, the opening and closing state signals of each load branch connected to the transformer are detected by a load state acquisition unit, including closing indication and opening indication; if the opening and closing state signal is the opening indication, the start-stop state of the load branch is set to 0; if the opening and closing state signal is the closing indication, the start-stop state of the load branch is set to 1; the start-stop states of the load branches are sorted in time sequence, and the start-stop states of adjacent time are compared: if the adjacent start-stop state values are different, it is judged that there is a jump event of the load branch; otherwise, the comparison is continued; after the preset statistical period ends, the cumulative result of all jump events is divided by the number of load branches to obtain the load jump number of the transformer.

4. The high-precision transformer fault detection method according to claim 3, wherein in step S2, the winding temperature of the transformer is collected by a temperature sensor, and the difference between the winding temperatures at the end time and the start time of the preset statistical period is taken as the temperature rise change value; the ratio of the temperature rise change value to the preset statistical period is taken as the winding temperature rise trend; the winding temperature rise trend and the load jump number are respectively standardized to obtain the load jump coefficient and the temperature rise trend coefficient. ​ ​ ​ The light load index is calculated based on the load jump coefficient and the temperature rise trend coefficient; If the light load index is greater than the preset light load threshold, it is determined that the transformer is in a light load state; Otherwise, it is determined that the transformer is not in a light load state.

5. The high-precision transformer fault detection method of claim 1, wherein: in step S3, when the transformer is in a light load state, the iron loss region of the transformer is regionally divided based on the structural composition of the transformer core; a preset sampling period is set, the magnetic flux response signals of each divided region are collected by the magnetic flux sensor, and the peak amplitude of the magnetic flux response signal is extracted within the preset sampling period, the peak amplitude being the maximum instantaneous amplitude reached by the magnetic flux response waveform.

6. The high-precision transformer fault detection method of claim 5, wherein: in step S3, the magnetic flux density data of each divided region is summed to obtain a total magnetic flux density; the magnetic flux density data is divided by the total magnetic flux density to obtain the magnetic flux distribution proportion of the divided region, and the magnetic flux distribution entropy value is calculated based on the magnetic flux distribution proportion; 7. The high-precision transformer fault detection method of claim 1, wherein: The iron loss imbalance feature of the transformer is evaluated by using the magnetic flux distribution entropy value: wherein, is the total number of divided regions, is the iron loss imbalance feature, is the magnetic flux distribution entropy value. in step S4, the temperature of the insulating oil of the transformer within the preset sampling period is detected by the platinum resistance thermometer, and the maximum value of the detected insulating oil temperature is taken as the oil temperature peak value; the dielectric loss factor of the winding insulation is obtained from the insulation monitoring database; the product of the oil temperature peak value and the dielectric loss factor after standardization processing is taken as the insulation degradation coefficient of the transformer.

8. The high-precision transformer fault detection method of claim 7, wherein: in step S4, the abnormal index is calculated by comprehensively considering the insulation degradation coefficient and the iron loss imbalance feature; the abnormal index and the preset abnormal threshold are used to determine whether an abnormal signal is generated: if the abnormal index is greater than the preset abnormal threshold, it is determined that an abnormal signal is generated; otherwise, it is determined that no abnormal signal is generated. The system comprises a voltage acquisition module, a load discrimination module, an iron loss analysis module, and an insulation evaluation module, and each module has the following functions:

9. A high-precision transformer fault detection system for implementing the high-precision transformer fault detection method of any one of claims 1-8, characterized in that: The voltage acquisition module is used to obtain the access voltage information of the transformer input end, set the analysis time, access the running database to obtain the access voltage reference, and combine the access voltage information to statistically calculate the low-load cumulative time and determine whether to enter the load discrimination module; The load discrimination module is used to detect the start-stop state of each load branch connected to the transformer and evaluate the load jump frequency, collect the winding temperature of the transformer, and calculate the winding temperature rise trend, and combine the load jump frequency to determine whether the transformer is in a light load state; The iron loss analysis module regionally divides the iron loss region of the transformer when the transformer is in a light load state, collects the magnetic flux density data of each divided region, and evaluates the iron loss imbalance feature using the magnetic flux density data; The insulation evaluation module is used to detect the oil temperature peak value of the transformer and the dielectric loss factor of the winding insulation, evaluate the insulation degradation coefficient of the transformer according to the detection results, and comprehensively consider the insulation degradation coefficient and the iron loss imbalance feature to determine whether an abnormal signal is generated. ​

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