Power distribution operation and maintenance management method for transformer substation

Abstract

This application relates to a method for power distribution operation and maintenance management in substations, comprising: collecting data on the temperature difference between the inner and outer surfaces of a transformer and its output power, as well as the number of maintenance operations performed on the transformer during a historical monitoring period; calculating a heat dissipation stability evaluation factor; constructing a power output function; calculating an aging degree evaluation index; determining the sampling frequency of the transformer's operating parameters during the current monitoring period; and collecting the transformer's operating parameters. The technical solution of this application can improve the efficiency and accuracy of power distribution operation and maintenance management in substations.

Description

Technical Field

[0001] This application generally relates to the field of power distribution operation and maintenance technology, and in particular to a power distribution operation and maintenance management method for substations. Background Technology

[0002] With the increasing demands of national economic development and the advancement of distribution network technology, the scale of my country's low-voltage distribution network is continuously expanding. The safe and stable operation of distribution network equipment is the foundation for the reliable operation of the power system. Once a distribution network device fails, it can, at best, disrupt normal production and daily life; at worst, cause safety accidents, resulting in significant economic losses and adverse social impacts. Therefore, real-time assessment of the operating status of distribution network equipment, early warning of potential faults, and timely maintenance and replacement of damaged equipment are of great significance for improving the reliability of power system operation.

[0003] Modern power distribution operation and maintenance utilizes advanced Internet of Things (IoT) information technology to conduct 24-hour real-time online monitoring and centralized surveillance of electrical equipment in power distribution rooms. It can comprehensively analyze various data from power distribution room equipment and access online monitoring data, equipment load data, environmental monitoring data, and video and audio information from the power distribution room through various interface methods to achieve remote inspection. This enables rapid fault diagnosis and handling, reduces equipment maintenance costs, minimizes power outage losses, and improves work efficiency.

[0004] Distribution network equipment is characterized by its wide variety, large quantity, complex parameters, and diverse operating environments, resulting in a massive amount of monitoring data. At the same time, the numerous sensor data also contain a great deal of redundant information, which brings great difficulties to subsequent data calculation and processing.

[0005] For a long time, the regular maintenance mechanism implemented by power companies for distribution network equipment has suffered from problems such as "insufficient maintenance" and "over-maintenance," which not only causes significant waste of resources but also affects the reliability of power supply to a certain extent. Currently, with the advancement of communication, computer, and control technologies, fault monitoring systems are widely used in distribution network equipment, accumulating massive amounts of data. How to deeply analyze this data to maintain the safe operation of distribution network equipment has become an urgent problem to be solved. Summary of the Invention

[0006] In order to solve one or more of the above-mentioned technical problems in the prior art, this application provides a method for power distribution operation and maintenance management of substations, aiming to improve the efficiency and accuracy of power distribution operation and maintenance management of substations.

[0007] This application provides a power distribution operation and maintenance management method for a substation, the substation including a transformer, comprising: collecting the internal and external surface temperature difference and output power of the transformer and the number of maintenance operations of the transformer during a historical monitoring period, wherein the historical monitoring period is one or more monitoring periods prior to the current monitoring period; calculating a heat dissipation stability evaluation factor based on the internal and external surface temperature difference; constructing a power output function based on the output power; calculating an aging degree evaluation index based on the heat dissipation stability evaluation factor, the power output function, and the number of maintenance operations; determining the sampling frequency of the transformer's operating parameters during the current monitoring period based on the aging degree evaluation index; and collecting the transformer's operating parameters during the current monitoring period based on the sampling frequency of the operating parameters.

[0008] In one embodiment, collecting the transformer's operating parameters within the current monitoring period according to the operating parameter sampling frequency includes: adjusting the length of the time window used for sampling within the current monitoring period based on the historical operation and maintenance data; and collecting the transformer's operating parameters within each time window based on the length of the time window.

[0009] In one embodiment, determining the sampling frequency of the transformer's operating parameters within the current monitoring cycle based on the aging degree evaluation index includes: obtaining the initial sampling frequency of the operating parameters; correcting the initial sampling frequency based on the aging degree evaluation index to obtain a corrected sampling frequency; and using the corrected sampling frequency as the sampling frequency of the operating parameters.

[0010] In one embodiment, calculating the aging degree evaluation index based on the heat dissipation stability evaluation factor, the power output function, and the number of maintenance cycles includes: calculating the aging degree evaluation index according to the following formula:

[0011]

[0012] in, This indicates the aging degree evaluation index. This represents the power output function. This indicates the search for the maximum value of the power output function. This indicates the number of maintenance operations.

[0013] In one embodiment, calculating the heat dissipation stability evaluation factor based on the temperature difference between the inner and outer surfaces includes: constructing a temperature difference sequence between the inner and outer surfaces based on the temperature difference between the inner and outer surfaces; and calculating the heat dissipation stability evaluation factor based on the temperature difference sequence between the inner and outer surfaces, wherein...

[0014]

[0015] in, L represents the heat dissipation stability evaluation factor, and L represents the total length of the temperature difference sequence between the inner and outer surfaces. This represents the t-th temperature difference value in the inner and outer surface temperature difference sequence. Let g represent the g-th temperature difference value in the inner and outer surface temperature difference sequence, abs() represent the absolute value function, and e represent the natural constant.

[0016] In one embodiment, constructing the power output function based on the output power includes: constructing the power output function according to the following relationship:

[0017]

[0018] in, This represents the power output function. This indicates the actual output power of the transformer. This indicates the rated power of the transformer. This represents the linear rectification function.

[0019] In one embodiment, correcting the initial sampling frequency based on the aging evaluation index to obtain a corrected sampling frequency includes: correcting the initial sampling frequency based on the aging evaluation index according to the following relationship:

[0020]

[0021] in, i The substation is represented by the first i One transformer, This indicates the corrected sampling frequency of the transformer. This indicates the initial sampling frequency of the transformer. This indicates the evaluation index of the aging degree of the transformer. This is an evaluation index representing the average aging level of all transformers in the substation.

[0022] In one embodiment, adjusting the length of the sampling time window within the current monitoring period based on the historical operation and maintenance data includes: for each time window, obtaining the local average power within the corresponding time period of the previous monitoring period; calculating the global average power corresponding to all time windows based on the local average power; and calculating the weight corresponding to each time window based on the local average power and the global average power.

[0023]

[0024] in, j Indicates the first jA time window, Indicates time window j The corresponding weights Indicates time window j The corresponding local average power, The global average power is represented; the weights of all time windows are normalized to obtain normalized weights; the length of each time window is calculated based on the normalized weights of each time window.

[0025]

[0026] Indicates time window j Normalized weights Indicates time window j The initial length.

[0027] The technical solution of this application has the following beneficial technical effects:

[0028] By dynamically adjusting the sampling frequency of transformer operating parameters according to the aging degree of the transformer, a lower sampling frequency is used for transformers with lower aging degree, which significantly reduces the amount of sampling data and improves the efficiency of subsequent data calculation and processing; on the other hand, a higher sampling frequency is used for transformers with higher aging degree, which enables more timely detection of faults in transformers with higher aging degree, thereby improving the reliability of power system operation.

[0029] Furthermore, based on the transformer's historical operation and maintenance data, the sampling time window length of the transformer within the current monitoring cycle is dynamically adjusted, which further reduces the amount of sampling data and improves the efficiency of subsequent data calculation and processing.

[0030] Furthermore, by comprehensively considering different monitoring items closely related to the degree of aging and constructing a transformer aging evaluation function accordingly, the degree of transformer aging can be evaluated more accurately, thereby improving the accuracy of sampling frequency adjustment. Attached Figure Description

[0031] The above and other objects, features, and advantages of exemplary embodiments of this application will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of this application are illustrated by way of example and not limitation, and the same or corresponding reference numerals denote the same or corresponding parts, wherein:

[0032] Figure 1 This is a schematic diagram of a substation to which the power distribution operation and maintenance management method for substations according to the embodiments of this application is applied;

[0033] Figure 2This is a flowchart of a power distribution operation and maintenance management method for a substation according to an embodiment of this application. Detailed Implementation

[0034] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0035] It should be understood that when the terms "first," "second," etc., are used in the claims, description, and drawings of this application, they are only used to distinguish different objects and not to describe a specific order. The terms "comprising" and "including" used in the description and claims of this application indicate the presence of the described features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof.

[0036] This application provides a method for power distribution operation and maintenance management of substations, wherein the substation includes one or more transformers. Figure 1 This is a schematic diagram of a substation to which the power distribution operation and maintenance management method for substations, according to an embodiment of this application, is applied. Figure 1 As shown, the substation includes multiple transformers. Each substation collects different types of monitoring data and stores them in a database in the cloud center. The server processes the collected data.

[0037] Figure 2 This is a flowchart of a power distribution operation and maintenance management method for a substation according to an embodiment of this application. The substation may be... Figure 1 The substation shown in the image. Figure 2 As shown, the power distribution operation and maintenance management method includes steps S201 to S206, which are described in detail below.

[0038] S201, Collect the temperature difference between the inner and outer surfaces of the transformer and its output power, as well as the number of maintenance operations of the transformer during the historical monitoring period, wherein the historical monitoring period is one or more monitoring periods prior to the current monitoring period.

[0039] As an example, the monitoring cycle is 24 hours, and the historical monitoring cycle is each day of the previous week. To collect the temperature difference between the inner and outer surfaces, two temperature sensors can be placed at the same location inside and outside the transformer, and their readings can be collected in real time, once every 30 minutes. The temperature difference between the inner and outer surfaces within each sampling cycle is calculated based on the readings of the two temperature sensors. The sampling frequency for output power is relatively high, for example, once per minute. The number of maintenance operations of the transformer is the number of maintenance operations from the time the transformer leaves the factory to the previous monitoring cycle, which can be obtained from the transformer's maintenance records. Since the operating time of each transformer under the control of the substation is different, their aging levels will also be different. This application uses the number of maintenance operations to reflect the operating time of the transformer, rather than directly using the operating time, because the transformer has a complex composition, and the quality of each component is different. Using the number of maintenance operations can more accurately evaluate the health of the transformer, and the data acquisition is more convenient and simplifies subsequent data processing.

[0040] S202, Calculate the heat dissipation stability evaluation factor based on the temperature difference between the inner and outer surfaces.

[0041] Specifically, calculating the heat dissipation stability evaluation factor based on the temperature difference between the inner and outer surfaces includes: constructing a temperature difference sequence between the inner and outer surfaces based on the temperature difference between the inner and outer surfaces; and calculating the heat dissipation stability evaluation factor based on the temperature difference sequence between the inner and outer surfaces, wherein...

[0042]

[0043] in, This represents the heat dissipation stability evaluation factor. L This represents the total length of the temperature difference sequence between the inner and outer surfaces. This represents the t-th temperature difference value in the inner and outer surface temperature difference sequence. Let g represent the g-th temperature difference value in the inner and outer surface temperature difference sequence, abs() represent the absolute value function, and e represent the natural constant.

[0044] In the above example, a total of 7 samples were collected in the previous week. 48 = 336 temperature differences, and by arranging all temperature difference values ​​in chronological order, a sequence of inner and outer surface temperature differences is obtained. H Its total length is 336. Based on the temperature difference sequence of the inner and outer surfaces... H The heat dissipation stability evaluation factor was calculated. D .

[0045] In the above formula, The value represents the absolute difference between the mean of the first g values ​​and the mean of the first g-1 values ​​in the sequence. If the temperature difference between the inside and outside of the transformer is relatively stable, then the differences in all terms of the stability formula approach zero, and the heat dissipation stability evaluation factor approaches 1. In this case, the temperature difference between the inside and outside of the transformer is considered to be well maintained, and the transformer is not prone to failure. Conversely, if the heat dissipation stability of the transformer is poor, the heat dissipation stability evaluation factor approaches 0. In this case, the transformer may fail due to internal heat accumulation. The higher the heat dissipation stability evaluation factor of the transformer, the better the internal temperature of the transformer is maintained, the less damage to the transformer, and the lower the degree of aging of the transformer itself. Conversely, it indicates that the internal heat of the transformer is accumulating, which may affect the operation of the transformer and accelerate the aging of the transformer.

[0046] S203, construct a power output function based on the output power.

[0047] Specifically, the power output function is constructed according to the following relationship:

[0048]

[0049] in, This represents the power output function. This indicates the actual output power of the transformer. This indicates the rated power of the transformer. This represents the linear rectification function.

[0050] For transformer operation, if the actual output power exceeds the rated power, the transformer will age faster due to excessive load, potentially leading to transformer damage. Therefore, the aforementioned power output function is constructed to evaluate the stability of the power output.

[0051] S204. Calculate the aging degree evaluation index based on the heat dissipation stability evaluation factor, the power output function, and the number of maintenance cycles.

[0052] Specifically, the aging evaluation index is calculated according to the following formula:

[0053]

[0054] in, This indicates the aging degree evaluation index. This represents the power output function. This indicates the search for the maximum value of the power output function. This indicates the number of maintenance operations.

[0055] In the above formula, This indicates that the transformer is in good working condition when its actual power is less than its rated power. The value is 1; conversely, when the actual power of the transformer exceeds the rated power, the transformer is operating under a heavy load, which can damage the transformer itself. The value will increase exponentially, at which point the degree of transformer aging will increase dramatically. The number of transformer maintenance cycles serves as a correction for evaluating the degree of aging, in order to ensure that transformers with different operating times can have an accurate assessment of their aging degree.

[0056] S205, Based on the aging degree evaluation index, determine the sampling frequency of the transformer's operating parameters within the current monitoring cycle.

[0057] After calculating the aging degree evaluation index, the sampling frequency of the transformer's operating parameters in the current monitoring cycle can be adjusted accordingly. Specifically, determining the sampling frequency of the transformer's operating parameters in the current monitoring cycle based on the aging degree evaluation index includes: obtaining the initial sampling frequency of the operating parameters; correcting the initial sampling frequency based on the aging degree evaluation index to obtain a corrected sampling frequency; and using the corrected sampling frequency as the sampling frequency of the operating parameters.

[0058] As an example, the operating parameters can be the operating parameters considered for calculating the aging degree evaluation index, such as the internal and external temperature difference of the transformer, or other operating parameters besides those mentioned above, such as the output voltage of the transformer. This application does not specifically limit these parameters. Here, we use the internal and external surface temperature difference of the transformer as an example to describe the operating parameters. These operating parameters have an initial sampling frequency, such as once every 30 minutes. =1 / 1800. Specifically, based on the aging degree evaluation index, the initial sampling frequency is corrected according to the following relationship:

[0059]

[0060] in, i The substation is represented by the first i One transformer, This indicates the corrected sampling frequency of the transformer. This indicates the initial sampling frequency of the transformer. This indicates the evaluation index of the aging degree of the transformer. This is an evaluation index representing the average aging level of all transformers in the substation.

[0061] The above formula takes into account all transformers in the substation because transformers in the same substation have certain similarities in operating conditions and their aging rates are correlated, which can be used as a reference for evaluating other transformers in the same substation.

[0062] S206, Based on the sampling frequency of the operating parameters, collect the operating parameters of the transformer within the current monitoring cycle.

[0063] As an example, in the above instance, the current transformer's correction sampling frequency is once every 60 minutes, that is... =1 / 3600 indicates that the current substation is in good health and its temperature sampling frequency can be appropriately reduced.

[0064] In the above description, after determining the corrected sampling frequency, transformer operating data is collected continuously for a sampling time window at equal time intervals (i.e., 60 minutes). However, this average sampling method is difficult to reflect the dynamic state changes of the transformer at different time periods within the current sampling period. For example, when the output power is high, the transformer temperature may also be correspondingly high; when the output power changes significantly, the transformer temperature may also change drastically. Therefore, this scheme, based on obtaining the corrected sampling frequency, dynamically adjusts the length of the sampling time window for each sampling of the transformer within the current monitoring period. The sampling duration is relatively reduced during periods when the transformer power is likely to be low, and relatively increased during periods when the transformer power is likely to be high. This makes the sampling duration more reasonable, improves the ability to detect transformer anomalies in a timely manner, and enhances transformer safety.

[0065] Specifically, collecting the transformer's operating parameters within the current monitoring period according to the operating parameter sampling frequency includes: adjusting the length of the sampling time window within the current monitoring period based on the historical operation and maintenance data; and collecting the transformer's operating parameters within each time window based on the length of the time window.

[0066] Further, adjusting the length of the sampling time window within the current monitoring cycle based on the historical operation and maintenance data includes: for each time window, obtaining the local average power within the corresponding time period of the previous monitoring cycle corresponding to the time window; calculating the global average power corresponding to all time windows based on the local average power; calculating the weight corresponding to each time window based on the local average power and the global average power; normalizing the weights of all time windows to obtain normalized weights; and calculating the length of each time window based on the normalized weights of each time window.

[0067] As an example, the weight for each time window is calculated according to the following formula:

[0068]

[0069] in, j Indicates the first j A time window, Indicates time window j The corresponding weights Indicates time window j The corresponding local average power, This represents the global average power.

[0070] As an example, the length of each time window is calculated according to the following formula:

[0071]

[0072] Indicates time window j Normalized weights Indicates time window j The initial length.

[0073] The reason why the above operation is feasible is that the operating conditions of the transformer are relatively stable in two adjacent monitoring cycles, so the operating status in the next monitoring cycle can be predicted based on the monitoring data in the previous monitoring cycle.

[0074] The technical principles and implementation details of the power distribution operation and maintenance management method for substations described above are introduced through specific embodiments and examples. According to the technical solution of this application, by dynamically adjusting the sampling frequency of transformer operating parameters based on the aging degree of the transformer, a lower sampling frequency is used for transformers with lower aging degrees, thereby significantly reducing the amount of sampled data and improving the efficiency of subsequent data calculation and processing. On the other hand, a higher sampling frequency is used for transformers with higher aging degrees, enabling more timely detection of faults in transformers with higher aging degrees, thereby improving the reliability of power system operation. Furthermore, based on the historical operation and maintenance data of the transformer, the sampling time window length of the transformer within the current monitoring cycle is dynamically adjusted, further reducing the amount of sampled data and improving the efficiency of subsequent data calculation and processing. In addition, by comprehensively considering different monitoring items closely related to the aging degree and constructing a transformer aging evaluation function accordingly, the aging degree of the transformer can be evaluated more accurately, thereby improving the accuracy of sampling frequency adjustment.

[0075] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0076] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A power distribution operation and maintenance management method for a substation, the substation comprising a transformer, characterized by, include: The data includes the temperature difference between the inner and outer surfaces of the transformer and its output power, as well as the number of times the transformer was overhauled during the historical monitoring period. The historical monitoring period refers to one or more monitoring periods prior to the current monitoring period. Based on the temperature difference between the inner and outer surfaces, a heat dissipation stability evaluation factor is calculated, including: constructing a temperature difference sequence between the inner and outer surfaces; and calculating the heat dissipation stability evaluation factor based on the temperature difference sequence. ,in, This represents the heat dissipation stability evaluation factor, where L represents the total length of the temperature difference sequence between the inner and outer surfaces. This represents the t-th temperature difference value in the sequence of temperature differences between the inner and outer surfaces. Let g represent the g-th temperature difference value in the sequence of temperature differences between the inner and outer surfaces, abs() represent the absolute value function, and e represent the natural constant. Construct a power output function based on the output power; Based on the heat dissipation stability evaluation factor, power output function, and maintenance frequency, the aging degree evaluation index is calculated, including: the aging degree evaluation index is calculated according to the following formula: ;in, Indicators representing the degree of aging Represents the power output function. This indicates finding the maximum value of the power output function. Indicates the number of maintenance visits; Based on the aging evaluation indicators, determine the sampling frequency of the transformer's operating parameters within the current monitoring cycle; Based on the sampling frequency of the operating parameters, the operating parameters of the transformer are collected within the current monitoring cycle.

2. The power distribution operation and maintenance management method for substations according to claim 1, characterized in that, The process of collecting transformer operating parameters within the current monitoring cycle based on the sampling frequency of operating parameters includes: Adjust the length of the time window used for sampling within the current monitoring cycle based on historical operation and maintenance data; Based on the length of the time window, the operating parameters of the transformer are collected within each time window.

3. The power distribution operation and maintenance management method for substations according to claim 1, characterized in that, The determination of the sampling frequency of transformer operating parameters within the current monitoring cycle based on aging degree evaluation indicators includes: Obtain the initial sampling frequency of the running parameters; Based on the aging degree evaluation index, the initial sampling frequency is adjusted to obtain the corrected sampling frequency; The sampling frequency is adjusted and used as the operating parameter.

4. The power distribution operation and maintenance management method for substations according to claim 1, characterized in that, The construction of the power output function based on the output power includes: constructing the power output function according to the following relationship: in, Represents the power output function. This indicates the actual output power of the transformer. This indicates the rated power of the transformer. This represents the linear rectifier function.

5. The power distribution operation and maintenance management method for substations according to claim 3, characterized in that, The step of adjusting the initial sampling frequency based on the aging degree evaluation index to obtain the adjusted sampling frequency includes: adjusting the initial sampling frequency according to the following relationship based on the aging degree evaluation index: in, i The number of substations i One transformer, Indicates the corrected sampling frequency of the transformer. This indicates the initial sampling frequency of the transformer. Indicators representing the aging degree of transformers This is an evaluation index representing the average aging level of all transformers in a substation.

6. The power distribution operation and maintenance management method for substations according to claim 2, characterized in that, The adjustment of the length of the sampling time window within the current monitoring cycle based on historical operation and maintenance data includes: For each time window, obtain the local average power within the corresponding time period of the previous monitoring cycle; Calculate the global average power for all time windows based on the local average power. Calculate the weight corresponding to each time window based on the local average power and the global average power. in, j Indicates the first j A time window, Indicates time window j The corresponding weights Indicates time window j The corresponding local average power, Indicates global average power; The weights of all time windows are normalized to obtain normalized weights; Calculate the length of each time window based on its normalized weight. Indicates time window j Normalized weights Indicates time window j The initial length.

Images