Distribution transformer capacity optimization configuration method and device considering load state

Abstract

The invention discloses a distribution transformer capacity optimization configuration method and device considering a load state, and belongs to the technical field of electric power, and the method comprises the steps: obtaining the capacity data of each distribution transformer in a target region and the load data in a preset period; determining the load state of each distribution transformer according to the load and capacity data; determining a tip load average value of each heavy overload distribution transformer and a daily maximum load average value of the light no-load distribution transformer based on a preset period and the load data; combining each heavy overload distribution transformer and each light no-load distribution transformer in the target area in pairs; and determining a matching difference degree of each distribution transformer combination based on the tip load average value, the daily maximum load average value and the capacity data in each distribution transformer combination, and selecting the distribution transformer combination based on the matching difference degree to carry out capacity optimization configuration. According to the invention, under the condition that the power grid cost is not increased, each distribution transformer resource in the target area is allocated, and the problem of coexistence of heavy overload and light no-load distribution transformers is solved.

Description

Background Art

[0002] Distribution transformer is the most important terminal equipment in the power system. Keeping it in a reasonable load state is very important for the economical and safe operation of the power grid.

[0003] Then, due to the influence of factors such as early planning, the growth rate of electricity demand, personnel flow, and climate change, the load conditions of each distribution transformer are different, and in the same area, distribution transformers are lightly unloaded and heavily overloaded. Some distribution transformers are in a lightly unloaded state, resulting in a waste of transformer capacity; some distribution transformers are in a heavily overloaded state, which poses certain safety risks. Therefore, how to reasonably allocate distribution transformer resources and improve the status quo of heavily overloaded and lightly unloaded distribution transformers has become an urgent problem to be solved. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and device for optimizing the capacity configuration of distribution transformers taking into account the load state in view of the deficiencies in the above-mentioned prior art, so as to solve the technical problems of unreasonable allocation of distribution transformer resources and the coexistence of heavily overloaded and lightly unloaded distribution transformers at the current stage.

[0005] The present invention adopts the following technical solutions: A method for optimizing the capacity configuration of a distribution transformer considering load conditions comprises the following steps: Obtain capacity data of each distribution transformer in the target area and load data within a preset period; Determine the load state of each distribution transformer according to the load data in a preset period and the capacity data of each distribution transformer in the target area; wherein the load state includes a heavy overload state and a light no-load state; Determine the average value of the tip load of the distribution transformers in each heavy overload state in the target area, and the average value of the daily maximum load of the distribution transformers in each light no-load state in the target area; Combining the distribution transformers in the target area in heavy overload state with the distribution transformers in the light no-load state in pairs, respectively, to obtain multiple distribution transformer combinations; The matching difference of each distribution transformer combination is determined based on the tip load average value, daily maximum load average value and capacity data within each distribution transformer combination, and the distribution transformer combination is selected for capacity optimization configuration based on the matching difference.

[0006] Preferably, the load state of each distribution transformer is determined as follows: According to the load data and capacity data corresponding to the distribution transformer, calculate the load rate of the distribution transformer at each collection point; If the load rate is not less than the first preset threshold, the collection point is determined to be a heavily overloaded point; if the load rate is less than the second preset threshold, the collection point is determined to be a lightly unloaded point; If there are continuous heavy overload points not less than the third preset threshold value on any day, it is determined that a heavy overload phenomenon occurs on that day; if all the collection points on any day are light no-load points, it is determined that a light no-load phenomenon occurs on that day; If the number of days when the heavy overload phenomenon occurs within the preset period is greater than the first preset ratio, it is determined that the load state of the distribution transformer within the preset period is a heavy overload state; If the light no-load phenomenon occurs every day within the preset period, it is determined that the load state of the distribution transformer within the preset period is the light no-load state.

[0007] Preferably, the average value of the tip load of the distribution transformer in each heavy overload state in the target area is determined as follows: Determine the daily maximum load and the daily second maximum load of the distribution transformer in the heavily overloaded state within a preset period based on the load data of the distribution transformer in the heavily overloaded state; Determine the tip load average value of the distribution transformer in the heavy overload state according to the daily maximum load, the daily second maximum load and the preset period within the preset period; The average daily maximum load of the distribution transformers in each light no-load state in the target area is determined as follows: Based on the load data of the distribution transformer in the light no-load state, a daily maximum load of the distribution transformer in the light no-load state within a preset period is determined.

[0008] The average daily maximum load of the distribution transformer in the light no-load state is determined according to the daily maximum load within the preset period and the preset period.

[0009] Preferably, the average tip load of any distribution transformer in a heavily overloaded state is and the average daily maximum load of the distribution transformer in the light no-load state Specifically:

[0010]

[0011] in, The first i The distribution transformer in the heavily overloaded state is x The maximum daily load of the day, The first i The distribution transformer in the heavily overloaded state is x The second largest load of the day, M is the largest load in the preset period iThe number of days that the distribution transformer in the heavy overload state experiences heavy overload phenomenon, For the i The cumulative value of the daily maximum load and the daily second largest load of the distribution transformer in the heavily overloaded state during the days when the heavy overload phenomenon occurs, N is the preset cycle, The first i The distribution transformer in a light no-load state x The maximum daily load of the day.

[0012] Preferably, the multiple distribution transformer combinations are obtained as follows: Based on the first preset combination principle, each heavily overloaded distribution transformer in the target area is combined with each lightly no-loaded distribution transformer in pairs to obtain multiple distribution transformer combinations; The first preset combination principle includes: In each distribution transformer combination, the capacity data of the distribution transformer in a heavily overloaded state is smaller than the capacity data of the distribution transformer in a lightly no-loaded state, and the tip load average value of the distribution transformer in the heavily overloaded state is within a first preset range of the capacity data of the distribution transformer in the lightly no-loaded state, and the maximum load average value of the distribution transformer in the lightly no-loaded state is within a second preset range of the capacity data of the distribution transformer in the heavily overloaded state.

[0013] Preferably, in each distribution transformer combination, the distribution transformer in a heavily overloaded state and the distribution transformer in a lightly no-loaded state in the combination meet the following principles: The capacity data of the distribution transformer in a heavily overloaded state is smaller than that in a lightly no-loaded state; The average value of the tip load of the distribution transformer in the heavy overload state is greater than 20% of the capacity data of the distribution transformer in the light no-load state, and less than 80% of the capacity data of the distribution transformer in the light no-load state; The average value of the maximum load of the distribution transformer in the light no-load state is greater than 20% of the capacity data of the distribution transformer in the heavy overload state, and is less than 80% of the capacity data of the distribution transformer in the heavy overload state.

[0014] Preferably, the matching difference of each distribution transformer combination is determined based on the tip load average value, the daily maximum load average value and the capacity data in each distribution transformer combination, and the distribution transformer combination is selected for capacity optimization configuration based on the matching difference, specifically: Based on the tip load average value, daily maximum load average value and capacity data in each distribution transformer combination, the matching difference of each distribution transformer combination is determined as follows: Determine the capacity matching degree of the heavily overloaded distribution transformer based on the daily maximum load average value of the distribution transformer in the light no-load state and the capacity data of the distribution transformer in the heavily overloaded state in the combination; Determine the light no-load distribution transformer capacity matching degree based on the tip load average value of the distribution transformer in the heavy overload state and the capacity data of the distribution transformer in the light no-load state in the combination; The sum of the capacity matching degree of the heavily overloaded distribution transformer and the capacity matching degree of the lightly unloaded distribution transformer is taken as the matching difference degree of the distribution transformer combination; It is preferred to swap the positions of the distribution transformer in a heavily overloaded state and the distribution transformer in a lightly no-loaded state in the distribution transformer combination with a smaller matching difference value.

[0015] Preferably, in any combination, the capacity matching degree of the heavily overloaded distribution transformer can be calculated by the following formula:

[0016] in, is the capacity matching degree of the heavily overloaded distribution transformer in the distribution transformer combination. It is the average daily maximum load of the distribution transformer in the light no-load state within the preset period in the distribution transformer combination. It is the capacity data of the distribution transformer in the distribution transformer combination in the heavily overloaded state.

[0017] Preferably, in any combination, the light no-load distribution transformer capacity matching degree in the distribution transformer combination is The calculation is as follows:

[0018] in, It is the capacity data of the distribution transformer in the light no-load state in the distribution transformer combination.

[0019] In a second aspect, an embodiment of the present invention provides a system for optimizing the capacity configuration of a distribution transformer considering a load state, comprising: An acquisition module, which acquires the capacity data of each distribution transformer in the target area and the load data within a preset period; A classification module, which determines the load state of each distribution transformer according to the load data in a preset period and the capacity data of each distribution transformer in the target area; wherein the load state includes a heavy overload state and a light no-load state; A calculation module, determining an average value of the tip load of the distribution transformers in each heavy overload state in the target area, and an average value of the daily maximum load of the distribution transformers in each light no-load state in the target area; A combination module combines the distribution transformers in the heavily overloaded state and the distribution transformers in the lightly no-loaded state in the target area in pairs to obtain multiple distribution transformer combinations; The optimization module determines the matching difference of each distribution transformer combination based on the tip load average value, daily maximum load average value and capacity data in each distribution transformer combination, and selects the distribution transformer combination for capacity optimization configuration based on the matching difference.

[0020] In a third aspect, a computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for optimizing capacity configuration of distribution transformers considering load status when executing the computer program.

[0021] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, comprising a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for optimizing the capacity configuration of distribution transformers considering load status.

[0022] Compared with the prior art, the present invention has at least the following beneficial effects: A method for optimizing the capacity configuration of distribution transformers considering load status is provided. The method determines whether the load status of each distribution transformer is a heavy overload status or a light no-load status through the capacity data of each distribution transformer in the target area and the load data in a preset period; then calculates the tip load average value corresponding to each distribution transformer in the heavy overload status and the daily maximum load average value corresponding to each distribution transformer in the light no-load status. Then, the distribution transformers in the two states are paired and combined to obtain multiple distribution transformer combinations. The matching difference of each distribution transformer combination is determined based on the tip load average value, the maximum load average value and the capacity data in each distribution transformer combination; wherein, the smaller the matching difference value, the higher the matching degree of the combination. Based on this logic, the load of each distribution transformer combination is optimized to achieve reasonable allocation of the resources of each distribution transformer in the target area without increasing the grid cost, and improve the coexistence of heavy overload and light no-load distribution transformers.

[0023] Furthermore, according to the business, the distribution transformer is prone to short-term load peaks during special periods, resulting in a short-term increase in the load rate of the distribution transformer. Therefore, it is not possible to judge that the distribution transformer is overloaded based solely on the heavy overload situation at a certain point in time. Therefore, the text sets 3 or more consecutive points (taking 1 collection point for 1 hour as an example, for two hours), indicating that the distribution transformer is overloaded on the same day. The transformer occasionally overloads within the preset period, but it is not frequent. It may be affected by factors such as personnel flow and holidays. It cannot represent that the transformer is in a state of heavy overload. When judging whether the transformer is in a state of heavy overload, the present invention sets a threshold value. When the number of days with heavy overload phenomena within the preset period is greater than the threshold value, it indicates that the transformer is in a state of heavy overload. The long-term load rate of the distribution transformer is too low, and resources cannot be effectively utilized, resulting in a waste of electric energy. At the same time, it will also reduce the power factor of the power grid and affect the quality of the power grid. Therefore, it is necessary to carry out a light no-load state analysis of the distribution network public transformer. The transformer load fluctuates with the electricity consumption of residents throughout the day. The peak hours are from 11:00 to 14:00 and from 18:00 to 21:00 in the evening, when the transformer load is large, but the transformer load is small in the early morning and other periods during the day. Therefore, when judging whether the transformer has a light no-load phenomenon, this article sets a threshold to judge the load rate of the distribution transformer throughout the day. If the load rate throughout the day does not exceed the threshold, it indicates that the transformer has a light no-load phenomenon on that day. If the distribution transformer has a light no-load phenomenon every day within the preset period, the distribution transformer is judged to be in a light no-load state within the preset period.

[0024] Further, after finding out the transformer in the heavy overload state through the threshold, it is necessary to quantify the degree of heavy overload of the transformer in the heavy overload state, and characterize the specific degree of heavy overload of the transformer in the heavy overload state through quantitative data. This paper selects the two largest load points every day, because there are many days in the preset cycle, in order to comprehensively reflect the degree of heavy overload of the distribution transformer in the heavy overload state, this paper adds and averages the two largest load points of the transformer every day in the preset cycle to characterize the specific degree of heavy overload of the transformer. After finding out the transformer in the light no-load state through the threshold, it is necessary to quantify the degree of light no-load of the transformer in the light no-load state, and characterize the specific degree of light no-load of the transformer in the light no-load state through quantitative data. This paper selects the two largest load points every day in the light no-load state, because there are many days in the preset cycle, in order to comprehensively reflect the degree of light no-load of the distribution transformer in the light no-load state, this paper adds and averages the two largest load points of the transformer every day in the preset cycle to characterize the specific degree of light no-load of the transformer.

[0025] Furthermore, the present invention rotates the transformers in the heavily overloaded state and the transformers in the lightly no-loaded state, but in the process of rotation selection, it is necessary to ensure that the distribution transformers in the heavily overloaded state cannot be in the heavily overloaded state or the lightly no-loaded state after the rotation, and the transformers in the lightly no-loaded state cannot be in the lightly no-loaded state or the heavily overloaded state after the rotation, otherwise the rotation will not be of much significance.

[0026] Furthermore, during the transformer rotation process, it is necessary to give priority to the distribution transformer combination pair with better effect after rotation. Therefore, it is necessary to calculate the load state of the heavy overload state transformer and the light no-load state transformer after rotation, and add the calculated capacity matching degree to obtain the matching difference value. The rotation selection order of the distribution transformer combination is comprehensively judged by the size of the matching difference value. The smaller the capacity matching degree after the heavy overload state distribution transformer is calculated, the better the effect of the heavy overload state distribution transformer in the combination pair after rotation. The smaller the capacity matching degree after the light no-load state distribution transformer is calculated, the better the effect of the light no-load state distribution transformer in the combination pair after rotation. Similarly, the smaller the matching difference, the distribution transformer combination can be selected first.

[0027] It can be understood that the beneficial effects of the second aspect mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0028] In summary, the present invention is used to solve the technical problem of how to carry out the rotation configuration of distribution transformers during the rotation process, and to solve the coexistence of heavily overloaded and lightly unloaded transformers in the region with greater efficiency. The present invention can effectively support power workers in carrying out the work of replacing the positions of distribution transformers in the region, and while alleviating the heavy overload of the distribution transformer, it also alleviates the lightly unloaded situation of the distribution transformer.

[0029] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A flow chart for implementing a method for optimizing the capacity of a distribution transformer taking load status into consideration provided by an embodiment of the present invention; Figure 2 A system architecture diagram of a method for optimizing the capacity of a distribution transformer taking load status into consideration provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of a device for optimizing the capacity configuration of a distribution transformer taking into account load conditions provided by an embodiment of the present invention; Figure 4 A schematic diagram of a computer device provided by an embodiment of the present invention; Figure 5 The present invention is a block diagram of an electronic device provided according to an embodiment. DETAILED DESCRIPTION

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

[0032] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0033] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an" and "the" are intended to include plural forms unless the context clearly indicates otherwise.

[0034] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0035] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0036] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0037] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0038] The present invention provides a method for optimizing the capacity configuration of distribution transformers considering the load state, obtaining the capacity data of each distribution transformer in the target area and the load data in a preset period; determining the load state of each distribution transformer according to the load and capacity data; determining the tip load average value of each heavily overloaded distribution transformer and the daily maximum load average value of each light no-loaded distribution transformer based on the preset period and load data; combining each heavily overloaded distribution transformer with each light no-loaded distribution transformer in the target area in pairs; determining the matching difference of each distribution transformer combination based on the tip load average value, daily maximum load average value and capacity data in each distribution transformer combination, and selecting the distribution transformer combination for capacity optimization configuration based on the matching difference. The present invention can allocate the distribution transformer resources in the target area without increasing the cost of the power grid, and improve the coexistence of heavily overloaded and light no-loaded distribution transformers.

[0039] See also Figure 1 The present invention provides a method for optimizing the capacity configuration of a distribution transformer considering load status, comprising the following steps: S1. Obtain the capacity data of each distribution transformer in the target area and the load data within a preset period; In this embodiment, the capacity data of each distribution transformer and the load data within the preset period are obtained from the power consumption information collection system. Among them, the archive information of the city, district, county, power supply station, etc. to which the distribution transformer belongs can also be obtained from the power consumption information collection system. The preset period can include multiple days, and each day can include 24 collection points; accordingly, for a distribution transformer, the load data within the preset period is the 24-point load data every day within the preset period.

[0040] After obtaining the capacity data of each distribution transformer and the load data within a preset period, the quality of the data needs to be checked to ensure its accuracy, completeness, consistency and standardization.

[0041] Specifically, since some of the 24-point load data in the system may be missing, which affects the accuracy of subsequent calculations, the load data of the previous hour is used to fill the current empty value in consideration of the special time sequence of load power consumption. At the same time, the overall data is tested for abnormal values. The abnormal detection standard adopts the Raida criterion, and those with a variance higher than three times are deleted. At the same time, some distribution transformers with excessive capacity are eliminated in combination with business knowledge.

[0042] S2. Determine the load state of each distribution transformer according to the load data and capacity data; wherein the load state includes a heavy overload state and a light no-load state; In an optional embodiment, determining the load state of each distribution transformer according to the load data and the capacity data includes: Perform the following steps for any distribution transformer: The load rate of the distribution transformer at each collection point is calculated according to the load data and capacity data corresponding to the distribution transformer.

[0043] If the load rate is not less than the first preset threshold, the collection point is determined to be a heavy overload point; if the load rate is less than the second preset threshold, the collection point is determined to be a light no-load point.

[0044] If there are continuous heavy overload points that are not less than the third preset threshold value on any day, it is determined that a heavy overload phenomenon occurs on that day; if all collection points on any day are light no-load points, it is determined that a light no-load phenomenon occurs on that day.

[0045] If the number of days when heavy overload occurs within the preset period is greater than the first preset ratio, it is determined that the load state of the distribution transformer within the preset period is a heavy overload state; if light no-load occurs every day within the preset period, it is determined that the load state of the distribution transformer within the preset period is a light no-load state.

[0046] The following takes a distribution transformer as an example to illustrate this embodiment. In this embodiment, the load data of each distribution transformer is mainly collected once a day at the top of the hour, a total of 24 times a day. For any distribution transformer, the load rate of each collection point is:

[0047] in, Load data for each collection point; CT is the current transformer ratio of the distribution transformer; PT is the voltage transformer ratio of the distribution transformer; CAP It is the capacity data of distribution transformer.

[0048] In order to determine the load attribute corresponding to the distribution transformer at the collection point, the collection point whose load rate is not less than the first preset threshold can be determined as a heavy overload point; the collection point whose load rate is less than the second preset threshold can be determined as a light no-load point; and the collection point whose load rate is less than the first preset threshold and not less than the second preset threshold can be determined as a normal point.

[0049] Among them, the first preset threshold is 75%, 80%, and 85%; the second preset threshold is 25%, 20%, and 15%. It should be understood that the above examples are only for illustration and do not constitute any limitation to this solution.

[0050] For any day, if the distribution transformer has a heavy overload point on that day, and the heavy overload points are continuous and the number is not less than the third preset threshold, it is determined that a heavy overload phenomenon occurs on that day; if all 24 collection points in that day are light no-load points, it is determined that a light no-load phenomenon occurs on that day.

[0051] For the distribution transformer, if the number of days with heavy overload phenomenon is greater than the first preset ratio during the entire collection period, the load state of the distribution transformer during the preset period is determined to be a heavy overload state; if the number of days with heavy overload phenomenon is not greater than the first preset ratio, the load state of the distribution transformer during the preset period is determined to be a normal state; if light no-load phenomenon occurs every day during the preset period, the load state of the distribution transformer during the preset period is determined to be a light no-load state. 1 Among them, the first preset ratio can be 2 / 5, 1 / 3, 1 / 4, etc., which is not limited here.

[0052] S3. Based on the preset cycle and the load data, determine the tip load average value of each distribution transformer in a heavy overload state and the daily maximum load average value of each distribution transformer in a light no-load state; In an optional embodiment, the preset period includes multiple days; based on the preset period and the load data, determining the tip load average value of the distribution transformer in each heavy overload state includes: Perform the following steps for each distribution transformer that is in a heavily overloaded state: Based on the load data of the distribution transformer in the heavily overloaded state, a daily maximum load and a daily second maximum load of the distribution transformer in the heavily overloaded state within a preset period are determined.

[0053] The tip load average value of the distribution transformer in the heavy overload state is determined based on the daily maximum load, the daily second largest load and the preset period within the preset period.

[0054] In this embodiment, for any distribution transformer in a heavily overloaded state, there are 24 points of load data every day within a preset period. The load data within the day can be arranged in ascending or descending order. If arranged in ascending order, the last load data is taken as the daily maximum load, and the second to last load data is taken as the daily second largest load; if arranged in descending order, the first load data is taken as the daily maximum load, and the second load data is taken as the daily second largest load.

[0055] Specifically, the average tip load of any distribution transformer in a heavily overloaded state can be calculated using the following formula:

[0056] in, The first i The average value of the tip load of the distribution transformer in the heavy overload state; The first i The distribution transformer in the heavily overloaded state is x The maximum daily load of the day, The first i The distribution transformer in the heavily overloaded state is x The second largest load of the day, M is the largest load in the preset period i The number of days that the distribution transformer in the heavy overload state experiences heavy overload phenomenon, For the i The cumulative value of the daily maximum load and the daily second largest load of a distribution transformer in a severe overload state during the days when severe overload occurs.

[0057] In an optional embodiment, determining the daily maximum load average value of each distribution transformer in a light no-load state based on a preset cycle and load data may include: Perform the following steps for each distribution transformer in a lightly no-load condition: Based on the load data of the distribution transformer in the light no-load state, a daily maximum load of the distribution transformer in the light no-load state within a preset period is determined.

[0058] The average daily maximum load of the distribution transformer in the light no-load state is determined according to the daily maximum load within the preset period and the preset period.

[0059] In this embodiment, the daily maximum load of the distribution transformer in a light no-load state within a preset period is also the maximum load data of the distribution transformer in a light no-load state on a daily basis. Specifically, for any distribution transformer in a light no-load state, its daily maximum load average value can be calculated by the following formula:

[0060] in, The first i The average daily maximum load of the distribution transformer in a light no-load state, The first i The distribution transformer in a light no-load state x The maximum daily load of the day, N The preset cycle.

[0061] S4, combining each heavily overloaded distribution transformer in the target area with each lightly no-loaded distribution transformer in pairs, to obtain a plurality of distribution transformer combinations; In an optional embodiment, each heavily overloaded distribution transformer in the target area is combined with each lightly no-loaded distribution transformer in pairs to obtain multiple distribution transformer combinations, which may include: Based on the first preset combination principle, the distribution transformers in the heavily overloaded state in the target area are respectively combined in pairs with the distribution transformers in the lightly no-loaded state to obtain a plurality of distribution transformer combinations.

[0062] Among them, the first preset combination principle includes: In each distribution transformer combination, the capacity data of the distribution transformer in a heavily overloaded state is smaller than the capacity data of the distribution transformer in a lightly no-loaded state, and the tip load average value of the distribution transformer in the heavily overloaded state is within a first preset range of the capacity data of the distribution transformer in the lightly no-loaded state, and the maximum load average value of the distribution transformer in the lightly no-loaded state is within a second preset range of the capacity data of the distribution transformer in the heavily overloaded state.

[0063] In this embodiment, the distribution transformers in a heavily overloaded state can be taken as a set, and the distribution transformers in a no-loaded state can be taken as a set. For any distribution transformer in a heavily overloaded state in the heavily overloaded state set, based on the first preset combination principle, a distribution transformer in a no-loaded state in the no-loaded state set is selected as a distribution transformer combination, and then to multiple distribution transformer combinations.

[0064] Exemplarily, the first preset range may be 20% to 80%, and the second preset range may be the same as the first preset range. Accordingly, in each distribution transformer combination, the distribution transformer in a heavily overloaded state and the distribution transformer in a lightly no-loaded state in the combination meet the following principles: 1. The capacity data of the distribution transformer in a heavily overloaded state is smaller than the capacity data of the distribution transformer in a lightly no-loaded state.

[0065] 2. The average value of the tip load of the distribution transformer in the heavy overload state is greater than 20% of the capacity data of the distribution transformer in the light no-load state, and is less than 80% of the capacity data of the distribution transformer in the light no-load state.

[0066] 3. The average maximum load value of the distribution transformer in the light no-load state is greater than 20% of the capacity data of the distribution transformer in the heavy overload state, and less than 80% of the capacity data of the distribution transformer in the heavy overload state.

[0067] S5. Determine the matching difference of each distribution transformer combination based on the tip load average value, daily maximum load average value and capacity data in each distribution transformer combination, and select the distribution transformer combination for capacity optimization configuration based on the matching difference.

[0068] In an optional embodiment, determining the matching difference of each distribution transformer combination based on the tip load average value, the daily maximum load average value and the capacity data in each distribution transformer combination may include: Perform the following steps for each distribution transformer combination: The capacity matching degree of the heavily overloaded distribution transformer is determined based on the daily maximum load average value of the distribution transformer in the light no-load state and the capacity data of the distribution transformer in the heavily overloaded state in the combination.

[0069] The capacity matching degree of the light no-load distribution transformer is determined based on the tip load average value of the distribution transformer in the heavy overload state and the capacity data of the distribution transformer in the light no-load state in the combination.

[0070] The sum of the capacity matching degree of the heavily overloaded distribution transformer and the capacity matching degree of the lightly unloaded distribution transformer is taken as the matching difference degree of the distribution transformer combination.

[0071] In this embodiment, the matching difference corresponding to each combination is: the sum of the heavy overload distribution transformer capacity matching degree of the distribution transformer in the heavy overload state in the combination and the light no-load distribution transformer capacity matching degree of the distribution transformer in the light no-load state in the combination.

[0072] In any combination, the capacity matching degree of the heavily overloaded distribution transformer can be calculated by the following formula:

[0073] in, is the capacity matching degree of the heavily overloaded distribution transformer in the distribution transformer combination. It is the average daily maximum load of the distribution transformer in the light no-load state within the preset period in the distribution transformer combination. It is the capacity data of the distribution transformer in the distribution transformer combination in the heavily overloaded state.

[0074] In any combination, the light no-load distribution transformer capacity matching degree is calculated by the following formula:

[0075] in, is the capacity matching degree of light no-load distribution transformer in the distribution transformer combination. It is the capacity data of the distribution transformer in the light no-load state in the distribution transformer combination.

[0076] In an optional embodiment, selecting a distribution transformer combination for capacity optimization configuration based on the matching difference may include: It is preferred to swap the positions of the distribution transformer in a heavily overloaded state and the distribution transformer in a lightly no-loaded state in the distribution transformer combination with a smaller matching degree value.

[0077] In this embodiment, the smaller the matching value is, the higher the matching degree of the combination is. After obtaining the matching difference value of each distribution transformer combination, the distribution transformer combinations can be sorted in order from small to large matching value, and the combination with the smaller matching value in the front can be prioritized for capacity optimization configuration, that is, the positions of the distribution transformer in the heavily overloaded state and the distribution transformer in the lightly unloaded state in the distribution transformer combination are interchanged.

[0078] The methods proposed in the above embodiments can be used as a functional module of a big data analysis platform. Figure 2 shown.

[0079] Figure 2 In the embodiment, a computer program is compiled according to the principle and flow chart of this embodiment, and the computer program is deployed on the computing server of the big data analysis platform. Through the power consumption information collection system and the distribution management system (MDS), the capacity data of each distribution transformer in the target area and the load data within the preset period are collected, and the collected data are uploaded to the big data analysis platform through a unified interface program. Then the compiled computer program is evaluated, and the screening results are stored in the database server of the big data analysis platform. Then the WEB server of the big data analysis platform responds to the requests of the power supply units at the provincial, municipal, county, and station levels, and displays the screening results to the monitoring terminals of the power supply units at the provincial, municipal, county, and station levels.

[0080] In summary, this embodiment takes into account the capacity, historical maximum load, etc. of heavily overloaded distribution transformers and lightly no-loaded distribution transformers in the same region, proposes a load optimization method for distribution transformers based on capacity and load status, and obtains the optimal combination of lightly no-loaded distribution transformers and heavily overloaded distribution transformers, assists in the formulation of distribution transformer resource optimization configuration plans, improves the current situation of coexistence of heavily overloaded and lightly no-loaded distribution transformers, improves the reliability of distribution network power supply, and enhances the power consumption satisfaction of power users.

[0081] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean 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 embodiment of the present invention.

[0082] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0083] The following is a system embodiment of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0084] See also Figure 3 In another embodiment of the present invention, a system for optimizing the capacity configuration of a distribution transformer taking load status into consideration is provided. The system can be used to implement the above-mentioned method for optimizing the capacity configuration of a distribution transformer taking load status into consideration. Specifically, the system for optimizing the capacity configuration of a distribution transformer taking load status into consideration includes an acquisition module 31, a classification module 32, a calculation module 33, a combination module 34 and an optimization module 35.

[0085] The acquisition module 31 is used to acquire the capacity data of each distribution transformer in the target area and the load data within a preset period; The classification module 32 is used to determine the load state of each distribution transformer according to the load data and the capacity data; wherein the load state includes a heavy overload state and a light no-load state; A calculation module 33, used to determine the tip load average value of the distribution transformers in each heavy overload state in the target area, and the daily maximum load average value of the distribution transformers in each light no-load state in the target area; A combination module 34 is used to respectively combine the distribution transformers in the heavily overloaded state and the distribution transformers in the lightly no-loaded state in the target area in pairs to obtain a plurality of distribution transformer combinations; The optimization module 35 is used to determine the matching degree of each distribution transformer combination based on the tip load average value, daily maximum load average value and capacity data in each distribution transformer combination, and select the distribution transformer combination for capacity optimization configuration based on the matching degree.

[0086] In a possible implementation, the calculation module 33 is specifically used for: Perform the following steps for each distribution transformer that is in a heavily overloaded state: Determine the daily maximum load and the daily second maximum load of the distribution transformer in the heavily overloaded state within a preset period based on the load data of the distribution transformer in the heavily overloaded state; The tip load average value of the distribution transformer in the heavy overload state is determined based on the daily maximum load, the daily second largest load and the preset period within the preset period.

[0087] In a possible implementation, the daily maximum load, the daily second maximum load, the preset period, and the average value of the tip load within the preset period satisfy the following relationship:

[0088] in, The first i The average value of the tip load of the distribution transformer in the heavy overload state; The first i The distribution transformer in the heavily overloaded state is x The maximum load of the day, The first i The distribution transformer in the heavily overloaded state is x The second largest load of the day, M is the maximum load in the preset cycle i The number of days that the distribution transformer in the heavy overload state experiences heavy overload phenomenon, For the i The cumulative value of the daily maximum load and the daily second largest load of a distribution transformer in a severe overload state during the days when severe overload occurs.

[0089] Perform the following steps for each distribution transformer in a lightly no-load condition: Based on the load data of the distribution transformer in the light no-load state, a daily maximum load of the distribution transformer in the light no-load state within a preset period is determined.

[0090] The average daily maximum load of the distribution transformer in the light no-load state is determined according to the daily maximum load within the preset period and the preset period.

[0091] In a possible implementation, the daily maximum load of the distribution transformer in a light no-load state within a preset period is also the maximum load data of the distribution transformer in a light no-load state on a daily basis. Specifically, for any distribution transformer in a light no-load state, its daily maximum load average value can be calculated by the following formula:

[0092] in, The first i The average daily maximum load of the distribution transformer in a light no-load state, The first i The distribution transformer in a light no-load state x The maximum daily load of the day, N In a possible implementation, the optimization module 35 is specifically used for: Perform the following steps for each distribution transformer combination: Determine the capacity matching degree of the heavily overloaded distribution transformer based on the daily maximum load average value of the distribution transformer in the light no-load state and the capacity data of the distribution transformer in the heavily overloaded state in the combination; Determine the light no-load distribution transformer capacity matching degree based on the tip load average value of the distribution transformer in the heavy overload state and the capacity data of the distribution transformer in the light no-load state in the combination; The sum of the capacity matching degree of the heavily overloaded distribution transformer and the capacity matching degree of the lightly unloaded distribution transformer is taken as the matching degree of the distribution transformer combination.

[0093] In a possible implementation, the daily maximum load average value, the capacity data of the distribution transformer in a heavily overloaded state, and the matching degree of the heavily overloaded distribution transformer capacity satisfy the following relationship:

[0094] in, is the capacity matching degree of the heavily overloaded distribution transformer in the distribution transformer combination. It is the average daily maximum load of the distribution transformer in the light no-load state within the preset period in the distribution transformer combination. It is the capacity data of the distribution transformer in the distribution transformer combination in the heavily overloaded state.

[0095] In a possible implementation, the tip load average value, the capacity data of the distribution transformer in a light no-load state, and the light no-load distribution transformer capacity matching degree satisfy the following relationship:

[0096] in, is the capacity matching degree of light no-load distribution transformer in the distribution transformer combination. It is the capacity data of the distribution transformer in the light no-load state in the distribution transformer combination.

[0097] In a possible implementation, the combining module 34 is specifically used for: Based on the first preset combination principle, each heavily overloaded distribution transformer in the target area is combined with each lightly no-loaded distribution transformer in pairs to obtain multiple distribution transformer combinations; Among them, the first preset combination principle includes: In each distribution transformer combination, the capacity data of the distribution transformer in a heavily overloaded state is smaller than the capacity data of the distribution transformer in a lightly no-loaded state, and the tip load average value of the distribution transformer in the heavily overloaded state is within a first preset range of the capacity data of the distribution transformer in the lightly no-loaded state, and the maximum load average value of the distribution transformer in the lightly no-loaded state is within a second preset range of the capacity data of the distribution transformer in the heavily overloaded state.

[0098] In a possible implementation, the optimization module 35 is specifically used to: It is preferred to swap the positions of the distribution transformer in a heavily overloaded state and the distribution transformer in a lightly no-loaded state in the distribution transformer combination with a smaller matching difference value.

[0099] In a possible implementation, the classification module 32 is specifically used for: Perform the following steps for any distribution transformer: Calculate the load rate of the distribution transformer at each collection point according to the load data and capacity data corresponding to the distribution transformer; If the load rate is not less than the first preset threshold, the collection point is determined to be a heavily overloaded point; if the load rate is less than the second preset threshold, the collection point is determined to be a lightly unloaded point; If there are continuous heavy overload points not less than the third preset threshold value on any day, it is determined that a heavy overload phenomenon occurs on that day; if all the collection points on any day are light no-load points, it is determined that a light no-load phenomenon occurs on that day; If the number of days when heavy overload occurs within the preset period is greater than the first preset ratio, it is determined that the load state of the distribution transformer within the preset period is a heavy overload state; if light no-load occurs every day within the preset period, it is determined that the load state of the distribution transformer within the preset period is a light no-load state.

[0100] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0101] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the capacity optimization configuration method of the distribution transformer considering the load state, including: Acquire the capacity data of each distribution transformer in the target area and the load data within a preset period; determine the load state of each distribution transformer according to the load data within the preset period and the capacity data of each distribution transformer in the target area; wherein the load state includes a heavy overload state and a light no-load state; determine the tip load average value of the distribution transformers in each heavy overload state in the target area, and the daily maximum load average value of the distribution transformers in each light no-load state in the target area; respectively combine the distribution transformers in each heavy overload state with the distribution transformers in each light no-load state in the target area in pairs to obtain a plurality of distribution transformer combinations; determine the matching difference of each distribution transformer combination based on the tip load average value, the daily maximum load average value and the capacity data in each distribution transformer combination, and select the distribution transformer combination for capacity optimization configuration based on the matching difference degree.

[0102] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device, and can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples (non-exhaustive list) of the computer-readable storage medium here include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0103] Computer readable storage media also include data signals propagated in baseband or as part of a carrier wave, which carry readable program codes. Such propagated data signals can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0104] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0105] One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the method for optimizing the capacity configuration of a distribution transformer considering the load state in the above embodiment; one or more instructions in the computer-readable storage medium may be loaded and executed by a processor as follows: Acquire the capacity data of each distribution transformer in the target area and the load data within a preset period; determine the load state of each distribution transformer according to the load data within the preset period and the capacity data of each distribution transformer in the target area; wherein the load state includes a heavy overload state and a light no-load state; determine the tip load average value of the distribution transformers in each heavy overload state in the target area, and the daily maximum load average value of the distribution transformers in each light no-load state in the target area; respectively combine the distribution transformers in each heavy overload state with the distribution transformers in each light no-load state in the target area in pairs to obtain a plurality of distribution transformer combinations; determine the matching difference of each distribution transformer combination based on the tip load average value, the daily maximum load average value and the capacity data in each distribution transformer combination, and select the distribution transformer combination for capacity optimization configuration based on the matching difference degree.

[0106] See also Figure 4 , the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the method for calculating the composition of fluid in the reservoir transformation wellbore in the embodiment is realized. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the calculation system for the composition of fluid in the reservoir transformation wellbore in the embodiment are realized. To avoid repetition, it is not described one by one here.

[0107] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 4 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0108] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, central processing units, graphics processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, data processing logic based on quantum computing, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0109] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0110] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0111] Any reference to a memory, database or other medium used in the embodiments provided in the present application may include at least one of a non-volatile and a volatile memory. Non-volatile memory may include a read-only memory (ROM), a magnetic tape, a floppy disk, a flash memory, an optical memory, a high-density embedded non-volatile memory, a resistive random access memory (ReRAM), a magnetic random access memory (MRAM), a ferroelectric random access memory (FRAM), a phase change memory (PCM), a graphene memory, etc. Volatile memory may include a random access memory (RAM) or an external cache memory, etc. As an illustration and not limitation, RAM may be in various forms, such as a static random access memory (SRAM) or a dynamic random access memory (DRAM), etc.

[0112] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0113] See also Figure 5 The terminal device 600 is an electronic device, and the electronic device is in the form of a general computing device. The components of the electronic device may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0114] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0115] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0116] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0117] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0118] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0119] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0120] Test verification: 1. Data preparation Assume that there are 10 distribution transformers in the target area, and the capacity parameters of each distribution transformer are shown in Table 1.

[0121] Table 1 Distribution transformer capacity parameters

[0122] 2. Determine the load status of the distribution transformer The preset cycle is one month (30 days). The load data of each transformer is recorded every day. Based on the transformer load data, the load rate of each collection point of each transformer is calculated. The load rate is used to determine the number of days when each distribution transformer has a heavy overload phenomenon or a light no-load phenomenon within the preset cycle. The statistical data is shown in Table 2.

[0123] Table 2 Distribution transformer load status

[0124] 3. Determine the average tip load and the average daily maximum load As shown in Table 2, the number of days when the distribution transformers with serial numbers 1 and 7 experienced heavy overload exceeded 1 / 3 of the preset period (30 days), so they were judged to be in a heavy overload state; the distribution transformers with serial numbers 3 and 6 experienced light no-load phenomena every day within the preset period, so they were judged to be in a light no-load state. Further, the average value of the tip load of the distribution transformer in the heavy overload state and the average value of the daily maximum load of the distribution transformer in the light no-load state were calculated, and the data results are shown in Table 3.

[0125] Table 3 Average tip load and average daily maximum load

[0126] 4. Determine the distribution transformer combination The heavily overloaded distribution transformers T1 and T7 are combined with the lightly unloaded distribution transformers T3 and T6 in pairs to obtain multiple distribution transformer combinations that meet the first preset combination principle. The two-by-two distribution transformer pairs are (T1 and T3), (T1 and T6), and (T6 and T7). 5. Calculate the matching difference of each distribution transformer combination (1) T1 and T3 distribution transformer combination The capacity matching degree of heavy overload distribution transformer is

[0127] Light no-load distribution transformer capacity matching for

[0128] The matching difference of the distribution transformer combination is

[0129] (2) T1 and T6 distribution transformer combination The capacity matching degree of heavy overload distribution transformer is

[0130] Light no-load distribution transformer capacity matching for

[0131] The matching difference of the distribution transformer combination is

[0132] (3) T6 and T7 distribution transformer combination The capacity matching degree of heavy overload distribution transformer is

[0133] Light no-load distribution transformer capacity matching for

[0134] The matching difference of the distribution transformer combination is

[0135] By comparison, among all distribution transformer combinations, the matching difference between T6 and T7 is the smallest, followed by T1 and T3. Therefore, (T6, T7) is preferred for capacity optimization configuration, followed by (T1, T3). In actual applications, more factors need to be considered, such as the physical location of the transformer, the grid structure, and the economic cost.

[0136] In summary, the present invention provides a method and device for optimizing the configuration of distribution transformer capacity taking into account load status, which can allocate the distribution transformer resources in the target area without increasing the grid cost, and improve the coexistence of heavy overload and light no-load distribution transformers.

[0137] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0138] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0139] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0140] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0141] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0142] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0143] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0144] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0145] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0146] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

Claims

1. A method for optimizing the capacity of a distribution transformer considering load conditions, characterized in that: The following steps are involved: Obtain capacity data of each distribution transformer in the target area and load data within a preset period; Determine the load state of each distribution transformer according to the load data in a preset period and the capacity data of each distribution transformer in the target area; wherein the load state includes a heavy overload state and a light no-load state; Determine the average value of the tip load of the distribution transformers in each heavy overload state in the target area, and the average value of the daily maximum load of the distribution transformers in each light no-load state in the target area; Combining the distribution transformers in the target area in heavy overload state with the distribution transformers in the light no-load state in pairs, respectively, to obtain multiple distribution transformer combinations; The matching difference of each distribution transformer combination is determined based on the tip load average value, daily maximum load average value and capacity data within each distribution transformer combination, and the distribution transformer combination is selected for capacity optimization configuration based on the matching difference.

2. The method for optimizing the capacity of distribution transformers considering load conditions according to claim 1, characterized in that: Determine the load status of each distribution transformer as follows: According to the load data and capacity data corresponding to the distribution transformer, calculate the load rate of the distribution transformer at each collection point; If the load rate is not less than the first preset threshold, the collection point is determined to be a heavily overloaded point; if the load rate is less than the second preset threshold, the collection point is determined to be a lightly unloaded point; If there are continuous heavy overload points not less than the third preset threshold value on any day, it is determined that a heavy overload phenomenon occurs on that day; if all the collection points on any day are light no-load points, it is determined that a light no-load phenomenon occurs on that day; If the number of days when the heavy overload phenomenon occurs within the preset period is greater than the first preset ratio, it is determined that the load state of the distribution transformer within the preset period is a heavy overload state; If the light no-load phenomenon occurs every day within the preset period, it is determined that the load state of the distribution transformer within the preset period is the light no-load state.

3. The method for optimizing the capacity of distribution transformers considering load conditions according to claim 1, characterized in that: The average tip load of the distribution transformer under each heavy overload state in the target area is determined as follows: Determine the daily maximum load and the daily second maximum load of the distribution transformer in the heavily overloaded state within a preset period based on the load data of the distribution transformer in the heavily overloaded state; Determine the tip load average value of the distribution transformer in the heavy overload state according to the daily maximum load, the daily second maximum load and the preset period within the preset period; The average daily maximum load of the distribution transformers in each light no-load state in the target area is determined as follows: Determine the daily maximum load of the distribution transformer in the light no-load state within a preset period based on the load data of the distribution transformer in the light no-load state; The average daily maximum load of the distribution transformer in the light no-load state is determined according to the daily maximum load within the preset period and the preset period.

4. The method for optimizing the capacity of distribution transformers considering load conditions according to claim 3, characterized in that: The average value of the tip load of any distribution transformer in a heavily overloaded state and the average daily maximum load of the distribution transformer in the light no-load state Specifically: in, The first i The distribution transformer in the heavily overloaded state is x The maximum daily load of the day, The first i The distribution transformer in the heavily overloaded state is x The second largest load of the day, M is the largest load in the preset period. i The number of days that the distribution transformer in the heavy overload state experiences heavy overload phenomenon, For the i The cumulative value of the daily maximum load and the daily second largest load of the distribution transformer in the heavily overloaded state during the days when the heavy overload phenomenon occurs, N is the preset cycle, The first i The distribution transformer in light no-load state x The maximum daily load of the day.

5. The method for optimizing the capacity of distribution transformers considering load conditions according to claim 1, characterized in that: The specific combinations of multiple distribution transformers are as follows: Based on the first preset combination principle, each heavily overloaded distribution transformer in the target area is combined with each lightly no-loaded distribution transformer in pairs to obtain multiple distribution transformer combinations; The first preset combination principle includes: In each distribution transformer combination, the capacity data of the distribution transformer in a heavily overloaded state is smaller than the capacity data of the distribution transformer in a lightly no-loaded state, and the tip load average value of the distribution transformer in the heavily overloaded state is within a first preset range of the capacity data of the distribution transformer in the lightly no-loaded state, and the maximum load average value of the distribution transformer in the lightly no-loaded state is within a second preset range of the capacity data of the distribution transformer in the heavily overloaded state.

6. The method for optimizing the capacity of distribution transformers considering load conditions according to claim 5, characterized in that: In each distribution transformer combination, the distribution transformers in the heavily overloaded state and the distribution transformers in the lightly no-loaded state in the combination meet the following principles: The capacity data of the distribution transformer in a heavily overloaded state is smaller than that in a lightly no-loaded state; The average value of the tip load of the distribution transformer in the heavy overload state is greater than 20% of the capacity data of the distribution transformer in the light no-load state, and less than 80% of the capacity data of the distribution transformer in the light no-load state; The average value of the maximum load of the distribution transformer in the light no-load state is greater than 20% of the capacity data of the distribution transformer in the heavy overload state, and is less than 80% of the capacity data of the distribution transformer in the heavy overload state.

7. The method for optimizing the capacity of distribution transformers considering load conditions according to claim 1, characterized in that: Based on the tip load average value, daily maximum load average value and capacity data in each distribution transformer combination, the matching difference of each distribution transformer combination is determined. Based on the matching difference, the distribution transformer combination is selected for capacity optimization configuration as follows: Based on the tip load average value, daily maximum load average value and capacity data in each distribution transformer combination, the matching difference of each distribution transformer combination is determined as follows: Determine the capacity matching degree of the heavily overloaded distribution transformer based on the daily maximum load average value of the distribution transformer in the light no-load state and the capacity data of the distribution transformer in the heavily overloaded state in the combination; Determine the light no-load distribution transformer capacity matching degree based on the tip load average value of the distribution transformer in the heavy overload state and the capacity data of the distribution transformer in the light no-load state in the combination; The sum of the capacity matching degree of the heavily overloaded distribution transformer and the capacity matching degree of the lightly unloaded distribution transformer is taken as the matching difference degree of the distribution transformer combination; It is preferred to swap the positions of the distribution transformer in a heavily overloaded state and the distribution transformer in a lightly no-loaded state in the distribution transformer combination with a smaller matching difference value.

8. The method for optimizing the capacity of distribution transformers considering load conditions according to claim 7, characterized in that: In any combination, the capacity matching degree of the heavily overloaded distribution transformer can be calculated by the following formula: in, is the capacity matching degree of the heavily overloaded distribution transformer in the distribution transformer combination. It is the average daily maximum load of the distribution transformer in the light no-load state within the preset period in the distribution transformer combination. It is the capacity data of the distribution transformer in the distribution transformer combination in the heavily overloaded state.

9. The method for optimizing the capacity configuration of distribution transformers considering load conditions according to claim 7, characterized in that: In any combination, the matching degree of light no-load distribution transformer capacity in the distribution transformer combination is The calculation is as follows: in, It is the capacity data of the distribution transformer in the light no-load state in the distribution transformer combination.

10. A system for optimizing the capacity of distribution transformers considering load conditions, characterized in that: include: An acquisition module, which acquires the capacity data of each distribution transformer in the target area and the load data within a preset period; A classification module, which determines the load state of each distribution transformer according to the load data in a preset period and the capacity data of each distribution transformer in the target area; wherein the load state includes a heavy overload state and a light no-load state; A calculation module, determining an average value of the tip load of the distribution transformers in each heavy overload state in the target area, and an average value of the daily maximum load of the distribution transformers in each light no-load state in the target area; A combination module combines the distribution transformers in the heavily overloaded state and the distribution transformers in the lightly no-loaded state in the target area in pairs to obtain multiple distribution transformer combinations; The optimization module determines the matching difference of each distribution transformer combination based on the tip load average value, daily maximum load average value and capacity data in each distribution transformer combination, and selects the distribution transformer combination for capacity optimization configuration based on the matching difference.

Images