A transformer load balancing optimization method based on user time domain load characteristics

Abstract

The application relates to the field of load balancing optimization, and discloses a transformer load balancing optimization method based on user time domain load characteristics, which comprises the following steps: firstly, different power users in a target park are subjected to power consumption test analysis to obtain load fluctuation rules and load characteristic groups of different power users, and target power consumption strategies are generated in combination with the characteristics of flexible resources in different dimensions in the target park; and finally, transformer load balancing optimization is carried out in a high-voltage transformer substation according to the target power consumption strategies. The application can combine the time domain load characteristics of power users in the power consumption process, realize the purpose of transformer load balancing optimization of a high-voltage transformer substation for power supply, improve the utilization rate of equipment, and avoid problems such as power grid redundant investment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of load balancing optimization, and particularly relates to a main transformer load balancing optimization method based on user time domain load characteristics. BACKGROUND

[0002] The main transformer load generally refers to the power or current condition of the main transformer (a transformer used in power plants and substations to deliver power to the power system or users) in the load running state. Such load can be an electrical equipment or other transformer in the power system, which is connected to the power grid through the main transformer and thus consumes power. When the substation outputs load to different power users, if the main transformer load in the substation is unbalanced, the transformers in the substation will bear too high load, resulting in overheating, insulation aging and other problems, and even causing failure or shutdown, while some transformers can be in a low load state, causing resource waste. Through balancing optimization, it can be ensured that each transformer is running within a reasonable load range, avoiding overload and underload phenomena, because overload and underload conditions will affect the power demand of power users, and some power users may not be supplied with enough power. At the same time, balancing the main transformer load can make the best use of power resources in the system, reduce energy waste caused by unbalanced load, and improve power transmission and reduce operating costs. Therefore, a main transformer load balancing optimization method based on user time domain load characteristics is proposed. SUMMARY

[0003] The present application overcomes the shortcomings of the prior art and provides a main transformer load balancing optimization method based on user time domain load characteristics.

[0004] To achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows:

[0005] The present application provides a main transformer load balancing optimization method based on user time domain load characteristics, comprising the following steps:

[0006] Perform power consumption test analysis through the high-voltage substation, and based on the power consumption test analysis results, calculate the load fluctuation law of the load characteristic change test curve of different power users and the load characteristic group;

[0007] Obtain the characteristics of flexible resources in the target park in different dimensions, and combine the characteristics of flexible resources in the target park in different dimensions and the load characteristic group of the load characteristic change test curve to obtain different power dispatching strategies;

[0008] Obtain the target power dispatching strategy, import the target power dispatching strategy into the main transformer load of the high-voltage substation for execution effect analysis, and based on the analysis results, balance and optimize the main transformer load in the high-voltage substation.

[0009] Further, in a preferred embodiment of the present application, the power consumption test analysis through the high-voltage transformer substation is based on the power consumption test analysis results to calculate the load fluctuation law and the load characteristic group of the load characteristic change test curve of different power consumption users, specifically:

[0010] Obtain a target park, wherein the target park includes different power consumption users, and obtain a high-voltage transformer substation, which is a power supply source for the target park;

[0011] A preset power consumption test time is set, and the total load of the high-voltage transformer substation is tested in real time within the power consumption test time, and the total load of the high-voltage transformer substation is analyzed to determine the load received by different power consumption users;

[0012] The parameters of the load received by different power consumption users are converted into a time sequence format, and based on the time sequence format of the load received by different power consumption users, a load characteristic change test curve of different power consumption users is constructed, and load characteristic indexes are extracted in the load characteristic change test curve, wherein the load characteristic indexes include peak load values, valley load values, and average load values in the load characteristic change test curve.

[0013] Based on the load characteristic indexes, the load fluctuation range of the load characteristic change test curve is calculated, and based on the load fluctuation range of the load characteristic change test curve, the load fluctuation law of the load characteristic change test curve is calculated, wherein the load fluctuation law of the load characteristic change test curve includes periodic change law and seasonal change law.

[0014] The peak load points on the load characteristic change test curve are calculated and marked, wherein the peak load points are time periods in which the load values in the load characteristic change test curve are greater than the average load values.

[0015] The peak load points on the load characteristic change test curve of all power consumption users are regularly analyzed, and based on the regular analysis results, a load characteristic group of the load characteristic change test curve of different power consumption users is generated.

[0016] Further, in a preferred embodiment of the present application, the peak load points on the load characteristic change test curve of all power consumption users are regularly analyzed, and based on the regular analysis results, a load characteristic group of the load characteristic change test curve of different power consumption users is generated, specifically:

[0017] A preset maximum threshold of the number of occurrences of the peak load points is set, and the peak load points on the load characteristic change test curve of the power consumption users are analyzed to calculate the time domain coincidence of the peak load points in the load characteristic change test curve within the power consumption test time.

[0018] The peak load time domain coincidence represents whether the number of occurrences of the peak load point is greater than a maximum threshold value of the number of occurrences.

[0019] The peak load time domain deviation of the load characteristic change test curve is calculated in the load characteristic change test curve in the power consumption test time, and the peak load time domain deviation is the difference between the load value at the peak load point and the average load value.

[0020] The peak load time domain coincidence and the peak load time domain deviation of the load characteristic change test curve are collectively referred to as the load characteristic group of the load characteristic change test curve.

[0021] Further, in a preferred embodiment of the present application, the characteristics of the flexible resources in the target park in different dimensions are obtained, and the different power consumption scheduling strategies are obtained by combining the characteristics of the flexible resources in the target park in different dimensions and the load characteristic group of the load characteristic change test curve, specifically:

[0022] In the target park, all flexible resources are obtained, wherein the all flexible resources include distributed new energy, distributed energy storage energy and adjustable load.

[0023] The power consumption data of all flexible resources are collected, and the power consumption data of all flexible resources are preprocessed, wherein the data preprocessing includes data cleaning and data standardization processing of the power consumption data of the flexible resources, to obtain the preprocessed power consumption data of the flexible resources.

[0024] Based on the preprocessed power consumption data of the flexible resources, the time characteristic analysis and the space characteristic analysis of the flexible resources are performed, the adjustment characteristics and the distribution characteristics of the flexible resources in the time dimension are obtained, and the adjustment characteristics and the distribution characteristics of the flexible resources in the space dimension are obtained.

[0025] The adjustment characteristics and the distribution characteristics of the flexible resources in the time dimension and the space dimension are combined, and the load characteristic change test curves of different users are obtained, to obtain the power consumption complementary characteristics of the target park.

[0026] A big data network is obtained, and based on the big data network, the high-voltage substation based on the power consumption complementary characteristics of the target park is searched, the total load generated in the target park is scheduled, the load scheduling strategy of the generated load acting on different power consumption users is calibrated as the power consumption scheduling strategy.

[0027] Further, in a preferred embodiment of the present application, the target power consumption scheduling strategy is introduced into the main transformer load of the high-voltage substation for execution effect analysis, and based on the analysis result, the main transformer load in the high-voltage substation is balanced and optimized, specifically:

[0028] Obtain the electricity planning of the target park, introduce a grey correlation method to calculate the correlation value between different electricity scheduling strategies and the electricity planning of the target park, and label it as a correlation value;

[0029] A preset correlation value threshold is selected, and the corresponding electricity scheduling strategy of the correlation value within the correlation value threshold is selected as the target electricity scheduling strategy;

[0030] A preset load output time is selected, and the total load value range of the high-voltage substation under the target electricity scheduling strategy is calculated, and the total load value range of the high-voltage substation under the target electricity scheduling strategy is calculated.

[0031] Obtain the load state parameters of the high-voltage substation under the target electricity scheduling strategy, and calculate the actual total load value of the high-voltage substation under the target electricity scheduling strategy based on the load state parameters of the high-voltage substation under the target electricity scheduling strategy.

[0032] If the actual total load value is maintained within the standard output total load value range, the main transformer load in the high-voltage substation is labeled as a qualified main transformer load.

[0033] The qualified main transformer load is balanced and optimized, so that the execution effect of the qualified main transformer load under the target electricity scheduling strategy meets the expected value.

[0034] Further, in a preferred embodiment of the present application, the qualified main transformer load is balanced and optimized, so that the execution effect of the qualified main transformer load under the target electricity scheduling strategy meets the expected value, specifically:

[0035] A main transformer load control device is connected to the qualified main transformer load, and the main transformer load control device can adjust the output load of the qualified main transformer load;

[0036] The target electricity scheduling strategy is introduced into the main transformer load control device, and the qualified main transformer load is run, and the load fluctuation law of different electricity users receiving load is calculated in real time during the running of the qualified main transformer load, and the similarity between the load fluctuation law of the electricity user receiving load and the load fluctuation law of the load characteristic change test curve is calculated, and the similarity is labeled as the load fluctuation law similarity;

[0037] A preset load fluctuation law similarity threshold is selected, and if the load fluctuation law similarity of all electricity users is maintained within the load fluctuation law similarity threshold, the qualified main transformer load does not need to be balanced and optimized;

[0038] If the load fluctuation regularity similarity of the power users does not maintain within the load fluctuation similarity threshold, the main transformer load control device is intelligently output load adjusted, so that the load fluctuation regularity similarity of all power users of the qualified main transformer load is maintained within the load fluctuation similarity threshold during operation.

[0039] The second aspect of the present application also provides a main transformer load balancing optimization system based on user time domain load characteristics, which comprises a memory and a processor, and the memory stores a main transformer load balancing optimization method.

[0040] Through power consumption test analysis of the high-voltage substation, the load fluctuation regularity and the load characteristic group of the load characteristic change test curve of different power users are calculated based on the power consumption test analysis results.

[0041] The characteristics of the flexible resources in the target park in different dimensions are obtained, and the different power dispatching strategies are obtained by combining the characteristics of the flexible resources in the target park in different dimensions and the load characteristic group of the load characteristic change test curve.

[0042] The target power dispatching strategy is obtained, the target power dispatching strategy is introduced into the main transformer load of the high-voltage substation for execution effect analysis, and the main transformer load in the high-voltage substation is balanced and optimized based on the analysis results.

[0043] The technical defects in the background art are solved, and the present application has the following beneficial effects: first, the power consumption of different power users in the target park is tested and analyzed to obtain the load fluctuation regularity and the load characteristic group of different power users, and the target power strategy is generated by combining the characteristics of the flexible resources in the target park in different dimensions. Finally, the main transformer load balancing optimization in the high-voltage substation is performed according to the target power strategy. The present application can combine the time domain load characteristics of the power users in the power consumption process to achieve the purpose of main transformer load balancing optimization of the high-voltage substation for power supply, improve the utilization rate of the equipment, and avoid problems such as redundant investment of the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings of embodiments according to these drawings without creative labor.

[0045] Figure 1 A flowchart of a main transformer load balancing optimization method based on user time domain load characteristics is shown.

[0046] Figure 2 A method flow chart for balancing optimization of main transformer load in a high-voltage substation is shown.

[0047] Figure 3 A program view of a main transformer load balancing optimization system based on user time domain load characteristics is shown. DETAILED DESCRIPTION

[0048] In order to enable a more clear understanding of the above-mentioned objects, features and advantages of the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0049] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, however, the present application can be practiced in other manners different from those described herein, therefore, the scope of protection of the present application is not limited by the specific embodiments disclosed below.

[0050] Figure 1 A flow chart of a main transformer load balancing optimization method based on user time domain load characteristics is shown, including the following steps:

[0051] S102: Perform power consumption test analysis through the high-voltage substation, and based on the power consumption test analysis result, calculate the load fluctuation law and load characteristic group of the load characteristic change test curve of different power consumption users;

[0052] S104: Obtain the characteristics of flexible resources in the target park in different dimensions, and combine the characteristics of flexible resources in the target park in different dimensions and the load characteristic group of the load characteristic change test curve to obtain different power consumption scheduling strategies;

[0053] S106: Obtain the target power consumption scheduling strategy, import the target power consumption scheduling strategy into the main transformer load of the high-voltage substation for execution effect analysis, and based on the analysis result, balance and optimize the main transformer load in the high-voltage substation

[0054] Further, in a preferred embodiment of the present application, the power consumption test analysis through the high-voltage substation, based on the power consumption test analysis result, calculating the load fluctuation law and load characteristic group of the load characteristic change test curve of different power consumption users, specifically:

[0055] Obtain a target park, wherein the target park includes different power consumption users, and obtain a high-voltage substation, which is the power supply source of the target park;

[0056] A preset power consumption test time is set, during which the total load of the high-voltage transformer substation is tested in real time, and the total load of the high-voltage transformer substation is analyzed to determine the loads received by different power consumption users;

[0057] The parameters of the loads received by different power consumption users are converted into a time sequence format, and based on the loads received by different power consumption users in the time sequence format, a load characteristic change test curve of different power consumption users is constructed, and a load characteristic index is extracted in the load characteristic change test curve, wherein the load characteristic index includes a peak load value, a valley load value and an average load value in the load characteristic change test curve.

[0058] Based on the load characteristic index, a load fluctuation range of the load characteristic change test curve is calculated, and based on the load fluctuation range of the load characteristic change test curve, a load fluctuation law of the load characteristic change test curve is calculated, wherein the load fluctuation law of the load characteristic change test curve includes a periodic change law and a seasonal change law.

[0059] A peak load point is calculated and marked on the load characteristic change test curve, wherein the peak load point is a period in which the load value in the load characteristic change test curve is greater than the average load value.

[0060] It should be noted that there are multiple different types of users in the target park, and different users need to consume electricity, but the electricity consumption levels of different users are different, so the loads of the transformer substations in the park when supplying power to different users are also different. Therefore, the main transformer load needs to be adjusted according to the electricity consumption level of the user, so that the load when supplying power to different users can meet the needs of the user. Before adjusting the main transformer load, the law of the load consumed by different users needs to be determined, so the electricity consumption of different users needs to be tested to determine the law of the electricity consumption of different users. First, the total load provided by the high-voltage transformer substation is determined, and based on the total load, the loads received by different users during the electricity consumption test time can be calculated, and a load characteristic change test curve of different users is constructed. To determine the load amount change trend with time, the parameters of the loads received by different power consumption users need to be converted into a time sequence format to facilitate the analysis of the load characteristics of different industrial users from the time domain. Based on the load characteristic change test curve, the load fluctuation law can be calculated, and the peak load point can be determined. The peak load point can be used to determine different load characteristic groups in the load characteristic change test curve, such as peak load time domain coincidence and peak load time domain deviation.

[0061] Further, in a preferred embodiment of the present application, the peak load point on the load characteristic change test curve of all power consumption users is regularly analyzed, and based on the regular analysis result, a load characteristic group of the load characteristic change test curve of different power consumption users is generated, specifically:

[0062] a preset maximum threshold of the number of occurrences of the peak load point, and analyzing the peak load point on the load characteristic change test curve of the power consumption user, and calculating the peak load time domain coincidence in the load characteristic change test curve within the power consumption test time;

[0063] The peak load time domain coincidence represents whether the number of occurrences of the peak load point is greater than the maximum threshold of the number of occurrences.

[0064] On the load characteristic change test curve of the power consumption user, the peak load time domain deviation of the load characteristic change test curve within the power consumption test time is calculated, wherein the peak load time domain deviation is the difference between the load value at the peak load point and the average load value.

[0065] The peak load time domain coincidence and the peak load time domain deviation of the load characteristic change test curve are collectively referred to as a load characteristic group of the load characteristic change test curve.

[0066] It should be noted that the peak load time domain coincidence is that multiple peak load points frequently occur in a certain time period, forming a coincidence phenomenon in the time domain. The peak load time domain coincidence helps to identify the key load period in the power system, and provides a basis for formulating power supply plans and adjusting power generation strategies, so it needs to be determined. The peak load time domain deviation is the degree of change of the peak load point from the expected compliance level. In general, the causes of deviation include but are not limited to sudden increase in power demand, equipment failure, weather changes, etc. The peak load time domain deviation can evaluate the stability and resilience of the power system, as well as the ability to respond to emergencies. After determining the peak load time domain coincidence and the peak load time domain deviation of the load characteristic change test curve of different power consumption users, the load characteristic group of the load characteristic change test curve can be obtained. The load characteristic group can be applied to balance optimization of the main transformer load.

[0067] Further, in a preferred embodiment of the present application, the characteristics of the flexible resources in the target park in different dimensions are obtained, and the different power consumption scheduling strategies are obtained by combining the characteristics of the flexible resources in the target park in different dimensions and the load characteristic group of the load characteristic change test curve, specifically:

[0068] In the target park, all flexible resources are obtained, wherein the all flexible resources include distributed new energy, distributed energy storage and adjustable load.

[0069] The power consumption data of all flexible resources are collected, and the power consumption data of all flexible resources are preprocessed, wherein the data preprocessing includes data cleaning and data standardization processing of the power consumption data of the flexible resources, to obtain preprocessed power consumption data of the flexible resources.

[0070] Based on the pre-processed flexible resource electricity data, time characteristic analysis and space characteristic analysis are performed on the flexible resource to obtain the adjustment characteristics and distribution characteristics of the flexible resource in the time dimension and the space dimension;

[0071] The adjustment characteristics and distribution characteristics of the flexible resource in the time dimension and the space dimension are combined with the load characteristic change test curve of different users to obtain target park electricity complementary characteristics;

[0072] A big data network is obtained, and based on the big data network, a high-voltage substation is searched based on target park electricity complementary characteristics, the total load generated in the target park is dispatched, the load scheduling strategy of the generated load acting on different electricity users is obtained, and the load scheduling strategy is calibrated as an electricity scheduling strategy.

[0073] It should be noted that the flexible resource refers to distributed new energy, distributed energy storage energy and adjustable load in the park. The traditional flexible resource refers to a resource that can improve the dynamic balance of the energy supply and demand system and realize the flexibility and elasticity of the system energy supply and demand level. Its main object is a power supply that has the ability to quickly adjust the output power and can be directly controlled and dispatched. After the load is transmitted to the flexible resource for load processing by the high-voltage substation, the load is transmitted to different electricity users. The electricity data of the flexible resource is collected and pre-processed, and the purpose is to unify the data in different flexible resources to facilitate subsequent processing. It is necessary to obtain the adjustment characteristics and distribution characteristics of the flexible resource in the time dimension and the space dimension, which reflects the flexibility of the flexible resource in the active distribution network and the volatility and uncertainty of renewable energy, and also reflects the characteristics of the flexible resource affected by the time sequence characteristics of the active distribution network and its own time sequence characteristics. By combining the adjustment characteristics and distribution characteristics of the flexible resource in the time dimension and the space dimension with the load characteristic change test curve of different users, the complementary characteristics can be determined. The complementary characteristics represent the relationship between the load growth rule of the users in the park and the characteristics of the flexible resource, and the complementary characteristics can be used to generate different electricity scheduling strategies. Among them, the purpose of the electricity scheduling strategy may be different, some of which is to try to be green and low carbon, and some of which is to maximize economic benefits in electricity scheduling. The big data network is a database that stores various electricity-related schemes.

[0074] Figure 2 A flowchart of a method for balancing and optimizing the main transformer load in the high-voltage substation is shown, which includes the following steps:

[0075] S202: Calculate the correlation value between the electricity planning of the target park and different electricity scheduling strategies by the grey correlation method, and determine the target electricity scheduling strategy based on the correlation value size;

[0076] S204: Calculate the actual total load value of the high-voltage substation output in the load output time.

[0077] Determine the qualified main transformer load;

[0078] S206: Balanced optimization is performed on the qualified main transformer load, so that the execution effect of the qualified main transformer load when executing the target power consumption scheduling strategy meets the expected value.

[0079] Further, in a preferred embodiment of the present application, the actual total load value of the high-voltage substation output in the load output time is calculated. The qualified main transformer load is determined, specifically:

[0080] A load output time is preset. In the load output time, the total load value range that the high-voltage substation needs to output after performing load output on all power consumption users under the target power consumption scheduling strategy is calculated, and is marked as the standard output total load value range.

[0081] The load state parameters of the high-voltage substation under the target power consumption scheduling strategy are obtained, and the actual total load value of the high-voltage substation output in the load output time is calculated based on the load state parameters of the high-voltage substation under the target power consumption scheduling strategy, and is marked as the actual output total load value.

[0082] If the actual output total load value is maintained within the standard output total load value range, the main transformer load in the high-voltage substation is marked as the qualified main transformer load.

[0083] It should be noted that in the high-voltage substation, there is a main transformer load, which refers to the main transformer, i.e. the load condition of the transformer in the power plant and the substation for delivering power to the power system or users during operation. Before balanced optimization is performed on the main transformer load, it is necessary to determine whether the main transformer load is qualified, i.e. whether the total load value of the main transformer load meets the standard requirement. The main transformer load can only be balanced and optimized when it is qualified, otherwise the main transformer load cannot achieve load processing. Under the target power consumption scheduling strategy, it is judged whether the actual total load value of the high-voltage substation output in the load output time is maintained within the standard value. If so, it is proved that the high-voltage substation is a qualified main transformer load.

[0084] Further, in a preferred embodiment of the present application, the actual total load value of the high-voltage substation output in the load output time is calculated. The qualified main transformer load is determined, specifically:

[0085] A main transformer load control device is connected in the qualified main transformer load, and the main transformer load control device can adjust the output load of the qualified main transformer load;

[0086] The target electricity scheduling strategy is introduced into the main transformer load control device, and the qualified main transformer load is run, and the fluctuation law of the load when different electricity users receive the load is calculated in real time during the running of the qualified main transformer load, and the similarity between the fluctuation law of the load when the electricity users receive the load and the fluctuation law of the load of the load characteristic change test curve is calculated, and is calibrated as the similarity of the load fluctuation law;

[0087] A preset load fluctuation law similarity threshold is set, and if the load fluctuation law similarity of all electricity users is maintained within the load fluctuation similarity threshold, the load balancing optimization of the qualified main transformer load is not needed;

[0088] If the load fluctuation law similarity of the electricity users is not maintained within the load fluctuation similarity threshold, the intelligent output load adjustment of the main transformer load control device is performed, so that the load fluctuation law similarity of all electricity users is maintained within the load fluctuation similarity threshold during the running of the qualified main transformer load.

[0089] It should be noted that after the qualified main transformer load is determined, the balancing optimization of the main transformer load is realized through the main transformer load control device according to the electricity law of the electricity users and the electricity data of all flexible resources. When the target electricity scheduling strategy acts on the qualified main transformer load, the load fluctuation law of different electricity users when receiving the load should be similar to the load fluctuation law of the load characteristic change test curve during the electricity test, and the similarity between the two is calculated. When the two are similar, the balancing optimization of the main transformer load is not needed, because the load fluctuation law of the load characteristic change test curve is the most suitable load fluctuation law of different electricity users after the test, so the balancing optimization of the main transformer load is needed, so that the actual output load fluctuation law is equal to the load fluctuation law of the load characteristic change test curve when the target electricity scheduling strategy is met. The intelligent output load adjustment of the main transformer load control device can realize the balancing optimization purpose of the main transformer load.

[0090] In addition, the main transformer load balancing optimization method based on the time domain load characteristics of the user further includes the following steps:

[0091] When the qualified main transformer load is intelligently adjusted by the main transformer load control device, if the load fluctuation law similarity of the electricity users is not maintained within the load fluctuation similarity threshold, the qualified main transformer load is calibrated as a to-be-detected qualified main transformer load;

[0092] When the main transformer load in the high-voltage transformer station is the to-be-detected qualified main transformer load, the wiring mode between different power equipment connected with the to-be-detected qualified main transformer load in the high-voltage transformer station is obtained, and the electrical parameter range of different power equipment in the high-voltage transformer station is obtained;

[0093] A mathematical model of the high-voltage substation is established based on the wiring mode between different power equipment and the electrical parameter range of the power equipment, wherein different nodes and corresponding node parameters exist in the mathematical model of the high-voltage substation, and the node parameters reflect the electrical parameters of the power equipment;

[0094] The node parameters of different nodes in the mathematical model of the high-voltage substation are calculated by a power flow calculation software, and a node whose node parameter is not within the electrical parameter range of the corresponding power equipment is marked as an abnormal node, and the node parameter of the abnormal node is obtained;

[0095] The power equipment corresponding to the abnormal node in the high-voltage substation is determined as an abnormal power equipment, and the abnormal position in the abnormal power equipment is determined based on the node parameter of the abnormal node;

[0096] A correction scheme of the abnormal position in the abnormal power equipment is retrieved based on a big data network and outputted, so that no abnormal node exists in all power equipment, and the intelligent output load adjustment of the to-be-detected qualified main transformer load is continued by a main transformer load control device, so that the similarity of the load fluctuation rules of all power users is maintained within the load fluctuation similarity threshold.

[0097] It should be noted that if the load fluctuation rule similarity of the power users is not maintained within the load fluctuation similarity threshold during the balancing and optimization of the qualified main transformer load, it is proved that the power equipment connected with the qualified main transformer load may have a problem, which leads to that even if the main transformer load is balanced and optimized, the load fluctuation rules of all power users cannot be within the preset range. Therefore, the power equipment needs to be subjected to power flow analysis to determine whether there is an abnormal node in the power equipment, and if so, it is determined that there is a fault in the power equipment that needs to be repaired. The power flow analysis can realize power flow calculation by using ETAP or the like to obtain electrical parameters such as voltage and power of each node, compare the electrical parameters with a standard range to obtain an abnormal node, obtain an abnormal position of the corresponding power equipment based on the abnormal node, and finally retrieve a repair scheme of the abnormal position by a big data network and output the repair scheme, so that all power equipment connected with the qualified main transformer load in the high-voltage substation is normal, the load of the main transformer load is adjusted, and the similarity of the load fluctuation rules of all power users is maintained within the load fluctuation similarity threshold.

[0098] As shown in Figure 3 The second aspect of the present application also provides a main transformer load balancing and optimization system based on user time domain load characteristics, which comprises a memory 31 and a processor 32, and the memory 31 stores a main transformer load balancing and optimization method. When the main transformer load balancing and optimization method is executed by the processor 32, the following steps are implemented:

[0099] The load fluctuation law and the load characteristic group of the load characteristic change test curve of different power users are calculated based on the power consumption test analysis result of the high-voltage transformer substation;

[0100] The characteristics of the flexible resources in different dimensions in the target park are obtained, and the different power dispatching strategies are obtained in combination with the characteristics of the flexible resources in different dimensions in the target park and the load characteristic group of the load characteristic change test curve;

[0101] The target power dispatching strategy is obtained, the target power dispatching strategy is introduced into the main transformer load of the high-voltage transformer substation for execution effect analysis, and the main transformer load in the high-voltage transformer substation is balanced and optimized based on the analysis result.

[0102] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A main transformer load balancing optimization method based on user time-domain load characteristics, characterized in that, Includes the following steps: By conducting power consumption test analysis at high-voltage substations, and based on the results of the power consumption test analysis, the load fluctuation law and load characteristic group of different power users' load characteristic change test curves are calculated. The characteristics of flexible resources within the target park under different dimensions are obtained, and different power dispatch strategies are obtained by combining the characteristics of flexible resources within the target park under different dimensions with the load characteristic change test curve. The target power consumption scheduling strategy is obtained, and then imported into the main transformer load of the high-voltage substation for execution effect analysis. Based on the analysis results, the main transformer load in the high-voltage substation is balanced and optimized. Specifically: Obtain the electricity consumption plan of the target park, and introduce the grey relational analysis method to calculate the correlation value between different electricity dispatch strategies and the electricity consumption plan of the target park, and label them as a type of correlation value; Select a type of power dispatching strategy whose correlation value is within the correlation value threshold and label it as the target power dispatching strategy; During the load output time, calculate the range of total load values ​​that the high-voltage substation needs to output after outputting load to all electricity users under the target power dispatch strategy, and calibrate it as the standard output total load value range. Obtain the load status parameters of the high-voltage substation under the target power dispatch strategy, and calculate the actual total load value output by the high-voltage substation during the load output time based on the load status parameters of the high-voltage substation under the target power dispatch strategy, and calibrate it as the actual total output load value. If the actual total output load value remains within the range of the standard total output load value, then the main transformer load in the high-voltage substation will be calibrated as a qualified main transformer load. Balance and optimize the load of qualified main transformers so that the performance of qualified main transformer loads when executing the target power dispatch strategy meets the expected value.

2. The main transformer load balancing optimization method based on user time-domain load characteristics as described in claim 1, characterized in that, The process involves conducting power consumption testing and analysis at a high-voltage substation. Based on the results of this analysis, the load fluctuation patterns and load characteristic groups of different power users are calculated from their load characteristic change test curves. Specifically: The target industrial park is identified, which includes different electricity users, and a high-voltage substation is identified, which is the power source for the target industrial park. A preset power consumption test time is set. During the power consumption test time, the total load of the high-voltage substation is tested in real time, and the total load of the high-voltage substation is analyzed to determine the load received by different power users. The parameter format of the load received by different electricity users is converted into a time series format. Based on the load received by different electricity users in the time series format, load characteristic change test curves of different electricity users are constructed, and load characteristic indicators are extracted from the load characteristic change test curves. The load characteristic indicators include the peak load value, valley load value and average load value in the load characteristic change test curves. Based on the load characteristic index, the load fluctuation range of the load characteristic change test curve is calculated. Based on the load fluctuation range of the load characteristic change test curve, the load fluctuation pattern of the load characteristic change test curve is calculated. The load fluctuation pattern of the load characteristic change test curve includes periodic change pattern and seasonal change pattern. Calculate and mark peak load points on the load characteristic change test curve, wherein the peak load points are the periods in the load characteristic change test curve where the load value is greater than the average load value; The peak load points on the load characteristic change test curves of all electricity users are analyzed for patterns, and load characteristic groups of different electricity users are generated based on the results of the pattern analysis.

3. The main transformer load balancing optimization method based on user time-domain load characteristics as described in claim 2, characterized in that, The process involves analyzing the patterns of peak load points on the load characteristic change test curves of all electricity users, and generating load characteristic groups for different electricity users based on the analysis results. Specifically: The maximum threshold for the occurrence of peak load points is preset, and the peak load points are analyzed on the load characteristic change test curve of the electricity user. The overlap of peak loads in the time domain of the load characteristic change test curve during the electricity consumption test time is calculated. Wherein, the overlap of peak load time domain represents whether the number of times the peak load point occurs is greater than the maximum threshold of the number of occurrences; On the load characteristic change test curve of the electricity user, calculate the peak load time domain deviation of the load characteristic change test curve during the electricity test time, wherein the peak load time domain deviation is the difference between the load value at the peak load point and the average load value. The coincidence of peak load in the time domain and the deviation of peak load in the time domain of the load characteristic change test curve are collectively referred to as the load characteristic group of the load characteristic change test curve.

4. The main transformer load balancing optimization method based on user time-domain load characteristics as described in claim 1, characterized in that, The process involves acquiring the characteristics of flexible resources within the target park under different dimensions, and combining these characteristics with the load characteristic group from the load characteristic change test curve to obtain different power dispatch strategies. Specifically: Within the target park, acquire all flexible resources, including distributed new energy sources, distributed energy storage, and adjustable loads; Collect electricity consumption data of all flexible resources and perform data preprocessing on the electricity consumption data of all flexible resources. The data preprocessing includes data cleaning and data standardization of the electricity consumption data of flexible resources to obtain preprocessed electricity consumption data of flexible resources. Based on the preprocessed electricity consumption data of flexible resources, time and spatial characteristic analysis is performed on the flexible resources to obtain the regulation and distribution characteristics of flexible resources in the time dimension and in the spatial dimension. By combining the adjustment and distribution characteristics of flexible resources in the time and space dimensions, as well as the load characteristic change test curves of different users, the electricity complementary characteristics of the target park are obtained. The system acquires a big data network and, based on this network, retrieves information about high-voltage substations that, based on the complementary power consumption characteristics of the target area, schedule the total load generated within the target area, and implement load scheduling strategies that apply the generated load to different power users. These strategies are then labeled as power consumption scheduling strategies.

5. The main transformer load balancing optimization method based on user time-domain load characteristics as described in claim 1, characterized in that, The aforementioned balancing optimization of qualified main transformer loads, ensuring that the performance of the qualified main transformer loads in implementing the target power dispatch strategy meets the expected value, specifically involves: A main transformer load control device is connected to a qualified main transformer load, and the main transformer load control device is capable of regulating the output load of the qualified main transformer load. The target power dispatch strategy is imported into the main transformer load control device, and a qualified main transformer load is run. During the operation of the qualified main transformer load, the load fluctuation pattern when different power users receive load is calculated in real time, and the similarity between the load fluctuation pattern when power users receive load and the load fluctuation pattern of the load characteristic change test curve is calculated and calibrated as the load fluctuation pattern similarity. If the load fluctuation pattern similarity threshold is preset, and the load fluctuation pattern similarity of all electricity users is maintained within the load fluctuation similarity threshold, then there is no need to perform load balancing optimization on qualified main transformer loads. If the load fluctuation similarity of some electricity users does not remain within the load fluctuation similarity threshold, the main transformer load control device will perform intelligent output load adjustment to ensure that the load fluctuation similarity of all electricity users remains within the load fluctuation similarity threshold during the operation of the qualified main transformer load.

6. A main transformer load balancing optimization system based on user time-domain load characteristics, characterized in that, The transformer load balancing optimization system includes a memory and a processor. The memory stores a transformer load balancing optimization method program. When the transformer load balancing optimization method program is executed by the processor, the transformer load balancing optimization method steps as described in any one of claims 1-5 are implemented.

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