A three-phase imbalance optimization control method and device for intelligent transformer area

Abstract

The application discloses the technical field of power distribution network control, and discloses a kind of intelligent district three-phase unbalance optimization regulation and control method and device, including verifying the integrity of received district operation data, if district operation data meets preset data integrity condition, then verify whether district operation data meets preset regulation and control strategy update condition, if meet, then strategy update is carried out using genetic algorithm, and regulation and control are carried out using updated district regulation and control strategy;The application is based on genetic algorithm to the district operation data of received intelligent district is analyzed and calculated periodically to formulate district regulation and control strategy, can be adjusted in time according to the power consumption law of district user control, improve the regulation and control capability of intelligent district, solve the technical problem that existing regulation and control capability is insufficient when relying on experience method to regulate and control intelligent district.

Description

Technical Field

[0001] This invention relates to the field of power distribution network control technology, and in particular to a method and device for optimizing and controlling three-phase imbalance in intelligent distribution areas. Background Technology

[0002] Energy loss occurs at every stage of the power grid, from generation and transmission to distribution. Low-voltage distribution networks, directly connected to users, account for over 60% of total grid losses due to their wide coverage, numerous distribution devices, and complex, dense lines. How to technically reduce losses at the distribution network level and lower its carbon emissions has always been a key focus and research challenge for the power industry. To address this issue, scholars and power industry professionals both domestically and internationally have proposed various loss reduction measures for low-voltage distribution networks, such as shortening low-voltage power supply line lengths, reactive power compensation, balancing three-phase loads, using energy-saving transformers, rationally configuring transformer capacity, and using large-diameter wire circuits. In three-phase four-wire low-voltage distribution networks with significant three-phase current imbalance, balancing the three-phase load is a breakthrough direction for effective loss reduction. Asymmetrical three-phase loads in the distribution network generate unbalanced currents in the system, leading to increased active power losses in transformers and lines under unbalanced operating conditions, reduced transformer output capacity, and decreased power quality. System three-phase imbalance not only increases transformer load losses but also affects transformer no-load losses.

[0003] According to the "Standards for Operation of Medium and Low Voltage Power Distribution," the three-phase load of a transformer should be balanced as much as possible, with an imbalance degree not exceeding 15%. To address the problem of three-phase load imbalance in a distribution transformer area, it is necessary to use relevant methods to rationally adjust the phase sequence of the electrical load among phases A, B, and C, ensuring a balanced distribution of the load across these three phases. Due to the randomness and uncertainty of electrical load, three-phase load imbalance occurs in real-time. Relying on manual adjustments based on the actual load imbalance is impossible and poses certain safety hazards. Offline adjustments to the load distribution on each phase of the low-voltage line will inevitably affect the reliability of power supply to users.

[0004] In recent years, with the widespread application of low-voltage intelligent equipment such as smart terminals and smart meters, the power sensing capabilities of low-voltage distribution networks have been continuously improved. However, existing intelligent distribution areas mainly remain at the level of observation and measurability, with generally low intelligent control capabilities. The handling of common problems such as single-phase overload tripping, three-phase imbalance, and low voltage at the end of the distribution network primarily relies on the knowledge and experience of personnel. This method is labor-intensive and suffers from problems such as disorder and lag. It fails to fully utilize the advantages brought by the modernization and intelligent development of distribution networks. How to fully utilize operational sensing data at the distribution network level to achieve efficient and accurate handling of common distribution network problems has always been a key focus and research challenge for the power industry. Summary of the Invention

[0005] This invention provides a method and device for optimizing and controlling three-phase imbalance in intelligent distribution transformer areas, which solves the technical problem that existing intelligent distribution transformer areas mainly rely on the knowledge and experience of practitioners for control, resulting in insufficient control capabilities.

[0006] The first aspect of this invention provides a method for optimizing and controlling three-phase imbalance in intelligent distribution transformer areas, comprising:

[0007] Verify the integrity of the received station operation data;

[0008] If the operating data of the transformer area meets the preset data integrity condition, then verify whether the operating data of the transformer area meets the preset control strategy update condition;

[0009] If the conditions are met, a genetic algorithm is used to update the strategy, and the updated control strategy for the transformer area is then used for control.

[0010] Optionally, the preset data integrity condition is that the integrity of the operating data of the transformer area within the preset data collection period is greater than or equal to the preset integrity threshold.

[0011] Optionally, the transformer area operation data includes load rate, three-phase imbalance and neutral current, and the preset control strategy update conditions are that the load rate is greater than a preset debt ratio threshold, the three-phase imbalance is greater than a preset imbalance threshold, and the neutral current is greater than a preset current threshold.

[0012] Optionally, the strategy update using a genetic algorithm includes:

[0013] Users in the distribution area are coded with chromosomes, where each chromosome represents a user's phase sequence combination;

[0014] Randomly generate an initial chromosome population;

[0015] The fitness function is composed of the user phase sequence combination and the three-phase current imbalance, and the fitness of each chromosome in the initial chromosome population is calculated based on the fitness function.

[0016] Based on the fitness, genetic operations are performed on the initial chromosome population until the required number of iterations is met. Then, the chromosome corresponding to the maximum fitness value is output as the updated control strategy for the transformer area.

[0017] Optionally, the chromosome encoding of users in the substation area includes:

[0018] Establish a set of random numbers for users that matches the number of low-voltage users;

[0019] Based on the preset random number interval in the user random number set, the user phase sequence combination corresponding to the random number is determined.

[0020] Optionally, performing genetic operations on the initialized chromosome population based on the fitness includes:

[0021] A selection operation is performed on the initial chromosome population based on multiple fitness values;

[0022] Based on the random crossover numbers generated for each chromosome in the initialized chromosome population, the crossover operation is performed;

[0023] Based on the initialized chromosome population after the crossover operation, random mutation numbers are randomly generated for each chromosome, and the mutation operation is performed.

[0024] Optionally, performing a selection operation on the initial chromosome population based on a plurality of said fitness values ​​includes:

[0025] The fitness sum is obtained by performing a summation operation on the fitness of all chromosomes in the initial chromosome population.

[0026] The selection probability value corresponding to each chromosome is obtained by performing a ratio calculation between the fitness of each chromosome and the sum of fitness values;

[0027] Compare each selected probability value with a preset standard probability threshold;

[0028] If the selection probability value is greater than the preset standard probability threshold, then the chromosome associated with the selection probability value will be retained.

[0029] If the selection probability value is less than or equal to the preset standard probability threshold, then the chromosome associated with the selection probability value is replaced.

[0030] Optionally, the step of generating random crossover numbers based on each chromosome in the initialized chromosome population and performing the crossover operation includes:

[0031] Randomly generate crossover random numbers for each chromosome within the initialized chromosome population;

[0032] Compare each of the stated crossover random numbers with the preset crossover probability value;

[0033] If the crossover random number is greater than or equal to the preset crossover probability value, then the chromosome associated with the crossover random number is copied into the new initialized chromosome population;

[0034] If the crossover random number is less than the preset crossover probability value, then the chromosome associated with the crossover random number will undergo gene exchange, and the gene-exchanged chromosome will be copied into the new initialized chromosome population.

[0035] Optionally, the step of randomly generating mutated random numbers for each chromosome in the initialized chromosome population after the crossover operation, and then performing the mutation operation, includes:

[0036] Randomly generate the mutation random number for each gene of each chromosome in the initialized chromosome population after the crossover operation is performed;

[0037] Compare each of the aforementioned mutated random numbers with the preset mutated probability value;

[0038] If the mutated random number is less than the preset mutation probability value, then the chromosome associated with the mutated random number is maintained;

[0039] If the mutated random number is greater than or equal to the preset mutation probability value, then the gene in the chromosome associated with the mutated random number will be mutated.

[0040] The second aspect of the present invention provides an intelligent three-phase imbalance optimization and control device for a distribution area, the control device comprising a communication unit, an ARM core processing unit, and a storage unit connected in sequence.

[0041] The communication unit is used to transmit the collected operating data of the transformer area and to transmit control commands.

[0042] The ARM core processing unit is used to execute the intelligent distribution area three-phase imbalance optimization and control method as described in the first aspect of the present invention.

[0043] The storage unit is used to store the generated control strategy for the transformer area;

[0044] The control device also includes an interaction unit that is communicatively connected to the ARM core processing unit.

[0045] The interactive unit is used to display the control strategy for the transformer area;

[0046] The control device is equipped with a power management unit.

[0047] The power management unit is used to provide operating power to the control device.

[0048] As can be seen from the above technical solutions, the present invention has the following advantages:

[0049] This invention provides a method and device for optimizing and controlling three-phase imbalance in intelligent distribution transformer areas. Based on a genetic algorithm, the method analyzes and calculates the received operating data of the intelligent distribution transformer area to periodically formulate control strategies. It can adjust and control the distribution transformer area in a timely manner according to the electricity consumption patterns of users in the distribution transformer area, thereby improving the control capability of the intelligent distribution transformer area and solving the technical problem of insufficient control capability in existing methods that rely on empirical methods to control intelligent distribution transformer areas. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a flowchart illustrating the steps of a smart transformer substation three-phase imbalance optimization and control method provided in Embodiment 1 of the present invention.

[0052] Figure 2 This is a flowchart illustrating the steps of a smart transformer substation three-phase imbalance optimization and control method provided in Embodiment 2 of the present invention.

[0053] Figure 3 This is a structural block diagram of an intelligent three-phase imbalance optimization and control device for a distribution area provided in Embodiment 3 of the present invention;

[0054] Figure 4 This is a schematic diagram of the operating logic of the ARM core processing unit. Detailed Implementation

[0055] This invention provides a method and apparatus for optimizing and controlling three-phase imbalance in intelligent distribution transformer areas, which solves the technical problem that existing intelligent distribution transformer areas mainly rely on the knowledge and experience of practitioners for control, resulting in insufficient control capabilities.

[0056] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0057] Example 1

[0058] Please see Figure 1 , Figure 1The flowchart illustrates the steps of an intelligent three-phase imbalance optimization and control method for a transformer substation, as provided in Embodiment 1 of the present invention.

[0059] This invention provides a method for optimizing and controlling three-phase imbalance in intelligent distribution transformer areas, comprising:

[0060] Step 101: Verify the integrity of the received station operation data.

[0061] Distribution area operation data refers to the basic operating parameters of a smart distribution area collected through intelligent terminals, including but not limited to the rated capacity of the distribution area, voltage and current information on the low-voltage outlet side of the distribution area, load rate, three-phase imbalance, neutral current, number of low-voltage users, low-voltage user number, and low-voltage user type (single-phase or three-phase).

[0062] In this embodiment of the invention, in response to a received request instruction for optimizing and controlling the smart distribution area, the integrity of the received distribution area operation data is verified, that is, the distribution area operation data is checked to determine the integrity of the currently received distribution area operation data.

[0063] Step 102: If the operating data of the transformer area meets the preset data integrity conditions, then verify whether the operating data of the transformer area meets the preset control strategy update conditions.

[0064] In this embodiment of the invention, if the integrity of the operating data of the transformer area meets the preset data integrity condition, then it is verified whether the transformer area meets the preset control strategy update condition, that is, it is determined whether the user access strategy of the smart transformer area needs to be adjusted.

[0065] Step 103: If the conditions are met, a genetic algorithm is used to update the strategy, and the updated control strategy for the transformer area is used for control.

[0066] In this embodiment of the invention, if the operating data of the transformer area meets the preset control strategy update conditions, a genetic algorithm is used to update the user access strategy, and the updated transformer area control strategy is used to control the smart transformer area.

[0067] In this invention, the integrity of the received transformer substation operation data is verified. If the substation operation data meets the preset data integrity conditions, it is then verified whether the substation operation data meets the preset control strategy update conditions. If it does, a genetic algorithm is used to update the strategy, and the updated substation control strategy is used for control. This solves the technical problem that existing smart transformer substations mainly rely on the knowledge and experience of practitioners for control, resulting in insufficient control capabilities. This invention analyzes and calculates the received smart transformer substation operation data using a genetic algorithm to periodically formulate substation control strategies, which can adjust and control in a timely manner according to the electricity consumption patterns of substation users, thereby improving the control capabilities of smart transformer substations.

[0068] Example 2

[0069] Please see Figure 2 , Figure 2 This is a flowchart illustrating the steps of a smart transformer substation three-phase imbalance optimization and control method provided in Embodiment 2 of the present invention.

[0070] This invention provides a method for optimizing and controlling three-phase imbalance in intelligent distribution transformer areas, comprising:

[0071] Step 201: Verify the integrity of the received station operation data.

[0072] In this embodiment of the invention, the operating data of the transformer substation is obtained from the smart terminal through the communication module. Due to signal transmission and other issues, the data obtained by the core processing unit may be incomplete. It is necessary to check it against the pre-established basic operating data ledger to determine the completeness of the currently obtained operating data of the transformer substation.

[0073] Transformer operation data includes, but is not limited to, the rated capacity S of the transformer area. n Information on voltage and current at the low-voltage outlet side of the distribution area, load rate, three-phase unbalance, neutral current, number of low-voltage users (n), low-voltage user number, and low-voltage user type (single-phase or three-phase).

[0074] Step 202: If the operating data of the transformer area meets the preset data integrity conditions, then verify whether the operating data of the transformer area meets the preset control strategy update conditions.

[0075] Furthermore, the preset data integrity condition is that the integrity of the station operation data within the preset data collection period is greater than or equal to the preset integrity threshold.

[0076] It is worth mentioning that completeness refers to the percentage of collected station operation data to standard station operation data; in other words, completeness can be understood as a percentage.

[0077] In this embodiment of the invention, the preset integrity threshold can be 98%. If the proportion (completeness) of the operating data of the transformer area in the standard operating data of the transformer area is greater than or equal to 98%, then it is verified whether the operating data of the transformer area meets the preset control strategy update conditions.

[0078] For ease of understanding, the following is an application example for judging the integrity of the operating data of a transformer substation:

[0079] Taking a specific day's data as an example, if the number of low-voltage users in the distribution area is n, then the complete meter information for that day should include the voltage, current, and phase information for each of the n users every fifteen minutes, as well as the voltage and current information for the low-voltage distribution area's outlet side every fifteen minutes. If the data completeness is greater than or equal to 98%, then proceed to the next step to determine whether the control strategy needs to be updated. If the data completeness is less than 98%, then continue operating according to the current low-voltage user access strategy of the smart distribution area.

[0080] Step 203: If satisfied, perform chromosome encoding on the users in the station area, where each chromosome represents a user sequence combination.

[0081] Furthermore, the operating data of the transformer area includes load rate, three-phase imbalance and neutral current. The preset control strategy update conditions are that the load rate is greater than the preset debt ratio threshold, the three-phase imbalance is greater than the preset imbalance threshold, and the neutral current is greater than the preset current threshold.

[0082] In this embodiment of the invention, the control strategy is updated only when the operating data of the distribution area meets the preset control strategy update conditions, that is, when the load rate, three-phase imbalance, and neutral current are all greater than the associated set thresholds. If any one of these conditions is not met, the strategy update is not triggered. The set thresholds include a preset load ratio threshold, a preset imbalance threshold, and a preset current threshold, which can be set based on user operating experience. When the operating data of the distribution area meets the preset control strategy update conditions, chromosome coding is performed on the users in the distribution area, where each chromosome represents a user phase sequence combination for that smart distribution area.

[0083] Furthermore, step 203 may include the following sub-steps:

[0084] S11. Establish a set of random numbers for users that is consistent with the number of low-voltage users.

[0085] S12. Determine the user phase sequence combination corresponding to the random number based on the preset random number interval in the user random number set.

[0086] For ease of understanding, the following are examples of the application of chromosome coding:

[0087] Assuming the user set of a transformer area is U={u1,u2,...,un} (where n is the number of low-voltage users in the transformer area), for a combination of user phase sequences in the transformer area, its chromosome code is...

[0088] Three-phase users: The connection phase of each phase load of a three-phase user can be adjusted to make the three-phase load in the transformer area tend to be balanced;

[0089] Single-phase users: The phase of connection for single-phase users can be adjusted to balance the three-phase load area in the transformer substation;

[0090] Randomly generate a set of random numbers between 0 and 1 of length n (where n is the number of low-voltage users in the distribution area).

[0091] If the i-th random number a∈(0, 1 / 3), then the phase sequence of the i-th user is A (single-phase user) or ABC (three-phase user).

[0092] If the i-th random number a∈[1 / 3, 2 / 3), then the phase sequence of the i-th user is B (single-phase user) or CAB (three-phase user, i.e., one forward rotation based on the original sequence).

[0093] If the i-th random number a∈[2 / 3,1), then the phase order of the i-th user is C (single-phase user) or BCA (three-phase user, i.e., two forward rotations on the original basis).

[0094] Step 204: Randomly generate an initial chromosome population.

[0095] In this embodiment of the invention, population initialization is performed by simulating natural selection, which requires iterative searching within an initial evolutionary population. Population initialization is typically accomplished using random number generation. In this invention, each chromosome in the population is initialized using a (0, 1) random number generation method.

[0096] If the population size is NP, a random matrix P is generated, with NP rows and n columns, where n is the number of low-voltage users in the area. Each column represents the corresponding code of one of the chromosomes in the population.

[0097] P

[0098] It should be noted that the random matrix P here... This is a randomly generated initialization matrix, whose matrix elements are randomly generated and do not need to be specifically described here.

[0099] Step 205: Based on the user phase sequence combination and the three-phase current imbalance, form a fitness function, and calculate the fitness of each chromosome in the initial chromosome population based on the fitness function.

[0100] In this embodiment of the invention, fitness evaluation is the core of the genetic algorithm. It is necessary to formulate clear selection rules to evaluate the fitness value of each chromosome and then distinguish the quality of chromosomes. The chromosome with the higher the fitness value is, the better it is. In this invention, the calculation of fitness is related to the three-phase imbalance of the user's phase sequence combination. The smaller the three-phase imbalance of the combination, the higher the fitness.

[0101] To facilitate understanding, the following are application examples of fitness functions:

[0102] In this invention, there are d single-phase low-voltage users and s three-phase low-voltage users in the distribution area (where d+s=n, that is, the sum of single-phase users and three-phase users is the total number of low-voltage users in the distribution area).

[0103] The current sequences of the three phases A, B, and C in this transformer area at a certain time j are as follows:

[0104] Phase A current ( ): Single-phase user with phase sequence A (d A The sum of the A-phase loads of (s) three-phase users and (s) three-phase users;

[0105]

[0106] I adij This represents the load current of the single-phase user with phase sequence A at time j.

[0107] I asij This represents the load current of phase A of the i-th three-phase user at time j;

[0108] Phase B current ( ): Single-phase user with phase sequence B (d B The sum of the B-phase loads of the three-phase users (s) and the three-phase users (s).

[0109]

[0110] I bdij This represents the load current of the single-phase user with phase sequence B at time j.

[0111] I bsij This represents the load current of phase B of the i-th three-phase user at time j;

[0112] C-phase current ( ): Single-phase user with phase sequence C (d C The sum of the C-phase loads of (s) three-phase users and (s) three-phase users;

[0113]

[0114] Icdij represents the load current of the single-phase user with phase sequence C at time j;

[0115] Icsij represents the load current of phase C of the i-th three-phase user at time j;

[0116] The three-phase imbalance Bj of a transformer in a certain area at a certain time j is:

[0117]

[0118] If the number of time points within a characteristic time period is m, then the average three-phase imbalance B during this period is:

[0119]

[0120] The ultimate goal of this invention is to find the minimum value of the average three-phase imbalance during the characteristic period of the low-voltage distribution area. The fitness function is defined as:

[0121]

[0122] In the formula, C represents a user phase sequence combination in the distribution area, Bc represents the average three-phase unbalance of the transformer in the distribution area corresponding to this phase sequence combination over a certain period of time, and the maximum value of the three-phase unbalance is 1.

[0123] It should be noted that the fitness of each chromosome is calculated using this fitness function, which can be understood as the fitness of a particular user's phase sequence combination. Simulating the different fitness outcomes of various phase sequence combinations randomly generated during the characteristic time period facilitates the search for the optimal solution.

[0124] Step 206: Based on fitness, perform genetic operations on the initialized chromosome population until the number of iterations is satisfied. Then, output the chromosome corresponding to the maximum fitness value as the updated control strategy for the transformer area.

[0125] Genetic operations include selection, crossover, and mutation.

[0126] Furthermore, step 206 may include the following sub-steps:

[0127] S21. Perform selection operations on the initial chromosome population based on multiple fitnesss.

[0128] Furthermore, S21 may include the following sub-steps:

[0129] S211. The fitness of all chromosomes in the initial chromosome population is used to perform a sum operation to obtain the fitness sum.

[0130] S212. The ratio of the fitness of each chromosome to the sum of fitness values ​​is calculated to obtain the selection probability value of each chromosome.

[0131] In this embodiment of the invention, the initial chromosome population size is NP, and the fitness of the i-th chromosome X is f. i The probability value p of being selected is... i for.

[0132] In practical implementation, to simplify the method, the above process can be converted into a formula encapsulation. The specific formula for selecting the probability value can be as follows:

[0133]

[0134] In the formula, This indicates the selection of a probability value. Let fitness be the value, where fitness here is determined by the fitness function in step 205. What was obtained This represents fitness and value.

[0135] S213. Compare each selected probability value with the preset standard probability threshold.

[0136] In this embodiment of the invention, each selected probability value is compared with a preset standard probability threshold.

[0137] S214. If the selected probability value is greater than the preset standard probability threshold, the chromosome associated with the selected probability value will be retained.

[0138] In this embodiment of the invention, if the selected probability value is greater than a preset standard probability threshold, the chromosome associated with the selected probability value will be retained in the population.

[0139] S215. If the selected probability value is less than or equal to the preset standard probability threshold, then the chromosome associated with the selected probability value will be replaced.

[0140] In this embodiment of the invention, if the selected probability value is less than or equal to a preset standard probability threshold, the chromosome associated with the selected probability value will be replaced.

[0141] It should be noted that, based on individual fitness values, the genetic concept of survival of the fittest in nature is simulated, and some superior individuals are selected to be passed on to the next generation of the population according to certain rules and methods.

[0142] This invention employs roulette wheel selection to select superior individuals. This is a proportion-based selection method, where the probability of an individual's offspring being retained is determined by the proportion of its fitness value within the population. As seen in the roulette wheel selection mechanism, the higher an individual's fitness, the greater its chance of being selected, and vice versa. However, due to the inherent randomness of the selection process, it cannot guarantee that every selection will choose the superior chromosomes; therefore, less desirable chromosomes are also given some survival space.

[0143] S22. Based on the random crossover numbers generated randomly for each chromosome in the initial chromosome population, perform the crossover operation.

[0144] Furthermore, S22 may include the following sub-steps:

[0145] S221. Randomly generate crossover random numbers for each chromosome in the initial chromosome population.

[0146] In this embodiment of the invention, a crossover random number p in the range of 0 to 1 is generated for each chromosome. jc .

[0147] S222. Compare each crossover random number with the preset crossover probability value.

[0148] In this embodiment of the invention, during the chromosome crossover stage, each chromosome has a preset crossover probability value p that allows crossover to occur. c The decision is that the default crossover probability value here can be 0.9.

[0149] S223. If the crossover random number is greater than or equal to the preset crossover probability value, then the chromosome associated with the crossover random number is copied into the new initialized chromosome population.

[0150] In this embodiment of the invention, if the crossover random number p jc Greater than or equal to the preset crossover probability value p c If the chromosome does not participate in the crossover operation, it is directly copied into the new initialized chromosome population.

[0151] S224. If the crossover random number is less than the preset crossover probability value, then the chromosome associated with the crossover random number will undergo gene exchange, and the gene-exchanged chromosome will be copied into the new initialized chromosome population.

[0152] In this embodiment of the invention, if the crossover random number is less than the preset crossover probability value, it means that the chromosome needs to undergo a crossover operation.

[0153] The specific implementation of the crossover operation is as follows:

[0154] Each pair of chromosomes selected according to their crossover probability values ​​undergoes a crossover operation, exchanging some of their genes to generate two new chromosomes.

[0155] The specific operation involves randomly generating two valid crossover positions, m and n, and then exchanging all genes located between m and n using two crossover chromosomes.

[0156] The user-ordered crossover operation is shown below. After the crossover operation, the size of the new initial chromosome population remains NP.

[0157] Intersection

[0158] Position m Position n

[0159] Parent user phase sequence combination 1 A,ABC,BCA...,ABC|A,C,B...ABC,CAB|BCA,A

[0160] Parent user phase sequence combination 2 B,CBA,CAB...,ABC|B,A,A...ABC,ABC|CAB,B

[0161] cross

[0162] Position m Position n

[0163] Child user phase sequence combination 1 A,ABC,BCA...,ABC|B,A,A...ABC,ABC|BCA,A

[0164] The child user phase sequence combination 2 is B,CBA,CAB...,ABC|A,C,B...ABC,CAB|CAB,B.

[0165] S23. Based on the initial chromosome population after the crossover operation, randomly generate mutated random numbers for each chromosome and perform the mutation operation.

[0166] Furthermore, S23 may include the following sub-steps:

[0167] S231. Randomly generate the mutation random number for each gene of each chromosome in the initialized chromosome population after the crossover operation.

[0168] In this embodiment of the invention, a random variable p in the range of 0 to 1 is generated for each gene within each chromosome. by .

[0169] S232. Compare each mutated random number with the preset mutated probability value.

[0170] In this embodiment of the invention, for each gene on each chromosome in the new population after crossover, each chromosome has n gene loci, and the mutation probability value p is preset. m Determine whether the gene has mutated.

[0171] S233. If the mutated random number is less than the preset mutation probability value, then the chromosome associated with the mutated random number is maintained.

[0172] In this embodiment of the invention, if the mutated random number p by Less than the preset mutation probability value p m If the chromosome associated with the mutated random number remains unchanged, then the chromosome remains unchanged.

[0173] S234. If the mutated random number is greater than or equal to the preset mutation probability value, then the gene in the chromosome associated with the mutated random number will be mutated.

[0174] In this embodiment of the invention, if the mutated random number is greater than or equal to a preset mutation probability value, then the gene in the chromosome associated with the mutated random number will be mutated.

[0175] The specific mutation operation is as follows: the value after the mutation of this gene locus is equal to the absolute number of the value before the mutation minus 1.

[0176] Mutation location: | |

[0177] Before mutation, the user phase sequence combination is A,|ABC|, BCA......|ABC|, CAB, BCA,|A|

[0178] Mutations

[0179] Before mutation, the user phase sequence combination is A,|CAB|, BCA......|BCA|, CAB, BCA,|B|

[0180] S24. Jump to step 205 until the number of iterations is satisfied, then output the chromosome corresponding to the maximum fitness value as the updated control strategy for the transformer area.

[0181] In this embodiment of the invention, the process jumps to step 205 until the number of iterations is satisfied. Then, the maximum value is selected from multiple fitness values, and the chromosome corresponding to the fitness value of the maximum value is used as the updated control strategy for the segregated area.

[0182] It should be noted that each chromosome corresponds to a certain user sequence combination.

[0183] Step 207: Implement control using the updated transformer area control strategy.

[0184] In this embodiment of the invention, an updated transformer area control strategy is used for control.

[0185] For ease of understanding, the following is a specific application example of the present invention:

[0186] A transformer substation in a certain area has a capacity of 315kVA, a load rate of 45% (greater than the set value), a neutral current of 83A (greater than the set value), and an imbalance of 35.7 (greater than the set value). There are 51 low-voltage users, including 43 with three-phase meters and 8 with single-phase meters.

[0187] (1) Chromosome encoding: Establish a user set containing 51 random numbers, which are generated from (0,1).

[0188] (2) Population initialization: Set an initial population with a population size of NP=100, which is equivalent to having 100 randomly generated chromosomes in the initial population, and each chromosome has 51 genes.

[0189] (3) Calculate the fitness of 100 chromosomes in the population respectively. In this invention, the fitness calculation is related to the three-phase imbalance of the phase sequence combination. The smaller the three-phase imbalance of the combination, the higher the fitness.

[0190] (4) Selection operation: The roulette wheel selection method is used to select a chromosome to be retained in the new generation population according to the fitness ratio.

[0191] (5) Crossover operation: exchange the positions of certain genes (phases of certain low-voltage users) on certain chromosomes (i.e., certain phase sequence combinations in the original population).

[0192] (6) Mutation operation: Mutate certain genes of certain chromosomes of the original population (i.e., a certain phase sequence combination of the substation area) (phase difference of certain low voltage users).

[0193] (7) Form a new generation of population, and repeat steps (3), (4), and (5) until the number of iterations is satisfied. Find the chromosome with the highest fitness in each generation and output the result.

[0194] In this invention, the integrity of the received transformer substation operation data is verified. If the substation operation data meets the preset data integrity conditions, it is then verified whether the substation operation data meets the preset control strategy update conditions. If it does, a genetic algorithm is used to update the strategy, and the updated substation control strategy is used for control. This solves the technical problem that existing smart transformer substations mainly rely on the knowledge and experience of practitioners for control, resulting in insufficient control capabilities. This invention analyzes and calculates the received smart transformer substation operation data using a genetic algorithm to periodically formulate substation control strategies, which can adjust and control in a timely manner according to the electricity consumption patterns of substation users, thereby improving the control capabilities of smart transformer substations.

[0195] Example 3

[0196] Currently available three-phase imbalance control products on the market have prominent problems and the following drawbacks:

[0197] 1. It is necessary to measure the basic operating data such as voltage and current on the low-voltage side of the distribution area again, which introduces testing equipment such as current transformers, increasing the risk of failure. In recent years, with the promotion and application of low-voltage intelligent devices such as smart terminals (smart gateways) and smart meters, the power sensing capability of low-voltage distribution networks has been continuously improved. Basic information such as voltage and current has already been collected by the existing smart terminals in the distribution area, so there is no need to repeat the sampling.

[0198] 2. Currently, three-phase imbalance control devices on the market are mainly designed for fixed transformer area topologies. However, due to factors such as peak summer demand, transformer areas may experience load shedding, and the transformer area topology will be dynamically updated, making it impossible to adapt to the needs of low-voltage transformer area topology changes in real time.

[0199] 3. Products that include data acquisition and analysis decision-making functions are generally large in size, making them inconvenient to install in small, enclosed spaces such as low-voltage power rooms.

[0200] Please see Figure 3-4 , Figure 3 This is a structural block diagram of an intelligent three-phase imbalance optimization and control device for a distribution area provided in Embodiment 3 of the present invention.

[0201] The present invention provides an intelligent three-phase imbalance optimization and control device for distribution transformer areas. The control device includes a communication unit, an ARM core processing unit, and a storage unit connected in sequence.

[0202] The communication unit is used to transmit the collected operating data of the transformer area and to transmit control commands.

[0203] An ARM core processing unit is used to execute the intelligent distribution area three-phase imbalance optimization and control method as described in the first aspect of the present invention.

[0204] Storage unit, used to store the generated transformer area control strategy;

[0205] The control device also includes an interaction unit that communicates with the ARM core processing unit;

[0206] The interactive unit is used to display the control strategy for the transformer area.

[0207] The control device has a built-in power management unit;

[0208] The power management unit is used to provide operating power to the control device.

[0209] In this embodiment of the invention, the device consists of an ARM core processing unit, a communication unit (supporting 4G, LoRa, wideband carrier, and RS485 communication methods), a human-machine interface interaction unit, a storage unit, and a power management unit.

[0210] ARM core processing unit: Using a high-speed ARM chip, it is responsible for conducting three-phase imbalance analysis of the transformer area based on the massive data collected by the smart meter every fifteen minutes, supplemented by the operating data collected by the public transformer smart terminal, and forming the transformer area control strategy.

[0211] The communication unit consists of a LoRa communication module, a broadband carrier module, an RS485 communication module, and a 4G module. The LoRa communication module can communicate with the phase-changing switches in the low-voltage distribution area and issue phase-changing commands. The broadband carrier module is responsible for information transmission and interaction between the device and the smart terminal in the low-voltage distribution area. On one hand, the communication unit transmits data collected by the smart terminal, including three-phase voltage and current at the low-voltage outlet side of the distribution area, the transformer-line-household relationship in the low-voltage distribution area, the number of smart meters, and the 15-minute voltage, current, and phase sequence information of the smart meters, to the device. On the other hand, the communication unit can transmit the governance decisions analyzed and processed by the device to the smart terminal or the back-end system.

[0212] Human-Machine Interface (HMI) Interaction Unit: Responsible for human-machine interaction functions, equipped with LCD display and touch input functions. It can display the control strategy of the distribution area for easy reference by front-line operators; at the same time, it can display key parameters of the equipment such as the feature analysis cycle and the set value of the distribution area access strategy adjustment for maintenance personnel.

[0213] Storage unit: Responsible for storing data transmitted from the communication unit and the management strategy after analysis and calculation by the ARM core processing unit.

[0214] Power management unit: Provides power to the device.

[0215] The ARM core processing unit includes:

[0216] The first verification module is used to verify the integrity of the received station operation data;

[0217] The second verification module is used to verify whether the operating data of the transformer area meets the preset control strategy update conditions if the operating data of the transformer area meets the preset data integrity conditions.

[0218] The strategy update module is used to update the strategy using a genetic algorithm if the conditions are met, and then use the updated control strategy for the transformer area to perform control.

[0219] Furthermore, the preset data integrity condition is that the integrity of the station operation data within the preset data collection period is greater than or equal to the preset integrity threshold.

[0220] Furthermore, the operating data of the transformer area includes load rate, three-phase imbalance and neutral current. The preset control strategy update conditions are that the load rate is greater than the preset debt ratio threshold, the three-phase imbalance is greater than the preset imbalance threshold, and the neutral current is greater than the preset current threshold.

[0221] Furthermore, the policy update module includes:

[0222] The encoding submodule is used to encode the chromosomes of users in the transformer area, where each chromosome represents the user's phase sequence combination;

[0223] The initialization submodule is used to randomly generate an initial chromosome population;

[0224] The fitness submodule is used to compose a fitness function based on the user's phase sequence combination and the three-phase current imbalance, and to calculate the fitness of each chromosome in the initial chromosome population based on the fitness function.

[0225] The genetic operations submodule is used to perform genetic operations on the initialized chromosome population based on fitness until the required number of iterations is met. Then, the chromosome corresponding to the maximum fitness value is output as the updated control strategy for the transformer area.

[0226] Furthermore, the encoding submodule includes:

[0227] The random number set unit is used to establish a user random number set consistent with the number of low-voltage users;

[0228] The user phase sequence combination unit is used to determine the user phase sequence combination corresponding to the random number based on the preset random number interval in the user random number set.

[0229] Furthermore, the genetic manipulation submodule includes:

[0230] The selection operation unit is used to perform selection operations on an initialized chromosome population based on multiple fitnesss.

[0231] The crossover operation unit is used to perform the crossover operation based on the random crossover number generated randomly for each chromosome in the initialized chromosome population;

[0232] The mutation operation unit is used to randomly generate mutated random numbers for each chromosome in the initialized chromosome population after the crossover operation is performed, and then perform the mutation operation.

[0233] Furthermore, the selection operation unit includes:

[0234] The fitness and value subunit is used to perform a sum operation on the fitness of all chromosomes in the initialized chromosome population to obtain the fitness and value.

[0235] A probability value subunit is selected to perform a ratio calculation between the fitness of each chromosome and the sum of fitness values ​​to obtain the selection probability value for each chromosome.

[0236] The first comparison subunit is used to compare each selected probability value with a preset standard probability threshold.

[0237] The first processing subunit is used to retain the chromosome associated with the selected probability value if the selected probability value is greater than a preset standard probability threshold.

[0238] The second processing subunit is used to replace the chromosome associated with the selected probability value if the selected probability value is less than or equal to a preset standard probability threshold.

[0239] Furthermore, the cross-operation unit includes:

[0240] The crossover random number subunit is used to randomly generate crossover random numbers for each chromosome in the initial chromosome population;

[0241] The second comparison subunit is used to compare each crossover random number with the preset crossover probability value;

[0242] The third processing subunit is used to copy the chromosome associated with the crossover random number into a new initialized chromosome population if the crossover random number is greater than or equal to the preset crossover probability value.

[0243] The fourth processing subunit is used to perform gene exchange on the chromosome associated with the crossover random number if the crossover random number is less than the preset crossover probability value, and then copy the gene-exchanged chromosome into the new initialized chromosome population.

[0244] Furthermore, the mutation operation unit includes:

[0245] The mutated random number subunit is used to randomly generate mutated random numbers for each gene of each chromosome in the initialized chromosome population after the crossover operation is performed;

[0246] The third comparison subunit is used to compare each mutated random number with the preset mutation probability value;

[0247] The fifth processing subunit is used to maintain the chromosome associated with the mutated random number if the mutated random number is less than the preset mutation probability value.

[0248] The sixth processing subunit is used to perform mutation operations on the genes in the chromosome associated with the mutated random number if the mutated random number is greater than or equal to the preset mutation probability value.

[0249] Please see Figure 4 , Figure 4 A schematic diagram of the operating logic of the ARM core processing unit:

[0250] 1. Establish a basic operational data ledger. Operational data is obtained from the smart terminal via a communication module. Basic information should include the rated capacity S of the distribution area. n The system displays information such as voltage and current at the low-voltage outlet side of the distribution area, load rate, three-phase imbalance, neutral current, number of low-voltage users N, low-voltage user number, and low-voltage user type (single-phase or three-phase). If the distribution area is equipped with intelligent phase-switching switches, the distribution information of the relevant intelligent switches can also be read through the LoRa communication module.

[0251] 2. Determine the feature analysis cycle. The analysis cycle (daily, weekly, monthly) can be set through the human-computer interface interaction unit, and an adjustment strategy can be set according to the cycle. For example, if the analysis cycle is set to daily, the data from 0:00 to 23:59 of the previous calendar day will be analyzed and calculated after 0:00 each day; if the cycle is set to weekly, the data from 0:00 to 23:59 of the previous seven calendar days will be analyzed and calculated each day; if the analysis cycle is set to monthly, the data from 0:00 to 23:59 of the previous thirty calendar days will be analyzed and calculated each day.

[0252] 3. Assess data integrity. Due to signal transmission issues, the data acquired by the core processing unit may be incomplete. It is necessary to verify the data against the basic information ledger to determine the completeness of the currently acquired characteristic data. Taking a specific day's data as an example, if the number of low-voltage users in the distribution area is n, then the complete meter information for that day should include voltage, current, and phase information for each of the n users every fifteen minutes, as well as complete voltage and current information for the low-voltage distribution area's outlet side every fifteen minutes. If the data integrity is greater than or equal to 98%, proceed to the step of formulating an adjustment strategy; if the data integrity is less than 98%, continue with the original strategy, without updates, and maintain the original access strategy for low-voltage users.

[0253] 4. To determine whether the low-voltage user access strategy of the distribution area needs to be updated, the main basis is the operating data of the low-voltage outlet side of the distribution area during the analysis period, including whether the load rate is greater than the preset debt ratio threshold, whether the neutral current is greater than the preset imbalance threshold, and whether the imbalance is greater than the preset current threshold.

[0254] 5. Update and formulate regional control strategies:

[0255] Based on the genetic algorithm, the meter information should include voltage, current, and phase information for n users every fifteen minutes, as well as complete voltage and current information for the low-voltage distribution area outlet side every fifteen minutes. For each type of phase sequence combination of distribution area users, based on the current sequence data of each user at a certain moment, without considering line loss, the current data of each phase A, B, and C of the distribution area transformer at that moment can be obtained by cumulative calculation, and then the three-phase imbalance at that moment can be calculated.

[0256] If user data for certain characteristic time periods is obtained, and the goal is to optimize the average three-phase imbalance of transformers in the distribution area within those periods, a genetic algorithm is used to search for the set of phase sequence combinations for users in the distribution area. Ultimately, an optimal phase sequence combination is found that minimizes the average imbalance of the transformers in that area, leading to the optimal distribution area load control strategy.

[0257] The main steps in calculating the optimal load adjustment strategy for transformer substations based on genetic algorithms are as follows:

[0258] (1) Chromosomal coding: Each chromosome represents a certain phase sequence composition of the region;

[0259] (2) Population initialization: Multiple randomly generated chromosomes form the initial population, and genetic evolution takes place within this population;

[0260] (3) Fitness evaluation: The core of the genetic algorithm is to simulate the natural selection law of natural selection and survival of the fittest in nature. Fitness evaluation is the method to evaluate the best individual. In this design, the optimal solution is to achieve the minimum three-phase imbalance of the transformer area.

[0261] (4) Selection operation: Select chromosomes from the population that are inherited into the new population to simulate the inheritance laws in nature;

[0262] (5) Crossover operation: Select chromosomes that exchange some genes in a population to simulate the mating process in a population in nature;

[0263] (6) Mutation operation: Select the gene that needs to be mutated in each chromosome of the population to simulate the mutation process within the population in nature.

[0264] 6. Display the adjustment plan, transmit the optimal control strategy of the transformer area obtained based on the genetic algorithm to the human-machine interface interaction unit or to the smart terminal through the communication unit, and then to the corresponding back-end system through the smart terminal, so as to facilitate the operation and maintenance personnel to query.

[0265] 7. Send commutation command: If the transformer area is equipped with an intelligent commutation switch, the ARM core processing unit can send a commutation command to the designated commutation switch through the LoRa communication module according to the optimal adjustment scheme.

[0266] In this invention, all basic operating data of the target distribution area are obtained from low-voltage intelligent devices such as smart terminals (smart gateways) and smart meters. This data includes rated capacity, low-voltage outlet voltage and current information, load rate, three-phase imbalance, neutral current, number of low-voltage users, low-voltage user ID, and low-voltage user type (single-phase or three-phase). It also supports information interaction with the intelligent phase-changing switches configured in the distribution area. Basic settings for control strategies can be configured according to the actual needs of maintenance personnel, such as the formulation of control strategy characteristic cycles and thresholds for strategy execution. Based on the actual electricity consumption of low-voltage users, a periodic control strategy for the distribution area is formulated using a genetic algorithm. The invention supports both human-machine interface display and background display of the control strategy. This invention can fully utilize low-voltage intelligent devices such as smart terminals (smart gateways) and smart meters to update the distribution area control strategy in a timely manner according to the electricity consumption patterns of users in the distribution area.

[0267] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0268] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0269] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0270] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0271] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing and controlling three-phase imbalance in intelligent distribution transformer areas, characterized in that, include: Verify the integrity of the received station operation data; If the operating data of the transformer area meets the preset data integrity condition, then verify whether the operating data of the transformer area meets the preset control strategy update condition; If the conditions are met, a genetic algorithm is used to update the strategy, and the updated control strategy for the transformer area is used for control. The preset data integrity condition is that the integrity of the station operation data within the preset data collection period is greater than or equal to a preset integrity threshold, wherein the preset integrity threshold is 98%. The transformer area operation data includes load rate, three-phase imbalance, and neutral current. The preset control strategy update conditions are that the load rate is greater than a preset load rate threshold, the three-phase imbalance is greater than a preset imbalance threshold, and the neutral current is greater than a preset current threshold. The strategy update using a genetic algorithm includes: Users in the distribution area are coded with chromosomes, where each chromosome represents a user's phase sequence combination; Randomly generate an initial chromosome population; The fitness function is composed of the user phase sequence combination and the three-phase current imbalance, and the fitness of each chromosome in the initial chromosome population is calculated based on the fitness function. Based on the fitness, genetic operations are performed on the initial chromosome population until the required number of iterations is met. Then, the chromosome corresponding to the maximum fitness is output as the updated control strategy for the transformer area. The chromosome encoding of users in the platform area includes: Establish a set of random numbers for users that matches the number of low-voltage users; Based on the preset random number interval in which the random number in the user random number set is located, the user phase sequence combination corresponding to the random number is determined. Here, a random number set in the interval of 0 to 1 with a length of n is randomly generated, where n is the number of low-voltage users in the transformer area. If the i-th random number a∈(0, 1 / 3), then the phase sequence of the i-th user is A single-phase user, ABC three-phase user. If the i-th random number a∈[1 / 3, 2 / 3), then the phase sequence of the i-th user is B single-phase user, CAB three-phase user, that is, the forward rotation sequence is 1 time on the original basis; If the i-th random number a∈[2 / 3,1), then the phase sequence of the i-th user is C (single-phase user) or BCA (three-phase user), which means the forward rotation is 2 times based on the original sequence.

2. The intelligent distribution area three-phase imbalance optimization and control method according to claim 1, characterized in that, The genetic operations performed on the initialized chromosome population based on the fitness include: A selection operation is performed on the initial chromosome population based on multiple fitness values; Based on the random crossover numbers generated for each chromosome in the initialized chromosome population, the crossover operation is performed; Based on the initialized chromosome population after the crossover operation, random mutation numbers are randomly generated for each chromosome, and the mutation operation is performed.

3. The intelligent distribution area three-phase imbalance optimization and control method according to claim 2, characterized in that, The step of performing a selection operation on the initialized chromosome population based on multiple fitness values ​​includes: The fitness sum is obtained by performing a summation operation on the fitness of all chromosomes in the initial chromosome population. The selection probability value corresponding to each chromosome is obtained by performing a ratio calculation between the fitness of each chromosome and the sum of fitness values; Compare each selected probability value with a preset standard probability threshold; If the selection probability value is greater than the preset standard probability threshold, then the chromosome associated with the selection probability value will be retained. If the selection probability value is less than or equal to the preset standard probability threshold, then the chromosome associated with the selection probability value is replaced.

4. The intelligent distribution area three-phase imbalance optimization and control method according to claim 2, characterized in that, The step of generating random crossover numbers based on each chromosome in the initialized chromosome population and performing the crossover operation includes: Randomly generate crossover random numbers for each chromosome within the initialized chromosome population; Compare each of the stated crossover random numbers with the preset crossover probability value; If the crossover random number is greater than or equal to the preset crossover probability value, then the chromosome associated with the crossover random number is copied into the new initialized chromosome population; If the crossover random number is less than the preset crossover probability value, then the chromosome associated with the crossover random number will undergo gene exchange, and the gene-exchanged chromosome will be copied into the new initialized chromosome population.

5. The intelligent distribution area three-phase imbalance optimization and control method according to claim 2, characterized in that, The step of randomly generating mutated random numbers for each chromosome in the initialized chromosome population after the crossover operation, and then performing the mutation operation, includes: Randomly generate the mutation random number for each gene of each chromosome in the initialized chromosome population after the crossover operation is performed; Compare each of the aforementioned mutated random numbers with the preset mutated probability value; If the mutated random number is less than the preset mutation probability value, then the chromosome associated with the mutated random number is maintained; If the mutated random number is greater than or equal to the preset mutation probability value, then the gene in the chromosome associated with the mutated random number will be mutated.

6. A smart transformer substation three-phase imbalance optimization and control device, characterized in that, The intelligent transformer area three-phase imbalance optimization and control method as described in claim 1 is implemented, wherein the control device comprises a communication unit, an ARM core processing unit, and a storage unit connected in sequence. The communication unit is used to transmit the collected operating data of the transformer area and to transmit control commands. The ARM core processing unit is used to process the operating data of the transformer area and generate the transformer area control strategy for the intelligent transformer area. The storage unit is used to store the generated control strategy for the transformer area; The control device also includes an interaction unit that is communicatively connected to the ARM core processing unit. The interactive unit is used to display the control strategy for the transformer area; The control device is equipped with a power management unit. The power management unit is used to provide operating power to the control device.

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