Distribution line protection method and device for distributed photovoltaic access

Abstract

The invention discloses a distributed photovoltaic access distribution line protection method and device, and the method comprises the steps: collecting distribution line parameters, and building a multi-target distribution line protection fixed value setting model with the shortest protection fixed value time and the optimal sensitivity as targets; taking the multi-target distribution line protection setting value setting model as an improved hawk algorithm fitness function, and solving the model by using the improved hawk algorithm to obtain an optimal protection setting value; and the optimal protection constant value is embedded into the switch equipment, and distribution line relay protection is carried out. According to the method, the protection constant value adaptive to the current system can be automatically generated without knowing the fault voltage and current change characteristics of the system, and the method can be well suitable for the distributed photovoltaic access power distribution network. The reliability of the system is improved by improving the hawk algorithm, and the method has practical engineering significance. And a distribution line protection fixed value setting algorithm is embedded into the equipment, so that the integration level of the distribution equipment and the sensing capability of the distribution line are improved, and the operation reliability of the distribution network equipment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems and automation thereof, and in particular to a method and device for protecting a distribution line of distributed photovoltaic access. Background Art

[0002] High proportion of distributed photovoltaic access will become an inevitable trend in the development of distribution network. However, photovoltaic power generation has significant volatility and randomness. With the continuous increase in the proportion of distributed photovoltaic power generation, the power system operation safety and stability face huge challenges. Traditional protection principles cannot be applied to new distribution networks with high proportion of distributed photovoltaic access. With the rapid development of artificial intelligence technology in recent years, many intelligent optimization algorithms have strong nonlinear model solving capabilities and have been applied in the field of power systems. Faced with complex distribution networks with high proportion of distributed photovoltaic access, the current intelligent optimization algorithm has lower convergence accuracy and lower relay protection sensitivity. A new optimization algorithm is urgently needed to optimize the constant to improve the reliability of relay protection devices. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a distribution line protection method and device for distributed photovoltaic access to solve the problem that the current traditional protection principle cannot fully adapt to the high-proportion distributed photovoltaic access to the new distribution network, and the new power system constant value setting is difficult.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a distribution line protection method for distributed photovoltaic access, including: collecting distribution line parameters, and constructing a multi-objective distribution line protection setting model with the shortest protection setting time and the optimal sensitivity as the goals;

[0008] The multi-objective distribution line protection setting value setting model is used as the fitness function of the improved Sky Eagle algorithm, and the improved Sky Eagle algorithm is used to solve the model to obtain the optimal protection setting value;

[0009] The optimal protection setting is embedded in the switchgear to perform distribution line relay protection.

[0010] As a preferred solution of the distributed photovoltaic access distribution line protection method described in the present invention, wherein: constructing a multi-objective distribution line protection setting value setting model includes: taking the range constraint, power flow constraint, sensitivity constraint, and selectivity constraint as constraint conditions, and the calculation formula is as follows:

[0011]

[0012] Where M is the target fitness value, k and k′ are both constants, H is the number of protections, G is the number of protection delays, a is the weight of the extreme difference constraint, b is the weight of the power flow constraint, c is the weight of the sensitivity constraint, and d is the weight of the selectivity constraint. It is the sum of all protection delay action times. is the penalty time for not complying with the extreme constraint, is the penalty time for not complying with the power flow constraints, is the penalty time for not complying with the sensitivity constraint, is the penalty time for not complying with the selective constraint, Z line.h (g) is the line current of protection h in g-stage protection, Z set.h (g) is the fixed value setting current of protection h in g-stage protection, It is the sensitivity of protection h in g segment protection.

[0013] As a preferred solution of the distributed photovoltaic access distribution line protection method of the present invention, it also includes: when the model protection setting time becomes shorter, the value of the fitness function becomes larger; when the model setting impedance value increases, the sensitivity also increases, and the fitness function value increases;

[0014] If the model does not meet any constraint condition, the time penalty value is increased through the weight, thereby reducing the value of the fitness function and screening out high-quality solutions; if the model meets the constraint condition, the sensitivity of the protection is enhanced and the overall result is optimized.

[0015] As a preferred solution of the distributed photovoltaic access distribution line protection method of the present invention, the improved Sky Eagle algorithm is used to solve the model, including initializing the population through Logistics chaos mapping, and the calculation formula is:

[0016]

[0017] in, is the position of the i-th eagle individual in the j dimension after being initialized by the Logistics chaotic map, is the chaotic variable, μ is the chaos coefficient, and μ=4.

[0018] As a preferred solution of the distributed photovoltaic access distribution line protection method described in the present invention, it also includes calculating the fitness of all Skyhawk individuals, determining the global optimal solution and the historical optimal solution, and using the adaptive weight method to update the Skyhawk position, which is expressed as:

[0019]

[0020] Among them, ω is the adaptive weight, k is the weight coefficient, is the search position after the t+1th iteration using the search method X3, α is the development coefficient, is the search position after the t+1th iteration using the search method X1, is the best position in the previous t iterations, T is the maximum number of iterations, and rand is a random number between 0 and 1. is the average value of search results after t iterations;

[0021] If the current number of iterations reaches the maximum number of iterations, the optimal protection setting for the distributed photovoltaic access to the distribution network is output; if the current number of iterations does not reach the maximum number of iterations, the iteration continues until the requirements are met.

[0022] As a preferred solution of the distribution line protection method for distributed photovoltaic access described in the present invention, wherein: the optimal protection setting is embedded in the switch device, and the distribution line relay protection also includes: dynamically matching the distributed photovoltaic access scenario, calculating the protection setting and embedding it in the switch device, and performing distribution line relay protection.

[0023] In a second aspect, the present invention provides a distributed photovoltaic access distribution line protection device, comprising:

[0024] Model building module, used to collect distribution line parameters and build a multi-objective distribution line protection setting model with the shortest protection setting time and optimal sensitivity as the goals;

[0025] A calculation module, used for using the multi-objective distribution line protection setting value setting model as the improved Sky Eagle algorithm fitness function, using the improved Sky Eagle algorithm to solve the model, and obtaining the optimal protection setting value;

[0026] The protection module is used to embed the optimal protection setting into the switchgear to perform relay protection of the distribution line.

[0027] As a preferred solution of the distributed photovoltaic access distribution line protection device described in the present invention, the model building module includes: taking the range constraint, power flow constraint, sensitivity constraint and selectivity constraint as constraint conditions, the calculation formula is as follows:

[0028]

[0029] Where M is the target fitness value, k and k′ are both constants, H is the number of protections, G is the number of protection delays, a is the weight of the extreme difference constraint, b is the weight of the power flow constraint, c is the weight of the sensitivity constraint, and d is the weight of the selectivity constraint. It is the sum of all protection delay action times. is the penalty time for not complying with the extreme constraint, is the penalty time for not complying with the power flow constraints, is the penalty time for not complying with the sensitivity constraint, is the penalty time for not complying with the selective constraint, Z line.h (g) is the line current of protection h in g-stage protection, Z set.h (g) is the fixed value setting current of protection h in g-stage protection, It is the sensitivity of protection h in g segment protection.

[0030] In a third aspect, the present invention provides a computing device, comprising:

[0031] Memory and processor;

[0032] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the distribution line protection method for distributed photovoltaic access are implemented.

[0033] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the distribution line protection method for distributed photovoltaic access.

[0034] Compared with the prior art, the present invention has the following beneficial effects: the present invention takes the relay protection speed, reliability and sensitivity as basic conditions, constructs a multi-objective distribution line protection setting model with the shortest protection setting time and the best sensitivity, and can automatically generate protection settings that adapt to the current system without knowing the system fault voltage and current change characteristics, and can be well applied to the distribution network with distributed photovoltaic access. The advantages of the Eagle algorithm's fast convergence speed and strong optimization ability are used to quickly and accurately solve the distribution line protection setting model, and the Eagle algorithm is optimized by introducing adaptive weights and Logistics chaotic sequences to prevent the algorithm from having local optimal problems caused by random initialization and step search, and can accurately and quickly derive the setting, thereby improving the system reliability and having a very light practical engineering significance.

[0035] The present invention also designs a "six-in-one" hyper-converged distribution primary and secondary pole-mounted switchgear covering a switch body, a disconnector, a feeder terminal, a sensor, a power extraction unit, and a digital backup power supply, and embeds a distribution line protection constant setting algorithm into the equipment. The equipment has a small size, a large measurement range, good temperature characteristics, and low power loss, thereby improving the integration of distribution equipment and the ability to perceive distribution lines, and realizing the perception, measurability, warning, and interaction of medium-voltage distribution network feeders in new power systems, thereby improving the operating reliability of distribution network equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0037] Figure 1 A schematic flow chart of a method and device for protecting a distributed photovoltaic access distribution line according to an embodiment of the present invention;

[0038] Figure 2 A physical diagram of a primary and secondary distribution pole switchgear of a distributed photovoltaic access distribution line protection method and device according to an embodiment of the present invention;

[0039] Figure 3 A feeder terminal data acquisition circuit diagram of a distributed photovoltaic access distribution line protection method and device according to an embodiment of the present invention;

[0040] Figure 4 A distributed photovoltaic access distribution network structure according to a distributed photovoltaic access distribution line protection method and device described in an embodiment of the present invention;

[0041] Figure 5 The present invention is a distributed photovoltaic access distribution line protection method and device according to an embodiment of the present invention, which uses an improved Eagle algorithm to solve a distribution line protection constant value setting model fitness diagram. DETAILED DESCRIPTION

[0042] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0043] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0044] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0045] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0046] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0047] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0048] Example 1

[0049] Reference Figure 1 to Figure 3 , is an embodiment of the present invention, which provides a distribution line protection method for distributed photovoltaic access, including:

[0050] S1: Collect distribution line parameters and build a multi-objective distribution line protection setting model with the shortest protection setting time and optimal sensitivity as the goals;

[0051] In the embodiment of the present application, the collected distribution line parameters include voltage, current, and impedance.

[0052] It should be noted that by collecting data from actual lines, an accurate mathematical model can be established to reflect the electrical characteristics of the lines. By continuously monitoring changes in line parameters, the protection settings can be dynamically adjusted to ensure that the protection devices can maintain optimal speed, reliability and sensitivity under various operating conditions, thereby ensuring stable operation of the power system.

[0053] In the embodiment of the present application, constructing a multi-objective distribution line protection setting model includes: taking range constraint, flow constraint, sensitivity constraint, and selectivity constraint as constraint conditions, and the calculation formula is as follows:

[0054]

[0055] Where M is the target fitness value, k and k′ are both constants, H is the number of protections, G is the number of protection delays, a is the weight of the extreme difference constraint, b is the weight of the power flow constraint, c is the weight of the sensitivity constraint, and d is the weight of the selectivity constraint. It is the sum of all protection delay action times. is the penalty time for not complying with the extreme constraint, is the penalty time for not complying with the power flow constraints, is the penalty time for not complying with the sensitivity constraint, is the penalty time for not complying with the selective constraint, Z line.h (g) is the line current of protection h in g-stage protection, Z set.h (g) is the fixed value setting current of protection h in g-stage protection, It is the sensitivity of protection h in g segment protection.

[0056] In the embodiment of the present application, the distribution line protection setting model is a multi-objective model that takes into account the shortest protection setting time and the optimal sensitivity, and takes the speed, reliability and sensitivity of the protection as constraints.

[0057] In the embodiment of the present application, it also includes: when the model protection setting time becomes shorter, the value of the fitness function becomes larger; when the model setting impedance value increases, the sensitivity also increases, and the fitness function value increases;

[0058] If the model does not meet any constraint, the time penalty value is increased through the weight, thereby reducing the value of the fitness function and screening out high-quality solutions; if the model meets the constraint, the sensitivity of the protection is enhanced and the overall result is optimized.

[0059] It should be noted that k′ is a smaller constant that can preferentially satisfy the constraint conditions.

[0060] S2: Taking the multi-objective distribution line protection setting value setting model as the improved Sky Eagle algorithm fitness function, the improved Sky Eagle algorithm is used to solve the model to obtain the optimal protection setting value;

[0061] It is worth noting that the Sky Eagle Algorithm is an intelligent algorithm that simulates the hunting of Sky Eagles. It has the advantages of fast convergence speed and strong optimization ability. It mainly includes five stages: initializing the population, increasing the search range, reducing the search range, expanding development, and reducing development.

[0062] (1) Initialize the population: The calculation formula for initializing the eagle individuals is:

[0063]

[0064] in, is the position of the i-th eagle individual in the j-th dimension, rand is a random number between 0 and 1; max j 、min j are the maximum and minimum values ​​that the j-th dimension individual can reach, respectively.

[0065] (2) Increasing the search range: The Skyhawk expands the search range by increasing the vertical height, which enables it to obtain the best prey. The calculation formula is:

[0066]

[0067] in, is the search position after the t+1th iteration using the search method X1, is the best position in the previous t iterations, T is the maximum number of iterations, is the average value of the search results after t iterations.

[0068] Specifically, the formula for calculating the average value of search results is:

[0069]

[0070] (3) Reduce the search range: After finding the prey, reduce the search range and circle around the prey, ready to attack at any time. The calculation expression is:

[0071]

[0072] in, is the search position after the t+1th iteration using the search method X2; Levy is the Skyhawk flight strategy.

[0073] (4) Expansion and development: In this stage, the eagle prepares to attack and slowly lowers its flight position to make its first attack to see how the prey reacts. The calculation formula is:

[0074]

[0075] in, is the search position after the t+1th iteration using the search method X3; α and δ are development coefficients, and their values ​​are 0.1.

[0076] (5) Reduce development: In this stage, the Skyhawk randomly attacks prey on the ground. The calculation formula is:

[0077]

[0078] in, It is the search position after the t+1th iteration using the search method X4, QF is the quality function, which is mainly used to balance the search, G1 and G2 are the movement of the eagle tracking the prey and the linearly decreasing flight slope, respectively.

[0079] In the embodiment of the present application, solving the model using the improved Sky Eagle algorithm includes: initializing the population through Logistics chaos mapping, and the calculation formula is:

[0080]

[0081] in, is the position of the i-th eagle individual in the j dimension after being initialized by the Logistics chaotic map, is the chaotic variable, μ is the chaos coefficient, and μ=4.

[0082] It should be noted that the chaotic sequence generated by the chaotic map has good randomness and ergodicity, which helps to improve the distribution of the initial population. Since the population initialized by the Sky Eagle algorithm is randomly generated, the population diversity is low, which affects the performance of the algorithm. This application introduces Logistics chaotic mapping to initialize the population and improves the Sky Eagle algorithm, thereby improving the overall performance of the Sky Eagle algorithm.

[0083] In the embodiment of the present application, it also includes: calculating the fitness of all eagle individuals, determining the global optimal solution and the historical optimal solution, and using the adaptive weight method to update the position of the eagle, which is expressed as:

[0084]

[0085] Among them, ω is the adaptive weight, k is the weight coefficient, is the search position after the t+1th iteration using the search method X3, α is the development coefficient, is the search position after the t+1th iteration using the search method X1, is the best position in the previous t iterations, T is the maximum number of iterations, and rand is a random number between 0 and 1. is the average value of search results after t iterations;

[0086] If the current number of iterations reaches the maximum number of iterations, the optimal protection setting for the distributed photovoltaic access to the distribution network is output; if the current number of iterations does not reach the maximum number of iterations, the iteration continues until the requirements are met.

[0087] It is worth noting that the present application adopts the adaptive weight method Sky Eagle algorithm to perform global search with a larger exponential weight in the early stage of the search, and in the later stage of the search, when approaching the optimal solution, a smaller weight is used to enhance the algorithm's local search capability.

[0088] It should be noted that the Sky Eagle algorithm is prone to fall into the problem of local optimality. The present application introduces an adaptive weight method to improve the algorithm's search capability and prevent the algorithm from falling into the local optimal solution.

[0089] S3: Embed the optimal protection setting into the switchgear to perform distribution line relay protection.

[0090] In an embodiment of the present application, the optimal protection setting is embedded in the switch device, and the distribution line relay protection also includes: dynamically matching the distributed photovoltaic access scenario, calculating the protection setting and embedding it in the switch device, and performing the distribution line relay protection.

[0091] It should be noted that the switchgear in the embodiment of the present application is a "six-in-one" super-integrated primary and secondary pole-mounted switchgear that covers the switch body, disconnector, feeder terminal, sensor, power supply unit, and backup power supply. This "six-in-one" super-integrated primary and secondary pole-mounted switchgear has an IP67 protection level, can withstand the erosion of super-strong wind, rain, and snow, as well as harsh natural environmental conditions such as dust, frost, ice, and white dew, adopts an integrated structural design, has the characteristics of deep integration of primary and secondary, and the primary collection transformers are all built-in electronic in the switch main box, with a large measurement range, good temperature characteristics, and low power loss.

[0092] It should also be noted that the data collection of the feeder terminal of the primary and secondary pole switches of the power distribution uses the DSP+ARM dual-core architecture to reduce hardware costs, achieve highly integrated collection and calculation, and efficiently process data interaction and collaboration between multiple cores. The sampling rate reaches 6.4kHz, which improves the accuracy of capturing transient fault quantities. The over-limit voltage is reduced by a fixed ratio and sampled and measured by sampling resistor voltage divider, achieving wide-range and high-precision measurement under strong noise conditions in the distribution network, ensuring that the primary current can still meet the 0.5-level accuracy requirement when it is 10A.

[0093] The above is a schematic scheme of a distributed photovoltaic access distribution line protection method of this embodiment. It should be noted that the technical scheme of the distributed photovoltaic access distribution line protection device and the technical scheme of the above-mentioned distributed photovoltaic access distribution line protection method belong to the same concept. The details of the technical scheme of the distributed photovoltaic access distribution line protection device in this embodiment that are not described in detail can all be referred to the description of the technical scheme of the above-mentioned distributed photovoltaic access distribution line protection method.

[0094] In this embodiment, a distributed photovoltaic access distribution line protection device includes:

[0095] Model building module, used to collect distribution line parameters and build a multi-objective distribution line protection setting model with the shortest protection setting time and optimal sensitivity as the goals;

[0096] A calculation module is used to use the multi-objective distribution line protection setting value setting model as the improved Sky Eagle algorithm fitness function, and use the improved Sky Eagle algorithm to solve the model to obtain the optimal protection setting value;

[0097] The protection module is used to embed the optimal protection setting into the switchgear for distribution line relay protection.

[0098] In the embodiment of the present application, the model building module includes: taking the range constraint, the flow constraint, the sensitivity constraint, and the selectivity constraint as the constraint conditions, the calculation formula is as follows:

[0099]

[0100] Where M is the target fitness value, k and k′ are both constants, H is the number of protections, G is the number of protection delays, a is the weight of the extreme difference constraint, b is the weight of the power flow constraint, c is the weight of the sensitivity constraint, and d is the weight of the selectivity constraint. It is the sum of all protection delay action times. is the penalty time for not complying with the extreme constraint, is the penalty time for not complying with the power flow constraints, is the penalty time for not complying with the sensitivity constraint, is the penalty time for not complying with the selective constraint, Z line.h (g) is the line current of protection h in g-stage protection, Z set.h (g) is the fixed value setting current of protection h in g-stage protection, It is the sensitivity of protection h in g segment protection.

[0101] This embodiment further provides a computing device, which is applicable to a distribution line protection method for distributed photovoltaic access, and includes:

[0102] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the distribution line protection method for distributed photovoltaic access proposed in the above embodiment.

[0103] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the distribution line protection method for implementing distributed photovoltaic access as proposed in the above embodiment is implemented.

[0104] The storage medium proposed in this embodiment and the distribution line protection method for realizing distributed photovoltaic access proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0105] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0106] Example 2

[0107] Reference Figure 4-5 , is an embodiment of the present invention. This embodiment is different from the first embodiment in that this embodiment verifies the beneficial effects of the present invention through comparative experiments.

[0108] This embodiment is built in the PSCAD / EMTDC simulation software as follows Figure 4 The distribution network model of distributed photovoltaic access is shown in the figure. The access capacity of distributed photovoltaic 1 is 50MW, and the access capacity of distributed photovoltaic 2 is 100WM. "Six-in-one" super-convergent distribution primary and secondary pole-mounted switchgear is installed at positions 1-6. Matlab is used for calculation. The fitness change of the distribution line protection setting value setting algorithm proposed by the present invention is as follows: Figure 5 As shown in the figure, when the number of iterations only needs to reach 50 times, the fitness value can reach a relatively large stable value, which indicates that the method of the present invention has a fast speed and high stability in solving the distribution line protection constant setting model and has a good solution effect.

[0109] Taking the short circuit faults occurring at lines f1 and f2 as an example, the conventional method and the improved Sky Eagle algorithm proposed in the present invention are compared, and the results are shown in Table 1:

[0110] Table 1 Comparison of fixed value setting results

[0111]

[0112]

[0113] As can be seen from Table 1, the setting value of the distribution line protection based on the improved Sky Eagle algorithm proposed by the present invention is larger than that of the traditional method, which improves the sensitivity of the protection and can be used as a backup protection for protection 2. The CD line range of the protection is longer, up to 62.72%, and does not reach the I-stage distance protection range of protection 3, and no false operation will occur. In terms of solution time, the method of the present invention has a faster solution time, and only takes 15 seconds to complete the model solution. The set value can be transmitted to the "six-in-one" super-converged distribution primary and secondary pole switchgear in time, and has a strong system adaptability.

[0114] The present invention forms a new type of distribution system line protection constant adaptive setting strategy for distributed photovoltaic access, and integrates the strategy into the "six-in-one" super-integrated distribution primary and secondary pole switches, which significantly improves the safe and reliable operation of the distribution network with a high proportion of distributed photovoltaic access, and further promotes the development and construction of new power systems.

[0115] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for protecting a distribution line for distributed photovoltaic access, characterized in that: include: Collect distribution line parameters and build a multi-objective distribution line protection setting model with the shortest protection setting time and optimal sensitivity as the goals; The multi-objective distribution line protection setting value setting model is used as the fitness function of the improved Sky Eagle algorithm, and the improved Sky Eagle algorithm is used to solve the model to obtain the optimal protection setting value; The optimal protection setting is embedded in the switchgear to perform distribution line relay protection.

2. The distributed photovoltaic access distribution line protection method according to claim 1, characterized in that: The construction of a multi-objective distribution line protection setting model includes: taking range constraint, power flow constraint, sensitivity constraint, and selectivity constraint as constraint conditions, and the calculation formula is as follows: Where M is the target fitness value, k and k′ are both constants, H is the number of protections, G is the number of protection delays, a is the weight of the extreme difference constraint, b is the weight of the power flow constraint, c is the weight of the sensitivity constraint, and d is the weight of the selectivity constraint. It is the sum of all protection delay action times. is the penalty time for not complying with the extreme constraint, is the penalty time for not complying with the power flow constraints, is the penalty time for not complying with the sensitivity constraint, is the penalty time for not complying with the selective constraint, Z line.h (g) is the line current of protection h in g-stage protection, Z set.h (g) is the fixed value setting current of protection h in g-stage protection, It is the sensitivity of protection h in g segment protection.

3. The distributed photovoltaic access distribution line protection method according to claim 2, characterized in that: Also includes: When the protection setting time of the model becomes shorter, the value of the fitness function becomes larger; When the model setting impedance value increases, the sensitivity will also increase, and the fitness function value will increase; If the model does not meet any constraint condition, the time penalty value is increased through the weight, thereby reducing the value of the fitness function and screening out high-quality solutions; if the model meets the constraint condition, the sensitivity of the protection is enhanced and the overall result is optimized.

4. The distributed photovoltaic access distribution line protection method according to claim 3, characterized in that: Solving the model using the improved Sky Eagle algorithm includes: initializing the population through Logistics chaos mapping, and the calculation formula is: in, is the position of the i-th eagle individual in the j dimension after being initialized by the Logistics chaotic map, is the chaotic variable, μ is the chaos coefficient, and μ=4.

5. The distributed photovoltaic access distribution line protection method according to claim 4, characterized in that: Also includes: Calculate the fitness of all individual eagles, determine the global optimal solution and the historical optimal solution, and use the adaptive weight method to update the position of the eagle, which is expressed as: Among them, ω is the adaptive weight, k is the weight coefficient, is the search position after the t+1th iteration using the search method X3, α is the development coefficient, is the search position after the t+1th iteration using the search method X1, is the best position in the previous t iterations, T is the maximum number of iterations, and rand is a random number between 0 and 1. is the average value of search results after t iterations; If the current number of iterations reaches the maximum number of iterations, the optimal protection setting for the distributed photovoltaic access to the distribution network is output; if the current number of iterations does not reach the maximum number of iterations, the iteration continues until the requirements are met.

6. The distributed photovoltaic access distribution line protection method according to claim 5, characterized in that: Embedding the optimal protection setting into the switch device to perform distribution line relay protection also includes: dynamically matching the distributed photovoltaic access scenario, calculating the protection setting and embedding it into the switch device to perform distribution line relay protection.

7. A distributed photovoltaic access distribution line protection device, characterized in that: include: Model building module, used to collect distribution line parameters and build a multi-objective distribution line protection setting model with the shortest protection setting time and optimal sensitivity as the goals; A calculation module, used for using the multi-objective distribution line protection setting value setting model as the improved Sky Eagle algorithm fitness function, using the improved Sky Eagle algorithm to solve the model, and obtaining the optimal protection setting value; The protection module is used to embed the optimal protection setting into the switchgear to perform relay protection of the distribution line.

8. The distributed photovoltaic access distribution line protection device according to claim 7, characterized in that: The model building module includes: taking range constraint, flow constraint, sensitivity constraint and selectivity constraint as constraint conditions, and the calculation formula is as follows: Where M is the target fitness value, k and k′ are both constants, H is the number of protections, G is the number of protection delays, a is the weight of the extreme difference constraint, b is the weight of the power flow constraint, c is the weight of the sensitivity constraint, and d is the weight of the selectivity constraint. It is the sum of all protection delay action times. is the penalty time for not complying with the extreme constraint, is the penalty time for not complying with the power flow constraints, is the penalty time for not complying with the sensitivity constraint, is the penalty time for not complying with the selective constraint, Z line.h (g) is the line current of protection h in g-stage protection, Z set.h (g) is the fixed value setting current of protection h in g-stage protection, It is the sensitivity of protection h in g segment protection.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the distribution line protection method for distributed photovoltaic access as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the distributed photovoltaic access distribution line protection method as described in any one of claims 1 to 6.

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