Power distribution network contact diagram generation method and power distribution network contact diagram generation device

Abstract

The invention provides a power distribution network contact diagram generation method and a power distribution network contact diagram generation device. The method comprises the following steps: firstly, based on a topological structure of a power distribution network, generating an initial population meeting radial topological constraints, and setting constraint conditions; performing load flow calculation on each individual in the initial population to obtain network loss and constraint violation degree; adjusting a penalty coefficient for the proportion of individuals violating the constraint condition in the initial population, and constructing a fitness function by combining the penalty coefficient, network loss and constraint violation degree; and finally, carrying out genetic operation on the initial population based on a fitness function until a power distribution network connection diagram which meets constraint conditions and has minimum network loss is generated. According to the invention, the generation efficiency of the power distribution network contact diagram is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent power distribution network, and particularly relates to a power distribution network connection diagram generation method and a power distribution network connection diagram generation device. BACKGROUND

[0002] With the continuous development of power systems and the increasing demand for intelligentization, the optimization design and operation management of power distribution networks have become an important issue in the field of electric power. As an important tool for describing the connection relationship between nodes, branches and switches in a power distribution network, the generation of a power distribution network connection diagram is of great significance for improving the power supply reliability of the power distribution network, reducing network loss and improving overall operation efficiency.

[0003] In the prior art, the generation of a power distribution network connection diagram mainly relies on manual experience and manual adjustment. In combination with on-site line patrol feedback, a dispatcher first circumscribes overloaded or unqualified branches, and then repeatedly adjusts the opening and closing combination of switches on a hand-drawn single-line diagram until a standby connection scheme that can quickly close and form a radial temporary power supply path during a fault is determined, thereby providing an emergency switching operation reference.

[0004] However, in the above-mentioned manner, there is a problem of low efficiency in generating a power distribution network connection diagram. SUMMARY

[0005] The embodiments of the present application provide a power distribution network connection diagram generation method and a power distribution network connection diagram generation device to improve the generation efficiency of a power distribution network connection diagram.

[0006] In a first aspect, the embodiments of the present application provide a power distribution network connection diagram generation method, comprising:

[0007] Based on the topological structure of the power distribution network, an initial population satisfying the radial topological constraint is generated, and a constraint condition is set;

[0008] A power flow calculation is performed on each individual in the initial population to obtain network loss and constraint violation degree, and the constraint violation degree includes voltage out-of-limit degree and branch overload degree;

[0009] The proportion of individuals in the initial population that violate the constraint condition is adjusted to obtain a penalty coefficient, and an adaptability function is constructed in combination with the penalty coefficient, the network loss and the constraint violation degree;

[0010] Based on the adaptability function, a genetic operation is performed on the initial population until a power distribution network connection diagram satisfying the constraint condition and having the minimum network loss is generated.

[0011] In a possible implementation, node parameters and branch connection relationships of the power distribution network are acquired, wherein the node parameters include load point positions, substation coordinates and power distribution transformer capacities, and the branch connection relationships include starting nodes, ending nodes, conductor types, resistance and reactance parameters of each line; the node parameters and the branch connection relationships are represented by a graph structure to generate a topology structure; and the total number of branches is determined based on the topology structure.

[0012] Integer coding is adopted to encode the branch switch states, and each individual is represented in the form of a binary code string, wherein the length of the binary code string is the total number of branches;

[0013] A preset number of individuals are randomly generated, and the topology feasibility is verified by depth-first search to generate an initial population satisfying the radial topology constraint.

[0014] In a possible implementation, the constraint conditions include voltage deviation constraints and branch current constraints, wherein the voltage deviation constraints include rated voltage values and a preset voltage fluctuation range, and the branch current constraints refer to maximum current values of each branch.

[0015] In a possible implementation, electrical characteristic parameters are acquired, including load power of each node, resistance and reactance values of each branch; and each individual in the initial population is subjected to power flow calculation in combination with the electrical characteristic parameters, the power flow calculation including initialization of the network, forward calculation and backward calculation to obtain voltage amplitudes of each node and branch current values; and network loss and constraint violation degree are calculated according to the voltage amplitudes, the branch current values and the resistance values of the branches.

[0016] In a possible implementation, the network loss is calculated by the formula:

[0017] wherein, is the network loss, is the total number of branches in the power distribution network, is the current value of the i th branch, is the resistance value of the i th branch. The voltage out-of-limit degree is calculated by the formula:

[0018]

[0019] wherein, is the voltage out-of-limit degree, is the total number of nodes in the power distribution network, is the voltage amplitude of the i th node, is the preset rated voltage, is the preset voltage deviation. ​​​​​

[0020] Through the formula: Calculate the branch overload degree;

[0021] in, This refers to the branch circuit overload. This refers to the maximum current value of each branch.

[0022] In one possible implementation, all individuals in the initial population are traversed to determine the number of individuals that do not meet the constraints; the proportion of the number of individuals that do not meet the constraints to the total number of individuals in the population is calculated as the constraint violation ratio; if the constraint violation ratio is greater than the violation threshold, the penalty coefficient is increased.

[0023] In one possible implementation, the formula is: Construct the fitness function;

[0024] in, This refers to the fitness function. This refers to the first weighting coefficient. This refers to the second weighting coefficient. This refers to network loss. This refers to voltage exceeding the limit. This refers to the branch circuit overload. This refers to the adjusted penalty coefficient.

[0025] In one possible implementation, a roulette wheel selection mechanism is used to select individuals from the initial population according to preset screening conditions to enter the mating pool; crossover and mutation operations are performed on the individuals in the mating pool to generate a new generation population. The crossover operation adopts a single-point crossover strategy, and the mutation operation randomly flips the gene positions encoded in the individual according to a preset mutation probability.

[0026] For the new generation of population, the steps of adjusting the penalty coefficient for the proportion of individuals that violate the constraints and calculating the value of the fitness function are repeated until the preset termination condition is met, and a simplified diagram of the distribution network interconnection is generated.

[0027] In one possible implementation, the preset termination condition includes any one of the following: the number of iterations exceeds a preset threshold, the change in fitness value for a consecutive first preset number of iterations is less than a preset convergence threshold, and all individuals in the new generation population satisfy the constraint conditions for a consecutive second preset number of iterations and the network loss no longer decreases.

[0028] Secondly, embodiments of this application provide a distribution network interconnection diagram generation device, comprising:

[0029] The first generation module is used to generate an initial population that satisfies the radial topology constraints based on the topology of the distribution network, and to set the constraint conditions.

[0030] The module is used to perform power flow calculations on each individual in the initial population to obtain network loss and constraint violation degree, which includes voltage overrun and branch overload.

[0031] The processing module is used to adjust the penalty coefficient for the proportion of individuals violating the constraints in the initial population, and to construct a fitness function by combining the penalty coefficient, network loss and the degree of constraint violation.

[0032] The second generation module is used to perform genetic operations on the initial population based on the fitness function until a simplified distribution network interconnection diagram that satisfies the constraints and minimizes network loss is generated.

[0033] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0034] The memory stores the instructions that the computer executes;

[0035] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0036] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0037] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0038] The distribution network interconnection diagram generation method and apparatus provided in this application first generate an initial population through radial topology constraints, and simultaneously embed constraints on distribution network security to ensure the structural feasibility of the initial individuals. Then, network losses, voltage exceedance rates, and branch overload rates for each topology are extracted through power flow calculations, achieving a quantitative assessment of constraint violations. Subsequently, the penalty coefficient is adjusted based on the proportion of violating individuals, and a fitness function that balances economy and security is constructed to evaluate the quality of individuals. Finally, a genetic operation is performed under this function, iteratively converging to a distribution network interconnection diagram that satisfies the constraints and minimizes losses, thus improving the generation efficiency of the distribution network interconnection diagram. Attached Figure Description

[0039] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0040] Figure 1A flowchart illustrating the method for generating simplified distribution network interconnection diagrams provided in this application embodiment. Figure 1 ;

[0041] Figure 2 A flowchart illustrating the method for generating simplified distribution network interconnection diagrams provided in this application embodiment. Figure 2 ;

[0042] Figure 3 This is a schematic diagram of the distribution network interconnection diagram generation device provided in the embodiments of this application;

[0043] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0044] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0045] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0046] First, let me explain the terms used in this application:

[0047] Distribution network interconnection diagram: This is a visual circuit diagram used to reflect the connection relationships of power sources, key nodes, and all branches. It can display the main power supply line in closed operation and mark disconnected backup interconnection lines with different styles, providing clear operational guidance for dispatchers. It is of great significance for improving the power supply reliability of the distribution network, reducing network losses, and improving overall operational efficiency.

[0048] DFS stands for Depth-First Search. DFS is a graph traversal algorithm whose core idea is to start from the starting point and explore as deeply as possible along a path until no further progress is possible. Then, backtrack to the previous node, choose another unexplored path, and continue in this way until all reachable nodes have been traversed.

[0049] Roulette wheel selection mechanism: This means that the better an individual's fitness, the larger the area it occupies on the roulette wheel, and the higher its probability of being selected.

[0050] Mating pool: refers to the place where individuals selected from the roulette wheel selection mechanism gather.

[0051] Single-point crossover strategy: This refers to a recombination operator widely used in genetic algorithms. Its core operation is to first randomly select a crossover point within the length range of the chromosome coding strings of a pair of parent individuals randomly chosen from the mating pool. Then, all gene segments after that crossover point are interchanged between the two parent individuals, thereby generating two new offspring individuals.

[0052] In existing technologies, patrol personnel mark overloaded or abnormal branches discovered on-site onto paper single-line diagrams. Dispatchers then rely on experience to repeatedly adjust the tie switch symbols until a backup tie scheme is determined that can be quickly closed in case of a fault, forming a radial temporary power supply path for reference during emergency switching operations. However, existing technologies rely on manual experience and adjustments, resulting in low efficiency in generating simplified distribution network tie diagrams.

[0053] In response, the inventors argue that, firstly, considering the need for individuals representing interconnection schemes to satisfy radial topological constraints, the abstract switching states are transformed into a computable coded form. Furthermore, a graph traversal algorithm is used to pre-filter invalid solutions, providing high-quality solutions for subsequent optimization. Simultaneously, considering both economy and security, network loss and constraint violation severity are integrated into a fitness function. The penalty coefficient is dynamically adjusted based on the proportion of individuals violating constraints in the current population, avoiding the imbalance between constraints and objectives under traditional fixed coefficients. This ensures that the optimization process prioritizes security constraints while gradually approaching economic optimality, ultimately generating a simplified distribution network interconnection diagram that satisfies the radial requirement and minimizes network loss, thereby improving the generation efficiency of the simplified distribution network interconnection diagram.

[0054] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0055] Figure 1 A flowchart illustrating the method for generating simplified distribution network interconnection diagrams provided in this application embodiment. Figure 1 ,like Figure 1 As shown, the method includes:

[0056] S101. Based on the topology of the distribution network, an initial population that satisfies the radial topology constraints is generated using integer encoding, and the constraints are set.

[0057] Among them, the topology refers to the structural diagram that reflects the connection method between nodes and branches of the distribution network.

[0058] Radial topology constraints refer to the requirements for the operation rules of a distribution network. Specifically, the network structure must be tree-like, and must meet the requirements of connectivity and acyclicity. This means that all load nodes originating from the power source must be reachable, and there must be no closed loops in the network.

[0059] The initial population refers to a batch of legal network operation schemes randomly generated at the beginning of the genetic algorithm, and each scheme is the individual in the embodiments of this application.

[0060] Constraints refer to the standards that a distribution network must meet during operation. In one possible implementation, constraints include voltage deviation constraints and branch current constraints.

[0061] Voltage deviation constraints refer to the requirements for the voltage at each node in a distribution network. These constraints include the rated voltage value and the preset voltage fluctuation range. In practical applications, the nominal bus voltage is used as the rated voltage value, and it is stipulated that the actual voltage at each node must fluctuate within a certain percentage of the rated voltage.

[0062] For example, a permissible deviation threshold of 5% is set, meaning the preset voltage fluctuation range is such that all node voltages must be maintained between 95% and 105% of the rated voltage value. If the voltage of a node exceeds this range, it is considered to have violated the voltage deviation constraint, and the degree of violation is quantified by calculating the sum of the differences between the actual voltage of each node and the permissible range.

[0063] Branch current constraint refers to the maximum current value of each branch. In practical applications, the branch current constraint needs to be set according to the physical characteristics of the line or the parameters of the protection device, defining the maximum current value allowed to pass through each branch.

[0064] For example, if a branch uses a specific type of conductor with a long-term allowable current carrying capacity of 500 amperes, then the maximum current threshold for that branch is set to 500 amperes. If the current in a branch exceeds this threshold in the power flow calculation, it is determined to be a violation of the branch current constraint, and the degree of overload is quantified by accumulating the values ​​of the actual current exceeding the threshold in each branch.

[0065] S102. Perform power flow calculations on each individual in the initial population to obtain network loss and constraint violation degree.

[0066] Power flow calculation refers to calculating the voltage of each node and the current of each branch in the entire network.

[0067] Network loss refers to the electrical energy lost as heat due to the resistance of the line.

[0068] The degree of constraint violation refers to the extent to which the scheme deviates from the safe operation standard. The degree of constraint violation includes voltage over-limit and branch overload.

[0069] Voltage over-limit refers to the degree to which the node voltage deviates from the rated voltage.

[0070] Branch overload refers to the degree to which the current on a line exceeds its safe carrying capacity.

[0071] In one possible implementation, electrical characteristic parameters are first obtained, and then power flow calculations are performed on each individual in the initial population based on these parameters. Then, network losses and constraint violations are calculated based on voltage magnitudes, branch current values, and branch resistance values.

[0072] The electrical characteristic parameters include the load power of each node and the resistance and reactance values ​​of the branches. In practical applications, these electrical characteristic parameters need to be entered based on actual power grid measurement data or typical design parameters to form a complete electrical parameter database.

[0073] Power flow calculation includes network initialization, forward calculation, and back-substitution calculation to obtain the voltage magnitude and branch current value of each node.

[0074] It should be understood that, due to the radial structure of the distribution network, the forward-backward substitution method is used for power flow solution. Specifically, network initialization determines the current topology based on the corresponding switch states of individual nodes, and constructs a radial network with substations or power supply nodes as root nodes. Forward calculation calculates the current distribution of each branch level by level from the network's end nodes to the power supply nodes. First, the current of the end node is calculated based on its load power, and then the current value is accumulated upstream of the branch until the root node. Backward substitution calculation starts from the power supply nodes and, based on the branch currents and line impedance parameters obtained from forward calculation, calculates the voltage values ​​of each node level by level. The root node voltage is set to the rated value, and the downstream node voltage is obtained by subtracting the branch voltage drop from the upstream voltage.

[0075] In one possible implementation, network loss and constraint violation severity can be calculated using the formula: Calculate network loss;

[0076] in, This refers to network loss. This refers to the total number of branches in the distribution network. It refers to the first The current value of each branch, It refers to the first The resistance value of each branch.

[0077] It should be understood that the calculation of network loss is to sum the resistance loss of all closed branches, that is, to multiply the square of the current of each branch by its resistance value to obtain the total loss of the distribution network, which is the network loss.

[0078] Through the formula: Calculate the voltage consumption exceeding the limit;

[0079] in, This refers to voltage exceeding the limit. This refers to the total number of nodes in the distribution network. It refers to the first The voltage amplitude at each node, This refers to the preset rated voltage. This refers to the preset voltage deviation.

[0080] It should be understood that voltage deviation is the sum of all node voltage deviations from the rated voltage, and only the portion exceeding the allowable deviation threshold is counted.

[0081] Through the formula: Calculate the branch overload degree;

[0082] in, This refers to the branch circuit overload. This refers to the maximum current value of each branch.

[0083] It should be understood that the calculation of branch overload is the sum of all branch currents exceeding the maximum allowable value.

[0084] In practical applications, network loss and constraint violation severity are used as evaluation metrics for each individual and passed to subsequent steps for fitness function construction. If non-convergence occurs in the power flow calculation, such as voltage collapse or exceeding the iteration limit, the individual is directly marked as an infeasible solution and assigned a very low fitness value to avoid entering the next generation of the new population.

[0085] S103. Adjust the penalty coefficient for individuals in the initial population that violate the constraints, and construct a fitness function by combining the penalty coefficient, network loss, and degree of constraint violation.

[0086] The penalty coefficient is used to adjust the weight of an individual when the proportion of individuals in the population that violate the constraints is reached.

[0087] One possible implementation of adjusting the penalty coefficient involves iterating through all individuals in the initial population to determine the number of individuals that do not meet the constraints. Then, the proportion of these individuals to the total population is calculated as the constraint violation rate. If the violation rate exceeds a violation threshold, the penalty coefficient is increased.

[0088] Among them, the violation threshold refers to the threshold that measures the proportion of violations of constraints.

[0089] In practical applications, during each generation of genetic iteration, all individuals in the initial population are traversed to check whether they meet the voltage deviation constraint and the branch current constraint. Specifically, for the voltage deviation constraint, it is determined whether the voltage of each node exceeds the allowable deviation range of the rated voltage (e.g., ±5%); for the branch current constraint, it is determined whether the current of each branch exceeds its maximum allowable value.

[0090] The number of individuals that do not meet any constraint simultaneously is counted, and the proportion of such individuals to the total number of individuals in the population is calculated and denoted as the constraint violation ratio.

[0091] For example, if a population contains 100 individuals, and 30 of them have voltage overruns or branch overloads, then the violation rate is 30%.

[0092] Next, the penalty coefficient is adjusted based on the comparison between the constraint violation rate and the preset threshold. If the constraint violation rate is greater than the violation threshold, it indicates that there are too many infeasible solutions in the population, and the penalty coefficient needs to be increased to strengthen the constraint handling. The adjustment formula is as follows:

[0093]

[0094] in, This represents the penalty coefficient for the current generation. This represents the penalty coefficient for the next generation, i.e., the adjusted penalty coefficient. and These represent the preset scaling factors for increasing and decreasing the penalty coefficient, respectively. It is the set threshold for the proportion of violations of constraints. This indicates a violation of the constraint ratio.

[0095] in, and It is usually set to 0.1~0.3 to control the range of change of the penalty coefficient.

[0096] The fitness function is used to select individuals. The higher the fitness function value, the more likely the corresponding individual will be selected for reproduction.

[0097] It should be understood that network losses directly reflect the economic efficiency of distribution network operation and need to be minimized. Voltage overruns and branch overloads are converted into negative impacts on fitness through penalty terms. Therefore, in one possible implementation, this is achieved through the formula: Construct the fitness function.

[0098] in, This refers to the fitness function. This refers to the first weighting coefficient. This refers to the second weighting coefficient. This refers to network loss. This refers to voltage exceeding the limit. This refers to the branch circuit overload. This refers to the adjusted penalty coefficient.

[0099] S104. Perform genetic operations on the initial population based on the fitness function until a simplified distribution network interconnection diagram that satisfies the constraints and minimizes network loss is generated.

[0100] Genetic manipulation is used to produce a better next generation from the current generation.

[0101] In one possible implementation, a roulette wheel selection mechanism is first used to select individuals from the initial population according to preset screening criteria to enter the mating pool. Then, crossover and mutation operations are performed on the individuals in the mating pool to generate a new generation population. Finally, for the new generation population, the steps of adjusting the penalty coefficient for the proportion of individuals violating the constraints and calculating the fitness function are repeated until a preset termination condition is met, generating a simplified distribution network interconnection diagram.

[0102] Among them, the preset screening conditions refer to the rules for selecting individuals from the initial population to enter the mating pool.

[0103] For example, individuals with smaller fitness function values ​​have a higher probability of being selected. Each individual's fitness function value is converted into relative fitness, which is the reciprocal of the fitness function value. For instance, an individual with a fitness of 1000 has a relative fitness of 1 / 1000.

[0104] Next, the sum of the relative fitness of all individuals is calculated, and the selection probability of each individual is calculated, where the selection probability is equal to the individual's relative fitness divided by the sum.

[0105] Then, a roulette wheel process is simulated by generating random numbers, and individuals of the same number as the population size are selected to enter the mating pool according to probability. If an individual has extremely low fitness (e.g., an infeasible solution), its probability of being selected approaches 0.

[0106] Crossover is used to simulate the recombination process of biological genes in genetic algorithms. Crossover employs a single-point crossover strategy.

[0107] For example, individuals in a randomized mating pool exchange partial gene segments at a preset crossover probability (80%). For instance, two parent individuals are coded as 1,0,1,1,0 and 0,1,0,0,1. The third position is randomly selected as the crossover point to generate offspring 1,0,0,0,1 and 0,1,1,1,0. After crossover, it is necessary to verify whether the offspring satisfy the radial topological constraints; if they are violated, crossover is repeated.

[0108] The mutation operation randomly flips gene loci in an individual's coding sequence according to a preset mutation probability. The preset mutation probability refers to the pre-defined probability value for a single gene locus to be flipped during the mutation operation.

[0109] For example, certain gene positions in an individual's code are randomly flipped according to a preset mutation probability (5%). For example, an individual might be coded as 1, 0, 1, 0, and after mutation, it might become 1, 1, 1, 0. After mutation, the topological feasibility must also be verified through a depth-first search; if it fails, the mutation is abandoned.

[0110] For the new generation of individuals, the steps of adjusting the penalty coefficient based on the proportion of individuals violating the constraints and calculating the fitness function are repeated. For example, if the proportion of individuals violating the constraints in the new generation is 25%, exceeding the threshold of 20%, the penalty coefficient is increased from 10 to 12, assuming an increase in the proportion factor of 0.2. The updated penalty coefficient is used to recalculate the fitness values ​​of all individuals.

[0111] The preset termination condition is used to stop the iteration process. In one possible implementation, the preset termination condition includes any one of the following: the number of iterations exceeds a preset number threshold, the change in fitness value for the first preset number of iterations is less than a preset convergence threshold, or all individuals in the new generation population satisfy the constraint conditions for the second preset number of iterations and the network loss no longer decreases.

[0112] It should be understood that each iteration includes selection, crossover, mutation, and calculation of the fitness function.

[0113] The iteration count refers to the number of times the iteration is performed. When this number exceeds a preset threshold, the iteration needs to be stopped.

[0114] The first preset iteration number refers to the threshold for measuring the number of consecutive iterations. During this continuous iteration process, if the change in fitness value is less than the preset convergence threshold, then the preset termination condition is met.

[0115] The preset convergence threshold is a threshold used to measure the change in fitness value over multiple consecutive iterations. For example, if the change in fitness value over 10 consecutive generations is less than 0.1% of the preset convergence threshold, then the preset termination condition is met.

[0116] The second preset iteration number also refers to the threshold for measuring the number of consecutive iterations. In this continuous iteration process, if all individuals meet the constraints and the network loss no longer decreases, then the preset termination condition is met.

[0117] Once the preset termination condition is met, in one possible implementation, the individual with the smallest fitness function value is selected from the final new generation population, its switching state code is parsed, and the corresponding distribution network interconnection diagram is generated. For example, an optimal individual is coded as 1, 0, 1, 0, 1, indicating that branches 1, 3, and 5 are closed, and the remaining branches are disconnected. This configuration minimizes network losses while satisfying voltage and current constraints.

[0118] The distribution network interconnection diagram generation method provided in this application sets constraints on the initial population of radial topological constraints to ensure that the initial individuals meet structural feasibility requirements. Simultaneously, power flow calculations are performed on the individuals in the initial population to determine network losses, voltage exceedance (representing node voltage exceedance), and branch overload (representing branch current overload). Then, based on the dynamic proportion of individuals violating constraints in the initial population, the penalty coefficient is adaptively adjusted to construct an augmented fitness function that balances economy and safety. Finally, a genetic operation is performed based on the fitness function, iteratively evolving until convergence, ultimately outputting the optimal distribution network interconnection diagram with minimum network losses and zero constraint violations, thus improving the generation efficiency of the distribution network interconnection diagram.

[0119] Figure 2 A flowchart illustrating the method for generating simplified distribution network interconnection diagrams provided in this application embodiment. Figure 2 ,like Figure 1 As shown, in this embodiment... Figure 3 Based on the embodiments, the method for generating simplified diagrams of distribution network interconnections is described in detail. The method includes:

[0120] S201. Obtain the node parameters and branch connection relationships of the distribution network.

[0121] Among them, node parameters refer to the information possessed by each node in the distribution network, including the location of load points, substation coordinates, and distribution transformer capacity.

[0122] Branch connection relationships refer to the information possessed by each line or switch in the distribution network. Branch connection relationships include the starting node, ending node, conductor type, resistance and reactance parameters of each line.

[0123] S202. Represent the node parameters and branch connection relationships using a graph structure to generate the topology;

[0124] S203. Determine the total number of branches based on the topology.

[0125] Representing node parameters and branch connections using a graph structure means that nodes represent electrical equipment connection points in the distribution network, and branches represent switches or lines, ensuring that the model can accurately reflect the physical connection relationships of the actual distribution network.

[0126] The total number of branches refers to the total number of all branches that make up the distribution network.

[0127] S204. Integer encoding is used to encode the branch switch status, and each individual is represented in the form of a binary encoded string.

[0128] Among them, the branch switch status refers to whether the switch on each line in the distribution network is closed or open.

[0129] For example, each branch corresponds to a gene bit, where a gene value of 0 indicates that the branch switch is in the open state, and a gene value of 1 indicates that it is in the closed state.

[0130] The length of the binary encoded string is equal to the total number of branches. That is, each individual is composed of a binary encoded string of length equal to the total number of branches, representing a switch configuration scheme.

[0131] For example, if the distribution network contains 50 branches, each individual branch is a 50-bit binary code, with each bit corresponding to the switching state of one branch.

[0132] S205. Randomly generate a preset number of individuals, and verify the topological feasibility through depth-first search to generate an initial population that satisfies the radial topological constraints.

[0133] The preset number refers to the set value of the number of individuals. In practical applications, the initial population size can be set according to the complexity of the problem, usually between 50 and 200 individuals.

[0134] The feasibility of the topology is verified using a depth-first search. In practical applications, starting from the power node, all nodes connected by closed branches are traversed. If all load nodes are visited and no loops exist after traversal, the structure is considered radial. Individuals that do not meet the constraints are directly removed and regenerated until the preset population size is reached. This process ensures that every individual in the initial population is a feasible radial network structure.

[0135] During the verification process, if a loop (i.e., a non-tree edge) or an isolated node is detected, the individual is deemed invalid. For example, if a closed branch in an individual's encoding forms a loop, the same node will be visited repeatedly during the DFS traversal, and the individual must be discarded. Ultimately, all individuals in the initial population satisfy the basic requirements for radial operation of the distribution network, providing a feasible solution space for subsequent genetic optimization.

[0136] The distribution network interconnection diagram generation method provided in this application provides complete data for topology modeling by collecting node parameters and branch connection relationships of the distribution network. Next, the node parameters and branch connection relationships are converted into a graph structure to achieve digital topology mapping of the distribution network. Then, the total number of branches is counted based on the graph structure to provide a quantitative basis for subsequent encoding length. Integer encoding is then used to represent the switching state of each branch, and these are concatenated into a binary string of the same length as the total number of branches, achieving a one-to-one correspondence between individual genotypes and network topology. Finally, after randomly generating a preset number of individuals, a depth-first search is used to detect radial connectivity and acyclicity to ensure that the initial population satisfies topological feasibility, thereby improving the accuracy of the initial population.

[0137] Figure 3This is a schematic diagram of the distribution network interconnection diagram generation device provided in the embodiments of this application, such as... Figure 4 As shown, the power distribution network interconnection diagram generation device 30 provided in this embodiment includes:

[0138] The first generation module 301 is used to generate an initial population that satisfies radial topological constraints based on the topology of the distribution network, and to set the constraint conditions.

[0139] Module 302 is obtained, which is used to perform power flow calculations on each individual in the initial population to obtain network loss and constraint violation degree, including voltage over-limit degree and branch overload degree.

[0140] The processing module 303 is used to adjust the penalty coefficient for the proportion of individuals violating the constraints in the initial population, and to construct a fitness function by combining the penalty coefficient, network loss and the degree of constraint violation.

[0141] The second generation module 304 is used to perform genetic operations on the initial population based on the fitness function until a simplified diagram of the distribution network connection that satisfies the constraints and minimizes network loss is generated.

[0142] In one possible implementation, the first generation module 301 is further used to obtain the node parameters and branch connection relationships of the distribution network, wherein the node parameters include the load point location, substation coordinates and distribution transformer capacity, and the branch connection relationships include the starting node, ending node, conductor type, resistance and reactance parameters of each line; the node parameters and branch connection relationships are represented by a graph structure to generate a topology structure; and the total number of branches is determined based on the topology structure.

[0143] The branch switch states are encoded using integer encoding, and each individual is represented in the form of a binary encoded string, where the length of the binary encoded string is the total number of branches. A preset number of individuals are randomly generated, and the feasibility of the topology is verified by depth-first search to generate an initial population that satisfies the radial topology constraints.

[0144] In one possible implementation, module 302 is further used to obtain electrical characteristic parameters, including the load power of each node, the resistance value and reactance value of the branch; combined with the electrical characteristic parameters, power flow calculation is performed on each individual in the initial population, including network initialization, forward calculation and back-substitution calculation, to obtain the voltage amplitude and branch current value of each node; based on the voltage amplitude, branch current value and branch resistance value, network loss and constraint violation degree are calculated.

[0145] In one possible implementation, module 302 is also used via the formula: Calculate network loss;

[0146] in, This refers to network loss. This refers to the total number of branches in the distribution network. It refers to the first The current value of each branch, It refers to the first The resistance value of each branch.

[0147] Through the formula: Calculate the voltage consumption exceeding the limit;

[0148] in, This refers to voltage exceeding the limit. This refers to the total number of nodes in the distribution network. It refers to the first The voltage amplitude at each node, This refers to the preset rated voltage. This refers to the preset voltage deviation.

[0149] Through the formula: Calculate the branch overload degree;

[0150] in, This refers to the branch circuit overload. This refers to the maximum current value of each branch.

[0151] In one possible implementation, the processing module 303 is further configured to traverse all individuals in the initial population, determine the number of individuals that do not meet the constraints, calculate the proportion of the number of individuals to the total number of individuals in the population as the constraint violation ratio, and increase the penalty coefficient if the constraint violation ratio is greater than the violation threshold.

[0152] In one possible implementation, module 302 is also used via the formula: Construct the fitness function; where, This refers to the fitness function. This refers to the first weighting coefficient. This refers to the second weighting coefficient. This refers to network loss. This refers to voltage exceeding the limit. This refers to the branch circuit overload. This refers to the adjusted penalty coefficient.

[0153] In one possible implementation, the second generation module 304 is further configured to use a roulette wheel selection mechanism to select individuals from the initial population according to preset screening conditions to enter the mating pool; perform crossover and mutation operations on the individuals in the mating pool to generate a new generation population. The crossover operation adopts a single-point crossover strategy, and the mutation operation randomly flips the gene positions in the individual encoding according to a preset mutation probability.

[0154] For the new generation of population, the steps of adjusting the penalty coefficient for the proportion of individuals that violate the constraints and calculating the value of the fitness function are repeated until the preset termination condition is met, and a simplified diagram of the distribution network interconnection is generated.

[0155] The distribution network interconnection diagram generation device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0156] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. ​ As shown, the electronic device 40 provided in this embodiment includes at least one processor 401 and a memory 402. Optionally, the electronic device 40 further includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus 404.

[0157] In a specific implementation, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above-described method.

[0158] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0159] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0160] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0161] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0162] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0163] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0164] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0165] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0166] The division of units is merely a logical functional division; 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 indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0167] 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.

[0168] In addition, 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.

[0169] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0170] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0171] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for generating a simplified diagram of a power distribution network interconnection, characterized in that, include: Based on the topology of the distribution network, an initial population that satisfies the radial topology constraints is generated, and the constraints are set. For each individual in the initial population, power flow calculation is performed to obtain network loss and constraint violation degree, which includes voltage limit exceedance degree and branch overload degree. The penalty coefficient is adjusted based on the proportion of individuals in the initial population that violate the constraints, and a fitness function is constructed by combining the penalty coefficient, the network loss, and the degree of constraint violation. Genetic operations are performed on the initial population based on the fitness function until a simplified distribution network interconnection diagram that satisfies the constraints and minimizes the network loss is generated.

2. The method according to claim 1, characterized in that, The distribution network-based topology generates an initial population that satisfies radial topological constraints, including: Obtain the node parameters and branch connection relationships of the power distribution network, wherein the node parameters include the load point location, substation coordinates and distribution transformer capacity, and the branch connection relationships include the starting node, ending node, conductor type, resistance and reactance parameters of each line; The node parameters and the branch connection relationships are represented using a graph structure to generate the topology. Based on the aforementioned topology, the total number of branches is determined; The branch switch status is encoded using integer encoding, and each individual is represented in the form of a binary encoded string, wherein the length of the binary encoded string is the total number of branches; A preset number of individuals are randomly generated, and the topological feasibility is verified by depth-first search to generate an initial population that satisfies the radial topological constraints.

3. The method according to claim 2, characterized in that, The constraints include voltage deviation constraints and branch current constraints. The voltage deviation constraints include the rated voltage value and the preset voltage fluctuation range. The branch current constraints refer to the maximum current value of each branch.

4. The method according to any one of claims 1-3, characterized in that, The process of performing power flow calculations on each individual in the initial population to obtain network loss and constraint violation levels includes: Obtain electrical characteristic parameters, including the load power of each node, the resistance value of the branch, and the reactance value; Based on the electrical characteristic parameters, power flow calculation is performed on each individual in the initial population. The power flow calculation includes network initialization, forward calculation, and back-substitution calculation to obtain the voltage magnitude and branch current value of each node. The network loss and the degree of constraint violation are calculated based on the voltage amplitude, the branch current value, and the branch resistance value.

5. The method according to claim 4, characterized in that, The step of calculating the network loss and the degree of constraint violation based on the voltage amplitude, the branch current value, and the branch resistance value includes: Through the formula: Calculate the network loss; Among them, the This refers to the network loss, the This refers to the total number of branches in the power distribution network. It refers to the first The current value of each branch, the It refers to the first The resistance value of each branch; Through the formula: Calculate the voltage over-limit consumption; Among them, the This refers to the voltage exceeding the limit, the This refers to the total number of nodes in the power distribution network. It refers to the first The voltage amplitude of each node, the This refers to the preset rated voltage. This refers to the preset voltage deviation; Through the formula: Calculate the overload degree of the branch; Among them, the This refers to the overload degree of the branch circuit. This refers to the maximum current value of each branch.

6. The method according to claim 1, characterized in that, The adjustment of the penalty coefficient for the proportion of individuals in the initial population that violate the constraints includes: Iterate through all individuals in the initial population and determine the number of individuals that do not meet the constraints. The proportion of the number of the stated individuals to the total number of individuals in the population is calculated as the violation rate of the constraint. If the proportion of violations of constraints is greater than the violation threshold, the penalty coefficient is increased.

7. The method according to claim 6, characterized in that, The process of constructing a fitness function by combining the penalty coefficient, the network loss, and the degree of constraint violation includes: Through the formula: Construct the fitness function; Among them, the This refers to the fitness function, the This refers to the first weighting coefficient, the aforementioned This refers to the second weighting coefficient, the aforementioned This refers to the network loss, the This refers to the voltage exceeding the limit, the This refers to the overload degree of the branch circuit. This refers to the adjusted penalty coefficient.

8. The method according to claim 1, characterized in that, The genetic operation on the initial population based on the fitness function until a simplified distribution network interconnection diagram that satisfies the constraints and minimizes the network loss is generated includes: A roulette wheel selection mechanism is used to select individuals from the initial population according to preset screening criteria to enter the mating pool; Crossover and mutation operations are performed on individuals in the mating pool to generate a new generation population. The crossover operation adopts a single-point crossover strategy, and the mutation operation randomly flips the gene positions in the individual's encoding according to a preset mutation probability. For the new generation population, the steps of adjusting the penalty coefficient for the proportion of individuals that violate the constraints and calculating the value of the fitness function are repeated until a preset termination condition is met, and the simplified diagram of the distribution network interconnection is generated.

9. The method according to claim 8, characterized in that, The preset termination conditions include any one of the following: the number of iterations exceeds a preset threshold, the fitness value changes less than a preset convergence threshold for a consecutive first preset number of iterations, or all individuals in the new generation population satisfy the constraint conditions for a consecutive second preset number of iterations and the network loss no longer decreases.

10. A device for generating simplified diagrams of power distribution network interconnections, characterized in that, include: The first generation module is used to generate an initial population that satisfies the radial topology constraints based on the topology of the distribution network, and to set the constraint conditions. The module is used to perform power flow calculations on each individual in the initial population to obtain network loss and constraint violation degree, which includes voltage over-limit degree and branch overload degree. The processing module is used to adjust the penalty coefficient for the proportion of individuals in the initial population that violate the constraints, and to construct a fitness function by combining the penalty coefficient, the network loss, and the degree of constraint violation. The second generation module is used to perform genetic operations on the initial population based on the fitness function until a simplified distribution network interconnection diagram that satisfies the constraints and minimizes the network loss is generated.

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