Distribution network reconstruction method based on chaos bacterial foraging algorithm

Abstract

The invention relates to a distribution network reconstruction method based on a chaos bacterial foraging algorithm, and belongs to the technical field of power system analysis and control. According to the method, an optimization model of distribution network reconstruction is established, binary coding is adopted to represent a network state, and a chaos bacterial foraging algorithm is used for iterative optimization to realize optimization reconstruction of a distribution network structure so as to reduce system network loss. Wherein the optimization model takes minimum active loss as a target, and simultaneously meets multiple constraint conditions such as topological radioactivity constraint, power flow constraint, branch transmission power constraint, node voltage constraint and the like; the network state adopts binary vector coding; a chaos bacterial foraging algorithm initializes bacterial populations, simulates motion, perception and migration behaviors of bacteria, introduces chaos mapping to increase randomness, and approaches an optimal solution through iteration. According to the method, the advantages of a chaos bacterial foraging algorithm are fully utilized, selectable schemes can be quickly and efficiently searched, and a distribution network reconstruction scheme with the minimum global optimal network loss under the multi-constraint condition is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy power systems, and in particular relates to a distribution network reconstruction method based on a chaotic bacteria foraging algorithm. Background Art

[0002] Distribution network reconstruction is an important task in the field of distribution automation. It aims to minimize the active power loss of the power grid and improve the economy and reliability of power grid operation by adjusting the switch state in the power grid. With the increasing complexity and scale of the power system, distribution network reconstruction has become a key link in achieving optimal grid dispatch. This technology is of great significance in improving energy utilization, reducing operating costs and promoting the access of renewable energy.

[0003] At present, there are some limitations and challenges in the mainstream distribution network reconstruction methods. The first is the limitation of deterministic methods: some methods use deterministic strategies, such as branch and bound algorithms and mixed integer quadratic cone optimization, to determine the switch state of the distribution network. These methods show good performance when dealing with small and medium-sized systems, but they have slow calculation speeds in large-scale systems and are prone to falling into local optimal solutions. The second is the challenge of evolutionary optimization algorithms: evolutionary optimization algorithms, such as genetic algorithms and particle swarm algorithms, are used to solve the distribution network reconstruction problem. Although they have improved the computational efficiency to a certain extent, they still face challenges such as multi-objective optimization and unstable convergence. In addition, for some specific problems, these methods may require long-term parameter adjustment and calculation. Finally, the limitations of heuristic algorithms: heuristic algorithms usually use rules and heuristic rules to determine the switch state. Although they have fast calculation speeds, they can often only obtain local optimal solutions and are not flexible enough for complex power grid systems.

[0004] The present invention proposes a distribution network reconstruction method based on a chaotic bacterial foraging algorithm, aiming to overcome the limitations of existing methods. First, the method introduces chaotic mapping, such as Tent mapping, to increase the randomness and diversity of the algorithm. Chaotic mapping will be combined with the movement strategy of bacteria, which will help to jump out of the local optimal solution and explore the search space more comprehensively. Secondly, the method utilizes the characteristics of the bacterial foraging algorithm to simulate the behavior of bacteria when looking for food, including movement, perception and migration processes. By combining chaotic mapping and bacterial foraging algorithm, a more comprehensive and efficient solution to the distribution network reconstruction problem is achieved. Finally, the method has the ability to handle multi-objective optimization problems, aiming to improve the economy, stability and reliability of the power grid and provide better solutions for the operation of the power system. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a distribution network reconstruction method based on a chaotic bacterial foraging algorithm, which can achieve the global optimal distribution network topology under multiple constraints and improve the economy and stability of the new power system. The technical solution adopted by the present invention is: a distribution network reconstruction method based on a chaotic bacterial foraging algorithm, comprising the following steps:

[0006] S1. Data collection and preparation: Collect necessary data of the distribution network, including network topology, electrical parameters (resistance, reactance, load, etc.), power supply information, switch status information, and current and voltage measurement data. Establish an accurate power grid model for subsequent optimization and simulation.

[0007] S2. Establishment of mathematical model for distribution network optimization; Design and establish a mathematical model suitable for distribution network reconstruction, including objective function and constraints. The objective function is usually to minimize active power loss, and the constraints include topological radioactivity constraint, power flow balance constraint, branch transmission power constraint and node voltage constraint, etc. Ensure that the established mathematical model can accurately describe the distribution network reconstruction problem, including considering multiple constraints.

[0008] S3. Network coding: Design and implement appropriate network coding schemes to map the device states in the power grid (such as switch states) into binary codes that can be processed by the algorithm. Represent the power grid device states in a form that can be recognized by the algorithm for subsequent optimization of the chaotic bacteria foraging algorithm.

[0009] S4. Optimization of chaotic bacterial foraging algorithm; implementation of chaotic bacterial foraging algorithm, including: bacterial population initialization, fitness calculation, motion simulation, speed update of bacteria according to chaotic mapping and bacterial behavior simulation, chemotaxis simulation, iterative repetition of the above steps until the stop condition is met.

[0010] Specifically, the distribution network reconstruction optimization mathematical model described in step S2 includes two parts: objective function and constraint conditions. Among them, considering the minimization of network loss, the objective function of distribution network reconstruction is:

[0011]

[0012] Among them, P loss The sum of the electric energy consumed by the distribution line resistance of all branches of the system; N b is the number of all branches in the power distribution network system; i is the number of a specific branch in the power distribution network system; R i is the resistance value of branch i; I i is the effective value of the current flowing through the branch i; β i is the switch state of branch i, β i =0 means branch i switch is off, β i =1 indicates that the switch of branch i is closed.

[0013] The constraints are:

[0014] 1) Topological radioactivity constraints:

[0015] T∈T s

[0016] Where T is the system topology, T s To meet the requirements of radioactivity and have no isolated nodes.

[0017] 2) Power flow constraints:

[0018]

[0019] The power flow constraint needs to satisfy the balance of active power and reactive power. Gi , Q Gi , are respectively the active and reactive power injected into the system by the generator at node i; P Di , Q Di is the active and reactive power consumed by the load in the system; b ij , g ij 、b sh，ij is the conductance, susceptance and equivalent capacitance to ground of branch ij; c(i) is the set of nodes directly connected to node i by branches; α ij represents the branch switch state between nodes i and j, α ij =1 means the branch switch is closed, α ij =0 means the branch switch is off;

[0020] 3) Branch transmission power constraints:

[0021] S i ≤S i，max , i = 1, 2, ..., N b

[0022] Among them, S i , S i，max are the apparent power and the maximum allowable value of branch i respectively;

[0023] 4) Node voltage constraints:

[0024] U i，min ≤U i ≤U i，max , i=1,2,…,N b

[0025] Among them, U i , U i，min and U i，max are the node voltage value and the upper and lower limits of the node voltage of node i respectively; N is the total number of nodes in the distribution network system.

[0026] Specifically, the network coding described in step S3 adopts binary coding. For swarm intelligence algorithms used to solve optimization problems, each individual is used to represent a possible solution in the potential solution space. In the power distribution network reconstruction problem, binary coding can be used to represent the position of each possible solution in the solution space, where each binary bit represents the state of a device (0 means off, 1 means on).

[0027] Assume that there are N devices in the power grid, and use an N-dimensional binary vector to represent the state of the power grid, where each dimension corresponds to a device. For each individual position (representing a potential solution).

[0028] Let X i represents the position vector of the i-th individual, where i = 1, 2, ..., M (M is the number of individuals in the population), and N represents the number of devices in the power grid. Then, X i It can be represented as an N-dimensional binary vector:

[0029]

[0030] in, represents the state of the first device of the i-th individual, which can be 0 or 1, indicating off or on. This state vector X i is the position of individual i.

[0031] In the algorithm, the positions of individuals are iterated through the update rules to find the optimal solution. Binary coding is used to represent the state of each device in the power grid, 0 for off and 1 for on. In this way, each particle can be represented by a binary vector to represent its position in the potential solution space. In the iteration of the algorithm, these positions are constantly adjusted according to the algorithm's update rules to find the optimal distribution network reconstruction solution.

[0032] Specifically, the chaotic bacteria foraging algorithm described in step S4 is an optimization algorithm used in the present invention to solve the distribution network optimization problem. The following is a combination of the chaotic bacteria foraging algorithm and the distribution network reconstruction problem, and a detailed application process description:

[0033] S4.1 Algorithm initialization: Set the number of bacterial populations N, which usually represents different distribution network reconstruction schemes. Initialize the position X of each bacterium i , where each X i Indicates the state of a power grid device, using binary coding (0 for off, 1 for on), and is related to the number of distribution branches N b Initialize the chemotactic factor C of each bacterium i, which is used to simulate the chemotactic behavior of bacteria during the search process. Chemotactic factors affect the information that bacteria consider when updating their positions, and can be seen as the degree of preference of bacteria for the optimal solution. Larger chemotactic factors will more strongly guide bacteria to move towards the direction of the global optimal solution, while smaller factors will make it easier for bacteria to fall into the local optimal solution.

[0034] S4.2 Fitness calculation: For each bacterium X i , and maps its state to the switch state in the distribution network (according to binary coding). Then calculate the network loss of the current scheme, using the following objective function:

[0035]

[0036] Among them, P loss The sum of the electric energy consumed by the distribution line resistance of all branches of the system; N b is the number of all branches in the power distribution network system; i is the number of a specific branch in the power distribution network system; R i is the resistance value of branch i; I i is the effective value of the current flowing through the branch i; β i is the switch state of branch i, β i =0 means branch i switch is off, β i =1 indicates that the switch of branch i is closed.

[0037] S4.3 Movement simulation: For each bacterium X i , calculate its velocity v i , the objective function is recorded as J(X i ),but

[0038]

[0039] Speed ​​calculation formula:

[0040]

[0041] Where Δt is the time step, U and D are the external attraction factor and diffusion term. The time step Δt represents the iteration step or time step of the algorithm. In the chaotic bacterial foraging algorithm, Δt is usually used to control the time interval for bacteria to update their position and velocity in each iteration. Its specific value can be selected according to the complexity of the problem and the requirements of the algorithm, and parameter tuning is usually required to obtain the best performance. The external attraction factor U represents the gravitational or attractive force of the external environment on the bacteria during the search process. It indicates the degree to which the bacteria move towards the global optimal solution of the optimization problem. A larger external attraction factor will more strongly guide the bacteria to move towards the global optimal solution, while a smaller factor will make the bacteria more likely to fall into the local optimal solution. The diffusion term D represents the diffusion or randomness of the bacteria during the search process. A larger diffusion term makes the bacteria tend to explore the search space more randomly, which helps to escape the local optimal solution. A smaller diffusion term tends to maintain directional movement to converge to the local optimal solution faster. and Represent the objective function J(X i ) for position X i The gradient and second-order partial derivative matrix of . Gradient It represents the objective function J(X i ) at a certain position X i Specifically, is a vector, each component of which corresponds to the objective function J(X i ) About position X i The direction of the gradient indicates the direction in which the objective function increases fastest, while the magnitude of the gradient indicates the rate of increase in that direction. In speed calculation, bacteria adjust their speed according to the gradient information and move in the direction in which the objective function decreases to find a better solution. is the objective function J(X i ). It contains the second-order derivative information of the objective function and is used to describe the change in the curvature of the objective function. The second-order partial derivative matrix is ​​usually a square matrix whose elements represent the interaction between different dimensions of the objective function. In speed calculation, the role of the second-order partial derivative matrix is ​​to adjust the speed of bacteria to take into account the curvature information of the objective function. A larger curvature may require bacteria to reduce their speed to avoid crossing the minimum point of the objective function, while a smaller curvature allows bacteria to move faster.

[0042] S4.4 Chemotaxis simulation: Update the position X of each bacterium i , to simulate the movement of bacteria. Position update formula:

[0043] X i =X i +v i

[0044] S4.5 Introduction of Chaotic Mapping: Chaotic mapping is introduced to increase the randomness of the algorithm. Chaotic mapping can be used to change the movement and tendency behavior of bacteria, increase the diversity of the algorithm, help to jump out of the local optimal solution, and explore the search space more comprehensively. The present invention selects Tent mapping, and can also select a suitable mapping function according to the nature of the problem and the requirements of the algorithm. The Tent mapping expression is as follows:

[0045]

[0046] Among them, chaos t is the current state, chaos t+1 is the next state, p is the mapping parameter, which usually takes values ​​in the interval [0, 2].

[0047] The output of the chaos map is combined with the position information of the bacteria to change the speed or position update of the bacteria. In general, the output of the chaos map can be regarded as a random perturbation added to the speed or position of the bacteria. The output of the chaos map can be added to the speed update formula of the bacteria to increase the randomness of the bacteria: The speed calculation formula added with chaos:

[0048]

[0049] Among them, chaos t is the output of the chaotic map, which changes at each time step t, increasing the randomness of the bacterial movement. The introduction of the chaotic map can make the bacterial foraging algorithm more exploratory and help to find potential optimization solutions more comprehensively in the search space.

[0050] S4.6. Repeat iteration: Repeat steps S4.2 to S4.5 until the stopping condition is met (for example, the maximum number of iterations is reached or the network loss meets certain requirements).

[0051] S4.7. Output result: Return the best position X found * The corresponding network distribution scheme and the minimized network loss value P loss , which is the optimal distribution network reconstruction solution.

[0052] The bacterial foraging algorithm combines the movement, perception and migration behaviors of bacteria to search for the best grid reconstruction scheme to minimize grid losses. The algorithm is able to search in different binary coding states and continuously optimize the solution to meet the constraints and reduce grid losses.

[0053] Beneficial effects of the present invention: The present invention realizes efficient optimization of power distribution network reconstruction by introducing a chaotic bacterial foraging algorithm. Compared with traditional methods, the present invention can find the optimal combination of equipment states in the power grid more quickly, thereby reducing the active power loss of the power grid and improving the economy and reliability of the power grid. In addition, the introduction of chaotic mapping increases the randomness of the algorithm, which helps to jump out of the local optimal solution and fully explore the search space. Through multi-objective optimization, the present invention also takes into account various performance indicators of the power grid, providing a more optimized solution for the operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flow chart of the main steps of the present invention;

[0055] Figure 2 This is a flow chart of the chaotic bacteria foraging algorithm of the present invention;

[0056] Figure 3 IEEE69 node system topology diagram in an embodiment of the present invention;

[0057] Figure 4 It is a graph of the number of iterations-objective function in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention, and are specifically described in combination with the embodiments below.

[0059] like Figure 1 and 2 As shown, the present invention comprises the following steps:

[0060] S1. Data collection and preparation: Collect necessary data of the distribution network, including network topology, electrical parameters (resistance, reactance, load, etc.), power supply information, switch status information, and current and voltage measurement data.

[0061] S2. Establish a mathematical model for distribution network optimization; design and establish a mathematical model suitable for distribution network reconstruction, including objective functions and constraints.

[0062] The objective function of distribution network reconstruction is:

[0063]

[0064] Among them, P loss The sum of the electric energy consumed by the distribution line resistance of all branches of the system; N bis the number of all branches in the power distribution network system; i is the number of a specific branch in the power distribution network system; R i is the resistance value of branch i; I i is the effective value of the current flowing through the branch i; β i is the switch state of branch i, β i =0 means branch i switch is off, β i =1 indicates that the switch of branch i is closed.

[0065] 1) Topological radioactivity constraints:

[0066] T∈T s

[0067] Where T is the system topology, T s To meet the requirements of radioactivity and have no isolated nodes.

[0068] 2) Power flow constraints:

[0069]

[0070] The power flow constraint needs to satisfy the balance of active power and reactive power. Gi , Q Gi , are respectively the active and reactive power injected into the system by the generator at node i; P Di , Q Di is the active and reactive power consumed by the load in the system; b ij , g ij 、b sh，uj is the conductance, susceptance and equivalent capacitance to ground of branch ij; c(i) is the set of nodes directly connected to node i by branches; α ij represents the branch switch state between nodes i and j, α ij =1 means the branch switch is closed, α ij =0 means the branch switch is off;

[0071] 3) Branch transmission power constraints:

[0072] S i ≤S i，max , i = 1, 2, ..., N b

[0073] Among them, S i , S i，max are the apparent power and the maximum allowable value of branch i respectively;

[0074] 4) Node voltage constraints:

[0075] U i，min ≤U i ≤U i，max, i = 1, 2, ..., N b

[0076] Among them, U i , U i，min and U i，max are the node voltage value and the upper and lower limits of the node voltage of node i respectively; N is the total number of nodes in the distribution network system.

[0077] S3. Network coding: Use binary coding to represent the position of each possible solution in the solution space, where each binary bit represents the state of a device (0 means off, 1 means on).

[0078] Assume that there are N devices in the power grid, and use an N-dimensional binary vector to represent the state of the power grid. Let X i represents the position vector of the i-th individual, where i = 1, 2, ..., M (M is the number of individuals in the population), and N represents the number of devices in the power grid. Then, X i It can be represented as an N-dimensional binary vector:

[0079]

[0080] S4. Optimization of chaotic bacterial foraging algorithm;

[0081] S4.1 Algorithm initialization: Set the number of bacterial populations N, which usually represents different distribution network reconstruction schemes. Initialize the position X of each bacterium i , using binary coding (0 for closed, 1 for open), and with the number of distribution branches N b Initialize the chemotactic factor C of each bacterium i .

[0082] S4.2 Fitness calculation: For each bacterium X i , and maps its state to the switch state in the distribution network (according to binary coding). Then calculate the network loss of the current scheme, using the following objective function:

[0083]

[0084] S4.3 Movement simulation: For each bacterium X i , calculate its velocity v i , the objective function is recorded as J(X i ), then the speed calculation formula is:

[0085]

[0086] Where Δt is the time step, U and D are the external attraction factor and the diffusion term. and Represent the objective function J(Xi ) for position X i The gradient and second-order partial derivative matrices of .

[0087] S4.4 Chemotaxis simulation: Update the position X of each bacterium i , to simulate the movement of bacteria. Position update formula:

[0088] X i =X i +v i

[0089] S4.5 introduces chaotic mapping; adds the chaotic speed calculation formula:

[0090]

[0091] Among them, chaos t is the output of the chaotic map, which changes at every time step t.

[0092] S4.6. Repeat iteration: Repeat steps S4.2 to S4.5 until the stopping condition is met (for example, the maximum number of iterations is reached or the network loss meets certain requirements).

[0093] S4.7. Output result: Return the best position X found * The corresponding network distribution scheme and the minimized network loss value P loss , which is the optimal distribution network reconstruction solution.

[0094] The effectiveness of the present invention is verified as follows:

[0095] use Figure 3 The IEEE 69-node power distribution test system shown in the figure is simulated and verified. The system contains 69 nodes, 68 loads, and the total load is 3.80MW+j2.69MVar. The network contains 5 load levels, namely:

[0096] Load 1: 12, load value is 0.00+j0.00;

[0097] Load 2: 29, load value is 0.09+j0.05;

[0098] Load 3: 4, load value is 0.42+j0.20;

[0099] Load 4: 18, load value is 0.67+j0.34;

[0100] Load 5: 5, the load value is 1.00+j0.50;

[0101] Set the parameters of the chaotic bacterial foraging algorithm: population size N = 50; maximum number of iterations Itermax =100; time step Δt=1; external attraction factor U=1; diffusion factor D=1; the chaotic map selects Logistic map. The programming language is C, the operating environment is Ubuntu 18.04., Intel Core i5-8265U CPU@1.60GHz processor, 16GB memory.

[0102] Algorithm operation results: The chaotic bacterial foraging algorithm was run 10 times independently. The iteration number-objective function curve of the first 4 times of operation number 1 is as follows: Figure 4 The results are summarized in Table 1:

[0103] Table 1 Results of 10 runs of the chaotic bacterial foraging algorithm (per unit value)

[0104] serial number <![CDATA[Optimal objective function value f opt > <![CDATA[The number of rounds n when the optimal solution appears opt > <![CDATA[Average value f av > 1 0.00143 37 0.00152 2 0.00152 22 0.00158 3 0.00149 31 0.00156 4 0.00148 29 0.00155 5 0.00146 40 0.00153 6 0.00145 36 0.00153 7 0.00142 38 0.00151 8 0.00147 33 0.00155 9 0.00144 35 0.00152 10 0.00141 39 0.00150

[0105] From Table 1, we can see that the optimal objective function value f of the 10 running results is opt The average optimal value f is between 0.00141 and 0.00152. av Between 0.00150 and 0.00158, it means that the algorithm can stably search for a better solution; the number of occurrences of the optimal solution n opt Most of them are between 30 and 40 times, indicating that the algorithm has a strong global search capability. In summary, in the reconstruction optimization problem of IEEE 69-node distribution network, the chaotic bacterial foraging algorithm of the present invention can quickly and effectively search for a better solution, verifying the effectiveness of the method.

[0106] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations within the meaning and scope of the elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A distribution network reconstruction method based on chaotic bacterial foraging algorithm, characterized in that: The following steps are involved: S1. Data collection and preparation; S2. Establishment of mathematical model for distribution network optimization; S3. Network coding; S4. Optimization of chaotic bacterial foraging algorithm; Wherein, the distribution network reconstruction optimization mathematical model described in step S2 includes: objective function and constraint conditions; The network coding described in step S3 adopts binary coding; The chaotic bacteria foraging algorithm described in step S4 includes: algorithm initialization, fitness calculation, motion simulation, chemotaxis simulation, introduction of chaotic mapping, and repeated iteration until a stop condition is met.

2. A distribution network reconstruction method based on chaotic bacteria foraging algorithm according to claim 1, characterized in that: The objective function of the distribution network reconstruction optimization mathematical model in step S2 is the minimum active power loss: Among them, P loss The sum of the electric energy consumed by the distribution line resistance of all branches of the system; N b is the number of all branches in the power distribution network system; i is the number of a specific branch in the power distribution network system; R i is the resistance value of branch i; I i is the effective value of the current flowing through the branch i; β i is the switch state of branch i, β i =0 means branch i switch is off, β i =1 means branch i switch is closed; The constraints include: 1) Topological radioactivity constraints: T∈T s Where T is the system topology, T s To meet the requirements of radioactivity and have no isolated nodes. 2) Power flow constraints: Among them, the power flow constraint needs to satisfy the balance of active power and reactive power; among them, P Gi , Q Gi , are respectively the active and reactive power injected into the system by the generator at node i; P Di , Q Di is the active and reactive power consumed by the load in the system; b ij , g ij 、b sh,ij is the conductance, susceptance and equivalent capacitance to ground of branch ij; c(i) is the set of nodes directly connected to node i by branches; α ij represents the branch switch state between nodes i and j, α ij =1 means the branch switch is closed, α ij =0 means the branch switch is off; 3) Branch transmission power constraints: S i ≤S i,max ,i=1,2,…,N b Among them, S i , S i,max are the apparent power and the maximum allowable value of branch i respectively; 4) Node voltage constraints: U i,min ≤U i ≤U i,max ,i=1,2,…,N b Among them, U i , U i,min and U i,max are the node voltage value and the upper and lower limits of the node voltage of node i respectively; N is the total number of nodes in the distribution network system.

3. A distribution network reconstruction method based on chaotic bacteria foraging algorithm according to claim 1, characterized in that: The chaotic mapping in step S4 selects Tent mapping; the Tent mapping expression is as follows: Among them, chaos t is the current state, chaos t+1 is the next state, p is the parameter of the mapping, which usually takes values ​​in the interval [0,2].

4. A distribution network reconstruction method based on chaotic bacteria foraging algorithm according to claim 1, characterized in that: In step S4, the output of the chaotic map is combined with the position information of the bacteria to change the speed or position update of the bacteria; the output of the chaotic map can be regarded as a random disturbance added to the speed or position of the bacteria; the output of the chaotic map can be added to the speed update formula of the bacteria to increase the randomness of the bacteria: the speed calculation formula adding chaos: Among them, chaos t is the output of the chaotic map, which changes at every time step t.

5. A distribution network reconstruction method based on chaotic bacteria foraging algorithm according to claim 1, characterized in that: Also includes: Output the best position X found in step S4 * The corresponding network distribution scheme and the minimized network loss value P loss .

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