Middle and low voltage distribution network fault recovery method based on BWO optimization algorithm

Abstract

The invention discloses a medium and low voltage power distribution network fault recovery method based on a BWO algorithm, and the method comprises the steps: S101, obtaining a target power distribution network data set, and constructing a distributed power supply and load system model; s102, carrying out preliminary island division by taking the minimum important load loss after the fault as a target; s103, solving is carried out according to a BWO optimization algorithm, and the population is continuously iterated and updated until the number of iterations is reached or a termination condition is met; and S104, carrying out fault recovery according to a solving result.

Description

Technical Field

[0001] The present invention relates to the field of power system optimization and intelligent algorithm application, and in particular to a medium and low voltage distribution network fault recovery method based on a BWO optimization algorithm. Background Art

[0002] The safe and stable operation of medium and low voltage distribution networks directly affects the quality and reliability of electricity consumption of power users. With the continuous expansion of the scale of power grids and the increasing complexity of their structures, the speed and accuracy of distribution network recovery when facing faults are facing severe challenges. How to quickly and accurately restore distribution network faults is an important research topic in the field of power system engineering and smart grid technology.

[0003] Existing technologies often require a long time to diagnose and isolate faults after a distribution network fault occurs. In the process of fault diagnosis and isolation, existing technologies are prone to misjudgment or omission, resulting in inaccurate recovery plans. Not only will the recovery time be prolonged, but the power grid may also be damaged secondary to incorrect recovery strategies, further reducing the reliability of the power system. Although the fault recovery method based on the optimization algorithm can achieve better recovery effects, its computational complexity is high and requires a lot of computing resources and time. This is often difficult to meet the real-time requirements in practical applications, limiting its wide application. Especially in resource-constrained distribution networks, how to balance computational complexity and recovery effects has become an urgent problem to be solved. Existing technologies have poor adaptability when facing a variety of complex fault scenarios. Once the grid structure or load changes, or a new type of fault occurs, it may be difficult for existing recovery methods to quickly adjust the recovery strategy, resulting in poor recovery effects. This lack of adaptability seriously affects the flexibility and reliability of the distribution network.

[0004] In order to meet the above challenges, the industry has begun to explore fault recovery methods based on the BWO algorithm. This method can quickly locate the fault point, effectively divide the island area, and optimize the switch operation strategy through global search and local optimization capabilities. At the same time, the preliminary island division is carried out with the goal of minimizing the loss of important loads after the fault, reducing network losses and the number of switch operations during the recovery process. It not only improves the power supply reliability and system stability, but also reduces the consumption of computing resources. More importantly, based on the results of the optimization algorithm, it is possible to obtain a fault recovery plan that combines island division with switch operation, and flexibly adjust the recovery strategy in different fault periods. This dynamic optimization capability enables the distribution network to quickly and accurately restore power supply when a fault occurs, improving fault recovery efficiency and power supply reliability.

[0005] The distribution network fault recovery technology based on the BWO algorithm provides new ideas and solutions to solve the shortcomings of existing technologies with its fast, accurate and efficient characteristics. With the continuous development and improvement of technology, this technology is expected to play a more important role in the future power system.

[0006] However, there are still some problems with the existing distribution network management technology and emergency plan decision-making technology:

[0007] 1. Slow recovery: Existing technologies require a long time to diagnose and isolate faults after a fault occurs, and then formulate and implement recovery plans. During this process, factors such as communication delays and insufficient computing resources may lead to slow recovery and affect the user's power experience.

[0008] 2. Low recovery accuracy: In the process of fault diagnosis and isolation, existing technologies are prone to misjudgment or omission, resulting in inaccurate recovery. This not only prolongs the recovery time, but may also cause the loss of important loads and reduce the reliability of the power system.

[0009] 3. High computational complexity: Although the fault recovery method based on the optimization algorithm can achieve better recovery results, it has high computational complexity and requires a lot of computing resources and time. This often makes it difficult to meet real-time requirements in practical applications, limiting its widespread application.

[0010] 4. Poor adaptability: Existing technologies have poor adaptability when facing a variety of complex fault scenarios. Once the grid structure or load changes, or a new type of fault occurs, the existing recovery method may be difficult to quickly adjust the recovery strategy, resulting in poor recovery results. Summary of the invention

[0011] In order to overcome the deficiencies in the prior art, the present invention provides a medium and low voltage distribution network fault recovery method based on the BWO optimization algorithm, and realizes the fault recovery of the medium and low voltage distribution network through the BWO optimization algorithm technology.

[0012] In order to achieve the above-mentioned invention object, the technical solution adopted to solve the technical problem is as follows:

[0013] The present invention discloses a method for fault recovery of a medium and low voltage distribution network based on a BWO optimization algorithm, comprising the following steps:

[0014] Step S101: Acquire a target distribution network data set and construct a distributed power source and load system model;

[0015] Step S102: Perform preliminary island division with the goal of minimizing the loss of important loads after a fault;

[0016] Step S103: solving according to the BWO optimization algorithm, and continuously iterating and updating the population until the number of iterations is reached or the termination condition is met;

[0017] Step S104: Perform fault recovery according to the solution result.

[0018] Furthermore, in step S101, the target distribution network data set includes distribution network topology, distributed power source information and power grid operation data, wherein the distributed power source information includes its access point, capacity and active power.

[0019] Furthermore, in step S102, the island partitioning strategy includes an island partitioning objective function:

[0020] The goal of island division is to minimize the loss of important load f1 after a fault, and the objective function is established as follows:

[0021]

[0022] Where: T is the total fault outage time; N is the total number of nodes in the distribution network; ω i is the load weight of node i; δ i，t It is a 0-1 variable, indicating the load input and removal status. If node i is removed during period t, the value is 1, otherwise, the value is 0; is the load power of node i in period t.

[0023] Furthermore, in step S102, the island partitioning strategy further includes an island partitioning constraint condition:

[0024] The constraints of the island power balance equation are:

[0025]

[0026] Where: P i，t , Q i，t is the active power and reactive power of node i in period t; U i，t , U j，t is the voltage amplitude of nodes i and j at time period t; G ij , B ij is the conductance and susceptance of branch ij; θ ij,t is the phase angle difference of branch ij during period t;

[0027] The node voltage constraints are:

[0028] U imin ≤U i ≤U imax

[0029] Where U imin , Uimax are the lower and upper limits of the voltage amplitude at node i;

[0030] The branch current constraints are:

[0031]

[0032] In the formula, I ij,t is the current value of branch ij during period t, is the maximum allowable current value of the branch;

[0033] The power constraints are:

[0034]

[0035] In the formula, is the active power of DG at node i during period t; is the predicted value of DG active power at node i in period t; is the DG reactive power at node i during period t; θ i DG is the DG power factor angle at node i during period t; is the capacity of DG at node i;

[0036] The radial operation structure constraints are:

[0037] g∈G

[0038] Where: g is the topological structure of the distribution network after island division; G is the set of structures satisfied by the radial operation of the distribution network.

[0039] Furthermore, in step S102, the island division strategy also includes an island division strategy process, the steps of which are as follows:

[0040] (1) Determine the active power output of the DG and the power-off load level after the fault;

[0041] (2) Taking the DG deployment node as the search starting point, using the breadth-first search algorithm, searching within the range of the active power output of the DG during the fault period, if island crossing occurs, island fusion is performed;

[0042] (3) Taking the unrestored nodes among the important load nodes as the search starting point, the depth-first search algorithm is used to traverse them and classify them into the islands with the closest electrical distance to verify whether the island division constraints are met; if the constraints are not met, unimportant loads are removed according to the importance of the loads in the island until the constraints are met and the island division results are obtained.

[0043] Furthermore, in step S103, the solution is obtained according to the BWO optimization algorithm, and the population is continuously updated iteratively until the number of iterations is reached or the termination condition is met. The BWO optimization algorithm includes constructing an objective function:

[0044] Taking the minimum weighted sum of network loss and switch operation times as the goal, the objective function is established as follows:

[0045]

[0046] Where: α and β are weight coefficients, focusing on network loss and taking into account the number of switches, α is 0.7, β is 0.3; l, L are the branch number and the total number of branches; is a [0-1] variable, indicating the opening and closing state of branch l. If branch l is closed, the value is 1, otherwise, the value is 0; Z l is the impedance of branch l; P l is the active power of the end node of branch l; Q l is the reactive power of the end node of branch l; U l is the voltage amplitude of the node at the end of branch l; c, C are the switch number and the total number of switches; It is a [0-1] variable, indicating the opening and closing state of switch c in time period t. If switch c is closed, the value is 1, otherwise, the value is 0.

[0047] Furthermore, in step S103, the solution is obtained according to the BWO optimization algorithm, and the population is continuously updated iteratively until the number of iterations is reached or the termination condition is met. The BWO optimization algorithm also includes BWO algorithm optimization:

[0048] The population position X can be described as:

[0049]

[0050] Where: n is the population size; d is the population dimension;

[0051] The corresponding population fitness function F formula is as follows:

[0052]

[0053] Where: f is the objective function;

[0054] The BWO algorithm switches from the search phase to the predation phase, which is determined by the balance factor B. f Control, which is defined as:

[0055]

[0056] Where: B0 is a random number between (0, 1); I is the current iteration number; I max is the maximum number of iterations.

[0057] Furthermore, the BWO algorithm includes a search phase:

[0058] In this stage, the position of the population is updated by the pairing behavior of the population, and different formulas are used to update the position of the population according to the parity of the random integer q (q = 1, 2, 3, ..., d) selected from the d dimension;

[0059] When q is an even number, we have:

[0060]

[0061] When q is an odd number, we have:

[0062]

[0063] Where: is the new position and current position of the p-th individual in the q-th dimension; is the position of the randomly selected rth individual in the first dimension; r1, r2 are random operators used to enhance the search phase, and are random numbers between (0, 1).

[0064] Furthermore, the BWO algorithm also includes a predation phase:

[0065] In this stage, the Levy flight strategy is introduced to enhance the convergence ability, and its mathematical model is:

[0066]

[0067] Where: is the new position of the pth individual; r3, r4 are random numbers between (0, 1); The best location for the population; is the current position of the pth individual; C1 is the random jump intensity of Levy flight; l F is the Levy flight function; is the current position of the randomly selected rth individual, where:

[0068]

[0069] Where: u, ν are normally distributed random numbers; σ is the parameter in the Levy flight function; β is a constant, which is 1.5; Γ is the gamma function.

[0070] Furthermore, the BWO algorithm also includes a falling phase:

[0071] In this stage, the position of the population and the step length of the population fall are used to update the position to ensure the constant size of the population. The mathematical model is:

[0072]

[0073] Where: r5, r6, r7 are random numbers between (0, 1); X step is the step length of the fall, where:

[0074]

[0075] C2=2W f n

[0076]

[0077] Where: U b , l b are the upper and lower limits of the search boundary; C2 is the step factor; W f For the falling probability, the position of the updated population is discretized, and the formula is as follows:

[0078]

[0079] Where: r and is a random number in the range of [0, 1]; S(x) is an S-type discrete function.

[0080] Furthermore, the solution steps based on the BWO optimization algorithm are as follows:

[0081] (1) Determine the remaining power-off load and available switch status information in the distribution network;

[0082] (2) Initialize the population and set parameters: including population size, population dimension, and number of iterations;

[0083] (3) Calculate the population fitness value, that is, the value of the objective function;

[0084] (4) Calculate the balance factor B f , falling probability W f ;

[0085] (5) According to the calculated balance factor B f The value of determines the stage of the population and updates the position of the population; obtains the current fitness value, compares and sorts, and obtains the optimal solution for the current iteration;

[0086] (6) Calculate the falling probability W f , compared with the balance factor B f The size of W f >B f , update the population position;

[0087] (7) Determine the relationship between the current number of iterations and the maximum number of iterations. If the current number of iterations reaches the maximum number of iterations, stop searching and output the optimal parameters; otherwise, return to continue a new round of optimization search.

[0088] Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the prior art:

[0089] 1. Improve fault recovery speed and accuracy: The present invention adopts a medium and low voltage distribution network fault recovery method based on the BWO algorithm. Through the global search and local optimization capabilities of the BWO algorithm, the fault point can be quickly located, the island area can be effectively divided, and the switch operation strategy can be optimized, which greatly reduces the loss of important loads. Compared with traditional methods, this technology not only reduces network losses, but also significantly reduces the number of switch operations during the recovery process, improving power supply reliability and system stability.

[0090] 2. Strong adaptability: The present invention can cope with a variety of complex fault scenarios and has strong adaptability. Through global search and local optimization, it can obtain the optimal results of distribution network fault recovery in different fault periods. This is obviously better than the existing technology. When facing a variety of complex fault scenarios, the existing technology has poor adaptability and it is difficult to quickly adjust the recovery strategy.

[0091] 3. Reduce computational complexity: The computational complexity of the present invention is low and can meet real-time requirements. Although the fault recovery method based on the optimization algorithm can achieve a better recovery effect, the computational complexity is high and requires a lot of computing resources and time. The present invention adopts the BWO algorithm, which can not only ensure the recovery effect, but also reduce the computational complexity and improve efficiency.

[0092] 4. Improve power supply reliability: The fault recovery method of the present invention establishes a solar storage and wind storage system model and a load model, and performs preliminary island division of the distribution network with the goal of minimizing the loss of important loads after a fault. This division method with the goal of minimizing the loss of important loads helps to ensure that important loads can be restored first when a fault occurs, thereby improving power supply reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0094] Figure 1 A flow chart of a method for fault recovery of medium and low voltage distribution networks based on the BWO algorithm proposed in the present invention;

[0095] Figure 2 A flow chart of the island division strategy provided in an embodiment of the present application;

[0096] Figure 3 This is a flow chart of the BWO optimization algorithm provided in an embodiment of the present application. DETAILED DESCRIPTION

[0097] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0098] like Figure 1 As shown, this embodiment discloses a method for medium and low voltage distribution network fault recovery based on the BWO algorithm, which includes but is not limited to the following steps:

[0099] Step S101: Acquire a target distribution network data set and construct a distributed power source and load system model;

[0100] Specifically, in step S101, the target distribution network data set includes distribution network topology, distributed power source information and power grid operation data, wherein the distributed power source information includes its access point, capacity and active power.

[0101] Step S102: Perform preliminary island division with the goal of minimizing the loss of important loads after a fault;

[0102] Specifically, in step S102, the island division strategy includes the following steps:

[0103] 1) Island partition objective function

[0104] The goal of island division is to minimize the loss of important load f1 after a fault, and the objective function is established as follows:

[0105]

[0106] Where: T is the total fault outage time; N is the total number of nodes in the distribution network; ω i is the load weight of node i; δ i，t It is a 0-1 variable, indicating the load input and removal status. If node i is removed during period t, the value is 1, otherwise, the value is 0; is the load power of node i in period t.

[0107] 2) Island division constraints

[0108] The constraints of the island power balance equation are:

[0109]

[0110] Where: P i，t , Q i，t is the active power and reactive power of node i in period t; U i，t , U j，t is the voltage amplitude of nodes i and j at time period t; G ij , B ij is the conductance and susceptance of branch ij; θ ij,t is the phase angle difference of branch ij in period t.

[0111] The node voltage constraints are:

[0112] U imin ≤U i ≤U imax

[0113] Where U imin , U imax are the lower and upper limits of the voltage amplitude at node i.

[0114] The branch current constraints are:

[0115]

[0116] In the formula, I ij,t is the current value of branch ij during period t, is the maximum allowable current value of the branch.

[0117] The power constraints are:

[0118]

[0119]

[0120] In the formula, is the active power of DG at node i during period t; is the predicted value of DG active power at node i in period t; is the DG reactive power at node i during period t; θ i DG is the DG power factor angle at node i during period t; is the capacity of DG at node i.

[0121] The radial operation structure constraints are:

[0122] g∈G

[0123] Where: g is the topological structure of the distribution network after island division; G is the set of structures satisfied by the radial operation of the distribution network.

[0124] 3) Island division strategy process

[0125] The specific steps of the island division strategy are as follows:

[0126] (1) Determine the active power output of the DG and the power-off load level after the fault.

[0127] (2) Taking the DG deployment node as the search starting point, the breadth-first search algorithm is used to search within the range of the active power output of the DG during the fault period. If island crossing occurs, island fusion is performed.

[0128] (3) Take the unrestored nodes among the important load nodes as the search starting point, use the depth-first search algorithm to traverse, and classify them into the island with the closest electrical distance to verify whether the island division constraint is met. If the constraint is not met, remove the unimportant loads according to the importance of the loads in the island until the constraint is met and the island division result is obtained. The island division strategy process is as follows: Figure 2 shown.

[0129] Step S103: solving according to the BWO optimization algorithm, and continuously iterating and updating the population until the number of iterations is reached or the termination condition is met;

[0130] Specifically, in step S103, the solution is obtained according to the BWO optimization algorithm, and the population is continuously updated iteratively until the number of iterations is reached or the termination condition is met. The BWO optimization algorithm includes the following steps:

[0131] 1) Construct the objective function

[0132] Taking the minimum weighted sum of network loss and switch operation times as the goal, the objective function is established as follows:

[0133]

[0134] Where: α and β are weight coefficients, focusing on network loss and taking into account the number of switches, α is 0.7, β is 0.3; l, L are the branch number and the total number of branches; is a [0-1] variable, indicating the opening and closing state of branch l. If branch l is closed, the value is 1, otherwise, the value is 0; Z l is the impedance of branch l; P l is the active power of the end node of branch l; Q l is the reactive power of the end node of branch l; U l is the voltage amplitude of the node at the end of branch l; c, C are the switch number and the total number of switches; It is a [0-1] variable, indicating the opening and closing state of switch c in time period t. If switch c is closed, the value is 1, otherwise, the value is 0.

[0135] 2) BWO algorithm optimization

[0136] The BWO algorithm includes three stages: search, predation, and falling:

[0137] The population position X can be described as:

[0138]

[0139] Where: n is the population size; d is the population dimension.

[0140] The corresponding population fitness function F formula is as follows:

[0141]

[0142] Where: f is the objective function.

[0143] The BWO algorithm switches from the search phase to the predation phase, which is determined by the balance factor B. f Control, which is defined as:

[0144]

[0145] Where: B0 is a random number between (0, 1); I is the current iteration number; I max is the maximum number of iterations.

[0146] (1) Search phase: In this phase, the position of the population is updated by the pairing behavior of the population. Different formulas are used to update the position of the population according to the parity of the random integer q (q = 1, 2, 3, ..., d) selected from the d dimension.

[0147] When q is an even number, we have:

[0148]

[0149] When q is an odd number, we have:

[0150]

[0151] Where: is the new position and current position of the p-th individual in the q-th dimension; is the position of the randomly selected rth individual in the first dimension; r1, r2 are random operators used to enhance the search phase, and are random numbers between (0, 1).

[0152] (2) Predation stage: In this stage, the Levy flight strategy is introduced to enhance the convergence ability. Its mathematical model is:

[0153]

[0154] Where: is the new position of the pth individual; r3, r4 are random numbers between (0, 1); The best location for the population; is the current position of the pth individual; C1 is the random jump intensity of Levy flight; l F is the Levy flight function; is the current position of the randomly selected rth individual, where:

[0155]

[0156] Where: u, ν are normally distributed random numbers; σ is the parameter in the Levy flight function; β is a constant, which is 1.5; Γ is the gamma function.

[0157] (3) Falling stage: In this stage, the position of the population and the step length of the population falling body are used to update the position to ensure the constant size of the population. Its mathematical model is:

[0158]

[0159] Where: r5, r6, r7 are random numbers between (0, 1); X step is the step length of the fall, where:

[0160]

[0161] C2=2W f n

[0162]

[0163] Where: U b , l b are the search boundaries: C2 is the step factor; W f is the probability of falling; the position of the updated population is discretized, and the formula is as follows:

[0164]

[0165] Where: r and is a random number in the range of [0, 1]; S(x) is an S-type discrete function.

[0166] like Figure 3 , the solution steps based on the BWO optimization algorithm are as follows:

[0167] (1) Determine the remaining power-off load and available switch status information in the distribution network;

[0168] (2) Initialize the population and set parameters: including population size, population dimension, number of iterations, etc.

[0169] (3) Calculate the population fitness value, that is, the value of the objective function;

[0170] (4) Calculate the balance factor B f , falling probability W f ;

[0171] (5) According to the calculated balance factor B f The value of determines the stage of the population and updates the position of the population; obtains the current fitness value, compares and sorts it, and obtains the optimal solution for the current iteration;

[0172] (6) Calculate the falling probability W f , compared with the balance factor B f The size of W f >B f , update the population position;

[0173] (7) Determine the relationship between the current number of iterations and the maximum number of iterations. If the current number of iterations reaches the maximum number of iterations, stop searching and output the optimal parameters; otherwise, return to continue a new round of optimization search.

[0174] Step S104: Perform fault recovery according to the solution result.

[0175] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for fault recovery of medium and low voltage distribution networks based on BWO optimization algorithm, comprising the following steps: Step S101: Acquire a target distribution network data set and construct a distributed power source and load system model; Step S102: Perform preliminary island division with the goal of minimizing the loss of important loads after a fault; Step S103: solving according to the BWO optimization algorithm, and continuously iterating and updating the population until the number of iterations is reached or the termination condition is met; Step S104: Perform fault recovery according to the solution result.

2. A method for medium and low voltage distribution network fault recovery based on BWO optimization algorithm according to claim 1, characterized in that: In step S101, the target distribution network data set includes distribution network topology, distributed power source information and power grid operation data, wherein the distributed power source information includes its access point, capacity and active power.

3. A method for medium and low voltage distribution network fault recovery based on BWO optimization algorithm according to claim 1, characterized in that: In step S102, the island partitioning strategy includes an island partitioning objective function: The goal of island division is to minimize the loss of important load f1 after a fault, and the objective function is established as follows: Where: T is the total fault outage time; N is the total number of nodes in the distribution network; ω i is the load weight of node i; δ i，t It is a 0-1 variable, indicating the load input and removal status. If node i is removed during period t, the value is 1, otherwise, the value is 0; is the load power of node i in period t.

4. A method for medium and low voltage distribution network fault recovery based on BWO optimization algorithm according to claim 3, characterized in that: In step S102, the island partitioning strategy further includes an island partitioning constraint condition: The constraints of the island power balance equation are: Where: P i，t , Q i，t is the active power and reactive power of node i in period t; U i，t , U j，t is the voltage amplitude of nodes i and j at time period t; G ij , B ij is the conductance and susceptance of branch ij; θ ij,t is the phase angle difference of branch ij during period t; The node voltage constraints are: IN imin ≤U i ≤U imax Where U imin , U imax is the lower and upper limits of the voltage amplitude at node i; The branch current constraints are: In the formula, I ij,t is the current value of branch ij during period t, is the maximum allowable current value of the branch; The power constraints are: In the formula, is the active power of DG at node i during period t; is the predicted value of DG active power at node i in period t; is the DG reactive power at node i during period t; θ i DG is the DG power factor angle at node i during period t; is the capacity of DG at node i; The radial operation structure constraints are: g∈G Where: g is the topological structure of the distribution network after island division; G is the set of structures satisfied by the radial operation of the distribution network.

5. A method for medium and low voltage distribution network fault recovery based on BWO optimization algorithm according to claim 4, characterized in that: In step S102, the island division strategy also includes an island division strategy process, the steps are as follows: (1) Determine the active power output of the DG and the power-off load level after the fault; (2) Taking the DG deployment node as the search starting point, using the breadth-first search algorithm, searching within the range of the active power output of the DG during the fault period, if island crossing occurs, island fusion is performed; (3) Taking the unrestored nodes among the important load nodes as the search starting point, the depth-first search algorithm is used to traverse them and classify them into the islands with the closest electrical distance to verify whether the island division constraints are met; if the constraints are not met, unimportant loads are removed according to the importance of the loads in the island until the constraints are met and the island division results are obtained.

6. A method for medium and low voltage distribution network fault recovery based on BWO optimization algorithm according to claim 1, characterized in that: In step S103, the solution is obtained according to the BWO optimization algorithm, and the population is continuously updated iteratively until the number of iterations is reached or the termination condition is met. The BWO optimization algorithm includes constructing an objective function: Taking the minimum weighted sum of network loss and switch operation times as the goal, the objective function is established as follows: Where: α and β are weight coefficients, focusing on network loss and taking into account the number of switches, α is 0.7, β is 0.3; l, L are the branch number and the total number of branches; is a [0-1] variable, indicating the opening and closing state of branch l. If branch l is closed, the value is 1, otherwise, the value is 0; Z l is the impedance of branch l; P l is the active power of the end node of branch l; Q l is the reactive power of the end node of branch l; U l is the voltage amplitude of the node at the end of branch l; c, C are the switch number and the total number of switches; It is a [0-1] variable, indicating the opening and closing state of switch c in time period t. If switch c is closed, the value is 1, otherwise, the value is 0.

7. A method for medium and low voltage distribution network fault recovery based on BWO optimization algorithm according to claim 6, characterized in that: In step S103, the solution is obtained according to the BWO optimization algorithm, and the population is continuously updated iteratively until the number of iterations is reached or the termination condition is met. The BWO optimization algorithm also includes BWO algorithm optimization: The population position X can be described as: Where: n is the population size; d is the population dimension; The corresponding population fitness function F formula is as follows: Where: f is the objective function; The BWO algorithm switches from the search phase to the predation phase, which is determined by the balance factor B. f Control, which is defined as: Where: B0 is a random number between (0, 1); I is the current iteration number; I max is the maximum number of iterations.

8. A method for medium and low voltage distribution network fault recovery based on BWO optimization algorithm according to claim 7, characterized in that: The BWO algorithm comprises a search phase: In this stage, the position of the population is updated by the pairing behavior of the population, and different formulas are used to update the position of the population according to the parity of the random integer q (q = 1, 2, 3, ..., d) selected from the d dimension; When q is an even number, we have: When q is an odd number, we have: Where: is the new position and current position of the p-th individual in the q-th dimension; is the position of the randomly selected rth individual in the first dimension; r1, r2 are random operators used to enhance the search phase, and are random numbers between (0, 1).

9. A method for medium and low voltage distribution network fault recovery based on BWO optimization algorithm according to claim 8, characterized in that: The BWO algorithm also includes a predation phase: In this stage, the Levy flight strategy is introduced to enhance the convergence ability, and its mathematical model is: Where: is the new position of the pth individual; r3, r4 are random numbers between (0, 1); The best location for the population; is the current position of the pth individual; C1 is the random jump intensity of Levy flight; l F is the Levy flight function; is the current position of the randomly selected rth individual, where: Where: u, ν are normally distributed random numbers; σ is the parameter in the Levy flight function; β is a constant, which is 1.5; Γ is the gamma function.

10. A method for medium and low voltage distribution network fault recovery based on BWO optimization algorithm according to claim 9, characterized in that: The BWO algorithm also includes a falling phase: In this stage, the position of the population and the step length of the population fall are used to update the position to ensure the constant size of the population. The mathematical model is: Where: r5, r6, r7 are random numbers between (0, 1); X step is the step length of the fall, where: C2=2W f n Where: U b , l b are the upper and lower limits of the search boundary; C2 is the step factor; W f For the falling probability, the position of the updated population is discretized, and the formula is as follows: Where: r and is a random number in the range of [0, 1]; S(x) is an S-type discrete function.

11. A method for medium and low voltage distribution network fault recovery based on BWO optimization algorithm according to claim 4, characterized in that: The solution steps based on the BWO optimization algorithm are as follows: (1) Determine the remaining power-off load and available switch status information in the distribution network; (2) Initialize the population and set parameters: including population size, population dimension, and number of iterations; (3) Calculate the population fitness value, that is, the value of the objective function; (4) Calculate the balance factor B f , falling probability W f ; (5) According to the calculated balance factor B f The value of determines the stage of the population and updates the position of the population; obtains the current fitness value, compares and sorts it, and obtains the optimal solution for the current iteration; (6) Calculate the falling probability W f , compared with the balance factor B f The size of W f >B f , update the population position; (7) Determine the relationship between the current number of iterations and the maximum number of iterations; If the current number of iterations reaches the maximum number of iterations, the search stops and the optimal parameters are output; otherwise, it returns to continue a new round of optimization search.

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