Fault location method and system for distribution network with distributed power supply

Abstract

The application discloses a kind of distribution network fault location methods containing distributed power supply, including obtaining the data information of target distribution network and the data information of FTU real-time feedback;Initialization solution set and parameter;Calculate the fitness function value of current solution set;Calculate generalized convergence index;Target distribution network is searched globally, locally and in fault area, and the final solution set is obtained;Complete distribution network fault location containing distributed power supply.The application also discloses a kind of system for realizing the distribution network fault location method containing distributed power supply of the application.The application introduces different search states and corresponding search state conversion rules, uses the advantage of search state itself, uses optimal solution and potential fault area solution set as guidance, realizes the fast positioning of potential global optimal solution area and fast convergence in optimal solution neighborhood and fault area solution set neighborhood;Therefore, the application has higher reliability, better accuracy, and faster positioning speed.

Description

Technical Field

[0001] This invention belongs to the field of electrical automation, and specifically relates to a method and system for locating faults in a power distribution network containing distributed power sources. Background Technology

[0002] With economic and technological development and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and daily life, bringing endless convenience. Therefore, ensuring a stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] Distributed generation (DG) refers to small-scale power generation devices, such as wind power and solar power, connected to a distribution network. The connection of these devices changes the traditional unidirectional power supply mode of the distribution network, making the direction of power flow in the distribution network more complex.

[0004] Traditional fault location methods for distribution networks primarily rely on measured values ​​of electrical parameters such as current and voltage, determining the fault location by comparing parameter changes under normal and fault conditions. However, due to the integration of distributed generation sources, the direction of power flow and the distribution of electrical parameters in the distribution network may change, making it impossible for traditional fault location methods to accurately pinpoint the fault location.

[0005] With the increasing application of Feeder Terminal Units (FTUs) in distribution networks, researchers have begun to utilize the fault information they transmit for fault location. Currently, common fault location schemes based on fault current information detected by FTUs often employ methods such as hybrid ant colony and particle swarm optimization algorithms, immune algorithms, and improved gray wolf algorithms. However, these commonly used fault location schemes often suffer from getting trapped in local optima during iterative solutions, resulting in low reliability and accuracy. Summary of the Invention

[0006] One of the objectives of this invention is to provide a fault location method for distribution networks containing distributed power sources that is highly reliable, accurate, and fast.

[0007] The second objective of this invention is to provide a system for implementing the fault location method for a distribution network containing distributed power sources.

[0008] The fault location method for distribution networks containing distributed power sources provided by this invention includes the following steps:

[0009] S1. Acquire data information from the target distribution network and real-time feedback data information from the FTU;

[0010] S2. Initialize the solution set and parameters;

[0011] S3. Based on the data obtained in step S1 and the initialization results in step S2, calculate the fitness function value of the current solution set;

[0012] S4. Calculate the generalized convergence index;

[0013] S5. Based on the generalized convergence index calculated in step S4, perform global search, local search and fault area search on the target distribution network to obtain the final solution set;

[0014] S6. Based on the solution set obtained in step S5, complete the fault location of the distribution network containing distributed generation.

[0015] The initialization of the solution set and parameters in step S2 specifically includes the following steps:

[0016] Initialize the number of solutions and the maximum number of iterations in the solution set; set the initial number of iterations to 1; initialize the search state to global search.

[0017] Step S3, which involves calculating the fitness function value of the current solution set, specifically includes the following steps:

[0018] The fitness function value f(L) of the current solution set is calculated using the following formula. * ):

[0019]

[0020] In the formula, N is the total number of solutions in the current solution set; S i Let S be the switching state of the i-th switch, and let S be the direction of the fault current detected by the FTU as being consistent with the specified positive direction. i =1, if the FTU detects that the fault current direction of switch i is opposite to the specified positive direction, then S i =-1, if FTU does not detect the fault current of switch i, then S i =0; w is the set weighting coefficient; M is the total number of faulty feeders; l N * For the current solution set L * The corresponding feeder N's ​​status is 0 if the feeder is normal, and 1 if the feeder is faulty; S i * (l1 * …l N * ) represents the current solution set L * The corresponding state function value of switch i and S i * (l1 * …lN * ) = S i_up * (l1 * …l N * )-S i_down * (l1 * …l N * ), S i_up * (l1 * …l N * ) represents the current solution set L * The upper half-zone switching state function of the corresponding switch i and u represents the total number of feeder segments in the upper half of switch i, M represents the total number of power supplies in the upper half of switch i, and K represents the total number of power supplies in the upper half of switch i. up Let K be the switching factor for the upper half of the power supply of switch i. If the upper half of the power supply of switch i is in operation, then K... up =1, if the upper half of the power supply of switch i is not in operation, then K up =0, S i_down * (l1 * …l N * ) represents the current solution set L * The corresponding lower half-zone switching state function of switch i and U represents the total number of feeder segments in the lower half of switch i, m represents the total number of power supplies in the lower half of switch i, and K represents the total number of power supplies in the lower half of switch i. down Let K be the switching factor for the lower half of the power supply of switch i. If the lower half of the power supply of switch i is in operation, then K... down =1, if the lower half of the power supply of switch i is not in operation, then K down =0; The target distribution network is divided into two parts with switch i as the dividing point. The part including the grid power supply is the upper half of switch i, and the part excluding the grid power supply is the lower half of switch i.

[0021] Step S4, which involves calculating the generalized convergence index, specifically includes the following steps:

[0022] The generalized convergence index (CGI) of the current solution set is calculated using the following formula:

[0023]

[0024] In the formula d avg The average discrimination between two adjacent optimal solutions in the solution set and n is the number of solutions in the solution set, d ij The discriminant between solution sets and d ij =|f[(Li * ) t ]-f[(L j * ) t ]|,f[(L i * ) t ] is (L i * ) t The fitness function value, (L i * ) t Let i be the i-th solution in the solution set of the t-th generation.

[0025] Step S5, which involves performing a global search, a local search, and a fault region search on the target distribution network based on the generalized convergence index calculated in step S4, to obtain the final solution set, specifically includes the following steps:

[0026] A. Evaluate the generalized convergence index CGI obtained in step S4:

[0027] If CGI < ε and t > k*t max Then, the target distribution network is divided into equivalent regions, and the over-limit information of switch fault current is preprocessed in each region, and then the process proceeds to step C.

[0028] Otherwise, proceed to step B;

[0029] Specifically, the equivalent region division is as follows: in the target distribution network, when a single point or double fault occurs in a region, it will not cause the switch state function corresponding to the switch outside the region to change, so the region is regarded as an equivalent region; the preprocessing is as follows: extract the switch data information at the port of each equivalent region from the data information uploaded by the FTU.

[0030] B. Maintain the search state as a global search state; calculate the trajectory vector corresponding to each solution in the current solution set using the following formula, and then proceed to step F:

[0031]

[0032] In the formula Let be the trajectory vector corresponding to the i-th solution in the t-th solution set; Let be the i-th solution in the t-th solution set; β is an adjustment parameter, taking values ​​from 0 to 1; λ3 is the third random number, λ4 is the fourth random number, and both λ3 and λ4 follow a standard normal distribution; v1 is the first tracking vector, and its calculation formula is... v2 is the second tracking vector and its calculation formula is: p1, p2, and p3 are integers randomly selected from 1 to n, and satisfy p1≠p2≠p3≠i;

[0033] C. Based on the results obtained in step A, determine whether a faulty area D exists. fault :

[0034] If there is no fault area D fault Then proceed to step D;

[0035] If fault area D exists fault Then determine the line fault status that follows the line fault in the fault area, and add the corresponding fault area into the fault area solution set, and go to step E.

[0036] The fault area D fault Defined as: dividing the target distribution network into equivalent regions, resulting in m regions D1 to D2. m Within each region, the over-limit information of the switch fault current fed back by the FTU is calculated. If a region satisfies S1⊙S2⊙...⊙S n =1, then this area is designated as fault area D. fault If there exists a region satisfying S1⊙S2⊙...⊙S n If the value is 0, then this region is considered as the normal region D. e S1~S n This refers to the switch state variables of all switches within this area;

[0037] D. Transform the search state into a local search state; calculate the trajectory vector corresponding to each solution in the current solution set using the following formula, and then proceed to step F;

[0038]

[0039] In the formula The i-th solution in the current solution set The corresponding trajectory vector; μ i The i-th solution in the current solution set The corresponding generalized average position and M is the optimal solution in the current solution set, and M is the average position of the solution set if and only if δ i The i-th solution in the current solution set The corresponding generalized standard deviation and η is the penalty factor and λ1 is the first random number, λ2 is the second random number, a is the fifth random number, and b is the sixth random number, and λ1, λ2, a and b are all random numbers between 0 and 1;

[0040] E. Change the search state to the fault area search state; calculate the trajectory vector corresponding to each solution in the current solution set using the following formula, and then proceed to step F;

[0041]

[0042] In the formula The i-th solution in the current solution set The corresponding trajectory vector; μ fi The i-th solution in the current solution set The corresponding generalized fault location and This is the best solution in the current solution set. Let M be the h-th solution in the current fault region solution set, and M be the average position of the solution set if and only if; δ fi The i-th solution in the current solution set The corresponding generalized fault standard deviation and μ i The i-th solution in the current solution set The corresponding generalized average position; η is the penalty factor;

[0043] F. Obtain each solution The corresponding better individuals are then added to the solution set, and... Remove from the solution set; use the following formula to obtain a better individual:

[0044]

[0045] In the formula To solve The corresponding superior individuals;

[0046] G. Determine if the current iteration count has reached the set maximum iteration count:

[0047] If the solution is reached, output the current solution set, and step S5 ends.

[0048] If the condition is not met, the iteration count is increased by 1, and the check is performed again.

[0049] If the current search state is a global search state, return to step E and proceed to the next iteration;

[0050] If the current search state is a local search state, return to step D and proceed to the next iteration;

[0051] If the current search status is a fault area search status, proceed to step H;

[0052] H. Use the following formula to determine:

[0053] like Then return to step D and proceed to the next iteration;

[0054] like Then, feedback is given in step E, and the next iteration begins;

[0055] in, This is the fitness function value corresponding to the nth solution in the current fault region solution set.

[0056] This invention also provides a system for implementing the fault location method for a distribution network containing distributed generation, comprising a data acquisition module, an initialization module, a fitness calculation module, a convergence calculation module, an iterative search module, and a fault location module; the data acquisition module, initialization module, fitness calculation module, convergence calculation module, iterative search module, and fault location module are connected in series; the data acquisition module is used to acquire data information of the target distribution network and real-time feedback data information from the FTU, and upload the data to the initialization module; the initialization module is used to initialize the solution set and parameters according to the received data, and upload the data to the fitness calculation module; the fitness calculation module is used to calculate the fitness function value of the current solution set according to the received data, and upload the data to the convergence calculation module; the convergence calculation module is used to calculate the generalized convergence index according to the received data, and upload the data to the iterative search module; the iterative search module is used to perform a global search, a local search, and a fault area search on the target distribution network according to the received data to obtain the final solution set, and upload the data to the fault location module; the fault location module is used to complete the fault location of the distribution network containing distributed generation according to the received data.

[0057] The fault location method and system for distribution networks with distributed power sources provided by this invention introduces different search states and corresponding search state transition rules, and utilizes the advantages of each search state. Guided by the optimal solution and the solution set of potential fault regions, it achieves rapid location of potential global optimal solution regions and rapid convergence within the neighborhood of the optimal solution and the neighborhood of the solution set of fault regions. Therefore, this invention has higher reliability, better accuracy, and faster location speed. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0059] Figure 2 This is a schematic diagram of the functional modules of the system of the present invention. Detailed Implementation

[0060] like Figure 1 The diagram shown is a flowchart of the method of the present invention: The fault location method for a distribution network containing distributed power sources disclosed in this invention includes the following steps:

[0061] S1. Acquire data information from the target distribution network and real-time feedback data information from the FTU;

[0062] S2. Initialize the solution set and parameters; specifically including the following steps:

[0063] Initialize the number of individuals in the solution set and the maximum number of iterations; simultaneously set the initial number of iterations to 1; initialize the search state to global search;

[0064] S3. Based on the data obtained in step S1 and the initialization results in step S2, calculate the fitness function value of the current solution set; specifically, this includes the following steps:

[0065] The fitness function value f(L) of the current solution set is calculated using the following formula. * ):

[0066]

[0067] In the formula, N is the total number of solutions in the current solution set; S i Let S be the switching state of the i-th switch, and let S be the direction of the fault current detected by the FTU as being consistent with the specified positive direction. i =1, if the FTU detects that the fault current direction of switch i is opposite to the specified positive direction, then S i =-1, if FTU does not detect the fault current of switch i, then S i =0; w is the set weighting coefficient; M is the total number of faulty feeders; l N * For the current solution set L * The corresponding feeder N's ​​status is 0 if the feeder is normal, and 1 if the feeder is faulty; S i * (l1 * …l N * ) represents the current solution set L * The corresponding state function value of switch i and S i * (l1 * …l N * ) = S i_up * (l1 * …l N * )-S i_down * (l1 * …l N * ), S i_up * (l1 * …l N* ) represents the current solution set L * The upper half-zone switching state function of the corresponding switch i and u represents the total number of feeder segments in the upper half of switch i, M represents the total number of power supplies in the upper half of switch i, and K represents the total number of power supplies in the upper half of switch i. up Let K be the switching factor for the upper half of the power supply of switch i. If the upper half of the power supply of switch i is in operation, then K... up =1, if the upper half of the power supply of switch i is not in operation, then K up =0, S i_down * (l1 * …l N * ) represents the current solution set L * The corresponding lower half-zone switching state function of switch i and U represents the total number of feeder segments in the lower half of switch i, m represents the total number of power supplies in the lower half of switch i, and K represents the total number of power supplies in the lower half of switch i. down Let K be the switching factor for the lower half of the power supply of switch i. If the lower half of the power supply of switch i is in operation, then K... down =1, if the lower half of the power supply of switch i is not in operation, then K down =0; The target distribution network is divided into two parts with switch i as the dividing point. The part including the grid power supply is the upper half of switch i, and the part excluding the grid power supply is the lower half of switch i.

[0068] S4. Calculate the generalized convergence index; specifically including the following steps:

[0069] The generalized convergence index (CGI) of the current solution set is calculated using the following formula:

[0070]

[0071] In the formula d avg The average discrimination between two adjacent optimal solutions in the solution set and n is the number of solutions in the solution set, d ij The discriminant between solution sets and d ij =|f[(L i * ) t ]-f[(L j * ) t ]|,f[(L i * ) t ] is (L i * ) t The fitness function value, (L i * ) t Let i be the i-th solution in the solution set of the t-th generation;

[0072] S5. Based on the generalized convergence index calculated in step S4, perform a global search, a local search, and a fault region search on the target distribution network to obtain the final solution set; specifically, this includes the following steps:

[0073] Global search state: The global search state represents the potential global optimal solution region searched by the method of this invention within the domain. It has strong search capabilities, but the convergence of the global search state is poor.

[0074] Local search state: Guided by the current optimal solution, searching within the adjacent solution set can significantly improve the efficiency of finding the global optimal solution; the local search state limits the search range, making it more capable of searching in the target region, while its search capability decreases in other regions, which helps to accelerate the rapid convergence of the overall solution process;

[0075] Fault Region Search Status: Based on the fault regions determined after region division and their corresponding potential fault region solution sets, the fault region search status seeks better solutions in the neighborhood of the fault region solution set, thereby improving the search capability in the neighborhood of the fault region solution set. At this time, the convergence of the method of the present invention is enhanced.

[0076] In the initial stage, in order to find a sufficient number of potential global optimal solution regions as soon as possible, the search state should be controlled as a global search state; when a sufficient number of potential global optimal solution regions are found, the search capability of the global search should be weakened and its convergence enhanced, that is, the search state should be converted to a local search state or a fault region search state.

[0077] When the generalized convergence index (CGI) approaches 1, it indicates that the distinction between two adjacent optimal solutions in the solution set is decreasing, and the optimal solution set is clustering. This indicates that the method of the present invention has found enough global optimal solutions. At this time, the search state should be changed to weaken the global search capability and strengthen the local search capability and the fault region search capability. When the generalized convergence index is much greater than 1, it indicates that the distinction between two adjacent solutions in the solution set is large, and the optimal solution set has not yet clustered. This indicates that the method of the present invention has not found enough global optimal solutions, and the search state should remain in the global search state.

[0078] Compared to the global search state, the fault region search state has a greater chance of finding the global optimum because: 1) The fault region search state can use the fault region solution set to narrow down and guide the search range, avoiding ineffective searches, which is more efficient than the extensive and blind search of the global search state; 2) The fault region solution set is more likely to contain the optimum solution, i.e., the true fault location, compared to the global search space; narrowing the search to this solution set increases the likelihood of finding the global optimum; 3) The fault region search state converges faster because the search range is narrowed, allowing for a faster approximation of the optimum solution; this is much more efficient than the lengthy search of the global search state. Therefore, if the fault region can be located, after meeting the conversion criteria, the global search state should be converted to the fault region search state. Compared to the global search state, the fault region search state has stronger search capabilities and faster convergence speed; it can more effectively search within the region most likely to contain the fault and find the global optimum.

[0079] The fault region search state and the local search state play a crucial role. To find the global optimum, the method of this invention needs to flexibly switch between these two search states. Specifically, if, in the fault region search state, the fitness value of the current optimal solution is better than the fitness values ​​of all solutions in the fault region, it proves that the global optimum is not in the current fault region solution set or its adjacent regions. In this case, the method of this invention needs to exit the fault region search state and enter the local search state to search within the global solution space based on the current optimal solution. Conversely, if, in the local search state, no solution better than the current optimal solution can be found, it needs to return to the fault region search state and continue searching within the fault region solution set to avoid missing the global optimum. By flexibly switching between these two complementary search states, the method of this invention can cover the global solution space and find the global optimum.

[0080] A. Evaluate the generalized convergence index CGI obtained in step S4:

[0081] If CGI < ε and t > k*t max Then, the target distribution network is divided into equivalent regions, and the over-limit information of switch fault current is preprocessed in each region, and then the process proceeds to step C.

[0082] Otherwise, proceed to step B;

[0083] Specifically, the equivalent region division is as follows: in the target distribution network, when a single point or double fault occurs in a region, it will not cause the switch state function corresponding to the switch outside the region to change, so the region is regarded as an equivalent region; the preprocessing is as follows: extract the switch data information at the port of each equivalent region from the data information uploaded by the FTU.

[0084] B. Maintain the search state as a global search state; calculate the trajectory vector corresponding to each solution in the current solution set using the following formula, and then proceed to step F:

[0085]

[0086] In the formula Let be the trajectory vector corresponding to the i-th solution in the t-th solution set; Let be the i-th solution in the t-th solution set; β is an adjustment parameter, taking values ​​from 0 to 1; λ3 is the third random number, λ4 is the fourth random number, and both λ3 and λ4 follow a standard normal distribution; v1 is the first tracking vector, and its calculation formula is... v2 is the second tracking vector and its calculation formula is: p1, p2, and p3 are integers randomly selected from 1 to n, and satisfy p1≠p2≠p3≠i;

[0087] C. Based on the results obtained in step A, determine whether a faulty area D exists. fault :

[0088] If there is no fault area D fault Then proceed to step D;

[0089] If fault area D exists fault Then determine the line fault status that follows the line fault in the fault area, and add the corresponding fault area into the fault area solution set, and go to step E.

[0090] The fault area D fault Defined as: dividing the target distribution network into equivalent regions, resulting in m regions D1 to D2. m Within each region, the over-limit information of the switch fault current fed back by the FTU is calculated. If a region satisfies S1⊙S2⊙...⊙S n =1, then this area is designated as fault area D. fault If there exists a region satisfying S1⊙S2⊙...⊙S n If the value is 0, then this region is considered as the normal region D. e S1~S n This refers to the switch state variables of all switches within this area;

[0091] D. Transform the search state into a local search state; calculate the trajectory vector corresponding to each solution in the current solution set using the following formula, and then proceed to step F;

[0092]

[0093] In the formula The i-th solution in the current solution set The corresponding trajectory vector; μi The i-th solution in the current solution set The corresponding generalized average position and M is the optimal solution in the current solution set, and M is the average position of the solution set if and only if δ i The i-th solution in the current solution set The corresponding generalized standard deviation and η is the penalty factor and λ1 is the first random number, λ2 is the second random number, a is the fifth random number, and b is the sixth random number, and λ1, λ2, a and b are all random numbers between 0 and 1;

[0094] E. Change the search state to the fault area search state; calculate the trajectory vector corresponding to each solution in the current solution set using the following formula, and then proceed to step F;

[0095]

[0096] In the formula The i-th solution in the current solution set The corresponding trajectory vector; μ fi The i-th solution in the current solution set The corresponding generalized fault location and This is the best solution in the current solution set. Let M be the h-th solution in the current fault region solution set, and M be the average position of the solution set if and only if; δ fi The i-th solution in the current solution set The corresponding generalized fault standard deviation and μ i The i-th solution in the current solution set The corresponding generalized average position; η is the penalty factor;

[0097] F. Obtain each solution The corresponding better individuals are then added to the solution set, and... Remove from the solution set; use the following formula to obtain a better individual:

[0098]

[0099] In the formula To solve The corresponding superior individuals;

[0100] G. Determine if the current iteration count has reached the set maximum iteration count:

[0101] If the solution is reached, output the current solution set, and step S5 ends.

[0102] If the condition is not met, the iteration count is increased by 1, and the check is performed again.

[0103] If the current search state is a global search state, return to step E and proceed to the next iteration;

[0104] If the current search state is a local search state, return to step D and proceed to the next iteration;

[0105] If the current search status is a fault area search status, proceed to step H;

[0106] H. Use the following formula to determine:

[0107] like Then return to step D and proceed to the next iteration;

[0108] like Then, feedback is given in step E, and the next iteration begins;

[0109] in, This is the fitness function value corresponding to the nth solution in the current fault region solution set;

[0110] S6. Based on the solution set obtained in step S5, complete the fault location of the distribution network containing distributed generation.

[0111] The method of the present invention will be further described below with reference to an embodiment:

[0112] This invention compares its fault location method with commonly used hybrid algorithms based on ant colony and particle swarm optimization (HAO), immune algorithms (IA), and improved gray wolf algorithms (IGWA). The relevant initialization parameters are set as follows: total number of feeder segments N = 33; number of solutions n = 60; maximum number of iterations t. max =100; set the threshold ε to 1.2 and k to 0.5. The positioning accuracy, average convergence algebra, and iteration time were used as performance evaluation metrics for the algorithm. The comparison results are shown in Table 1.

[0113] Table 1. Comparison Results Data Illustration

[0114] Method of the present invention 96.8 15 0.864 HAO 66.3 56 1.256 IA 70.2 70 3.589 IGWA 90.5 30 4.236

[0115] As can be seen from the comparison data in Table 1, the method of the present invention is significantly superior to the existing fault location methods. Therefore, the method of the present invention has higher reliability, better accuracy, and faster location speed.

[0116] like Figure 2The diagram shows the functional modules of the system of this invention: The system for implementing the fault location method of a distribution network containing distributed power sources disclosed in this invention includes a data acquisition module, an initialization module, a fitness calculation module, a convergence calculation module, an iterative search module, and a fault location module; the data acquisition module, initialization module, fitness calculation module, convergence calculation module, iterative search module, and fault location module are connected in series; the data acquisition module is used to acquire data information of the target distribution network and data information fed back by the FTU in real time, and upload the data to the initialization module; the initialization module is used to initialize the solution set and parameters according to the received data, and upload the data to the fitness calculation module; the fitness calculation module is used to calculate the fitness function value of the current solution set according to the received data, and upload the data to the convergence calculation module; the convergence calculation module is used to calculate the generalized convergence index according to the received data, and upload the data to the iterative search module; the iterative search module is used to perform a global search, a local search, and a fault area search on the target distribution network according to the received data to obtain the final solution set, and upload the data to the fault location module; the fault location module is used to complete the fault location of the distribution network containing distributed power sources according to the received data.

Claims

1. A method for fault location in a distribution network containing distributed generation, comprising the following steps: S1. Obtain data information from the target distribution network and real-time feedback data from the FTU; S2. Initialize the solution set and parameters; S3. Based on the data obtained in step S1 and the initialization results in step S2, calculate the fitness function value of the current solution set; specifically, this includes the following steps: The fitness function value of the current solution set is calculated using the following formula. : In the formula, N is the total number of solutions in the current solution set; Let i be the switching state of the i-th switch, and let FTU detect that the direction of the fault current of switch i is consistent with the specified positive direction. If the FTU detects that the fault current direction of switch i is opposite to the specified positive direction, then... If the FTU does not detect the fault current of switch i, then ; The weighting coefficients are set. This represents the total number of faulty feeders; For the current solution set The corresponding feeder N's ​​status is 0 if the feeder is in normal condition and 1 if the feeder is in fault condition. For the current solution set The corresponding state function value of switch i and , For the current solution set The upper half-zone switching state function of the corresponding switch i and u represents the total number of feeder segments in the upper half of switch i, and M represents the total number of power supplies in the upper half of switch i. Let be the switching factor for the upper half of the power supply of switch i. If the upper half of the power supply of switch i is put into operation, then... If the upper half of switch i is not in operation, then , For the current solution set The corresponding lower half-zone switching state function of switch i and U represents the total number of feeder segments in the lower half of switch i, and m represents the total number of power supplies in the lower half of switch i. Let be the switching factor for the lower half of the power supply of switch i. If the lower half of the power supply of switch i is put into operation, then... If the lower half of the power supply of switch i is not in operation, then ; The target distribution network is divided into two parts with switch i as the dividing point. The part including the grid power supply is the upper half of switch i, and the part excluding the grid power supply is the lower half of switch i. S4. Calculate the generalized convergence index; specifically including the following steps: The generalized convergence index of the current solution set is calculated using the following formula. : In the formula The average discrimination between two adjacent optimal solutions in the solution set and , where n is the number of solutions in the solution set. For the discriminant between solution sets and , for The fitness function value, Let i be the i-th solution in the solution set of the t-th generation; S5. Based on the generalized convergence index calculated in step S4, perform global search, local search and fault region search on the target distribution network to obtain the final solution set; S6. Based on the solution set obtained in step S5, complete the fault location of the distribution network containing distributed generation.

2. The fault location method for a distribution network containing distributed power sources according to claim 1, characterized in that... The initialization of the solution set and parameters in step S2 specifically includes the following steps: Initialize the number of individuals in the solution set and the maximum number of iterations; set the initial number of iterations to 1; initialize the search state to global search.

3. The fault location method for a distribution network containing distributed power sources according to claim 2, characterized in that... Step S5, which involves performing a global search, a local search, and a fault region search on the target distribution network based on the generalized convergence index calculated in step S4, to obtain the final solution set, specifically includes the following steps: A. The generalized convergence index calculated in step S4 Make a judgment: like and Then, the target distribution network is divided into equivalent regions, and the over-limit information of switch fault current is preprocessed in each region, and then the process proceeds to step C. Otherwise, proceed to step B; Specifically, the equivalent region division is as follows: in the target distribution network, when a single point or double fault occurs in a region, it will not cause the switch state function corresponding to the switch outside the region to change, so the region is regarded as an equivalent region; the preprocessing is as follows: extract the switch data information at the port of each equivalent region from the data information uploaded by the FTU. B. Maintain the search state as a global search state; calculate the trajectory vector corresponding to each solution in the current solution set using the following formula, and then proceed to step F: In the formula Let be the trajectory vector corresponding to the i-th solution in the t-th solution set; Let i be the i-th solution in the solution set of the t-th generation; To adjust the parameters, and the values ​​range from 0 to 1; The third random number, It is the fourth random number, and and All follow a standard normal distribution; The first tracking vector is calculated using the following formula: , The second tracking vector is calculated using the following formula: , , and Let n be an integer randomly selected from 1 to n, and satisfy the following conditions: ; C. Based on the results obtained in step A, determine whether a fault area exists. : If no fault area exists Then proceed to step D; If a fault area exists Then determine the line fault status that follows the line fault in the fault area, and add the corresponding fault area into the fault area solution set, and go to step E. The aforementioned fault area Defined as: dividing the target distribution network into equivalent regions to obtain m regions. Within each region, the over-limit information of the switch fault current fed back by the FTU is calculated. If a region meets the requirements... Then this area will be designated as the fault area. If a region satisfies Then this area will be considered a normal area. ,in This refers to the switch state variables of all switches within this area; D. Transform the search state into a local search state; calculate the trajectory vector corresponding to each solution in the current solution set using the following formula, and then proceed to step F; In the formula The i-th solution in the current solution set The corresponding trajectory vector; The i-th solution in the current solution set The corresponding generalized average position and , This is the best solution in the current solution set. The average position of the solution set is if and ; The i-th solution in the current solution set The corresponding generalized standard deviation and ; As a penalty factor and , The first random number, The second random number, The fifth random number, It is the sixth random number, and , , and All numbers are random numbers between 0 and 1; E. Change the search state to the fault area search state; calculate the trajectory vector corresponding to each solution in the current solution set using the following formula, and then proceed to step F; In the formula The i-th solution in the current solution set The corresponding trajectory vector; The i-th solution in the current solution set The corresponding generalized fault location and , This is the best solution in the current solution set. This is the h-th solution in the current fault region solution set. Let be the average position of the solution set; The i-th solution in the current solution set The corresponding generalized fault standard deviation and , The i-th solution in the current solution set The corresponding generalized average position; As a penalty factor; F. Obtain each solution The corresponding better individuals are then added to the solution set, and... Remove from the solution set; use the following formula to obtain a better individual: In the formula To solve The corresponding superior individuals; G. Determine if the current iteration count has reached the set maximum iteration count: If the solution is reached, output the current solution set, and step S5 ends. If the condition is not met, the iteration count is increased by 1, and the check is performed again. If the current search state is a global search state, return to step E and proceed to the next iteration; If the current search state is a local search state, return to step D and proceed to the next iteration; If the current search status is a fault area search status, proceed to step H; H. Use the following formula to make the judgment: like If so, return to step D and proceed to the next iteration; like If the feedback is positive, then step E is performed and the next iteration begins; in, This is the fitness function value corresponding to the nth solution in the current fault region solution set.

4. A system for implementing the fault location method for a distribution network containing distributed power sources as described in any one of claims 1 to 3, characterized in that... The system comprises a data acquisition module, an initialization module, a fitness calculation module, a convergence calculation module, an iterative search module, and a fault location module. These modules are connected in series. The data acquisition module acquires data from the target distribution network and real-time feedback data from the FTUs, and uploads the data to the initialization module. The initialization module initializes the solution set and parameters based on the received data and uploads the data to the fitness calculation module. The fitness calculation module calculates the fitness function value of the current solution set based on the received data and uploads the data to the convergence calculation module. The convergence calculation module calculates the generalized convergence index based on the received data and uploads the data to the iterative search module. The iterative search module performs global, local, and fault area searches on the target distribution network based on the received data to obtain the final solution set and uploads the data to the fault location module. The fault location module locates faults in the distribution network containing distributed generation sources based on the received data.

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