Cooperative suppression method for subway stray current in urban power grid

Abstract

The invention discloses a cooperative suppression method for subway stray current in an urban power grid. The method specifically comprises the following steps: establishing a stray current distribution analysis model in the urban power grid; establishing an optimization model which takes the minimum cost as a target and takes transformer neutral point current and zero sequence current protection sensitivity meeting requirements as a constraint; monte Carlo sampling is carried out on the train position-current data of the subway, and the neutral point current of each transformer in an urban power grid is calculated by using a stray current distribution analysis model in the urban power grid; using the optimization model to calculate the installation positions of the uniform straight type and blocking straight type suppression devices meeting the optimization target and the constraint condition, and the equivalent resistance of the uniform straight type suppression device; and recording an optimization result meeting the requirement, and selecting a scheme with the minimum sum of the current amplitudes of the neutral points of the transformer as a final collaborative suppression scheme of the subway stray current. According to the invention, the installation position optimization of the direct current blocking and uniformizing type suppression device in the urban power grid is realized, and the cost is saved for stray current suppression.

Description

Technical Field

[0001] This invention belongs to the field of safe operation of power systems, and particularly relates to a collaborative suppression method for stray currents in subways in urban power grids. Background Technology

[0002] my country's subways typically use DC power supply and utilize rail return current; however, because rails are not completely insulated, some DC current leaks to the ground through the rails, forming stray currents. These stray currents can enter the urban power grid from the transformer neutral point through the ground, cable armor, and other paths, leading to DC bias in the transformer, increased corrosion of the grounding grid, and even malfunctioning protection systems.

[0003] Currently, methods for suppressing stray currents from subways in power grids mainly include: connecting a DC blocking suppression device (capacitive type) in series at the transformer neutral point to block the flow path of stray current (referred to as "DC blocking method"); connecting a DC equalization suppression device (resistive type) in series at the neutral point to reduce the amplitude of stray current (referred to as "DC equalization method"); and connecting a reverse compensation power supply to the neutral point to offset stray currents. However, because subway stray currents fluctuate continuously with train operation, the control process of the reverse compensation power supply method is extremely complex, and its accuracy and reliability are both low. Therefore, in practical engineering, the DC blocking method or the DC equalization method is generally used to suppress the intrusion of subway stray currents in urban power grids.

[0004] However, while DC blocking can effectively suppress stray current intrusion into the transformer, it is highly likely to cause passive DC overshoot at the neutral point of adjacent transformers, and DC blocking suppression devices are expensive. While equalizing DC suppression devices are significantly cheaper than capacitor-based DC blocking devices, they alter the zero-sequence impedance distribution of the power grid, potentially affecting the correct operation of zero-sequence current protection. Therefore, existing stray current suppression methods for subways in urban power grids are insufficient when used alone. Thus, it is necessary to combine the advantages of both suppression devices and propose a stray current suppression method for urban power grids that integrates DC blocking and equalizing DC suppression devices. Summary of the Invention

[0005] To address the aforementioned problems, the present invention aims to provide a stray current suppression method that synergistically utilizes DC blocking and DC equalization suppression devices. By employing capacitive DC blocking and resistive DC equalization suppression devices and utilizing a quantum genetic algorithm, stray current suppression in the power grid can be achieved, along with optimization of the installation positions of the capacitive DC blocking and resistive DC equalization suppression devices and the installation resistance value of the resistive DC equalization suppression devices.

[0006] The present invention provides a method for collaborative suppression of stray currents in subway systems within an urban power grid, comprising the following steps:

[0007] Step 1: Based on the topology of the urban power grid and subway network, the electrical parameters of the equipment, geographical location information, and the installation location and parameters of the subway stray current suppression device, establish an analytical model of stray current distribution in the urban power grid.

[0008] Step 2: Using the installation locations of the DC-suppressing and DC-blocking suppression devices and the equivalent resistance of the DC-suppressing device as optimization variables, establish an optimization model with the goal of minimizing cost and the requirement of satisfying the sensitivity of transformer neutral point current and zero-sequence current protection.

[0009] Step 3: Perform Monte Carlo sampling on the train position-current data of the subway, use the analytical model of stray current distribution in the urban power grid established in Step 1 to calculate the neutral point current of each transformer in the urban power grid; and use the optimization model established in Step 2 to calculate the installation location of the DC-suppressing and DC-blocking suppression devices that meet the optimization objectives and constraints, as well as the equivalent resistance of the DC-suppressing devices.

[0010] Step 4: Record the optimization results that meet the requirements in Step 3, and select the scheme with the smallest sum of transformer neutral point current amplitudes as the final cooperative suppression scheme for subway stray current.

[0011] Furthermore, step 1 specifically involves:

[0012] Step 1.1: Collect the equivalent resistance of transmission lines, transformer windings, overhead ground wires, substation grounding resistance, cable armor, and stray current suppression devices in the urban power grid.

[0013] Step 1.2: Collect the equivalent resistance of the up and down contact networks in the subway power supply network, the voltage of the traction substation, the equivalent internal resistance of the traction substation, the transition conductance of the rail to the grounding system, and the train operation diagram of each subway line.

[0014] Step 1.3: Collect the network topology between the urban power grid and the subway power supply system, and the installation location of stray current suppression devices.

[0015] Step 1.4: Establish an equivalent resistance model of the cable armor between the main subway station and the urban power grid substation, and use the resistance network modeling method to establish an analytical model of stray current distribution in the urban power grid.

[0016] Furthermore, step 2 specifically involves:

[0017] Step 2.1: The objective function for cooperative inhibition optimization is:

[0018]

[0019] Where, N c Number of DC blocking suppression devices installed, M cThe cost of a DC blocking suppression device, N r To determine the number of straight-line suppression devices to be installed, M r The cost of a set of straight-line suppression devices.

[0020] Step 2.2: The DC constraint condition for the transformer neutral point during the collaborative suppression optimization process is as follows:

[0021]

[0022] Among them, I i I represents the neutral point DC of the i-th transformer. set This indicates the set DC threshold for the transformer neutral point, and n indicates that there are a total of n neutral point grounded transformers.

[0023] Step 2.3: The constraint conditions for zero-sequence current protection of the power grid during the collaborative suppression optimization process are as follows:

[0024]

[0025] Where K lm This is expressed as the sensitivity coefficient of the transformer zero-sequence overcurrent protection. It represents the sensitivity coefficient of the second stage of the zero-sequence current protection for the line.

[0026] Furthermore, the calculation method for the sensitivity coefficient of the zero-sequence current protection of the urban power grid in step 2.3 is as follows:

[0027] Step 2.3.1: Based on the zero-sequence impedance data of each component of the urban power grid, establish the zero-sequence network of the urban power grid, and collect the setting values ​​of the zero-sequence overcurrent protection of each neutral-point grounding transformer and the second stage of the zero-sequence current protection of the line.

[0028] Step 2.3.2: Set the short-circuit point. The short-circuit point for both transformer zero-sequence overcurrent protection and line zero-sequence current protection is set at the end of the protection.

[0029] Step 2.3.3: Calculate the zero-sequence current of the branch using the node voltage method, and divide the branch current by the zero-sequence current protection setting value of the component to obtain the zero-sequence current protection sensitivity coefficient.

[0030] Step 2.3.4: Change the short circuit point and proceed to step 2.3.3 to start the calculation again. Calculate the sensitivity coefficient of the zero-sequence current protection stage II of all neutral-point grounded transformers and line zero-sequence current protection.

[0031] Furthermore, step 3 specifically involves:

[0032] Step 3.1: Optimize the installation location of the suppression device using a quantum genetic algorithm. Initialize the population size for each installation location to 50, the number of iterations to be 'a', and the number of individuals to be N. i, 1≤i≤50, its length is n, the data in each individual matrix is ​​a binary number of 0 or 1 with a probability of 50%, where 1 indicates that a suppression device needs to be installed at the neutral point of the transformer.

[0033] Step 3.2: Optimize the installation type of the suppression device using a quantum genetic algorithm. Initialize the population size of the suppression device installation type to 50, the number of iterations to be b, and the number of individuals to be M. x Its length is n, and the matrix of all individuals is M. x The corresponding N i The position of =1 is a binary number of 0 or 1 with a probability of 50%, where 1 indicates that a DC blocking suppression device needs to be installed at the neutral point of the transformer.

[0034] Step 3.3: Optimize the installation resistance of the straight-type suppression device using the particle swarm optimization algorithm. Initialize the number of particles to 20, with each particle having a resistance of y. x Its length is n, the particle coordinate change step size is set to 1, and a single particle matrix is ​​composed of the maximum resistance that can be installed at the neutral point of the transformer; collect the maximum access resistance data R of the neutral point of each transformer. max , where M corresponds x The position y where (j)=0 x (j) Take R max (j), corresponding to M x The position where (j)=1 is directly set to 0.

[0035] Step 3.4: Input the number of times d is used to sample the train position.

[0036] Step 3.5: Based on the collected train timetable, use the Monte Carlo sampling simulation method to sample all train position-current data at any time for each subway line.

[0037] Step 3.6: Calculate the sensitivity coefficient and determine whether the constraints in Step 2.3 are met. If they are met, proceed to Step 3.8; otherwise, proceed to Step 3.7.

[0038] Step 3.7: Optimize the resistance value of the installed straight-line suppression device using the particle swarm optimization algorithm, and determine whether all resistance values ​​are 0. If yes, proceed to step 3.8; otherwise, proceed to step 3.6.

[0039] Step 3.8: Input the optimized resistance distribution of the particle swarm optimization, update the grid resistance network, and set the resistance of the DC blocking suppression device to 10. 5 Ω, calculate the magnitude of the neutral point current |I n,x (j)│, where x corresponds to the value in step 3.2, and j represents the j-th transformer. Determine whether the neutral point current constraint condition in step 2.2 is satisfied. If the entire population does not satisfy the constraint, proceed to step 3.2. If there exists a population M that satisfies the constraint...x Then the number of samplings d = d-1. If d ≤ 0, proceed to step 3.9; otherwise, proceed to step 3.5.

[0040] Step 3.9: The cost of a single DC blocking suppression device is M. c The cost of a single straight-line suppression device is M. r Statistics M x The number of 1s in the middle is N c (x), the number of 0s is N. r (x), calculate the cost size T=N c ∙ M c + N r ∙M r And compare the cost values, selecting the value with the lowest cost, denoted as W. l , where l indicates that the installation type has been optimized l times.

[0041] Step 3.10: Determine if l is 1. If it is, directly set the first generation as the parent and proceed to step 3.12. If not, proceed to step 3.11.

[0042] Step 3.11: Compare with the previous minimum cost; if W... l >W l-1 Then W l-1 The corresponding one is M x Parent generation, otherwise W l The corresponding M x For the parent generation, and record W. l The corresponding M x (j), y x (j), |I nx (j)│, and calculate l=l+1. If l<b, proceed to step 3.12; otherwise, compare all recorded W values. l The minimum cost is selected and denoted as Q. i Proceed to step 3.13.

[0043] Step 3.12: Set the rotation angle of the parent input quantum rotation gate to 0.05π, and change M. x The probability of 0 and 1 at each position in (j) changes the value of the binary number, and the offspring is obtained. Step 3.3 is repeated.

[0044] Step 3.13: Record the cost of the optimal installation type for each installation location as Q1, Q2, ..., Q 50 .

[0045] Step 3.14: Compare Q1, Q2, ..., Q 50 The minimum cost is selected and denoted as P. m , m indicates that the installation location was optimized m times.

[0046] Step 3.15: Determine if m is 1. If it is, directly use the first generation as the parent and proceed to step 3.17. If not, proceed to step 3.16.

[0047] Step 3.16: Compare with the previous minimum cost; if P... m >P m-1 Then P m-1 The corresponding N i Parent generation, otherwise P m The corresponding N i For the parent generation, and record P. m The corresponding chromosome matrix N i (j), and calculate m=m+1. If m>a, proceed to step 3.18; otherwise, proceed to step 3.17.

[0048] Step 3.17: Input the parent into the quantum rotation gate, set the rotation angle to 0.05π, and change N. i The probabilities of 0 and 1 values ​​at each position in (j) are used to obtain the offspring chromosomes, and then proceed to step 3.2.

[0049] Step 3.18: Compare all recorded P values. m Filter out the minimum value corresponding to M. x y x 、│I n,x │, where there may be entities with the same cost but M x y x Different results.

[0050] Furthermore, step 4 selects the final optimized solution after the optimization process in step 3, obtaining the optimal installation type population under the optimal installation location population. At this point, it is necessary to compare the suppression effect. The smaller the value, the better the suppression effect. The individual with the best suppression effect is selected as the final optimized solution set.

[0051] The beneficial technical effects of this invention are as follows:

[0052] 1. This invention is based on quantum genetic algorithm to optimize the installation location of DC blocking and DC equalization suppression devices in urban power grids, thereby saving costs for stray current suppression.

[0053] 2. This invention integrates the impact of the DC-type suppression device on the sensitivity of the power grid zero-sequence current protection and its suppression effect on stray current. Based on the particle swarm optimization algorithm, it optimizes the installation resistance of the DC-type suppression device, providing a basis for the installation resistance of the DC-type suppression device. Attached Figure Description

[0054] Figure 1This is a flowchart of the collaborative suppression method for stray currents in subway systems in urban power grids according to the present invention. Detailed Implementation

[0055] The present invention will be further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0056] The process of a collaborative suppression method for stray currents in subways in an urban power grid according to the present invention is as follows: Figure 1 As shown, the specific steps include:

[0057] Step 1: Based on the topology of the urban power grid and subway network, the electrical parameters of the equipment, geographical location information, and the installation location and parameters of the subway stray current suppression device, establish an analytical model of stray current distribution in the urban power grid.

[0058] Step 1.1: Collect the equivalent resistance of transmission lines, transformer windings, overhead ground wires, substation grounding resistance, cable armor, and stray current suppression devices in the urban power grid.

[0059] Step 1.2: Collect the equivalent resistance of the up and down contact networks in the subway power supply network, the voltage of the traction substation, the equivalent internal resistance of the traction substation, the transition conductance of the rail to the grounding system, and the train operation diagram of each subway line.

[0060] Step 1.3: Collect the network topology between the urban power grid and the subway power supply system, and the installation location of stray current suppression devices.

[0061] Step 1.4: Establish an equivalent resistance model of the cable armor between the main subway station and the urban power grid substation, and use the resistance network modeling method to establish an analytical model of stray current distribution in the urban power grid.

[0062] Step 2: Using the installation locations of the DC-suppressing and DC-blocking suppression devices and the equivalent resistance of the DC-suppressing device as optimization variables, establish an optimization model with the goal of minimizing cost and the requirement of satisfying the sensitivity of transformer neutral point current and zero-sequence current protection.

[0063] Step 2.1: The objective function for cooperative inhibition optimization is:

[0064]

[0065] Where, N c Number of DC blocking suppression devices installed, M c The cost of a DC blocking suppression device, N r To determine the number of straight-line suppression devices to be installed, M r The cost of a set of straight-line suppression devices.

[0066] Step 2.2: The DC constraint condition for the transformer neutral point during the collaborative suppression optimization process is as follows:

[0067]

[0068] Among them, I i I represents the neutral point DC of the i-th transformer. set This indicates the set DC threshold for the transformer neutral point, and n indicates that there are a total of n neutral point grounded transformers.

[0069] Step 2.3: The constraint conditions for zero-sequence current protection of the power grid during the collaborative suppression optimization process are as follows:

[0070]

[0071] Where K lm This is expressed as the sensitivity coefficient of the transformer zero-sequence overcurrent protection. It represents the sensitivity coefficient of the second stage of the zero-sequence current protection for the line.

[0072] The calculation method for the sensitivity coefficient of the zero-sequence current protection of the urban power grid in step 2.3 is as follows:

[0073] Step 2.3.1: Based on the zero-sequence impedance data of each component of the urban power grid, establish the zero-sequence network of the urban power grid, and collect the setting values ​​of the zero-sequence overcurrent protection of each neutral-point grounding transformer and the second stage of the zero-sequence current protection of the line.

[0074] Step 2.3.2: Set the short-circuit point. The short-circuit point for both transformer zero-sequence overcurrent protection and line zero-sequence current protection is set at the end of the protection.

[0075] Step 2.3.3: Calculate the zero-sequence current of the branch using the node voltage method, and divide the branch current by the zero-sequence current protection setting value of the component to obtain the zero-sequence current protection sensitivity coefficient.

[0076] Step 2.3.4: Change the short circuit point and proceed to step 2.3.3 to start the calculation again. Calculate the sensitivity coefficient of the zero-sequence current protection stage II of all neutral-point grounded transformers and line zero-sequence current protection.

[0077] Step 3: Perform Monte Carlo sampling on the train position-current data of the subway, use the analytical model of stray current distribution in the urban power grid established in Step 1 to calculate the neutral point current of each transformer in the urban power grid; and use the optimization model established in Step 2 to calculate the installation location of the DC-suppressing and DC-blocking suppression devices that meet the optimization objectives and constraints, as well as the equivalent resistance of the DC-suppressing devices.

[0078] Step 3.1: Optimize the installation location of the suppression device using a quantum genetic algorithm. Initialize the population size for each installation location to 50, the number of iterations to be 'a', and the number of individuals to be N.i , 1≤i≤50, its length is n, the data in each individual matrix is ​​a binary number of 0 or 1 with a probability of 50%, where 1 indicates that a suppression device needs to be installed at the neutral point of the transformer.

[0079] Step 3.2: Optimize the installation type of the suppression device using a quantum genetic algorithm. Initialize the population size of the suppression device installation type to 50, the number of iterations to be b, and the number of individuals to be M. x Its length is n, and the matrix of all individuals is M. x The corresponding N i The position of =1 is a binary number of 0 or 1 with a probability of 50%, where 1 indicates that a DC blocking suppression device needs to be installed at the neutral point of the transformer.

[0080] Step 3.3: Optimize the installation resistance of the straight-type suppression device using the particle swarm optimization algorithm. Initialize the number of particles to 20, with each particle having a resistance of y. x Its length is n, the particle coordinate change step size is set to 1, and a single particle matrix is ​​composed of the maximum resistance that can be installed at the neutral point of the transformer; collect the maximum access resistance data R of the neutral point of each transformer. max , where M corresponds x The position y where (j)=0 x (j) Take R max (j), corresponding to M x The position where (j)=1 is directly set to 0.

[0081] Step 3.4: Input the number of times d is used to sample the train position.

[0082] Step 3.5: Based on the collected train timetable, use the Monte Carlo sampling simulation method to sample all train position-current data at any time for each subway line.

[0083] Step 3.6: Calculate the sensitivity coefficient and determine whether the constraints in Step 2.3 are met. If they are met, proceed to Step 3.8; otherwise, proceed to Step 3.7.

[0084] Step 3.7: Optimize the resistance value of the installed straight-line suppression device using the particle swarm optimization algorithm, and determine whether all resistance values ​​are 0. If yes, proceed to step 3.8; otherwise, proceed to step 3.6.

[0085] Step 3.8: Input the optimized resistance distribution of the particle swarm optimization, update the grid resistance network, and set the resistance of the DC blocking suppression device to 10. 5 Ω, calculate the magnitude of the neutral point current |I n,x(j)│, where x corresponds to the value in step 3.2, and j represents the j-th transformer. Determine whether the neutral point current constraint condition in step 2.2 is satisfied. If the entire population does not satisfy the constraint, proceed to step 3.2. If there exists a population M that satisfies the constraint... x Then the number of samplings d = d-1. If d ≤ 0, proceed to step 3.9; otherwise, proceed to step 3.5.

[0086] Step 3.9: The cost of a single DC blocking suppression device is M. c The cost of a single straight-line suppression device is M. r Statistics M x The number of 1s in the middle is N c (x), the number of 0s is N. r (x), calculate the cost size T=N c ∙ M c + N r ∙M r And compare the cost values, selecting the value with the lowest cost, denoted as W. l , where l indicates that the installation type has been optimized l times.

[0087] Step 3.10: Determine if l is 1. If it is, directly set the first generation as the parent and proceed to step 3.12. If not, proceed to step 3.11.

[0088] Step 3.11: Compare with the previous minimum cost; if W... l >W l-1 Then W l-1 The corresponding one is M x Parent generation, otherwise W l The corresponding M x For the parent generation, and record W. l The corresponding M x (j), y x (j), |I nx (j)│, and calculate l=l+1. If l<b, proceed to step 3.12; otherwise, compare all recorded W values. l The minimum cost is selected and denoted as Q. i Proceed to step 3.13.

[0089] Step 3.12: Set the rotation angle of the parent input quantum rotation gate to 0.05π, and change M. x The probability of 0 and 1 at each position in (j) changes the value of the binary number, and the offspring is obtained. Step 3.3 is repeated.

[0090] Step 3.13: Record the cost of the optimal installation type for each installation location as Q1, Q2, ..., Q 50 .

[0091] Step 3.14: Compare Q1, Q2, ..., Q 50 The minimum cost is selected and denoted as P. m , m indicates that the installation location was optimized m times.

[0092] Step 3.15: Determine if m is 1. If it is, directly use the first generation as the parent and proceed to step 3.17. If not, proceed to step 3.16.

[0093] Step 3.16: Compare with the previous minimum cost; if P... m >P m-1 Then P m-1 The corresponding N i Parent generation, otherwise P m The corresponding N i For the parent generation, and record P. m The corresponding chromosome matrix N i (j), and calculate m=m+1. If m>a, proceed to step 3.18; otherwise, proceed to step 3.17.

[0094] Step 3.17: Input the parent into the quantum rotation gate, set the rotation angle to 0.05π, and change N. i The probabilities of 0 and 1 values ​​at each position in (j) are used to obtain the offspring chromosomes, and then proceed to step 3.2.

[0095] Step 3.18: Compare all recorded P values. m Filter out the minimum value corresponding to M. x y x 、│I n,x │, where there may be entities with the same cost but M x y x Different results.

[0096] Step 4: Record the optimization results that meet the requirements in Step 3, and select the scheme with the smallest sum of transformer neutral point current amplitudes as the final cooperative suppression scheme for subway stray current.

[0097] The final optimized solution is obtained after the optimization process in step 3, which yields the optimal installation type population under the optimal installation location population. At this point, it is necessary to compare the suppression effect. The smaller the value, the better the suppression effect. The individual with the best suppression effect is selected as the final optimized solution set.

Claims

1. A collaborative suppression method for stray currents in subway systems within an urban power grid, characterized in that, Includes the following steps: Step 1: Based on the topology of the urban power grid and subway network, the electrical parameters of the equipment, geographical location information, and the installation location and parameters of the subway stray current suppression device, establish an analytical model of stray current distribution in the urban power grid; Step 2: Using the installation location of the DC-suppressing and DC-blocking suppression devices and the equivalent resistance of the DC-suppressing devices as optimization variables, establish an optimization model with the goal of minimizing cost and the requirement of satisfying the sensitivity of transformer neutral point current and zero-sequence current protection. Step 3: Perform Monte Carlo sampling on the train position-current data of the subway, use the analytical model of stray current distribution in the urban power grid established in Step 1 to calculate the neutral point current of each transformer in the urban power grid; and use the optimization model established in Step 2 to calculate the installation location of the DC-suppressing and DC-blocking suppression devices that meet the optimization objectives and constraints, as well as the equivalent resistance of the DC-suppressing devices. Step 4: Record the optimization results that meet the requirements in Step 3, and select the scheme with the smallest sum of transformer neutral point current amplitudes as the final cooperative suppression scheme for subway stray current.

2. The method for collaborative suppression of stray currents in urban power grids via subways according to claim 1, characterized in that, Step 1 specifically involves: Step 1.1: Collect the equivalent resistance of transmission lines, transformer windings, overhead ground wires, substation grounding resistance, cable armor, and stray current suppression devices in the urban power grid; Step 1.2: Collect the equivalent resistance of the up and down contact networks in the subway power supply network, the voltage of the traction substation, the equivalent internal resistance of the traction substation, the transition conductance of the rail to the grounding system, and the train operation diagram of each subway line. Step 1.3: Collect information on the network topology between the urban power grid and the subway power supply system, and the installation locations of stray current suppression devices; Step 1.4: Establish an equivalent resistance model of the cable armor between the main subway station and the urban power grid substation, and use the resistance network modeling method to establish an analytical model of stray current distribution in the urban power grid.

3. The method for collaborative suppression of stray currents in urban power grids via subways according to claim 1, characterized in that, Step 2 specifically involves: Step 2.1: The objective function for cooperative inhibition optimization is: ； Where, N c Number of DC blocking suppression devices installed, M c The cost of a DC blocking suppression device, N r To determine the number of straight-line suppression devices to be installed, M r The cost of a set of straight-line suppression devices; Step 2.2: The DC constraint condition for the transformer neutral point during the collaborative suppression optimization process is as follows: ； Among them, I i I represents the neutral point DC of the i-th transformer. set This indicates the set DC threshold for the transformer neutral point, where n represents the total number of n neutral-point grounded transformers. Step 2.3: The constraint conditions for zero-sequence current protection of the power grid during the collaborative suppression optimization process are as follows: ； Where K lm This is expressed as the sensitivity coefficient of the transformer zero-sequence overcurrent protection. It represents the sensitivity coefficient of the second stage of the zero-sequence current protection for the line.

4. The method for collaborative suppression of stray currents in urban power grids via subways according to claim 3, characterized in that, The method for calculating the sensitivity coefficient of the zero-sequence current protection of the urban power grid in step 2.3 is as follows: Step 2.3.1: Based on the zero-sequence impedance data of each component of the urban power grid, establish the zero-sequence network of the urban power grid, and collect the setting values ​​of the zero-sequence overcurrent protection of each neutral grounding transformer and the second stage of the zero-sequence current protection of the line. Step 2.3.2: Set the short-circuit point. The short-circuit point for both transformer zero-sequence overcurrent protection and line zero-sequence current protection is set at the end of the protection. Step 2.3.3: Calculate the branch zero-sequence current using the nodal voltage method, and divide the branch current by the zero-sequence current protection setting value of the component to obtain the zero-sequence current protection sensitivity coefficient. Step 2.3.4: Change the short circuit point and proceed to step 2.3.3 to start the calculation again. Calculate the sensitivity coefficient of the zero-sequence current protection stage II of all neutral-point grounded transformers and line zero-sequence current protection.

5. The method for collaborative suppression of stray currents in subway systems in urban power grids according to claim 3, characterized in that, Step 3 specifically involves: Step 3.1: Optimize the installation location of the suppression device using a quantum genetic algorithm. Initialize the population size for each installation location to 50, the number of iterations to be 'a', and the number of individuals to be N. i , 1≤i≤50, its length is n, the data in each individual matrix is ​​a binary number of 0 or 1 with a probability of 50%, where 1 indicates that a suppression device needs to be installed at the neutral point of the transformer; Step 3.2: Optimize the installation type of the suppression device using a quantum genetic algorithm. Initialize the population size of the suppression device installation type to 50, the number of iterations to be b, and the number of individuals to be M. x Its length is n, and the matrix of all individuals is M. x The corresponding N i The position of =1 is a binary number of 0 or 1 with a probability of 50%, where 1 indicates that a DC blocking suppression device needs to be installed at the neutral point of the transformer. Step 3.3: Optimize the installation resistance of the straight-type suppression device using the particle swarm optimization algorithm. Initialize the number of particles to 20, with each particle having a resistance of y. x Its length is n, the particle coordinate change step size is set to 1, and a single particle matrix is ​​composed of the maximum resistance that can be installed at the neutral point of the transformer; collect the maximum access resistance data R of the neutral point of each transformer. max , where M corresponds x The position y where (j)=0 x (j) Take R max (j), corresponding to M x The position where (j)=1 is directly set to 0; Step 3.4: Input the number of times d is used to sample the train position; Step 3.5: Based on the collected train timetable, use the Monte Carlo sampling simulation method to sample all train position-current data at any time for each subway line; Step 3.6: Calculate the sensitivity coefficient and determine whether the constraints in Step 2.3 are met. If they are met, proceed to Step 3.8; otherwise, proceed to Step 3.

7. Step 3.7: Optimize the resistance value of the installed straight-line suppression device using the particle swarm optimization algorithm, and determine whether all resistance values ​​are 0. If yes, proceed to step 3.8; otherwise, proceed to step 3.

6. Step 3.8: Input the optimized resistance distribution of the particle swarm optimization, update the grid resistance network, and set the resistance of the DC blocking suppression device to 10. 5 Ω, calculate the magnitude of the neutral point current |I n,x (j)│, where x corresponds to the value in step 3.2, and j represents the j-th transformer. Determine whether the neutral point current constraint condition in step 2.2 is satisfied. If the entire population does not satisfy the constraint, proceed to step 3.

2. If there exists a population M that satisfies the constraint... x Then the number of samples d = d-1. If d ≤ 0, proceed to step 3.9; otherwise, proceed to step 3.

5. Step 3.9: The cost of a single DC blocking suppression device is M. c The cost of a single straight-line suppression device is M. r Statistics M x The number of 1s in the middle is N c (x), the number of 0s is N. r (x), calculate the cost size T=N c ∙ M c + N r ∙M r And compare the cost values, selecting the value with the lowest cost, denoted as W. l , where l indicates that the installation type has been optimized l times; Step 3.10: Determine if l is 1. If it is, directly set the first generation as the parent and proceed to step 3.

12. If not, proceed to step 3.

11. Step 3.11: Compare with the previous minimum cost; if W... l >W l-1 Then W l-1 The corresponding one is M x Parent generation, otherwise W l The corresponding M x For the parent generation, and record W. l The corresponding M x (j), y x (j), |I nx (j)│, and calculate l=l+1. If l<b, proceed to step 3.12; otherwise, compare all recorded W values. l The minimum cost is selected and denoted as Q. i Proceed to step 3.13; Step 3.12: Set the rotation angle of the parent input quantum rotation gate to 0.05π, and change M. x The probability of 0 and 1 at each position in (j) changes the value of the binary number, and the offspring is obtained. Step 3.3 is repeated. Step 3.13: Record the cost of the optimal installation type for each installation location as Q1, Q2, ..., Q 50 ; Step 3.14: Compare Q1, Q2, ..., Q 50 The minimum cost is selected and denoted as P. m m indicates that the installation location was optimized m times; Step 3.15: Determine if m is 1. If it is, directly use the first generation as the parent and proceed to step 3.

17. If not, proceed to step 3.

16. Step 3.16: Compare with the previous minimum cost; if P... m >P m-1 Then P m-1 The corresponding N i Parent generation, otherwise P m The corresponding N i For the parent generation, and record P. m The corresponding chromosome matrix N i (j), and calculate m=m+1. If m>a, proceed to step 3.18; otherwise, proceed to step 3.

17. Step 3.17: Input the parent into the quantum rotation gate, set the rotation angle to 0.05π, and change N. i The probabilities of 0 and 1 values ​​at each position in (j) are used to obtain the offspring chromosomes, and then proceed to step 3.2; Step 3.18: Compare all recorded P values. m Filter out the minimum value corresponding to M. x y x 、│I n,x │, where there may be entities with the same cost but M x y x Different results.

6. The method for collaborative suppression of stray currents in urban power grids via subways according to claim 5, characterized in that, Step 4, which selects the final optimized solution, involves obtaining the optimal installation type population under the optimal installation location population after the optimization process in step 3. At this point, it is necessary to compare the suppression effect. The smaller the value, the better the suppression effect. The individual with the best suppression effect is selected as the final optimized solution set.

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