A power grid maintenance plan arrangement method and terminal based on a maintenance influence domain

Abstract

The application discloses a kind of power grid overhaul plan arrangement methods based on overhaul influence domain, solve the deficiency of prior art, including the following steps: S1, the ground state power flow when no equipment overhaul is calculated;S2, the power flow after any equipment is individually overhauled is calculated, and the influence domain of equipment overhaul is determined by comparison with ground state power flow;S3, the correlation analysis is carried out to the overhaul equipment of influence domain overlap, and repeated outage overhaul and conflict overhaul are judged;S4, the linear mixed integer programming model of overhaul plan optimization is established;S5, the linear mixed integer programming model of overhaul plan optimization is solved using branch cut plane method;S6, according to the overhaul optimization result, the various operation modes appearing in overhaul cycle are generated, and the overhaul related sub-failure set under the mode is determined according to overhaul influence domain;S7, the static security check is carried out to each overhaul mode and corresponding overhaul related sub-failure set;If static security check passes, then overhaul plan optimization ends.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method and terminal for scheduling power grid maintenance based on the maintenance influence domain. Background Technology

[0002] Power equipment maintenance is a crucial part of the daily operations of power supply companies. Regular or irregular maintenance and upkeep of power equipment to keep it in good working order is essential for improving equipment health, ensuring the safe operation of the power grid, and guaranteeing a continuous and reliable power supply. The development of power grid maintenance plans is a core component of power equipment maintenance for power supply companies at all levels. Typically, the power grid dispatch center compiles and coordinates maintenance tasks reported or assigned by different departments, ensuring that the resulting maintenance plans meet the needs of safe and reliable power grid operation while also ensuring reasonable coordination between maintenance departments and avoiding redundant power outages.

[0003] Electrical equipment will be taken out of service during maintenance, weakening the power grid's supply capacity and reducing its reliability. Current maintenance plans are generally drafted manually based on experience, leading to inefficient scheduling, increased outage time, and further reduced grid reliability. Summary of the Invention

[0004] This invention provides a method and terminal for scheduling power grid maintenance plans based on the maintenance influence domain, which effectively optimizes the scheduling of power grid maintenance plans and improves the optimization solution speed, meeting the requirements of large power grid applications.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] A method for scheduling power grid maintenance based on the maintenance influence domain includes the following steps:

[0007] S1. Calculate the ground state power flow when there is no equipment maintenance;

[0008] S2. Calculate the power flow after any single device is repaired, and determine the influence domain of the device repair by comparing it with the ground state power flow.

[0009] S3. Conduct correlation analysis on maintenance equipment with overlapping influence domains to determine duplicate power outage maintenance and conflicting maintenance.

[0010] S4. Establish a linear mixed integer programming model for maintenance plan optimization;

[0011] S5. Solve the linear mixed integer programming model for maintenance plan optimization using the branch-cut plane method;

[0012] S6. Generate various operating modes that occur during the maintenance cycle based on the maintenance optimization results, and determine the maintenance-related sub-fault set under each mode based on the maintenance influence domain;

[0013] S7. Perform static safety checks on each maintenance method and the corresponding maintenance-related sub-fault sets. If the static safety check passes, the maintenance plan optimization ends. Otherwise, add safety constraints corresponding to unmet time periods and anticipated faults to the maintenance plan optimization model and return to S5 to resolve the linear mixed integer programming model.

[0014] Preferably, step S2 specifically includes:

[0015] The power flow after individual maintenance of any equipment is calculated using the Newton-Raphson method or the PQ decomposition method, and compared with the ground state power flow. If the absolute value of the change in power flow of a branch before and after maintenance divided by the ground state power flow of the maintained equipment is greater than a set threshold, then the maintenance is considered to have a significant impact on the active power flow of that branch. The set of these branches is called the influence domain of the maintenance. The set of branches in the influence domain of the maintenance that have the same power flow change direction as the ground state power flow constitutes the positive influence domain, while those with different directions constitute the negative influence domain.

[0016] As described above, the maintenance impact domain defines the range of branches affected by equipment maintenance. Branches not within the impact domain can be considered to be less affected by the maintenance and can be ignored.

[0017] Preferably, step S3 specifically includes:

[0018] S31. Conduct simultaneous shutdown detection on maintenance equipment with overlapping influence areas. If two single maintenance operations have the same power outage equipment, it is judged as repeated power outage maintenance, and maintenance is scheduled at the same time in the maintenance schedule.

[0019] S32. For maintenance equipment with overlapping influence domains, conduct maintenance conflict detection. If a new power failure occurs when two equipment are being maintained at the same time, it is judged as a conflict maintenance and maintenance is scheduled to be carried out at different times.

[0020] As described above, performing correlation analysis on maintenance equipment with overlapping influence domains will avoid combining any two maintenance equipment for analysis, greatly improving the efficiency of maintenance conflict detection.

[0021] Preferably, the linear mixed-integer programming model in step S4 is as follows:

[0022]

[0023] In the formula, I represents the number of maintenance items, and i represents the maintenance item number; D i Let A be an integer variable representing the number of days the maintenance item i is postponed; i The variable is 0-1, indicating whether the original maintenance schedule for maintenance item i should be adjusted; 0 indicates that the original maintenance time should be maintained, and 1 indicates that the maintenance time should be adjusted. CIndicates the weight of the cancelled maintenance items; C i W is a 0-1 variable representing whether maintenance item i is cancelled; 0 indicates that the maintenance plan is retained, and 1 indicates that the maintenance plan is cancelled. O Indicates the weight of power equipment outages; O k,t S is a 0-1 variable representing whether power equipment k experiences a power outage on day t, where 1 indicates a power outage and 0 indicates no power outage; K The set of equipment subject to repeated power outages refers to the collection of equipment for which multiple maintenance items will cause power outages; T represents the total number of days in the maintenance plan optimization period.

[0024] The maintenance plan model considers four objectives: (1) minimizing the number of days for maintenance plan time adjustments; (2) minimizing the number of items for maintenance plan time adjustments; (3) minimizing the number of items for maintenance plan cancellations; and (4) minimizing the number of days for equipment power outages caused by maintenance. Weighting these four objectives yields equation (1).

[0025] In practical applications, the number of days of equipment outages due to maintenance should be minimized as much as possible, therefore W O A larger value should be chosen, and the cancellation of maintenance items should be avoided. Therefore, W C It should be a sufficiently large value, hence W C >>W O >>W A >>1. Aiming to minimize the number of maintenance adjustments and the number of adjustment days helps to schedule as many maintenance items as possible at their most desired times and reduces the workload of communication and coordination between the control center and the department that reports the maintenance plan.

[0026] Introducing variables D represents i That is, adjusting the number of days, then there is

[0027]

[0028]

[0029] The linear mixed-integer programming model for optimizing the maintenance plan includes eight constraints:

[0030] Constraints include: initial maintenance frequency, maintenance days, whether maintenance items maintain the original maintenance time, maintenance adjustment days, prohibited maintenance time, non-changeable maintenance, mutually exclusive maintenance, and simultaneous shutdown maintenance.

[0031] Preferably, step S6 specifically includes:

[0032] S61. Generate various operating modes that occur during the maintenance cycle based on the maintenance optimization results obtained in S5. Since an operating mode may run for multiple days during the maintenance cycle, merging the operating modes of these days helps to reduce the computational scale.

[0033] S62. Determine the maintenance-related sub-fault sets under each operating mode based on the maintenance impact domain.

[0034] As can be seen from the above description, the present invention improves computational efficiency by merging the same operating modes on different days and only considering anticipated faults within the maintenance impact domain, thereby reducing the size of the anticipated fault set.

[0035] Preferably, step S7 specifically includes:

[0036] S71. Perform static safety verification on the maintenance methods and corresponding maintenance-related sub-fault sets given in S6;

[0037] S72. If all maintenance methods and corresponding maintenance-related sub-fault sets can pass the static safety check, the maintenance plan optimization ends, and the maintenance plan given in S5 is the optimal arrangement.

[0038] S73. If the verification results show that the maintenance method cannot meet the static safety requirements under a certain fault, then add the safety constraints corresponding to the unmet time period and the expected fault to the maintenance plan optimization model, and return to S5 to solve again.

[0039] Preferably, the safety constraint used in step S73 is:

[0040] If, under a certain operating mode, the forward power flow of the bottleneck branch exceeds the limit under a certain anticipated fault, the positive influence domain of each maintenance equipment under that operating mode is searched, including the fact that maintenance of the bottleneck branch cannot be scheduled simultaneously. Similarly, if, under a certain anticipated fault, the reverse power flow of the bottleneck branch exceeds the limit, the negative influence domain of each maintenance equipment under that operating mode is searched, including the fact that maintenance of the bottleneck branch cannot be scheduled simultaneously.

[0041] As described above, this invention considers power grid safety constraints by adding a cutting plane to the maintenance optimization model, striving to minimize the number of added constraints in order to improve computational efficiency.

[0042] A power grid maintenance planning terminal based on maintenance influence domain includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the aforementioned power grid maintenance planning method based on maintenance influence domain.

[0043] The beneficial effects of this invention are: (1) Based on various considerations for power grid maintenance planning in engineering applications, a power grid maintenance planning optimization model is established, which improves the rationality of the optimization results. (2) In view of the large computational scale and large computational workload of the power grid maintenance planning optimization problem, the influence domain of a single maintenance is defined. By performing correlation analysis only on maintenance with intersections in the influence domain, and performing static safety verification of maintenance methods only on anticipated faults within the maintenance influence domain, the overall solution speed of the maintenance planning optimization problem is greatly improved, meeting the requirements of large power grid applications. Attached Figure Description

[0044] Figure 1 This is an overall flowchart of a power grid maintenance planning method based on the maintenance influence domain, according to an embodiment of the present invention.

[0045] Figure 2 This is a flowchart illustrating a power grid maintenance planning method based on the maintenance influence domain, according to an embodiment of the present invention.

[0046] Figure 3 This is a wiring diagram of the IEEE 39-node test system used in a power grid maintenance planning scheme based on the maintenance influence domain, according to an embodiment of the present invention.

[0047] Figure 4 This is a schematic diagram of a power grid maintenance planning terminal based on the maintenance influence domain, according to an embodiment of the present invention.

[0048] Among them: 1. A power grid maintenance planning terminal based on maintenance influence domain; 2. Memory; 3. Processor. Detailed Implementation

[0049] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0050] Example 1: A method for scheduling power grid maintenance based on the maintenance influence domain, such as... Figure 1 , Figure 2 As shown, it includes the following steps:

[0051] S1. Calculate the ground state power flow when there is no equipment maintenance;

[0052] S2. Calculate the power flow after any single device is repaired, and determine the influence domain of the device repair by comparing it with the ground state power flow.

[0053] S3. Conduct correlation analysis on maintenance equipment with overlapping influence domains to determine duplicate power outage maintenance and conflicting maintenance.

[0054] S4. Establish a linear mixed integer programming model for maintenance plan optimization;

[0055] S5. Solve the linear mixed integer programming model for maintenance plan optimization using the branch-cut plane method;

[0056] S6. Generate various operating modes that occur during the maintenance cycle based on the maintenance optimization results, and determine the maintenance-related sub-fault set under each mode based on the maintenance influence domain;

[0057] S7. Perform static safety checks on each maintenance method and the corresponding maintenance-related sub-fault sets. If the static safety check passes, the maintenance plan optimization ends. Otherwise, add safety constraints corresponding to unmet time periods and anticipated faults to the maintenance plan optimization model and return to S5 to resolve the linear mixed integer programming model.

[0058] Step S2 specifically involves:

[0059] The power flow after individual maintenance of any equipment is calculated using the Newton-Raphson method or the PQ decomposition method, and compared with the ground state power flow. If the absolute value of the change in power flow of a branch before and after maintenance divided by the ground state power flow of the maintained equipment is greater than a set threshold (e.g., 5%), then the maintenance of the equipment is considered to have a significant impact on the active power flow of that branch. The set of these branches is called the influence domain of the equipment maintenance. The set of branches in the influence domain of the equipment maintenance whose power flow changes are in the same direction as the ground state power flow constitutes the positive influence domain, while those whose directions are not the same constitute the negative influence domain.

[0060] As described above, the maintenance impact domain defines the range of branches affected by equipment maintenance. Branches not within the impact domain can be considered to be less affected by the maintenance and can be ignored.

[0061] The specific steps of step S3 are as follows:

[0062] S31. Conduct simultaneous shutdown detection on maintenance equipment with overlapping influence areas. If two single maintenance operations have the same power outage equipment, it is judged as repeated power outage maintenance, and maintenance is scheduled at the same time in the maintenance schedule.

[0063] S32. For maintenance equipment with overlapping influence domains, conduct maintenance conflict detection. If a new power failure occurs when two equipment are being maintained at the same time, it is judged as a conflict maintenance and maintenance is scheduled to be carried out at different times.

[0064] As described above, performing correlation analysis on maintenance equipment with overlapping influence domains will avoid combining any two maintenance equipment for analysis, greatly improving the efficiency of maintenance conflict detection.

[0065] The linear mixed-integer programming model in step S4 is specifically as follows:

[0066]

[0067] In the formula, I represents the number of maintenance items, and i represents the maintenance item number; Di Let A be an integer variable representing the number of days the maintenance item i is postponed; i The variable is 0-1, indicating whether the original maintenance schedule for maintenance item i should be adjusted; 0 indicates that the original maintenance time should be maintained, and 1 indicates that the maintenance time should be adjusted. C Indicates the weight of the cancelled maintenance items; C i W is a 0-1 variable representing whether maintenance item i is cancelled; 0 indicates that the maintenance plan is retained, and 1 indicates that the maintenance plan is cancelled. O Indicates the weight of power equipment outages; O k,t S is a 0-1 variable representing whether power equipment k experiences a power outage on day t, where 1 indicates a power outage and 0 indicates no power outage; K The set of equipment subject to repeated power outages refers to the collection of equipment for which multiple maintenance items will cause power outages; T represents the total number of days in the maintenance plan optimization period.

[0068] The maintenance plan model considers four objectives: (1) minimizing the number of days for maintenance plan time adjustments; (2) minimizing the number of items for maintenance plan time adjustments; (3) minimizing the number of items for maintenance plan cancellations; and (4) minimizing the number of days for equipment power outages caused by maintenance. Weighting these four objectives yields equation (1).

[0069] In practical applications, the number of days of equipment outages due to maintenance should be minimized as much as possible, therefore W O A larger value should be chosen, and the cancellation of maintenance items should be avoided. Therefore, W C It should be a sufficiently large value, hence W C >>W O >>W A >>1. Aiming to minimize the number of maintenance adjustments and the number of adjustment days helps to schedule as many maintenance items as possible at their most desired times and reduces the workload of communication and coordination between the control center and the department that reports the maintenance plan.

[0070] Introducing variables Represents |D i |, that is, adjusting the number of days, then we have

[0071]

[0072]

[0073] The linear mixed-integer programming model for optimizing the maintenance plan includes eight constraints:

[0074] Constraints include: initial maintenance frequency, maintenance days, whether maintenance items maintain the original maintenance time, maintenance adjustment days, prohibited maintenance time, non-changeable maintenance, mutually exclusive maintenance, and simultaneous shutdown maintenance.

[0075] 1) Initial maintenance frequency constraint

[0076] If maintenance item i is cancelled within the maintenance cycle, the initial maintenance count is 0; otherwise, it is 1. Therefore,

[0077]

[0078] In the formula, s i,t Indicates whether maintenance item i begins during time period t; T i S T i E T i F T i T T represents the allowed start date, allowed end date, originally planned start date, and originally planned end date of maintenance item i, respectively. These are all known constants in the maintenance plan optimization; i E -(T i T -T i F This corresponds to the latest start date for maintenance. In practical applications, it is generally undesirable for maintenance and adjustment time to be too long. This constraint can be achieved by adjusting the earliest allowed maintenance date and the latest allowed maintenance end date for the unit.

[0079] 2) Maintenance days constraints

[0080] When preparing maintenance plans, the dispatch center typically only adjusts the start date of the maintenance, without changing the number of maintenance days. For maintenance item i, constraints on maintenance status and maintenance start status can be constructed:

[0081]

[0082] In the formula, u i,t Indicates whether maintenance item i is to be maintained during time period t; for t-△t <T i S In this case, s i,(t-△t) Set it to 0.

[0083] 3) Whether the maintenance items maintain the original maintenance time constraints.

[0084] For the i-th maintenance item, the constraint on whether to maintain the original maintenance time can be described as follows:

[0085]

[0086] If the original maintenance time remains unchanged, that is, the first Ti F Maintenance began on [date]. and A i=0, otherwise there is and A i =1.

[0087] 4) Maintenance and adjustment days constraints

[0088] The maintenance and adjustment days constraint can be described as follows:

[0089]

[0090] 5) Restrictions on prohibited maintenance time

[0091] For certain maintenance items, there may be times when maintenance is not permitted. For example, maintenance of hydropower units is generally prohibited during the high-water season. If maintenance item i is prohibited on day t, then...

[0092] u i,t =0 (8)

[0093] 6) Maintenance constraints cannot be changed.

[0094] In practical applications, the timing of some maintenance items cannot be changed, such as maintenance plans formulated by the superior dispatching department, maintenance carried over from the previous maintenance period to the current cycle, and fault repairs. For maintenance that cannot be changed, the earliest maintenance date is allowed to correspond to the planned start date of maintenance, and the latest maintenance end date is allowed to correspond to the planned end date of maintenance.

[0095] 7) Mutually exclusive maintenance constraints

[0096] If performing two maintenance tasks simultaneously would cause a power outage or equipment overload, then these two maintenance tasks cannot be scheduled at the same time.

[0097]

[0098] 8) Same-stop maintenance constraints

[0099] If two maintenance items both cause a power outage on a certain piece of equipment, these two maintenance items should be scheduled to be carried out at the same time period as much as possible to avoid duplicate power outages and minimize the outage time. Assuming that maintenance items i and j both cause a power outage on equipment k, then...

[0100] As can be seen from the above description, the maintenance plan optimization model used in this invention is a linear mixed integer programming problem, which can be reliably solved using the branch-cut plane method.

[0101] Step S6 specifically involves:

[0102] S61. Generate various operating modes that occur during the maintenance cycle based on the maintenance optimization results obtained in S5. Since an operating mode may run for multiple days during the maintenance cycle, merging the operating modes of these days helps to reduce the computational scale.

[0103] S62. Determine the maintenance-related sub-fault sets under each operating mode based on the maintenance impact domain.

[0104] As can be seen from the above description, the present invention improves computational efficiency by merging the same operating modes on different days and only considering anticipated faults within the maintenance impact domain, thereby reducing the size of the anticipated fault set.

[0105] Step S7 specifically involves:

[0106] S71. Perform static safety verification on the maintenance methods and corresponding maintenance-related sub-fault sets given in S6;

[0107] S72. If all maintenance methods and corresponding maintenance-related sub-fault sets can pass the static safety check, the maintenance plan optimization ends, and the maintenance plan given in S5 is the optimal arrangement.

[0108] S73. If the verification results show that the maintenance method cannot meet the static safety requirements under a certain fault, then add the safety constraints corresponding to the unmet time period and the expected fault to the maintenance plan optimization model, and return to S5 to solve again.

[0109] The safety constraints used in step S73 are:

[0110] If, under a certain operating mode, the forward power flow of the bottleneck branch exceeds the limit under a certain anticipated fault, the positive influence domain of each maintenance equipment under that operating mode is searched, including the fact that maintenance of the bottleneck branch cannot be scheduled simultaneously. Similarly, if, under a certain anticipated fault, the reverse power flow of the bottleneck branch exceeds the limit, the negative influence domain of each maintenance equipment under that operating mode is searched, including the fact that maintenance of the bottleneck branch cannot be scheduled simultaneously.

[0111] As described above, this invention considers power grid safety constraints by adding a cutting plane to the maintenance optimization model, striving to minimize the number of added constraints in order to improve computational efficiency.

[0112] In this embodiment, based on various considerations for power grid maintenance planning in engineering applications, a power grid maintenance plan optimization model is established, improving the rationality of the optimization results. Addressing the large computational scale and high computational cost of the power grid maintenance plan optimization problem, an influence domain for a single maintenance is defined. By performing correlation analysis only on maintenances with overlapping influence domains and conducting static safety checks on maintenance methods only for anticipated faults within the maintenance influence domain, the overall solution speed for the maintenance plan optimization problem is significantly improved, meeting the requirements of large-scale power grid applications.

[0113] In this embodiment, the following is adopted: Figure 3 The IEEE 39-bus system is used as the basis for simulating a power grid maintenance planning method based on maintenance influence domain in this embodiment. Table 1 shows the sample monthly maintenance plan results.

[0114] Table 1 Optimization Results of IEEE 39 Maintenance Plan

[0115]

[0116] As can be seen from Table 1, the maintenance plan before optimization was very unreasonable:

[0117] The simultaneous maintenance shutdowns (such as branch line 19->33 and gen-33) were scheduled at different time periods, causing repeated power outages for gen-33 (days 1-10). After optimizing the maintenance plan, the simultaneous maintenance shutdowns (such as branch line 19->33 and gen-33) were scheduled at the same time, thus avoiding repeated power outages for unit 33.

[0118] Conflicting maintenance (such as branch lines 1->2 and 8->9) scheduled at the same time would have caused power outages on buses 1, 9, and 39. After optimizing the maintenance plan, the maintenance of branch line 8->9 was postponed by 5 days, thus avoiding a power outage on the buses.

[0119] The optimized maintenance plan yields a reasonable and feasible scheduling scheme, which can provide decision support for the control center to rationally arrange power grid maintenance plans.

[0120] Example 2: Figure 4 As shown, a power grid maintenance planning terminal 1 based on maintenance influence domain includes a memory 2, a processor 3, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the aforementioned power grid maintenance planning method based on maintenance influence domain.

[0121] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Other variations and modifications are possible without departing from the technical solutions described in the claims.

Claims

1. A method for scheduling power grid maintenance based on the maintenance influence domain, characterized in that, Includes the following steps: S1. Calculate the ground state power flow when there is no equipment maintenance; S2. Calculate the power flow after any single device is repaired, and determine the influence domain of the device repair by comparing it with the ground state power flow. S3. Conduct correlation analysis on maintenance equipment with overlapping influence domains to determine duplicate power outage maintenance and conflicting maintenance. S4. Establish a linear mixed integer programming model for maintenance plan optimization; S5. Solve the linear mixed integer programming model for maintenance plan optimization using the branch-cut plane method; S6. Generate various operating modes that occur during the maintenance cycle based on the maintenance optimization results, and determine the maintenance-related sub-fault set under each mode based on the maintenance influence domain; S7. Perform static safety verification on each maintenance method and the corresponding maintenance-related sub-fault set; If the static safety check passes, the maintenance plan optimization ends; otherwise, add safety constraints corresponding to the time period and expected fault that do not meet the requirements to the maintenance plan optimization model, and return to S5 to solve the linear mixed integer programming model again. Step S2 specifically involves: The power flow after any single equipment maintenance is calculated using the Newton-Raphson method or the PQ decomposition method, and compared with the ground state power flow. If the absolute value of the change in power flow of a branch before and after maintenance divided by the ground state power flow of the maintained equipment is greater than a set threshold, then the maintenance is considered to have a significant impact on the active power flow of that branch. The set of these branches is called the influence domain of the maintenance. The set of branches in the influence domain of the maintenance that have the same power flow change direction as the ground state power flow constitutes the positive influence domain, while those that have different directions constitute the negative influence domain. The linear mixed-integer programming model in step S4 is specifically as follows: （1） In the formula, Indicates the number of maintenance items. Indicates the maintenance item number; An integer variable representing maintenance items. Number of days of postponement; A variable consisting of 0 or 1, representing maintenance items. Whether to adjust the original maintenance schedule: 0 indicates to maintain the original maintenance schedule, 1 indicates to adjust the maintenance schedule; Indicates the weight of the cancelled maintenance item; A variable consisting of 0 or 1, representing maintenance items. Whether to cancel: 0 indicates that the maintenance plan is retained, and 1 indicates that the maintenance plan is canceled; Indicates the weight of power equipment outages; A 0-1 variable, representing electrical equipment. In the Is there a power outage? 1 indicates a power outage, 0 indicates no power outage. This refers to a set of equipment that experiences repeated power outages, meaning a collection of equipment for which multiple maintenance tasks will cause a power outage. This indicates the total number of days in the maintenance plan optimization period; Introducing variables express That is, adjusting the number of days, then there is （2） （3） The linear mixed-integer programming model for optimizing the maintenance plan includes eight constraints: Constraints include: initial maintenance frequency, maintenance days, whether maintenance items maintain the original maintenance time, maintenance adjustment days, prohibited maintenance time, non-changeable maintenance, mutually exclusive maintenance, and simultaneous shutdown maintenance.

2. The method for scheduling power grid maintenance based on the maintenance influence domain according to claim 1, characterized in that, The specific steps of step S3 are as follows: S31. Conduct simultaneous shutdown detection on maintenance equipment with overlapping influence areas. If two single maintenance operations have the same power outage equipment, it is judged as repeated power outage maintenance, and maintenance is scheduled at the same time in the maintenance schedule. S32. For maintenance equipment with overlapping influence domains, conduct maintenance conflict detection. If a new power failure occurs when two equipment are being maintained at the same time, it is judged as a conflict maintenance and maintenance is scheduled to be carried out at different times.

3. The method for scheduling power grid maintenance based on the maintenance influence domain according to claim 1, characterized in that, Step S6 specifically involves: S61. Generate various operating modes that occur during the maintenance cycle based on the maintenance optimization results obtained in S5. Since an operating mode may run for multiple days during the maintenance cycle, merge the operating modes of these days. S62. Determine the maintenance-related sub-fault sets under each operating mode based on the maintenance impact domain.

4. The method for scheduling power grid maintenance based on the maintenance influence domain according to claim 3, characterized in that, Step S7 specifically involves: S71. Perform static safety verification on the maintenance methods and corresponding maintenance-related sub-fault sets given in S6; S72. If all maintenance methods and corresponding maintenance-related sub-fault sets can pass the static safety check, the maintenance plan optimization ends, and the maintenance plan given in S5 is the optimal arrangement. S73. If the verification results show that the maintenance method cannot meet the static safety requirements under a certain fault, then add the safety constraints corresponding to the unmet time period and the expected fault to the maintenance plan optimization model, and return to S5 to solve again.

5. The method for scheduling power grid maintenance based on the maintenance influence domain according to claim 4, characterized in that, The safety constraints used in step S73 are: If, under a certain operating mode, the forward power flow of the bottleneck branch exceeds the limit under a certain anticipated fault, the positive influence domain of each maintenance equipment under that operating mode is searched, including the fact that maintenance of the bottleneck branch cannot be scheduled simultaneously. Similarly, if, under a certain anticipated fault, the reverse power flow of the bottleneck branch exceeds the limit, the negative influence domain of each maintenance equipment under that operating mode is searched, including the fact that maintenance of the bottleneck branch cannot be scheduled simultaneously.

6. A power grid maintenance planning terminal based on the maintenance influence domain, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in the power grid maintenance planning method based on the maintenance influence domain as described in any one of claims 1-5.

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