Generator control method and device, computer device and readable storage medium

Abstract

The application provides a generator control method and device, computer equipment and a readable storage medium. The method comprises the following steps: obtaining an out-of-limit condition matrix of a specified line set; determining an out-of-limit line in the specified line set when each power transmission line fails based on the out-of-limit condition matrix; determining a target generator related to current supply of the out-of-limit line in a generator set corresponding to the specified line set; determining a power regulation upper limit set of the generator set based on a power regulation upper limit value of the target generator; generating an output constraint range of the generator set based on the power regulation upper limit set of the generator set corresponding to each power transmission line; and determining an optimal power of each generator in the generator set in the output constraint range through a preset particle swarm algorithm. The technical scheme can reduce the iteration number of the particle swarm algorithm, reduce the time cost and computing resources consumed by searching, and improve the accuracy of the calculation result.

Description

[Technical Field]

[0001] This application relates to the field of power control technology, and in particular to a generator control method and apparatus, computer equipment and readable storage medium. [Background Technology]

[0002] In power systems, diversified power generation resources, ever-increasing load demand, and the large-scale integration of new energy sources make optimal power dispatching more challenging. One issue is the phenomenon of transmission lines exceeding their limits during power system faults. Related technologies suggest using particle swarm optimization algorithms to calculate the required generator output level when transmission lines exceed their limits, and adjusting generator power based on the calculation results to mitigate this issue and ensure the safe operation of the power system.

[0003] However, particle swarm optimization (PSO) essentially selects the optimal value within a specified adjustable range, and related technologies often use the upper limit of the operating power of each generator in a generator unit as the upper limit of this adjustable range. This results in a very large search range for PSO, a high number of iterations, and extremely high time and computational costs. Furthermore, because the search range is too large, the final results are not accurate, affecting the rationality of power adjustment for generator units, hindering the effective handling of transmission line overruns, and ultimately failing to guarantee the safe operation of the power system.

[0004] Therefore, in the process of searching for the optimal power of a generator unit based on the particle swarm optimization algorithm, how to improve the accuracy of the calculation results while reducing the search cost has become an urgent technical problem to be solved. [Summary of the Invention]

[0005] This application provides a generator control method and apparatus, a computer device, and a readable storage medium, aiming to solve the technical problem in the related art that it is impossible to balance low cost and high accuracy in the process of searching for the optimal power of a generator set based on the particle swarm algorithm.

[0006] In a first aspect, embodiments of this application provide a generator control method, including:

[0007] Obtain the limit violation matrix of a specified set of transmission lines, wherein the limit violation matrix is ​​used to reflect the limit violation probability parameters of all transmission lines in the specified set of transmission lines when each transmission line in the specified set of transmission lines experiences a fault;

[0008] Based on the over-limit situation matrix, determine the over-limit lines in the specified line set when each transmission line experiences a fault;

[0009] In the generator set corresponding to the specified set of lines, identify the target generator that is related to the current supply of the over-limit line;

[0010] Based on the power regulation upper limit value of the target generator, determine the power regulation upper limit set of the generator set;

[0011] Based on the power regulation upper limit set of the generator set corresponding to each transmission line, the output constraint range of the generator set is generated.

[0012] The optimal power of each generator in the generator set is determined using a preset particle swarm optimization algorithm within the power output constraint range.

[0013] In one embodiment of this application, optionally, obtaining the limit violation matrix of the specified line set includes:

[0014] Traverse each transmission line in the specified line set and obtain the current value of each transmission line when a fault occurs in each transmission line.

[0015] Based on the current values ​​of all transmission lines when each transmission line experiences a fault, and the upper limit values ​​of the current of all transmission lines when each transmission line is fault-free, the over-limit probability parameters of each transmission line when each transmission line experiences a fault are determined.

[0016] The limit-crossing probability parameter is used to generate the limit-crossing case matrix.

[0017] In one embodiment of this application, optionally, traversing each transmission line in the specified line set includes:

[0018] Based on the predetermined risk coefficient of the transmission lines, the ranking of all transmission lines in the specified line set is determined from high to low;

[0019] Iterate through each transmission line in the specified line set according to the sorting.

[0020] In one embodiment of this application, optionally, generating the output constraint range of the generator set based on the power regulation upper limit set of the generator set corresponding to each transmission line includes:

[0021] For every at least two transmission lines in the specified line set, search for the intersection of the power regulation upper limit sets corresponding to each of the at least two transmission lines;

[0022] The output constraint range of the generator set is determined to be the intersection of s intersections in all intersections of the search results, and the intersection of the s intersections is a non-empty set.

[0023] In one embodiment of this application, optionally, generating the output constraint range of the generator set based on the power regulation upper limit set of the generator set corresponding to each transmission line includes:

[0024] For every at least two transmission lines in the specified line set, search for the intersection of the power regulation upper limit sets corresponding to each of the at least two transmission lines;

[0025] If the predetermined number of searches v is less than s, the output constraint range of the generator set is determined to be the intersection of v intersections in the total intersection of the search results, and the intersection of the v intersections is a non-empty set;

[0026] If the predetermined number of searches v is greater than or equal to s, the output constraint range of the generator set is determined to be the intersection of s intersections in the total intersection of the search results, and the intersection of the s intersections is a non-empty set.

[0027] Secondly, embodiments of this application provide a generator control device, including:

[0028] The over-limit situation matrix acquisition unit is used to acquire the over-limit situation matrix of a specified line set, wherein the over-limit situation matrix is ​​used to reflect the over-limit probability parameters of all transmission lines in the specified line set when each transmission line in the specified line set experiences a fault.

[0029] The over-limit line determination unit is used to determine the over-limit lines in the specified line set when each transmission line has a fault, based on the over-limit situation matrix.

[0030] The target generator determination unit is used to determine, from the generator set corresponding to the specified line set, a target generator that is related to the current supply of the over-limit line;

[0031] A power regulation upper limit determination unit is used to determine the power regulation upper limit set of the generator set based on the power regulation upper limit value of the target generator;

[0032] The output constraint range determination unit is used to generate the output constraint range of the generator set based on the power adjustment upper limit set of the generator set corresponding to each transmission line;

[0033] The optimal power search unit is used to determine the optimal power of each generator in the generator set within the output constraint range using a preset particle swarm algorithm.

[0034] Optionally, in one embodiment of this application, the over-limit case matrix acquisition unit is used for:

[0035] Traverse each transmission line in the specified line set to obtain the current value of each transmission line when a fault occurs; based on the current value of each transmission line when a fault occurs and the upper limit value of the current of each transmission line when no fault occurs, determine the over-limit probability parameter of each transmission line when a fault occurs; generate the over-limit situation matrix based on the over-limit probability parameter.

[0036] Optionally, in one embodiment of this application, the over-limit case matrix acquisition unit is used for:

[0037] Based on the predetermined risk coefficient of the transmission lines from high to low, the order of all transmission lines in the specified line set is determined; each transmission line in the specified line set is traversed according to the order.

[0038] Optionally, in one embodiment of this application, the output constraint range determination unit is used for:

[0039] For every at least two transmission lines in the specified line set, search for the intersection of the power regulation upper limit sets corresponding to each of the at least two transmission lines; determine the output constraint range of the generator set as the intersection of s intersections in all intersections in the search results, where the intersection of the s intersections is a non-empty set.

[0040] Optionally, in one embodiment of this application, the output constraint range determination unit is used for:

[0041] For every at least two transmission lines in the specified line set, search for the intersection of the power regulation upper limit sets corresponding to each of the at least two transmission lines; if the predetermined number of searches v is less than s, determine that the output constraint range of the generator set is the intersection of v intersections in the entire intersection set of the search results, and the intersection of the v intersections is a non-empty set; if the predetermined number of searches v is greater than or equal to s, determine that the output constraint range of the generator set is the intersection of s intersections in the entire intersection set of the search results, and the intersection of the s intersections is a non-empty set.

[0042] Thirdly, embodiments of this application provide a computer device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform the method described in the first aspect above.

[0043] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions for performing the method described in the first aspect above.

[0044] The above technical solution addresses the technical challenge of balancing low cost and high accuracy in the process of searching for the optimal power of generator units using particle swarm optimization (PSO). By narrowing the range of generator power sought by the PSO algorithm, the number of iterations is reduced, thus decreasing the time and computational costs. Furthermore, by considering the upper limit of power adjustment for each target generator, the range of generator power sought by the PSO algorithm becomes more reasonable, or in other words, it is narrowed to a level closer to the optimal adjustment result. This facilitates improving the accuracy of the calculation results while reducing search costs. [Attached Image Description]

[0045] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 A flowchart of a generator control method according to an embodiment of this application is shown;

[0047] Figure 2 A flowchart of a generator control method according to another embodiment of this application is shown;

[0048] Figure 3 A wiring diagram of a New England 10-machine 39-node standard test system according to an embodiment of this application is shown;

[0049] Figure 4 A schematic diagram showing the iterative process of solving the problem using the particle swarm optimization algorithm before optimization is presented.

[0050] Figure 5 A schematic diagram illustrating the iterative process of solving using the particle swarm optimization algorithm according to an embodiment of this application is shown;

[0051] Figure 6 A schematic diagram showing the changes in the active power output of the generator before and after optimization using the technical solution of this application is shown;

[0052] Figure 7 A block diagram of a computer device according to one embodiment of this application is shown;

[0053] Figure 8 A block diagram of a computer device according to one embodiment of this application is shown.

Detailed Implementation Methods

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Example 1

[0056] Figure 1 A flowchart of a generator control method according to an embodiment of this application is shown.

[0057] like Figure 1 As shown, a generator control method according to an embodiment of this application includes:

[0058] Step 102: Obtain the limit violation matrix of the specified line set, wherein the limit violation matrix is ​​used to reflect the limit violation probability parameters of all transmission lines in the specified line set when each transmission line in the specified line set experiences a fault.

[0059] Step 104: Based on the over-limit situation matrix, determine the over-limit lines in the specified line set when each transmission line experiences a fault.

[0060] The specified line set is the set of lines that need to be monitored for power safety. For example, it can be all the transmission lines involved in the specified power equipment, or all the transmission lines involved in the specified function.

[0061] The limit-crossing condition matrix for a specified set of transmission lines describes the current state of each transmission line in the specified set when a fault occurs on any of the transmission lines. For example, if the specified set of transmission lines includes n transmission lines, the element in the x-th row and y-th column of the limit-crossing condition matrix refers to the current behavior of the y-th transmission line when the x-th transmission line in the specified set experiences a fault. The current state includes three types: fault, normal, and limit-crossing.

[0062] Optionally, if the element in the x-th row and y-th column of the over-limit situation matrix is ​​0, it is determined that the y-th transmission line is in a normal state when the x-th transmission line has a fault; if the element in the x-th row and y-th column of the over-limit situation matrix is ​​1, it is determined that the y-th transmission line is in an over-limit state when the x-th transmission line has a fault; if the element in the x-th row and y-th column of the over-limit situation matrix is ​​2, it is determined that the y-th transmission line is in a fault state when the x-th transmission line has a fault.

[0063] This allows for the identification of over-limit lines in the designated set of lines when a fault occurs on each transmission line.

[0064] Step 106: In the generator set corresponding to the specified line set, determine the target generator that is related to the current supply of the over-limit line.

[0065] When an over-limit line occurs, the immediate priority is to reduce the current in the over-limit line. The lower the power of the target generator that is correlated with the current supply of the over-limit line, the lower the current in the over-limit line. Therefore, the target generator that is correlated with the current supply of the over-limit line can be identified from the generator set, so that the current in the over-limit line can be reduced to a non-over-limit state by controlling the power of the target generator.

[0066] Step 108: Based on the power regulation upper limit of the target generator, determine the power regulation upper limit set of the generator set.

[0067] The power regulation upper limit refers to the power of the target generator when the current of the over-limit line is reduced to a non-over-limit state.

[0068] Optionally, for each transmission line fault, multiple target generators corresponding to the over-limit line in that case are identified.

[0069] Optionally, the power of each of the multiple target generators is reduced. That is, the power of one target generator is reduced at a time, while the power of the other target generators remains unchanged, until the current of the over-limit line returns to a safe range. The power reduction value of the target generator is the upper limit of the power regulation of the target generator.

[0070] Optionally, the power of one target generator is reduced at a time, while the power of the other target generators remains unchanged. If the current in the over-limit line has not recovered to the safe range when the power of the first target generator is reduced to 0, then another target generator is selected from the other target generators, and its power is reduced again until the current in the over-limit line recovers to the safe range. At this point, the reduction value when the power of the first target generator is reduced to 0 is the upper limit of the power regulation of that target generator.

[0071] Finally, for each transmission line in the specified line set, the target generator corresponding to the line that exceeds the limit when a fault occurs is determined, and all the determined target generators are taken as the generator set, and the power regulation upper limit value of all the determined target generators is taken as the power regulation upper limit set.

[0072] Step 110: Based on the power regulation upper limit set of the generator set corresponding to each transmission line, generate the output constraint range of the generator set.

[0073] Step 112: Determine the optimal power of each generator in the generator set within the power output constraint range using a preset particle swarm optimization algorithm.

[0074] It is understandable that for each target generator, its power regulation upper limit is less than its own operational power upper limit. In other words, the range defined by the power regulation upper limit set, and even the range defined by the output constraint range generated based on the power regulation upper limit set, is reduced relative to the entire power adjustable range of the target generator itself.

[0075] Therefore, the above technical solution, in the process of searching for the optimal power of the generator unit based on the particle swarm optimization (PSO) algorithm, narrows the range of generator power searched by the PSO algorithm, thereby reducing the number of iterations and the time and computational resources consumed in the search. Simultaneously, by considering the upper limit of the power adjustment for each target generator, the range of generator power searched by the PSO algorithm is made more reasonable; in other words, the range of generator power searched by the PSO algorithm is narrowed to a level closer to the optimal adjustment result, thus facilitating improved accuracy of the calculation results while reducing search costs.

[0076] In one possible design, step 102 includes: traversing each transmission line in the specified line set to obtain the current value of each transmission line when a fault occurs; determining the over-limit probability parameter of each transmission line when a fault occurs based on the current value of each transmission line when a fault occurs and the upper limit value of the current of each transmission line when no fault occurs; and generating the over-limit situation matrix based on the over-limit probability parameter.

[0077] In other words, when a fault occurs in each transmission line, the current performance of each transmission line is determined by comparing its current value with the preset upper limit of current under safe conditions.

[0078] Optionally, when a fault occurs on each transmission line, if the current value of a transmission line is greater than its preset safe current limit, the transmission line is determined to have exceeded the limit, and its exceedance probability parameter is set to 1. If the current value of the transmission line is greater than zero and less than or equal to its preset safe current limit, the transmission line is determined to be safe, and its exceedance probability parameter is set to 0. If the current value of the transmission line is equal to zero, the transmission line is determined to have a fault, and its exceedance probability parameter is set to 2.

[0079] In one possible design, when traversing each transmission line in the specified line set, the transmission lines in the specified line set can first be sorted from high to low based on their predetermined risk coefficients. Then, each transmission line in the specified line set is traversed according to this sorting. That is, the transmission lines with higher predetermined risk coefficients can be prioritized for determining the lines that will exceed the limits during a fault, so that when generating the output constraint range of the generator set, priority is given to ensuring that the exceedances caused by faults in transmission lines with higher predetermined risk coefficients are properly resolved.

[0080] In one possible design, step 110 includes: for every at least two transmission lines in the specified line set, searching for the intersection of the power regulation upper limit sets corresponding to each of the at least two transmission lines; if the search result shows that the power regulation upper limit sets corresponding to each of the at least two transmission lines have an intersection, incorporating the intersection into the output constraint range of the generator set.

[0081] In other words, it is possible to iterate through all the fault conditions of the transmission lines, find all feasible generator output constraints, and finally merge all feasible generator output constraints into the final output constraint range.

[0082] In another possible design, step 110 includes: for every at least two transmission lines in the specified line set, searching for the intersection of the power regulation upper limit sets corresponding to each of the at least two transmission lines; determining the output constraint range of the generator set as the intersection of s intersections in all intersections in the search results, wherein the intersection of the s intersections is a non-empty set.

[0083] Optionally, the intersection of the power regulation upper limit sets of at least two transmission lines is obtained, and then the non-empty intersection of the s intersections obtained from the multiple intersections is taken as the final output constraint range.

[0084] Optionally, during the search process, the intersection of generator constraint solutions can be determined first for transmission lines with higher predetermined risk coefficients, based on the order of their predetermined risk coefficients from high to low.

[0085] In another possible design, step 110 includes: for every at least two transmission lines in the specified line set, searching for the intersection of the power regulation upper limit sets corresponding to each of the at least two transmission lines; if the predetermined number of searches v is less than s, determining that the output constraint range of the generator set is the intersection of v intersections in the entire intersection set of the search results, wherein the intersection of the v intersections is a non-empty set; if the predetermined number of searches v is greater than or equal to s, determining that the output constraint range of the generator set is the intersection of s intersections in the entire intersection set of the search results, wherein the intersection of the s intersections is a non-empty set.

[0086] In other words, a predetermined number of searches can be set, thus eliminating the need to traverse all combinations of lines in the specified line set, reducing the computational load of the search, and further improving the computational efficiency of the generator output range.

[0087] All of the above methods can reduce the generator output constraint range used in the particle swarm optimization search to varying degrees, thereby achieving a low-cost and high-accuracy search.

[0088] Example 2

[0089] Figure 2 A flowchart of a generator control method according to another embodiment of this application is shown;

[0090] Combination Figure 2 As shown, this embodiment considers the generator output constraint under fault factors. Based on the risk map of the transmission line, the danger queue of the transmission line is obtained. Before solving the over-limit situation of the transmission line, the active power generated by the generator is constrained, and then the particle swarm algorithm is used to solve it.

[0091] The principle for adjusting generator output is to reduce the degree of over-limit behavior of the entire system. Specifically, this includes two aspects:

[0092] First, reduce the transmission power of the lines that exceed the limits, thereby reducing the degree of exceeding the limits or reducing the number of lines that exceed the limits;

[0093] Second, while reducing the number of lines exceeding the limit, new lines may be created, but the overall situation is improved compared to before the adjustment. The specific steps will be explained in detail below.

[0094] A. Construct a dangerous queue.

[0095] Each transmission line fault is set as a working condition ρ. i Since there are n lines in the system, n operating conditions can be set, i = (1, 2, ..., n).

[0096] Based on the hazard coefficients corresponding to the transmission lines in the risk map, the fault scenarios of the transmission lines are ranked to obtain ρ1, ρ2, ρ3…ρ n .

[0097] For example, in the 46 transmission lines of the New England 10-unit 39-node system, according to the risk map, the lines are arranged from high to low risk coefficient as L2-3, L1-2, L2-25, ..., L12-11, that is, ρ1 represents a fault in line L2-3, ρ2 represents a fault in line L1-2, ρ3 represents a fault in line L2-25, and ρ... 46 This indicates a fault has occurred on line L12-11.

[0098] ρ i =[ρ1, ρ2, ρ3…ρ n ]

[0099] B. Construct a transmission line over-limit model.

[0100] After a line fault, the over-limit situations of other transmission lines can be detected using matrix a. ij express.

[0101]

[0102] Among them, a ij This represents the situation where the j-th line exceeds the limit under operating condition i, where 1 represents line j exceeding the limit, 0 represents line j not exceeding the limit, and 2 represents a fault.

[0103] C. Determine the generator constraint model

[0104] Set the active power of the generator in the system to an initial value, namely P. g =[P g1 P g2 P g3 … P gN ], where N is the number of generators. For example, the initial power in a New England 10-machine 39-node system is:

[0105] P g =[250 677.87 650 632 508 650 560 540 830 1000]

[0106] The generator output range corresponding to different operating conditions is Ω ρ =[Ω ρ1 Ω ρ2 Ω ρ3 … Ω ρn According to matrix a] ij If the system does not exceed the limit, then Ω ρ =P g .

[0107] If line L exceeds the limit in operating condition i, the generators adjacent to line L will be adjusted to reduce the active power transmitted in line L, thereby reducing the power exceeding the limit or reducing the number of lines exceeding the limit.

[0108]

[0109] According to the hazard coefficient queue search, ρ8 indicates that a fault has occurred on line L16-21. The fault caused line L23-24 to exceed the limit, and the active power of G35 can be adjusted.

[0110]

[0111] Continuing the search based on the risk factor, the generator was adjusted for several high-risk operating conditions, resulting in:

[0112]

[0113] After searching for the s-th operating condition, the optimal generator output constraint value can be obtained:

[0114] Ω=Ω ρ1 ∩Ω ρ2 ∩Ω ρ3 ∩…∩Ω ρs

[0115] Since the constraint solution of the next level of working condition may not have any intersection with the previous level, the number of searches can be determined from two aspects.

[0116] The first method involves searching along the danger queue until no intersection is found. This method selects as many intersections as possible of the generator constraint solutions, i.e.:

[0117] Ω=Ω ρ1 ∩Ω ρ2 ∩Ω ρ3 ∩…∩Ω ρs

[0118] Where s is the maximum number of working conditions whose intersection is a non-empty set.

[0119] The second approach involves determining the number of searches, x, and then searching for the optimal value within that specified number of searches.

[0120]

[0121] The final generator output constraint model is as follows:

[0122]

[0123] D. Solve for the generator constraint solution

[0124] Using the particle swarm optimization algorithm, the output of each generator is used as a benchmark to fluctuate around the target value, allowing the particles to search for the optimal solution in this optimization space. By constraining the generator output, the particle optimization space can be optimized, making the target solution set have the property of having a smaller risk of exceeding the limit. Then, according to the set objective function, the optimal solution can be obtained faster and more accurately.

[0125] Example 3

[0126] Taking a 10-machine, 39-node line in New England as an example, the wiring of the standard test system for a 10-machine, 39-node line in New England is as follows: Figure 3 As shown.

[0127] A. Constrain generators according to the danger queue.

[0128] After a line fault, the over-limit situations of other transmission lines can be detected using matrix a. ij express.

[0129]

[0130] The initial power in the New England 10-machine 39-node system is:

[0131] P g =[250 677.87 650 632 508 650 560 540 830 1000]

[0132] Wherein, ρ5 indicates that a fault occurred in line 4-14, which caused line 6-11 to exceed its limit, and the active power of G32 can be adjusted.

[0133]

[0134] P g =[250 677.87 489 632 508 650 560 540 830 1000]

[0135] According to the risk factor queue search, ρ8 indicates that a fault occurred on line L16-21. The fault caused line L23-24 to exceed the limit. The active power of G35 and G36 can be adjusted. According to the transmission line limit power analysis, Pg35+Pg36<1123. After adjustment, the extreme value of the corresponding generator under this operating condition is obtained.

[0136]

[0137]

[0138] P g =[250 677.87 489 632 508 560 564 540 830 1000]

[0139] According to the hazard coefficient queue search, ρ13 indicates a fault in line L21-22, which causes line L16-24 to exceed its limit. The active power of lines G35 and G36 can be adjusted. Based on the transmission line limit power analysis, Pg35 + Pg36 < 600. Adjustment can mitigate the limit exceedance of line L16-24, but it will cause other transmission lines in the system to exceed their limits, and the exceedances will be more severe. Therefore, in this situation, no further adjustments to G35 and G36 are taken.

[0140] Continuing the search based on the risk factor, the generator was adjusted for several high-risk operating conditions, resulting in:

[0141] P g =[400 791.9145 489 580 585 560 564 410 860 1053.2548]

[0142] B. Simulation verification based on particle swarm optimization algorithm and verification results.

[0143] The model was analyzed using the particle swarm optimization algorithm in Matlab software, and case 39 in Matpower was used for the analysis.

[0144] Set dimension d = 9; population size popsize = 10; set maximum number of iterations maxgen = 10; objective function function value = langer(x), with the objective function being to minimize the total number of out-of-range events in the system; in Matlab, fun_range is set to fluctuate around Pg by 10.

[0145] fun_range=[240,260; 640,660; 622,642; 498,518; 640,660; 550,570; 530,550; 820,840; 990,1100].

[0146] The iterative process of solving the problem using the particle swarm optimization algorithm before optimization is as follows: Figure 4 As shown in the figure. The minimum value of the objective function is 19, and the program runs for 637.01 seconds.

[0147] The iterative process of solving the problem using the particle swarm optimization algorithm after optimization using the above-mentioned technical solution of this application is as follows: Figure 5 As shown, the minimum value of the objective function is 8, and the program runs for 81.54 seconds.

[0148] fun_range=[390,400;479,489;570,580;575,585;550,560;554,564;400,410;950,960;1100,1200].

[0149] In addition, the changes in the output of each generator before and after optimization are shown in Table 1 below. The changes in the active power output of the generators before and after optimization are as follows: Figure 6 As shown.

[0150] Table 1

[0151] Generator serial number Connected nodes Active power / MW before optimization Optimized active power / MW Output adjustment amount / MW 1 30 250 400 150 2 31 677.8711258 495.4624127 -182.408713 3 32 650 489 -161 4 33 632 580 -52 5 34 508 585 77 6 35 650 560 -90 7 36 560 564 4 8 37 540 410 -130 9 38 830 860 30 10 39 1000 1053.2548 53.2548

[0152] C. Simulation Verification Conclusion

[0153] With the same population size and maximum number of iterations, without considering dangerous queues, the required iteration time for solving the generator's active power output using the particle swarm optimization algorithm is 637.01 seconds, and the number of system overruns is 19.

[0154] The iteration time for solving the generator's active power output using the particle swarm optimization algorithm under the consideration of dangerous queues was 81.54 seconds, and the number of system overruns was 8.

[0155] Through simulation comparison and analysis, the generator output is first constrained based on the danger queue, and then the generator output value is solved using the particle swarm optimization algorithm. This not only improves the accuracy of the solution, but also effectively reduces the number of iterations and the time required by the algorithm.

[0156] The above technical solutions can improve optimization efficiency. By introducing a new model considering dangerous queues and a particle optimization space, the number of iterations of the particle swarm optimization algorithm in finding the optimal solution for unit output in a power system can be effectively reduced, thus significantly improving computational efficiency. Simultaneously, the optimization time can be shortened. Optimization is performed under given active power output constraints and maximum active power output limits. These constraints limit the search space, thereby reducing problem complexity and allowing the algorithm to converge to the optimal solution more quickly. Therefore, under the same accuracy requirements, the optimization process required using this application will be significantly reduced.

[0157] Furthermore, this approach can improve the accuracy of the solution and reduce the risk of exceeding limits. Specifically, by using dangerous queue constraints and a particle optimization space, the output range of the generators is selectively filtered. This allows the algorithm to focus on optimizing effective solutions, thereby increasing the likelihood of finding the global optimum and enhancing the accuracy of the solution. Simultaneously, by setting a maximum active power output limit for the generators, system requirements are met while ensuring that the equipment's safe operating range is not exceeded. This helps reduce the risk of exceeding limits and improves the reliability and stability of the power system. Moreover, this technical solution has broad applicability in practical power systems and can be effectively applied to the unit output optimization problem of various existing and new power supply systems. This provides strong support for achieving efficient, safe, and sustainable operation of power systems.

[0158] In summary, this application demonstrates significant technical advantages in improving optimization efficiency, shortening optimization time, increasing solution accuracy, and reducing the risk of exceeding limits. Compared with traditional particle swarm optimization algorithms, the method presented in this patent exhibits superior performance in solving power system unit output optimization problems and possesses strong practical value.

[0159] Additionally, this application provides a generator control device, including:

[0160] The over-limit situation matrix acquisition unit is used to acquire the over-limit situation matrix of a specified line set, wherein the over-limit situation matrix is ​​used to reflect the over-limit probability parameters of all transmission lines in the specified line set when each transmission line in the specified line set experiences a fault.

[0161] The over-limit line determination unit is used to determine the over-limit lines in the specified line set when each transmission line has a fault, based on the over-limit situation matrix.

[0162] The target generator determination unit is used to determine, from the generator set corresponding to the specified line set, a target generator that is related to the current supply of the over-limit line;

[0163] A power regulation upper limit determination unit is used to determine the power regulation upper limit set of the generator set based on the power regulation upper limit value of the target generator;

[0164] The output constraint range determination unit is used to generate the output constraint range of the generator set based on the power adjustment upper limit set of the generator set corresponding to each transmission line;

[0165] The optimal power search unit is used to determine the optimal power of each generator in the generator set within the output constraint range using a preset particle swarm algorithm.

[0166] Optionally, in one embodiment of this application, the over-limit case matrix acquisition unit is used for:

[0167] Traverse each transmission line in the specified line set to obtain the current value of each transmission line when a fault occurs; based on the current value of each transmission line when a fault occurs and the upper limit value of the current of each transmission line when no fault occurs, determine the over-limit probability parameter of each transmission line when a fault occurs; generate the over-limit situation matrix based on the over-limit probability parameter.

[0168] Optionally, in one embodiment of this application, the over-limit case matrix acquisition unit is used for:

[0169] Based on the predetermined risk coefficient of the transmission lines from high to low, the order of all transmission lines in the specified line set is determined; each transmission line in the specified line set is traversed according to the order.

[0170] Optionally, in one embodiment of this application, the output constraint range determination unit is used for:

[0171] For every at least two transmission lines in the specified line set, search for the intersection of the power regulation upper limit sets corresponding to each of the at least two transmission lines; determine the output constraint range of the generator set as the intersection of s intersections in all intersections in the search results, where the intersection of the s intersections is a non-empty set.

[0172] Optionally, in one embodiment of this application, the output constraint range determination unit is used for:

[0173] For every at least two transmission lines in the specified line set, search for the intersection of the power regulation upper limit sets corresponding to each of the at least two transmission lines; if the predetermined number of searches v is less than s, determine that the output constraint range of the generator set is the intersection of v intersections in the entire intersection set of the search results, and the intersection of the v intersections is a non-empty set; if the predetermined number of searches v is greater than or equal to s, determine that the output constraint range of the generator set is the intersection of s intersections in the entire intersection set of the search results, and the intersection of the s intersections is a non-empty set.

[0174] The generator control device uses the solution described in any one of the above embodiments, and therefore has all the above-mentioned technical effects, which will not be repeated here.

[0175] In another embodiment, this application provides a computer device, which may be a server, and its internal structure diagram may be as follows. Figure 7 As shown. The computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When the computer program is executed by the processor, it can implement the generator control method described in any of the above embodiments.

[0176] In one embodiment, this application also provides a computer device, which can be a client, and its internal structure diagram can be as follows: Figure 8As shown, the computer device includes a processor, memory, network interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it can implement the generator control method described in any of the above embodiments.

[0177] Any of the computer devices described in the embodiments of this application exist in various forms, including but not limited to:

[0178] (1) Mobile communication devices: These devices are characterized by their mobile communication capabilities and primarily aim to provide voice and data communication. These terminals include: smartphones (e.g., iPhones), multimedia phones, feature phones, and low-end phones, etc.

[0179] (2) Ultra-mobile personal computer devices: These devices fall under the category of personal computers, possessing computing and processing capabilities, and generally also have mobile internet access features. These terminals include PDAs, MIDs, and UMPCs, such as the iPad.

[0180] (3) Portable entertainment devices: These devices can display and play multimedia content. This category includes: audio and video players (such as iPods), handheld game consoles, e-books, as well as smart toys, wearable devices, and portable car navigation devices.

[0181] (4) Server: A device that provides computing services. The components of a server include a processor, hard disk, memory, system bus, etc. Servers are similar to general computer architectures, but because they need to provide highly reliable services, they have higher requirements in terms of processing power, stability, reliability, security, scalability, and manageability.

[0182] (5) Other electronic devices with data interaction functions.

[0183] Additionally, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which are used to perform the following steps:

[0184] Obtain the limit violation matrix of a specified set of transmission lines, wherein the limit violation matrix is ​​used to reflect the limit violation probability parameters of all transmission lines in the specified set of transmission lines when each transmission line in the specified set of transmission lines experiences a fault;

[0185] Based on the over-limit situation matrix, determine the over-limit lines in the specified line set when each transmission line experiences a fault;

[0186] In the generator set corresponding to the specified set of lines, identify the target generator that is related to the current supply of the over-limit line;

[0187] Based on the power regulation upper limit value of the target generator, determine the power regulation upper limit set of the generator set;

[0188] Based on the power regulation upper limit set of the generator set corresponding to each transmission line, the output constraint range of the generator set is generated.

[0189] The optimal power of each generator in the generator set is determined using a preset particle swarm optimization algorithm within the power output constraint range.

[0190] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0191] The technical solution of this application has been described in detail above with reference to the accompanying drawings. Through this technical solution, in the process of searching for the optimal power of a generator unit based on the particle swarm optimization (PSO) algorithm, the range of generator power searched by the PSO algorithm is narrowed, which reduces the number of iterations of the PSO algorithm and decreases the time cost and computational resources consumed in the search. At the same time, by considering the upper limit of the power adjustment of each target generator, the range of generator power searched by the PSO algorithm is made more reasonable; in other words, the range of generator power searched by the PSO algorithm is narrowed to a level closer to the optimal adjustment result, thereby facilitating the improvement of the accuracy of the calculation results while reducing search costs.

[0192] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0193] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0194] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0195] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.

[0196] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0197] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A generator control method, characterized in that, include: Obtain the limit violation matrix of a specified set of transmission lines, wherein the limit violation matrix is ​​used to reflect the limit violation probability parameters of all transmission lines in the specified set of transmission lines when each transmission line in the specified set of transmission lines experiences a fault; Based on the over-limit situation matrix, determine the over-limit lines in the specified line set when each transmission line experiences a fault; In the generator set corresponding to the specified set of lines, identify the target generator that is related to the current supply of the over-limit line; Based on the power regulation upper limit value of the target generator, determine the power regulation upper limit set of the generator set; Based on the power regulation upper limit set of the generator set corresponding to each transmission line, the output constraint range of the generator set is generated. The optimal power of each generator in the generator set is determined using a preset particle swarm optimization algorithm within the power output constraint range.

2. The generator control method according to claim 1, characterized in that, The step of obtaining the limit violation matrix for the specified set of lines includes: Traverse each transmission line in the specified line set and obtain the current value of each transmission line when a fault occurs in each transmission line. Based on the current values ​​of all transmission lines when each transmission line experiences a fault, and the upper limit values ​​of the current of all transmission lines when each transmission line is fault-free, the over-limit probability parameters of each transmission line when each transmission line experiences a fault are determined. The limit-crossing probability parameter is used to generate the limit-crossing case matrix.

3. The generator control method according to claim 2, characterized in that, The step of traversing each transmission line in the specified set of lines includes: Based on the predetermined risk coefficient of the transmission lines, the ranking of all transmission lines in the specified line set is determined from high to low; Iterate through each transmission line in the specified line set according to the sorting.

4. The generator control method according to any one of claims 1 to 3, characterized in that, The step of generating the output constraint range of the generator set based on the power regulation upper limit set of the generator set corresponding to each transmission line includes: For every at least two transmission lines in the specified line set, search for the intersection of the power regulation upper limit sets corresponding to each of the at least two transmission lines; The output constraint range of the generator set is determined to be the intersection of s intersections in all intersections of the search results, and the intersection of the s intersections is a non-empty set.

5. The generator control method according to claim 3, characterized in that, The step of generating the output constraint range of the generator set based on the power regulation upper limit set of the generator set corresponding to each transmission line includes: For every at least two transmission lines in the specified line set, search for the intersection of the power regulation upper limit sets corresponding to each of the at least two transmission lines; If the predetermined number of searches v is less than s, the output constraint range of the generator set is determined to be the intersection of v intersections in the total intersection of the search results, and the intersection of the v intersections is a non-empty set; If the predetermined number of searches v is greater than or equal to s, the output constraint range of the generator set is determined to be the intersection of s intersections in the total intersection of the search results, and the intersection of the s intersections is a non-empty set.

6. A generator control device, characterized in that, include: The over-limit situation matrix acquisition unit is used to acquire the over-limit situation matrix of a specified line set, wherein the over-limit situation matrix is ​​used to reflect the over-limit probability parameters of all transmission lines in the specified line set when each transmission line in the specified line set experiences a fault. The over-limit line determination unit is used to determine the over-limit lines in the specified line set when each transmission line has a fault, based on the over-limit situation matrix. The target generator determination unit is used to determine, from the generator set corresponding to the specified line set, a target generator that is related to the current supply of the over-limit line; A power regulation upper limit determination unit is used to determine the power regulation upper limit set of the generator set based on the power regulation upper limit value of the target generator; The output constraint range determination unit is used to generate the output constraint range of the generator set based on the power adjustment upper limit set of the generator set corresponding to each transmission line; The optimal power search unit is used to determine the optimal power of each generator in the generator set within the output constraint range using a preset particle swarm algorithm.

7. The generator control device according to claim 6, characterized in that, The over-limit case matrix acquisition unit is used for: Traverse each transmission line in the specified line set and obtain the current value of each transmission line when a fault occurs in each transmission line. Based on the current values ​​of all transmission lines when each transmission line experiences a fault, and the upper limit values ​​of the current of all transmission lines when each transmission line is fault-free, the over-limit probability parameters of each transmission line when each transmission line experiences a fault are determined. The limit-crossing probability parameter is used to generate the limit-crossing case matrix.

8. The generator control device according to claim 7, characterized in that, The over-limit case matrix acquisition unit is used for: Based on the predetermined risk coefficient of the transmission lines from high to low, the order of all transmission lines in the specified line set is determined; each transmission line in the specified line set is traversed according to the order.

9. A computer device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, the instructions being configured to perform the method according to any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that, The device stores computer-executable instructions for performing the method as described in any one of claims 1 to 5.

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