Power grid voltage difference adjusting method and device, electronic equipment and storage medium

Abstract

The invention provides a power grid voltage difference adjusting method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a network voltage in response to the detection that the voltage of a fan grid-connected point of a target power grid exceeds a voltage threshold value; taking the network point voltage as an initial particle position, and establishing a reactive power particle set and a network voltage active support system state equation; iterative calculation is carried out, and a candidate network point voltage value and a damping ratio of each iteration are determined; and determining target particles output by current iteration until the voltage values and the damping ratios of the candidate network points meet iteration ending conditions, taking reactive power corresponding to the target particles as target reactive power, and adjusting the fan grid-connected points of the target power grid based on the target reactive power. Therefore, the target reactive power is selected through the damping ratio and the particle swarm algorithm, the stability of the power grid can be ensured while the selected reactive power is ensured to be the optimal scheme, and the stability and the adjustment precision of the target power grid voltage are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent control technology, and in particular to a method, device, electronic device and storage medium for regulating voltage difference in a power grid. Background Art

[0002] New energy power represented by photovoltaic power generation and wind power generation is developing rapidly and has broad prospects. However, due to the inherent characteristics of sunlight and wind energy, photovoltaic power generation and wind power generation are highly random and volatile, which poses a huge challenge to the economic and safe operation of distribution networks under high penetration of new energy. Summary of the invention

[0003] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0004] To this end, an object of the present disclosure is to provide a method for regulating grid voltage difference.

[0005] A second objective of the present disclosure is to provide a grid voltage difference regulating device.

[0006] A third objective of the present disclosure is to provide an electronic device.

[0007] A fourth object of the present disclosure is to provide a non-transitory computer-readable storage medium.

[0008] A fifth object of the present disclosure is to provide a computer program product.

[0009] To achieve the above-mentioned purpose, the first aspect of the present disclosure proposes a grid voltage difference regulation method, including: in response to detecting that the voltage of the wind turbine grid-connected point of the target grid exceeds the voltage threshold, obtaining the grid voltage of the wind turbine grid-connected point; using the grid voltage as the initial particle position, and establishing a reactive power particle set of the wind turbine grid-connected point and a grid voltage active support system state equation of the target grid, wherein the reactive power particle set includes multiple particles, and the particle is a reactive power; iteratively calculating the reactive power particle set based on the particle swarm algorithm and the initial particle position, determining the candidate grid voltage value of each iteration output particle, and determining the damping ratio based on the candidate grid voltage value and the grid voltage active support system state equation; until the candidate grid voltage value and the damping ratio meet the iteration end condition, determining the target particle outputted in the current iteration, using the reactive power corresponding to the target particle as the target reactive power, and adjusting the wind turbine grid-connected point of the target grid based on the target reactive power.

[0010] According to one embodiment of the present disclosure, judging whether the candidate network point voltage value and the damping ratio satisfy an iteration end condition includes: comparing the candidate network point voltage value with the voltage threshold, and comparing the damping ratio with the damping ratio threshold; updating the reactive power particle set and the particle position of the particle swarm algorithm based on the comparison result, and comparing the current number of iterations with the iteration number threshold; in response to the current number of iterations being greater than or equal to the iteration number threshold, determining that the iteration end condition is satisfied.

[0011] According to one embodiment of the present disclosure, the method also includes: in response to the candidate grid voltage value being greater than the voltage threshold, and / or the damping ratio being less than the damping ratio threshold, randomly generating a new reactive power particle set and determining a new particle position; iteratively calculating the new reactive power particle set based on the particle swarm algorithm and the new particle position until an iteration end condition is met.

[0012] According to one embodiment of the present disclosure, the damping ratio is determined based on the candidate grid point voltage value and the grid voltage active support system state equation, including: obtaining the candidate grid point voltage value corresponding to the particle of the current iteration; substituting the candidate grid point voltage value into the grid voltage active support system state equation to determine the current system characteristic root; and determining the damping ratio based on the system characteristic root.

[0013] According to an embodiment of the present disclosure, determining the damping ratio based on the system characteristic root includes: acquiring the real part and the imaginary part of the system characteristic root; and calculating the damping ratio based on the real part and the imaginary part.

[0014] According to one embodiment of the present disclosure, establishing the state equation of the grid voltage active support system includes: acquiring the environmental parameters and operating parameters of the target grid; establishing the state matrix, input matrix, output matrix and direct transfer matrix of the target grid based on the environmental parameters and the operating parameters; and establishing the state equation of the grid voltage active support system based on the state matrix, the input matrix, the output matrix and the direct transfer matrix.

[0015] According to one embodiment of the present disclosure, the iterative calculation of the reactive power particle set based on the particle swarm algorithm and the initial particle position to determine the candidate network point voltage value of the particle output in each iteration includes: obtaining the current number of iterations; updating the position and speed of each particle in the particle set based on the number of iterations; processing each particle in the updated particle set by the particle swarm algorithm to determine the candidate optimal particle of this iteration, and using the network point voltage value obtained by the reactive power calculation corresponding to the candidate optimal particle as the candidate network point voltage value.

[0016] To achieve the above-mentioned purpose, the second aspect of the present disclosure proposes a grid voltage difference regulating device, including: an acquisition module, which is used to obtain the grid voltage of the wind turbine grid-connected point in response to detecting that the voltage of the wind turbine grid-connected point of the target grid exceeds the voltage threshold; an establishment module, which is used to use the grid voltage as the initial particle position, and establish a reactive power particle set of the wind turbine grid-connected point and a grid voltage active support system state equation of the target grid, wherein the reactive power particle set includes multiple particles, and the particle is a reactive power; a calculation module, which is used to iteratively calculate the reactive power particle set based on a particle swarm algorithm and the initial particle position, determine the candidate grid voltage value of each iteration output particle, and determine the damping ratio based on the candidate grid voltage value and the grid voltage active support system state equation; an adjustment module, which is used to determine the target particle output of the current iteration until the candidate grid voltage value and the damping ratio meet the iteration end condition, use the reactive power corresponding to the target particle as the target reactive power, and adjust the wind turbine grid-connected point of the target grid based on the target reactive power.

[0017] To achieve the above-mentioned purpose, the third aspect embodiment of the present disclosure proposes an electronic device, comprising: 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, and the instructions are executed by the at least one processor to implement the grid voltage difference regulation method as described in the first aspect embodiment of the present disclosure.

[0018] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present disclosure proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the grid voltage difference regulation method as described in the first aspect embodiment of the present disclosure.

[0019] To achieve the above-mentioned purpose, a fifth aspect of the present disclosure provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the grid voltage difference regulation method as described in the first aspect of the present disclosure.

[0020] Therefore, by selecting the target reactive power through the damping ratio and the particle swarm algorithm, it is possible to ensure the stability of the power grid while ensuring that the selected reactive power is the optimal solution, and to improve the stability and regulation accuracy of the target power grid voltage. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a schematic diagram of a grid voltage difference regulation method according to an embodiment of the present disclosure;

[0022] Figure 2 is a schematic diagram of a power grid automatic regulation system according to one embodiment of the present disclosure;

[0023] Figure 3 is a schematic diagram of another grid voltage difference regulation method according to an embodiment of the present disclosure;

[0024] Figure 4 is a schematic diagram of another grid voltage difference regulation method according to an embodiment of the present disclosure;

[0025] Figure 5 is a schematic diagram of a power grid voltage difference regulating device according to an embodiment of the present disclosure;

[0026] Figure 6 is a schematic diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0028] The acquisition, storage, use, and processing of data in the technical solution disclosed in this disclosure are in compliance with the relevant provisions of relevant laws and regulations.

[0029] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0030] Figure 1 is a schematic diagram of a grid voltage difference regulation method according to an embodiment of the present disclosure, such as Figure 1 As shown, the grid voltage difference regulation method includes the following steps:

[0031] S101, in response to detecting that the voltage of a wind turbine grid connection point of a target power grid exceeds a voltage threshold, obtaining a grid point voltage of the wind turbine grid connection point.

[0032] The grid voltage difference regulation method of the embodiment of the present application can be applied to the scenario of intelligent control of grid voltage. The executor of the grid voltage difference regulation of the embodiment of the present application can be the grid voltage difference regulation device of the embodiment of the present application, and the grid voltage difference regulation device can be set on an electronic device.

[0033] It should be noted that the voltage threshold is a critical value to ensure the stability of the grid voltage. The voltage threshold in the embodiment of the present disclosure is designed in advance and can be defined according to the actual design parameters of the grid.

[0034] In the embodiments of the present disclosure, Figure 2 As shown, Figure 2 This is a schematic diagram of a power grid automatic regulation system according to an embodiment of the present disclosure. When the voltage at the wind turbine grid connection point of the target grid exceeds the voltage threshold, the active support control strategy will control according to the excess voltage, generate reactive power compensation Q_{sup}, and add this compensation to the reactive voltage control on the wind turbine side to improve the voltage stability of the grid. The figure includes three elements: grid connection point voltage detection, proportional integral (PI) controller and K gain module.

[0035] Among them, grid connection point voltage detection: the box on the left represents the grid connection point voltage detection device, which monitors and records the voltage value U_{sup} of the grid connection point.

[0036] K gain module: The middle box represents a gain module, which is usually used to amplify or reduce the signal. Here K represents the gain coefficient, which is multiplied by the voltage value U_{sup} detected by the grid voltage to generate a new voltage value KU_{sup}.

[0037] Proportional Integral (PI) Controller: The box on the right represents a PI controller, which receives two inputs: one is KU_{sup} and the other is the reference reactive current I_{qref}. The purpose of the PI controller is to regulate the output current I_{q} so that it is as close as possible to the reference reactive current I_{qref}.

[0038] Q_{sup}: The arrow points to the top of the PI controller, indicating the reactive power compensation amount Q_{sup}. This amount is calculated by the active support control strategy and is used to compensate for the voltage at the grid point that exceeds the threshold.

[0039] Therefore, it is necessary to accurately calculate the reactive power that needs to be compensated to improve the stability of the power grid.

[0040] S102, taking the grid voltage as the initial particle position, and establishing a reactive power particle set of the wind turbine grid connection point and a grid voltage active support system state equation of the target grid, wherein the reactive power particle set includes multiple particles, and a particle is a reactive power.

[0041] In the embodiment of the present disclosure, the reactive power particle set is a set including multiple particles, each particle represents a reactive power, that is, a regulation scheme, and the reactive power particle set is a set of regulation schemes.

[0042] It should be noted that the grid voltage active support system state equation of the target grid is a set that can represent the current state of the target grid.

[0043] In the disclosed embodiment, historical operation data of the target power grid and surrounding environmental data may be collected and analyzed to generate a state equation of the active support system for the power grid voltage.

[0044] In another possible implementation, the historical operation data and environmental data of the target power grid can be input into the grid voltage active support system state equation establishment model to generate the grid voltage active support system state equation of the target power grid. The grid voltage active support system state equation establishment model is trained in advance and can be stored in the storage space of the electronic device to facilitate retrieval and use when needed.

[0045] S103, iteratively calculating the reactive power particle set based on the particle swarm algorithm and the initial particle position, determining the candidate network point voltage value of the output particle in each iteration, and determining the damping ratio based on the candidate network point voltage value and the state equation of the grid voltage active support system.

[0046] In the disclosed embodiment, the fitness of each particle in the reactive power particle set can be calculated, and then the individual extreme value and the global extreme value can be calculated according to the fitness of each particle. Then, the weight value is updated based on the individual extreme value and the global extreme value, and the speed and position of the particle are updated, and then each particle in the updated reactive power particle set is processed to determine the optimal particle for this iteration, and the network point voltage value corresponding to the particle is used as the candidate network point voltage value.

[0047] In the disclosed embodiments, the particle swarm algorithm (PSO) is a heuristic global optimization method, which was originally proposed by being inspired by the foraging behavior of bird flocks. The core idea of ​​the PSO algorithm is to simulate the cooperation and competition relationship between individuals in a group, and to solve the problem by continuously searching for the optimal solution. The particle swarm algorithm in the disclosed embodiments can be multiple, without any limitation here. For example, it can be an improved particle swarm optimization algorithm (IPSO), an adaptive particle swarm optimization algorithm (APSO), a discrete particle swarm optimization algorithm (DPSO), etc.

[0048] It should be noted that the damping ratio is a parameter proposed in the present disclosure for judging the stability of the target power grid with respect to the current iteration result. The larger the damping ratio, the higher the stability of the target power grid at this time.

[0049] S104, until the voltage value and damping ratio of the candidate network point meet the iteration end condition, determine the target particle output by the current iteration, use the reactive power corresponding to the target particle as the target reactive power, and adjust the wind turbine connection point of the target power grid based on the target reactive power.

[0050] In the disclosed embodiment, first, in response to detecting that the voltage of the wind turbine grid connection point of the target power grid exceeds the voltage threshold, the grid voltage of the wind turbine grid connection point is obtained, and then the grid voltage is used as the initial particle position, and the reactive power particle set of the wind turbine grid connection point and the grid voltage active support system state equation of the target power grid are established, wherein the reactive power particle set includes multiple particles, and the particle is a reactive power, and then the reactive power particle set is iteratively calculated based on the particle swarm algorithm and the initial particle position, and the candidate grid voltage value of each iteration output particle is determined, and the damping ratio is determined based on the candidate grid voltage value and the grid voltage active support system state equation, and finally until the candidate grid voltage value and the damping ratio meet the iteration end condition, the target particle output of the current iteration is determined, and the reactive power corresponding to the target particle is used as the target reactive power, and the wind turbine grid connection point of the target power grid is adjusted based on the target reactive power. Therefore, by selecting the target reactive power through the damping ratio and the particle swarm algorithm, the stability of the power grid can be guaranteed while ensuring that the selected reactive power is the optimal solution, and the stability and regulation accuracy of the target power grid voltage can be improved.

[0051] It should be noted that before iteratively calculating the reactive power particle set based on the particle swarm algorithm and the initial particle position, the particle swarm algorithm needs to be initialized. The initialization may include initializing the speed of each particle, setting the population size, maximum speed, learning factor, number of iterations, inertia weight, etc.

[0052] In an embodiment of the present disclosure, the reactive power particle set is iteratively calculated based on the particle swarm algorithm and the initial particle position to determine the candidate network point voltage value of the particle output in each iteration. The current number of iterations can be first obtained, and then the position and speed of each particle in the particle set are updated based on the number of iterations. Finally, each particle in the updated particle set is processed by the particle swarm algorithm to determine the candidate optimal particle for this iteration, and the network point voltage value obtained by the reactive power calculation corresponding to the candidate optimal particle is used as the candidate network point voltage value.

[0053] In one possible implementation, in response to the iteration number being 1, the position and velocity of each particle are randomly initialized, and in response to the iteration number being greater than 1, the historical result of the last iteration and the historical velocity and historical position of each particle configuration of the last iteration are obtained, and the target position and target velocity of each particle configuration required for this iteration are determined based on the historical results, historical velocity and historical position, and the position and velocity of each particle are updated based on the target position and target velocity.

[0054] It should be noted that, in order to determine the target position and target speed for each particle in this iteration based on the historical results, historical speed and historical position, the inertia weight, the first learning factor, the second learning factor, the first random number and the second random number can be obtained first, and the individual historical optimal position, the group historical optimal position, the individual historical optimal fitness value and the group historical optimal fitness value of the previous iteration can be obtained, and then the target speed is calculated based on the inertia weight, the first learning factor, the second learning factor, the historical speed, the first random number and the second random number, and the target position is calculated based on the target speed and the historical position.

[0055] It should be noted that the inertia weight ω represents the influence of the speed of the previous generation of particles on the speed of the current generation of particles, or the degree of trust that the particles have in their current state of motion. The particles move inertially according to their own speed. The inertia weight enables the particles to maintain the inertia of motion and the tendency to search for expansion space. The larger the ω, the stronger the ability to explore new areas, the stronger the global optimization ability, but the weaker the local optimization ability. Conversely, the weaker the global optimization ability, the stronger the local optimization ability. A larger ω is conducive to global search, jumping out of local extremes, and not falling into local optimality; while a smaller ω is conducive to local search, allowing the algorithm to converge quickly to the optimal solution. When the problem space is large, in order to achieve a balance between search speed and search accuracy, it is usually practiced to make the algorithm have a higher global search ability in the early stage to obtain suitable seeds, and a higher local search ability in the later stage to improve convergence accuracy, so ω should not be a fixed constant.

[0056] The learning factor is also called the acceleration coefficient or acceleration factor. The first learning factor in the present disclosure represents the weight of the particle's next action derived from its own experience, which is the acceleration weight that pushes the particle to the individual optimal position. The second learning factor in the present disclosure represents the weight of the particle's next action derived from the experience of other particles, which is the acceleration weight that pushes the particle to the group's optimal position.

[0057] It should be noted that the purpose of setting the first random number and the second random number is to increase the randomness of the search, and the first random number and the second random number are usually set to random numbers in the interval [0,1]. For example, the first random number can be 0.5, and the second random number can be 0.5.

[0058] The inertia weight, the first learning factor, the second learning factor, the first random number, and the second random number are designed in advance and can be changed according to actual design needs, and no limitation is made here. For example, the inertia weight can be set to ω=1.2, the first learning factor c1=1.5, and the second learning factor c2=1.8.

[0059] It should be noted that, to calculate the target speed based on the inertia weight, the first learning factor, the second learning factor, the historical speed, the first random number and the second random number, the inertia weight and the historical speed can be first multiplied to calculate the first factor; and, the first difference between the individual historical optimal position and the historical position is calculated, and the first difference, the first learning factor and the first random number are multiplied to calculate the second factor; and, the second difference between the group's historical optimal position and the historical position is calculated, and the second difference, the second learning factor and the second random number are multiplied to calculate the third factor; finally, the first factor, the second factor and the third factor are added to calculate the target speed.

[0060] The formula for calculating the target speed is as follows:

[0061]

[0062] in, is the target speed, ω is the inertia weight, is the historical speed, c1 is the first learning factor, is the first random number, is the individual's best historical position, is the historical position, c2 is the second learning factor, is the second random number, gBest d is the historical optimal position of a group.

[0063] It should be noted that the target position is calculated based on the target speed and the historical position, and the target speed and the historical position are added to calculate and obtain the target position.

[0064] The formula for calculating the target position is as follows:

[0065]

[0066] in, is the target location, For historical location, is the target speed.

[0067] In the above embodiment, it is judged whether the voltage value and damping ratio of the candidate network point meet the iteration end condition, and it is also possible to Figure 3 Explaining further, the method includes:

[0068] S301, comparing the voltage value of the candidate network point with the voltage threshold, and comparing the damping ratio with the damping ratio threshold.

[0069] It should be noted that the damping ratio threshold is designed in advance and can be changed according to actual design requirements, and no limitation is made here. For example, the damping ratio threshold can be 0.15.

[0070] S302: based on the comparison result, the reactive power particle set and the particle position of the particle swarm algorithm are updated, and the current number of iterations is compared with the iteration number threshold.

[0071] In the embodiment of the present disclosure, in response to the candidate network point voltage value being greater than a voltage threshold, and / or the damping ratio being less than a damping ratio threshold, a new reactive power particle set is randomly generated, and a new particle position is determined.

[0072] In response to the candidate network point voltage value being less than or equal to the voltage threshold, and the damping ratio being greater than or equal to the damping ratio threshold, the reactive power particle set and the particle position of the particle swarm algorithm are not adjusted.

[0073] S303: In response to the number of current iterations being greater than or equal to an iteration number threshold, determining that an iteration end condition is satisfied.

[0074] In the disclosed embodiment, the iteration number threshold is designed in advance and can be changed according to actual design requirements, and no limitation is made here.

[0075] In the disclosed embodiment, the voltage value of the candidate network point is first compared with the voltage threshold, and the damping ratio is compared with the damping ratio threshold, then the reactive power particle set and the particle position of the particle swarm algorithm are updated based on the comparison result, the current number of iterations is compared with the iteration number threshold, and finally, in response to the current number of iterations being greater than or equal to the iteration number threshold, it is determined that the iteration end condition is met. Thus, by setting the iteration number threshold, it is possible to prevent the iteration from falling into a loop, and at the same time, it is possible to ensure that the reactive power iterated by the current particle swarm algorithm is the optimal solution as much as possible.

[0076] In the above embodiment, the damping ratio is determined based on the candidate network point voltage value and the grid voltage active support system state equation, and the damping ratio can also be determined by Figure 4 Explaining further, the method includes:

[0077] S401, obtain the candidate node voltage value corresponding to the particle of the current iteration.

[0078] S402, substituting the candidate network point voltage value into the grid voltage active support system state equation to determine the current system characteristic root.

[0079] It should be noted that to establish the state equation of the grid voltage active support system, the environmental parameters and operating parameters of the target grid can be obtained first, and then the state matrix, input matrix, output matrix and direct transfer matrix of the target grid are established based on the environmental parameters and operating parameters. Finally, based on the state matrix, input matrix, output matrix and direct transfer matrix, the state equation of the grid voltage active support system is established.

[0080] In a possible implementation method, the state equation of the grid voltage active support system can be expressed as follows:

[0081]

[0082] y=Cx+Du

[0083] in, represents the derivative of the system state variable x; A represents the state matrix of the system; B represents the input matrix; C represents the output matrix; D represents the direct transfer matrix; y represents the system output.

[0084] It should be noted that the system state variables can be multiple, and no limitation is made here. For example, the system state variables can be the voltage, current, speed, etc. of the current system, and the voltage, current, speed, etc. of the fan.

[0085] It should be noted that the system characteristic root can be obtained according to the A matrix in the state equation: λ = eig(A)

[0086] S403: Determine a damping ratio based on the system characteristic root.

[0087] In the embodiment of the present disclosure, after the system characteristic root is obtained, the real part and the imaginary part of the system characteristic root may be obtained, and then the damping ratio may be calculated based on the real part and the imaginary part.

[0088] In one possible implementation, the formula for calculating the damping ratio may be as follows:

[0089]

[0090] Among them, ζ is the damping ratio, σ is the real part of the characteristic root, and ω is the imaginary part of the characteristic root.

[0091] Corresponding to the grid pressure difference regulation method provided in the above-mentioned embodiments, an embodiment of the present disclosure also provides a grid pressure difference regulation device. Since the grid pressure difference regulation device provided in the embodiment of the present disclosure corresponds to the grid pressure difference regulation method provided in the above-mentioned embodiments, the implementation method of the above-mentioned grid pressure difference regulation method is also applicable to the grid pressure difference regulation device provided in the embodiment of the present disclosure, and will not be described in detail in the following embodiments.

[0092] Figure 5 Schematic diagram of a grid voltage difference regulating device according to an embodiment of the present disclosure. Figure 5 As shown, the grid voltage difference regulating device 500 includes: an acquisition module 510 , an establishment module 520 , a calculation module 530 and a regulating module 540 .

[0093] The acquisition module 510 is used to acquire the grid point voltage of the wind turbine grid connection point in response to detecting that the voltage of the wind turbine grid connection point of the target power grid exceeds the voltage threshold.

[0094] Establishing module 520, used for taking the grid voltage as the initial particle position, and establishing the reactive power particle set of the wind turbine grid connection point and the grid voltage active support system state equation of the target grid, wherein the reactive power particle set includes multiple particles, and a particle is a reactive power.

[0095] The calculation module 530 is used to iteratively calculate the reactive power particle set based on the particle swarm algorithm and the initial particle position, determine the candidate network point voltage value of each iteration output particle, and determine the damping ratio based on the candidate network point voltage value and the state equation of the grid voltage active support system.

[0096] The adjustment module 540 is used to determine the target particle output by the current iteration until the voltage value and damping ratio of the candidate network point meet the iteration end condition, use the reactive power corresponding to the target particle as the target reactive power, and adjust the wind turbine grid connection point of the target power grid based on the target reactive power.

[0097] In one possible implementation, the adjustment module 540 is also used to: compare the candidate network point voltage value with the voltage threshold, and compare the damping ratio with the damping ratio threshold; update the reactive power particle set and the particle position of the particle swarm algorithm based on the comparison result, and compare the current number of iterations with the iteration number threshold; in response to the current number of iterations being greater than or equal to the iteration number threshold, determine that the iteration end condition is met.

[0098] In one possible implementation, the adjustment module 540 is further used to randomly generate a new reactive power particle set and determine a new particle position in response to a candidate network point voltage value being greater than a voltage threshold and / or a damping ratio being less than a damping ratio threshold.

[0099] In one possible implementation, the calculation module 530 is also used to: obtain the candidate grid point voltage value corresponding to the particle of the current iteration; substitute the candidate grid point voltage value into the state equation of the grid voltage active support system to determine the current system characteristic root; and determine the damping ratio based on the system characteristic root.

[0100] In a possible implementation, the calculation module 530 is further used to: obtain the real part and the imaginary part of the system characteristic root; and calculate the damping ratio based on the real part and the imaginary part.

[0101] In one possible implementation, module 520 is established and is also used to: obtain environmental parameters and operating parameters of the target power grid; establish a state matrix, input matrix, output matrix and direct transfer matrix of the target power grid based on the environmental parameters and operating parameters; and establish a state equation of the power grid voltage active support system based on the state matrix, input matrix, output matrix and direct transfer matrix.

[0102] In one possible implementation, the calculation module 530 is also used to: obtain the current number of iterations; update the position and speed of each particle in the particle set based on the number of iterations; process each particle in the updated particle set through a particle swarm algorithm to determine the candidate optimal particle for this iteration, and use the grid point voltage value obtained by calculating the reactive power corresponding to the candidate optimal particle as the candidate grid point voltage value.

[0103] Therefore, by selecting the target reactive power through the damping ratio and the particle swarm algorithm, it is possible to ensure the stability of the power grid while ensuring that the selected reactive power is the optimal solution, and to improve the stability and regulation accuracy of the target power grid voltage.

[0104] In order to implement the above embodiment, the present disclosure further provides an electronic device 600, Figure 6 is a schematic diagram of an electronic device according to an embodiment of the present disclosure, such as Figure 6 As shown, the electronic device 600 includes: a processor 601 and a memory 602 that is communicatively connected to the processor, the memory 602 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 601 to implement the present disclosure. Figure 1-Figure 4 A method for regulating grid voltage difference according to an embodiment.

[0105] In order to implement the above embodiments, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to implement the above embodiments. Figure 1-Figure 4 A method for regulating grid voltage difference according to an embodiment.

[0106] In order to implement the above embodiments, the present disclosure also provides a computer program product, including a computer program. When the computer program is executed by a processor, the computer program implements the above embodiments. Figure 1-Figure 4 A method for regulating grid voltage difference according to an embodiment.

[0107] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0108] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0109] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0110] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0111] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0112] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that contains, stores, communicates, propagates or transmits a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0113] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0114] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0115] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0116] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for regulating a voltage difference in a power grid, characterized in that: include: In response to detecting that the voltage of the wind turbine grid connection point of the target power grid exceeds a voltage threshold, acquiring the grid point voltage of the wind turbine grid connection point; The grid point voltage is used as the initial particle position, and a reactive power particle set of the wind turbine grid connection point and a grid voltage active support system state equation of the target grid are established, wherein the reactive power particle set includes a plurality of particles, and the particle is a reactive power; Iteratively calculating the reactive power particle set based on the particle swarm algorithm and the initial particle position, determining the candidate network point voltage value of the particle output in each iteration, and determining the damping ratio based on the candidate network point voltage value and the state equation of the grid voltage active support system; Until the candidate grid voltage value and the damping ratio meet the iteration end condition, the target particle output by the current iteration is determined, the reactive power corresponding to the target particle is used as the target reactive power, and the wind turbine grid connection point of the target power grid is adjusted based on the target reactive power.

2. The method according to claim 1, characterized in that Determining whether the candidate network point voltage value and the damping ratio meet an iteration end condition includes: Comparing the candidate network point voltage value with the voltage threshold, and comparing the damping ratio with the damping ratio threshold; Based on the comparison result, the reactive power particle set and the particle position of the particle swarm algorithm are updated, and the current number of iterations is compared with the iteration number threshold; In response to the number of current iterations being greater than or equal to the iteration number threshold, it is determined that an iteration end condition is met.

3. The method according to claim 2, characterized in that The updating of the reactive power particle set and the particle positions of the particle swarm algorithm based on the comparison result includes: In response to the candidate grid voltage value being greater than the voltage threshold, and / or the damping ratio being less than the damping ratio threshold, a new reactive power particle set is randomly generated, and a new particle position is determined.

4. The method according to claim 1, characterized in that The determining of the damping ratio based on the candidate network point voltage value and the grid voltage active support system state equation comprises: Get the candidate network point voltage value corresponding to the particle of the current iteration; Substituting the candidate network point voltage value into the grid voltage active support system state equation to determine the current system characteristic root; The damping ratio is determined based on the system characteristic root.

5. The method according to claim 4, characterized in that The determining the damping ratio based on the system characteristic root comprises: Obtaining the real part and the imaginary part of the system characteristic root; The damping ratio is calculated based on the real part and the imaginary part.

6. The method according to claim 1, characterized in that Establishing the state equation of the grid voltage active support system includes: Acquiring environmental parameters and operating parameters of the target power grid; Establishing a state matrix, an input matrix, an output matrix and a direct transfer matrix of the target power grid based on the environmental parameters and the operating parameters; The state equation of the grid voltage active support system is established based on the state matrix, the input matrix, the output matrix and the direct transfer matrix.

7. The method according to claim 1, characterized in that The iterative calculation of the reactive power particle set based on the particle swarm algorithm and the initial particle position to determine the candidate network point voltage value of the particle outputted in each iteration includes: Get the current number of iterations; Update the position and velocity of each particle in the particle set based on the number of iterations; The particle swarm algorithm is used to process each particle in the updated particle set to determine the candidate optimal particle of this iteration, and the grid point voltage value obtained by reactive power calculation corresponding to the candidate optimal particle is used as the candidate grid point voltage value.

8. A power grid voltage difference regulating device, characterized in that: include: An acquisition module, configured to acquire a grid point voltage of a wind turbine grid connection point in response to detecting that a voltage of the wind turbine grid connection point of a target power grid exceeds a voltage threshold; An establishment module is used to use the grid point voltage as the initial particle position, and establish a reactive power particle set of the wind turbine grid connection point and a grid voltage active support system state equation of the target grid, wherein the reactive power particle set includes a plurality of particles, and the particle is a reactive power; A calculation module, configured to iteratively calculate the reactive power particle set based on a particle swarm algorithm and the initial particle position, determine a candidate network point voltage value of each iteration output particle, and determine a damping ratio based on the candidate network point voltage value and a state equation of the grid voltage active support system; The adjustment module is used to determine the target particle output by the current iteration until the voltage value of the candidate network point and the damping ratio meet the iteration end condition, use the reactive power corresponding to the target particle as the target reactive power, and adjust the wind turbine grid connection point of the target power grid based on the target reactive power.

9. An electronic device, characterized in that: Including memory. processor; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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