SVG static var generator control method and system

Abstract

The invention relates to the technical field of generator control, and particularly discloses an SVG static var generator control method and system, and the method comprises the steps: constructing a reactive-voltage sensitivity matrix containing a node sensitivity weight based on the real-time measurement data of a power grid, and obtaining a node sensitivity matrix; a dynamic sorting strategy of device priority grading is generated, a distributed compensation priority queue of all devices is obtained, and the devices comprise an SVG cluster and a photovoltaic inverter cluster; executing reactive power distribution under a capacity constraint condition based on the distributed compensation priority queue, and generating a target reactive power compensation amount with the capacity constraint condition; generating a hierarchical coordination control instruction of the SVG and the inverter according to the target reactive compensation amount, and obtaining a hierarchical control instruction set; executing the distributed cooperative compensation driven by the hierarchical control instruction set, and completing the dynamic regulation of the power grid voltage; the reactive power regulation capability of different devices in a power grid can be fully utilized, the accuracy and response speed of reactive power distribution are improved, and resource waste and excessive compensation are avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of generator control, and relates to a control method and system for an SVG static var generator. Background Art

[0002] In the scenario of new energy grid connection, the reactive power control of SVG (static var generator) and wind turbine / photovoltaic inverter is of great importance and necessity. This is because with the large-scale access of new energy, the reactive power demand and voltage stability problems of the power grid become increasingly complex. New energy generation has the characteristics of volatility and intermittency, which is likely to cause large fluctuations in the grid voltage and even may lead to local voltage collapse. As a fast-response reactive power compensation device, SVG can provide precise reactive power regulation within milliseconds and quickly respond to voltage fluctuations. At the same time, wind turbines and photovoltaic inverters themselves have a certain reactive power regulation ability and can provide flexible reactive power support for the power grid by optimizing the control strategy. Therefore, combining the reactive power control of SVG with that of wind turbine / photovoltaic inverter can not only improve the voltage support ability of new energy grid connection but also enhance the safety, stability and economy of power grid operation.

[0003] However, in the prior art, the reactive power control of SVG and wind turbine / photovoltaic inverter in the new energy grid connection scenario generally lacks a unified scheduling and coordination mechanism, resulting in the following defects and drawbacks at the technical level: First, due to the lack of comprehensive analysis of the reactive power regulation ability of each device and the voltage sensitivity of nodes, problems such as "over-compensation" or "under-compensation" are likely to occur. For example, some nodes may have excessive reactive power due to repeated regulation, which may lead to voltage increase or even over-voltage problems; while other nodes may have voltage reduction or even low-voltage problems due to insufficient reactive power support. Second, there is a lack of dynamic optimization strategies for the reactive power distribution between SVG and inverters in the prior art. Usually, fixed priorities or static distribution methods are adopted, which cannot adapt to the complexity of new energy output fluctuations and load dynamic changes, resulting in the regulation result lagging behind the actual demand. In addition, in the existing distributed reactive power control technologies, the control instructions between devices lack coordination, which may cause mutual interference between devices and even trigger control oscillations, further reducing the operation stability of the power grid. Summary of the Invention

[0004] In view of the above problems existing in the prior art, the present invention provides a control method and system for an SVG static var generator to solve the above technical problems.

[0005] To achieve the above object and other objects, the technical solution adopted by the present invention is as follows: On the one hand, the present invention provides a control method for an SVG static var generator, and the method includes the following steps: Step S1: Construct a reactive power-voltage sensitivity matrix including node sensitivity weights based on real-time grid measurement data to obtain a node sensitivity matrix; Step S2: Generate a dynamic sorting strategy for device priority classification according to the node sensitivity matrix to obtain a distributed compensation priority queue for all devices, where the devices include SVG clusters and photovoltaic inverter clusters; Step S3: Perform reactive power distribution under capacity constraints based on the distributed compensation priority queue to generate a target reactive power compensation amount with capacity constraints; Step S4: Generate hierarchical coordinated control instructions for SVG and inverters according to the target reactive power compensation amount to obtain a hierarchical control instruction set; Step S5: Execute distributed cooperative compensation driven by the hierarchical control instruction set to complete dynamic regulation of the grid voltage.

[0006] On the other hand, the present invention provides an SVG static var generator control system, which includes a node sensitivity matrix acquisition module, a device priority queue generation module, a reactive power compensation amount calculation module, a control instruction set acquisition module, and a voltage dynamic regulation module. Each module is connected by wired and / or wireless connection methods to realize data transmission between modules; Node sensitivity matrix acquisition module: Construct a reactive power-voltage sensitivity matrix including node sensitivity weights based on real-time grid measurement data to obtain a node sensitivity matrix; Device priority queue generation module: Generate a dynamic sorting strategy for device priority classification according to the node sensitivity matrix to obtain a distributed compensation priority queue for all devices, where the devices include SVG clusters and photovoltaic inverter clusters; Reactive power compensation amount calculation module: Perform reactive power distribution under capacity constraints based on the distributed compensation priority queue to generate a target reactive power compensation amount with capacity constraints; Control instruction set acquisition module: Generate hierarchical coordinated control instructions for SVG and inverters according to the target reactive power compensation amount to obtain a hierarchical control instruction set; Voltage dynamic regulation module: Execute distributed cooperative compensation driven by the hierarchical control instruction set to complete dynamic regulation of the grid voltage.

[0007] As described above, the SVG static var generator control method and system provided by the present invention have at least the following beneficial effects: There are significant differences in the voltage sensitivity of each node in the power grid. Traditional static reactive power distribution strategies cannot fully consider the differences between nodes, which easily leads to waste of compensation resources or insufficient regulation effects. By constructing a sensitivity matrix through real-time measurement data, the response degree of each node to voltage changes can be dynamically evaluated, thereby optimizing the reactive power distribution strategy, enabling the compensation resources to act concentratedly on the nodes that are most sensitive to voltage stability, and improving the overall regulation efficiency of the power grid. Secondly, the dynamic sorting strategy for equipment priority grading can dynamically adjust the regulation order of SVG and photovoltaic inverters according to real-time operating conditions, avoiding the regulation lag problem caused by fluctuations in new energy output or load changes in traditional fixed priority strategies. Through the dynamic adjustment of the priority queue, the voltage fluctuations of the power grid can be quickly responded to, enhancing the real-time and flexibility of regulation. In addition, reactive power distribution under capacity constraints can ensure that the equipment operates within a safe range, avoiding equipment failures or shortened service life caused by overload operation, and also avoiding waste of resources due to some equipment not being fully utilized. Generating a hierarchical coordinated control instruction set based on the target reactive power compensation amount can achieve the coordinated regulation of SVG and photovoltaic inverters, avoiding control conflicts or repeated regulation problems between equipment, and enhancing the overall coordination and stability of distributed compensation. Finally, through the distributed cooperative compensation driven by the hierarchical control instruction set, the closed-loop control of dynamic voltage regulation of the power grid can be achieved, enhancing the adaptability of the power grid to voltage fluctuations brought by new energy access, and ensuring the safety, stability, and economy of power grid operation. From a technical perspective, this method can not only solve problems such as overcompensation, undercompensation, and regulation lag existing in traditional regulation strategies, but also fully tap the regulation potential of distributed equipment, providing important support for building an efficient and intelligent power grid regulation system. Description of the Drawings

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0009] Figure 1 Schematic diagram of the connection of each step of the method of the present invention.

[0010] Figure 2 Schematic diagram of the connection of each module of the system of the present invention. Detailed Embodiments

[0011] The following will combine the embodiments of the present invention. The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution, as long as they do not deviate from the concept of the invention or exceed the scope defined in this book, they should fall within the protection scope of the present invention.

[0012] Embodiment 1 Please refer to Figure 1 As shown, a control method for an SVG static var generator, the method includes the following steps: Step S1: Based on the real-time measurement data of the power grid, construct a reactive power-voltage sensitivity matrix containing node sensitivity weights to obtain a node sensitivity matrix; Generate a dynamic sorting strategy for device priority grading according to the node sensitivity matrix to obtain a distributed compensation priority queue for all devices, where the devices include an SVG cluster and a photovoltaic inverter cluster; Step S3: Perform reactive power distribution under capacity constraint conditions based on the distributed compensation priority queue to generate a target reactive power compensation amount with capacity constraint conditions; Step S4: Generate hierarchical coordinated control instructions for the SVG and the inverter according to the target reactive power compensation amount to obtain a hierarchical control instruction set; Step S5: Execute the distributed cooperative compensation driven by the hierarchical control instruction set to complete the dynamic regulation of the power grid voltage.

[0013] The operation logic of Step S1 is as follows: Step S11: Collect the voltage amplitude data of the key nodes of the power grid, the reactive power output data of the SVG, and the reactive power output data of the photovoltaic inverter through the SCADA system; among them, the voltage amplitude data of the key nodes of the power grid includes the three-phase voltage effective value, voltage phase angle, and voltage unbalance degree; the reactive power output data of the SVG includes the real-time reactive power output value, available capacity, and response time parameter; the reactive power output data of the photovoltaic inverter includes the current reactive power output, the maximum adjustable capacity, and the power factor adjustment step size; Step S12: Calculate the sensitivity factors of each reactive power source to the voltage weak nodes to obtain a set of node sensitivity factors; Step S13: Perform dynamic weight correction on the sensitivity factors, and the weight coefficient is positively correlated with the square root of the node voltage deviation rate to obtain a set of corrected sensitivity factors; Step S14: Finally, construct a reactive power-voltage sensitivity matrix containing node sensitivity weights.

[0014] In the embodiments of the present invention, the three-phase effective voltage values (including positive-sequence and negative-sequence components), voltage phase angles, and voltage unbalance degrees of key grid nodes are collected in real time through the SCADA system. At the same time, the real-time reactive power output value, available capacity, and response time parameters of the static var generator (SVG), as well as the current reactive power output, maximum adjustable capacity, and power factor adjustment step of the photovoltaic inverter, are obtained. Based on the grid topological connection relationship, a nodal admittance matrix is generated, and the Jacobian matrix method is used to calculate the sensitivity factors of each reactive power source to the voltage weak nodes, which is specifically expressed as the ratio of the change in the nodal voltage to the change in the reactive power output. The initial sensitivity value is determined through the partial derivative relationship between the active power of the node and the voltage phase angle, and the reactive power and the voltage amplitude in the Jacobian matrix. For nodes with a voltage deviation rate exceeding a preset threshold (such as 2%), a dynamic weight correction mechanism is introduced, and its weight coefficient is dynamically adjusted according to the square root of the percentage of the actual voltage of the node deviating from the rated value. The weight increase of the core nodes reaches 1.2 times that of ordinary nodes. After range normalization, the corrected sensitivity factors are used to construct a two-dimensional matrix according to the corresponding relationship between the weak nodes and the reactive power sources. The rows of the matrix are arranged in ascending order of the nodal voltage sensitivity, and the columns are arranged in descending order of the comprehensive sensitivity of the equipment, forming a reactive power-voltage sensitivity decision matrix reflecting the real-time state of the power grid.

[0015] It should be added that the key grid nodes are defined as the nodes that undertake the power transmission hub function or directly affect the system stability in the topological structure, including the endpoints of regional tie lines, the low-voltage side buses of main transformers, the grid connection points of new energy clusters, and the important load access points. They are screened through the nodal weight factor evaluation method. The weight factor is calculated by weighted calculation of the nodal degree centrality (the number of branches directly connected to the node), the betweenness centrality (the proportion of all shortest paths passing through the node), and the voltage level. The nodes with the top 20% of the weight factor rankings are selected as key nodes; the voltage weak nodes are defined as the nodes with the operating voltage deviating from the rated value by ±5% and the voltage sensitivity being higher than 1.5 times the system average. A two-stage screening mechanism is adopted: first, based on the SCADA historical data, the voltage over-limit frequency of each node is statistically analyzed, and the candidate nodes with a monthly average over-limit number > 3 are screened out. Then, the sensitivity coefficient of the candidate nodes to the reactive power disturbance is calculated in combination with the real-time Jacobian matrix. Finally, the nodes with the sensitivity coefficient exceeding the preset threshold are selected as the voltage weak nodes.

[0016] The overall logic of step S12 is as follows: Step S121: Based on the grid topological connection relationship provided by the SCADA system, first extract the impedance parameters of transmission lines, the transformer turns ratio data, and the physical access locations of reactive power compensation devices; establish a π-type equivalent circuit model including shunt admittance for each transmission line, and calculate the complex admittance values of each branch, where the real part is the line conductance value and the imaginary part is the line susceptance value; when constructing the nodal admittance matrix, the diagonal elements are composed of the sum of the admittances of all branches connected to the node plus the nodal shunt admittance, and the off-diagonal elements are taken as the negative of the admittance of the branch between adjacent nodes; finally, generate a nodal admittance matrix dimension consistent with the total number of grid nodes. Step S122: Construct a sensitivity matrix of the nodal voltage magnitude with respect to the phase angle change, which reflects the change in voltage magnitude caused by a small phase angle perturbation; at the same time, construct a sensitivity matrix of reactive power with respect to the phase angle change, and calculate its inverse matrix to reflect the reverse influence of phase angle change on reactive power; multiply the above two matrices and take the negative value to obtain the voltage-reactive power sensitivity sub-matrix; the rows of this sub-matrix correspond to the voltage weak nodes, the columns correspond to the reactive power source devices, and the matrix elements represent the change in nodal voltage caused by a unit change in reactive power output. Step S123: First, locate the physical access nodes of reactive power source devices through the nodal admittance matrix and determine the set of voltage weak nodes; then, screen out the rows corresponding to the voltage weak nodes and the columns corresponding to the access nodes of reactive power source devices in the voltage-reactive power sensitivity sub-matrix, and extract the matrix elements at the intersection positions, which are the sensitivity factors of each reactive power source to the target voltage weak node; each factor represents the linear change in the voltage magnitude of the voltage weak node caused by a unit change in the reactive power output of the reactive power source; finally, integrate the sensitivity factors between all voltage weak nodes and reactive power sources according to the node-device mapping relationship to form a set of nodal sensitivity factors.

[0017] Based on the grid topological connection relationship provided by the SCADA system, the embodiments of the present invention extract the impedance parameters of transmission lines, the transformer turns ratio data, and the physical access locations of reactive power compensation devices; for each transmission line, a π-type equivalent circuit model including shunt admittance to the ground is established, and the real part line conductance value and the imaginary part line susceptance value are calculated branch by branch through the complex admittance calculation method; when constructing the nodal admittance matrix, the admittance superposition principle is adopted, the diagonal elements are composed of the sum of the admittances of all associated branches of the node and the nodal shunt admittance to the ground, and the off-diagonal elements are the negative values of the admittances of the branches between adjacent nodes. Finally, a nodal admittance matrix with the same dimension as the total number of grid nodes is generated, and its dimension is N×N (N is the total number of nodes). Based on the nodal admittance matrix, a sensitivity matrix (∂V / ∂θ) of the nodal voltage magnitude with respect to the phase angle change is constructed, and this matrix solves the linear influence of a small phase angle perturbation on the voltage magnitude through a partial differential equation; simultaneously, a sensitivity matrix (∂Q / ∂θ) of reactive power with respect to the phase angle change is constructed, and its inverse matrix (∂θ / ∂Q) is obtained through matrix inversion operation to quantify the reverse regulation effect of the phase angle change on reactive power; multiplying ∂V / ∂θ by (∂θ / ∂Q) and taking the negative value generates a voltage-reactive power sensitivity sub-matrix (-∂V / ∂Q), whose row vectors correspond to the voltage weak nodes, the column vectors map to the nodes where reactive power source devices are connected, and the matrix element values represent the increment or attenuation of the target node voltage magnitude caused by the change in unit reactive power output. At the same time, based on the nodal admittance matrix, the actual access nodes of reactive power source devices (such as capacitor banks, static var compensators) are determined through topological mapping, and the nodes with voltage magnitudes lower than the threshold in the grid are screened to form a set of voltage weak nodes; subsequently, in the voltage-reactive power sensitivity sub-matrix, the intersection elements of the row vectors corresponding to the voltage weak nodes and the column vectors corresponding to the nodes where reactive power source devices are connected are extracted to obtain the sensitivity factors of each reactive power source to the specified weak node, and its physical meaning is: when a certain reactive power source outputs unit reactive power, the adjustment amplitude of the target weak node voltage magnitude; finally, through the node-device association mapping table, the sensitivity factors of all weak nodes and all reactive power sources are structurally integrated to form a set of node sensitivity factors. This set is stored in the form of a matrix or a list, quantifying the regulation capabilities of different reactive power sources on the voltage weak area, providing core data support for grid dynamic reactive power optimization, and ensuring the accuracy and economy of reactive power compensation strategies.

[0018] The overall logic of step S2 is as follows: Step S21: Extract device-level sensitivity factors from the node sensitivity matrix to generate a set of device sensitivity levels; Step S22: Real-time evaluate the margin coefficients of the SVG cluster and the photovoltaic inverter cluster to generate a set of margin coefficients; Step S23: Construct a dynamic weight allocation model to generate a set of device priority indices; Step S24: Generate a distributed compensation priority queue based on the priority index to complete the dynamic sorting of devices.

[0019] In the embodiment of the present invention, first, the maximum sensitivity factors of each reactive power compensation device (including SVG and photovoltaic inverters) to the voltage weak nodes are extracted from the node sensitivity matrix. The specific method is as follows: for each reactive power device, find the maximum value of the sensitivity factor among all weak nodes, and use this maximum value as the representative sensitivity value of the device. Subsequently, the representative sensitivity values of all devices are normalized, and the numerical range is linearly mapped to between 0 and 1 to generate the device sensitivity level. In the normalization process, the typical minimum sensitivity value of the system 0.015 and the maximum value 0.062 are set as the reference parameters. At the same time, to distinguish the device types, the SVG device is marked with the type identifier 1, and the photovoltaic inverter is marked with the type identifier 0. For the SVG device, by measuring its DC bus voltage and AC output current, combined with the current active power output value, the real-time available reactive power capacity is calculated using the vector operation rule, and the margin coefficient is obtained by dividing it by the rated capacity of the device. For the photovoltaic inverter, according to its current active power output and the rated apparent power, the remaining available reactive power capacity is calculated through the right triangle relationship, and the ratio of it to the rated capacity is used as the margin coefficient. The margin coefficients of all devices are sorted and stored according to the device number to form a margin coefficient set. A dynamic weight allocation model is constructed to weightedly fuse the device sensitivity level and the margin coefficient according to the system voltage deviation rate. When the system voltage deviation rate exceeds 8%, the sensitivity weight is set to 70% and the margin weight is set to 30%; when the voltage deviation rate is between 3% and 8%, the weights of both account for 50%; when the voltage deviation rate is lower than 3%, the sensitivity weight is reduced to 30% and the margin weight is increased to 70%. On this basis, an additional device type addition factor is added: due to the response speed advantage of the SVG device, its priority index is additionally increased by 15% of the base value. Finally, the calculation result is subjected to non-linear compression processing, and the S-shaped curve function is used to map the priority index to the range of 0-1 to avoid the influence of extreme values on the queue stability. All devices are sorted in descending order according to the priority index to generate an initial priority queue. For devices with similar indices (the difference between the two is less than 0.1) during the sorting process, the type priority rule is enforced: the SVG device is always ranked before the photovoltaic inverter. To prevent the device position from changing frequently in adjacent calculation cycles, a queue update lag interval is set, and only when the change range of the priority index of a certain device exceeds 5% is it allowed to adjust its queue position.

[0020] The overall logic of step S3 is as follows: Step S31: Trigger the calculation of the total system compensation demand to generate the reference total reactive power compensation amount; Step S32: Allocate the initial compensation amount according to the priority queue ratio to generate the original compensation instruction set; Step S33: Reconstruct the compensation instruction by applying the capacity constraint to generate an incremental allocation scheme for non-overlimit devices; Step S34: Verify the deviation of the total compensation amount and dynamically correct it, and output the final target reactive power compensation amount.

[0021] In the embodiment of the present invention, when it is detected that the voltage at the grid center point deviates from the rated value by more than 2% for 5 consecutive seconds, the calculation of the compensation demand is started. The calculation mode is selected according to the voltage deviation direction: if the voltage is lower than the rated value, the total compensation demand is the base capacity (taking the minimum three-phase short-circuit capacity of the grid, with a typical value of 100 MVA) multiplied by the voltage loss percentage (the ratio of the difference between the rated voltage and the actual voltage to the rated voltage); if the voltage is higher than the rated value, the calculation method is the same but the direction is opposite. When the demand fluctuation between two adjacent calculation cycles exceeds 30%, the historical data smoothing mechanism is enabled, and the weight ratio of the current value to the previous cycle is 6:4 to suppress the impact of short-term violent fluctuations on the system. Extract the priority index and real-time parameters of each device from the distributed compensation priority queue, and allocate the initial compensation amount according to the index ratio. The specific operation is as follows: divide the priority index of a single device by the sum of the indexes of all devices in the queue, and multiply the obtained ratio by the total compensation demand to obtain the initial target value of the device. Generate an instruction set data packet including the device number, device type flag, initial compensation amount, and available capacity, where the SVG type flag is the number 1 and the photovoltaic inverter flag is 0. The data packet is stored in a nested structure, and an example is as follows: The photovoltaic inverter numbered PV_05 is allocated an initial compensation of 8.3 Mvar, its available capacity is 12.1 Mvar, and the priority index is 0.17. Compare the initial compensation amount of each device with the available capacity: for compliant devices with non-overlimit initial amounts, directly retain the original value; for devices with overlimit initial amounts, forcefully lower their compensation amounts to the upper limit of the available capacity, and the excess part accumulates to form a redistribution surplus pool. For low-priority compliant devices, perform secondary allocation: the allocation weight of SVG devices is increased to 1.2 times the original value, and that of photovoltaic inverters is reduced to 0.8 times, and the total amount of the surplus pool is allocated to this group of devices according to the adjusted weight ratio. For example, a low-priority SVG originally accounted for 8% of the weight ratio, and after gain, it is increased to 9.6% to obtain additional incremental compensation. Accumulate the final allocation amounts of all devices, record it as the final target reactive power compensation amount, and calculate the deviation rate between it and the actual total demand. When the deviation rate exceeds 5%: for the case of insufficient compensation (the actual allocation amount is less than the demand), select high-priority devices from the head of the queue to supplement step by step by 30% of the missing amount; for the case of excessive compensation (the actual allocation amount is greater than the demand), gradually reduce it from the tail of the queue by 120% of the excess amount.

[0022] The overall logic of Step S4 is as follows: Step S41: Analyze the target reactive power compensation amount data to generate device-level control parameters; Step S42: Generate a millisecond-level fast response instruction for the SVG cluster; Step S43: generating a second-level economic adjustment instruction for the photovoltaic inverter cluster; Step S44: Execute the spatiotemporal coordinated scheduling of hierarchical instructions and output the final hierarchical control instruction set.

[0023] The embodiment of the present invention parses the target reactive power compensation data packet and extracts the device number, compensation value and device type identifier. For SVG devices, the fast response mode is automatically bound, requiring that the entire delay from the issuance of the command to the completion of the execution shall not exceed 50 milliseconds; the photovoltaic inverter enables the economic adjustment mode, allowing a maximum response time of 2 seconds. The physical coordinates of the SVG device are obtained through the device geographic information system, and its electrical distance from the central point of the power grid is calculated. The response delay of the near-zone SVG within 5 kilometers is compressed to 30 milliseconds, and the far-zone equipment outside 5 kilometers is relaxed to 50 milliseconds. In order to improve the control accuracy of SVG, a feedforward compensation amount is superimposed on the target compensation amount, and its value is 20% of the target value multiplied by the voltage change rate (the standard value of the voltage change per second) in the last 10 milliseconds. For photovoltaic inverter clusters, an adjustment rate limit is set to prevent power mutations: the single-step adjustment amount shall not exceed 20% of the target value or a minimum of 0.5 megavars. The regulation path is constructed with the goal of minimizing equipment loss. The loss calculation comprehensively evaluates the current heat loss (accounting for 50%), the mechanical loss of the switching device (accounting for 30%) and the temperature rise efficiency loss (accounting for 20%). The dynamic programming algorithm is used to select the optimal regulation scheme, and finally a multicast instruction package containing the equipment group number, target compensation amount, regulation rate and completion time limit is generated. For example, a certain inverter group needs to complete 8.3 megavar capacitive compensation in 17 steps at a rate of 0.5 megavar per second within 2 seconds. Finally, time-space collaborative scheduling is performed: the instruction execution timing relationship diagram is established. When it is detected that the SVG fast instruction and the inverter slow instruction overlap within 50 milliseconds, the inverter instruction start time is automatically postponed until the SVG instruction is completed; spatial coordination is implemented for equipment groups with an electrical distance of less than 2 kilometers, requiring SVG to perform compensation 5 milliseconds in advance to reserve voltage stability margin; and finally a hierarchical control instruction set in XML format is generated, for example, a certain SVG is specified to complete compensation within a 0-30 millisecond window, and a certain photovoltaic inverter is adjusted in steps within a 30-2030 millisecond window.

[0024] Example 2 like Figure 2 As shown, a SVG static VAR generator control system includes a node sensitivity matrix acquisition module, a device priority queue generation module, a reactive power compensation amount calculation module, a control instruction set acquisition module and a voltage dynamic adjustment module, wherein each module is connected by wired and / or wireless connection to realize data transmission between each module; Node sensitivity matrix acquisition module: constructs a reactive power-voltage sensitivity matrix including node sensitivity weights based on real-time grid measurement data to obtain the node sensitivity matrix; Device Priority Queue Generation Module: Generate a dynamic sorting strategy for device priority classification according to the node sensitivity matrix, and obtain a distributed compensation priority queue for all devices, where the devices include SVG clusters and photovoltaic inverter clusters; Reactive Power Compensation Quantity Calculation Module: Perform reactive power distribution under capacity constraints based on the distributed compensation priority queue, and generate a target reactive power compensation quantity with capacity constraints; Control Instruction Set Acquisition Module: Generate hierarchical coordinated control instructions for SVG and inverters according to the target reactive power compensation quantity, and obtain a hierarchical control instruction set; Voltage Dynamic Regulation Module: Perform distributed collaborative compensation driven by the hierarchical control instruction set to complete the dynamic regulation of the grid voltage.

[0025] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0026] It should be understood that determining B based on A does not mean determining B only based on A, but also B can be determined based on A and / or other information.

[0027] As described above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope described.

[0028] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A control method for an SVG static var generator, characterized in that, Including: Step S1: Based on the real-time measurement data of the power grid, construct a reactive power-voltage sensitivity matrix including node sensitivity weights to obtain a node sensitivity matrix; Step S2: Generate a dynamic sorting strategy for device priority classification according to the node sensitivity matrix to obtain a distributed compensation priority queue for all devices, where the devices include SVG clusters and photovoltaic inverter clusters; Step S3: Perform reactive power allocation under capacity constraints based on the distributed compensation priority queue to generate a target reactive power compensation amount with capacity constraints; Step S4: Generate hierarchical coordination control instructions for SVG and inverters according to the target reactive power compensation amount to obtain a hierarchical control instruction set; Step S5: Execute the distributed cooperative compensation driven by the hierarchical control instruction set to complete the dynamic regulation of the power grid voltage.

2. The control method of an SVG static var generator according to claim 1, wherein The operation logic of Step S1 is as follows: Step S11: Collect the voltage amplitude data of key nodes of the power grid, the reactive power output data of SVG, and the reactive power output data of photovoltaic inverters through the SCADA system; Step S12: Calculate the sensitivity factors of each reactive power source to the voltage weak nodes to obtain a set of node sensitivity factors; Step S13: Perform dynamic weight correction on the sensitivity factors. The weight coefficient is positively correlated with the square root of the node voltage deviation rate to obtain a set of corrected sensitivity factors; Step S14: Finally, construct a reactive power-voltage sensitivity matrix including node sensitivity weights.

3. A control method for an SVG static var generator according to claim 2, characterized in that The overall logic of Step S12 is as follows: Step S121: Based on the power grid topology connection relationship provided by the SCADA system, first extract the transmission line impedance parameters, transformer turns ratio data, and the physical access locations of reactive power compensation devices; establish a π-type equivalent circuit model including shunt admittance for each transmission line, calculate the complex admittance values of each branch, where the real part is the line conductance value and the imaginary part is the line susceptance value; when constructing the node admittance matrix, the diagonal elements are composed of the sum of the admittances of all branches connected to the node plus the node shunt admittance, and the off-diagonal elements are the negative of the branch admittance between adjacent nodes; finally, generate a node admittance matrix dimension consistent with the total number of power grid nodes; Step S122: Construct a sensitivity matrix of the node voltage amplitude to the phase angle change, which reflects the voltage amplitude change caused by a small phase angle perturbation; at the same time, construct a sensitivity matrix of reactive power to the phase angle change, and calculate its inverse matrix to reflect the reverse influence of the phase angle change on reactive power; multiply the above two matrices and take the negative value to obtain a voltage-reactive power sensitivity sub-matrix; the rows of this sub-matrix correspond to voltage weak nodes, the columns correspond to reactive power source devices, and the matrix elements represent the node voltage change caused by a unit change in reactive power output. Step S123: First, locate the physical access nodes of reactive power source devices through the nodal admittance matrix and determine the set of voltage weak nodes. Then, in the voltage-reactive power sensitivity sub-matrix, select the rows corresponding to the voltage weak nodes and the columns corresponding to the access nodes of reactive power source devices, and extract the matrix elements at the cross positions, which are the sensitivity factors of each reactive power source to the target voltage weak node. Each factor represents the linear change in the voltage amplitude of the voltage weak node caused by a unit change in the reactive power output of the reactive power source. Finally, integrate the sensitivity factors between all voltage weak nodes and reactive power sources according to the node-device mapping relationship to form a set of node sensitivity factors.

4. A control method for an SVG static var generator according to claim 1, characterized in that, The overall logic of Step S2 is as follows: Step S21: Extract device-level sensitivity factors from the node sensitivity matrix to generate a set of device sensitivity levels. Step S22: Evaluate the margin coefficients of the SVG cluster and the photovoltaic inverter cluster in real time to generate a set of margin coefficients. Step S23: Construct a dynamic weight allocation model to generate a set of device priority indices. Step S24: Generate a distributed compensation priority queue based on the priority indices to complete the dynamic sorting of devices.

5. A control method for an SVG static var generator according to claim 1, characterized in that, The overall logic of Step S3 is as follows: Step S31: Trigger the calculation of the total system compensation demand to generate a reference total reactive power compensation amount. Step S32: Allocate the initial compensation amount according to the proportion of the priority queue to generate an original compensation instruction set. Step S33: Reconstruct the compensation instruction by applying the capacity constraint to generate an incremental allocation plan for non-overlimit devices. Step S34: Verify the deviation of the total compensation amount and dynamically correct it to output the final target reactive power compensation amount.

6. The control method of an SVG static var generator according to claim 1, wherein The overall logic of Step S4 is as follows: Step S41: Analyze the target reactive power compensation amount data to generate device-level control parameters. Step S42: Generate millisecond-level fast response instructions for the SVG cluster. Step S43: Generate second-level economic regulation instructions for the photovoltaic inverter cluster. Step S44: Execute the spatio-temporal coordinated scheduling of hierarchical instructions to output the final hierarchical control instruction set.

7. A control system for an SVG static var generator, characterized in that, It is implemented based on the SVG static var generator control method described in any one of claims 1-6, and includes: Node sensitivity matrix acquisition module: Construct a reactive power-voltage sensitivity matrix containing node sensitivity weights based on real-time grid measurement data to obtain the node sensitivity matrix. Device priority queue generation module: Generate a dynamic sorting strategy for device priority classification according to the node sensitivity matrix to obtain a distributed compensation priority queue for all devices, where the devices include the SVG cluster and the photovoltaic inverter cluster. Reactive power compensation amount calculation module: Perform reactive power allocation under capacity constraint conditions based on the distributed compensation priority queue to generate a target reactive power compensation amount with capacity constraint conditions. Control instruction set acquisition module: Generate hierarchical coordinated control instructions for SVG and inverters according to the target reactive power compensation amount to obtain the hierarchical control instruction set. Voltage dynamic regulation module: Execute distributed cooperative compensation driven by the hierarchical control instruction set to complete the dynamic regulation of the grid voltage.

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