Offshore wind power analysis method, system, device and medium considering prediction information

Abstract

The present application relates to the technical field of new energy power system dispatching, and particularly relates to a method, system, device and medium for offshore wind power analysis considering prediction information, the method comprising: constructing a key node set of offshore wind power group, adjusting active power output of each power source involved in power flow calculation, inputting a virtual phase modifier at the key node, obtaining real-time / predicted node voltage-virtual phase modifier reactive power output curve of each key node, and determining the reactive power margin of the weakest node; constructing a key node set of offshore wind power grid strength, generating an interimpedance matrix online based on a preset strength standard, analyzing new energy multi-station short-circuit ratio of the offshore wind power group, and evaluating the grid strength; sorting and analyzing the voltage static stability margin of the key node of the offshore wind power group, sorting and analyzing the grid strength of the key node of the offshore wind power grid strength, determining the weakest node, analyzing the change trend, and comprehensively evaluating the operation risk based on the early warning threshold, and early warning the risk node.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new energy power system dispatching, and in particular to a method, system, device and medium for offshore wind power analysis considering prediction information. BACKGROUND

[0002] Under the background of low-carbon transformation of the energy industry, the eastern coastal areas of China have formed a development mode of large-scale development and centralized grid connection of offshore wind power, forming a multi-loop DC feeding receiving end power grid with large-scale offshore wind power access. The strength of the power grid has an important influence on the instability mode of the large-scale offshore wind power grid connection system.

[0003] With the continuous increase of new energy installed capacity and the exit of traditional power sources, the proportion of new energy such as wind power continues to increase, which reduces the relative strength of the main power grid and further increases the risk of offshore wind power oscillation instability under weak power grid. In order to improve the stable operation margin of the power grid and prevent wide frequency oscillation accidents in the scenario of large-scale offshore wind power access, it is urgent to form a practical risk early warning analysis method for offshore wind power grid connection operation to ensure the safe and stable operation of the power system with high proportion of offshore wind power access.

[0004] However, the existing analysis method can only monitor the current operation state of offshore wind power, cannot master the deep information such as the strength of the power grid of offshore wind power group in real time, and cannot evaluate the operation risk and safety margin of the current offshore wind power, lacking quantitative evaluation method for offshore wind power operation reactive voltage safety margin and weak link analysis and risk evaluation method of power grid strength.

[0005] The information disclosed in this BACKGROUND section is only intended to enhance the understanding of the general background of the present application, and should not be construed as recognition or implication that this information constitutes prior art that is already known to those of ordinary skill in the art. SUMMARY

[0006] The present application provides a method, system, device and medium for offshore wind power analysis considering prediction information, thereby effectively solving the problems in the background art.

[0007] In order to achieve the above purpose, the technical solution adopted by the present application is as follows: a method for offshore wind power analysis considering prediction information, comprising the following steps:

[0008] Constructing a set of key nodes of offshore wind power group, adjusting the active power output of each power source involved in the power flow calculation, inputting a virtual phase modifier at the key nodes, obtaining real-time / predicted node voltage-virtual phase modifier reactive power output curves of each key node, analyzing voltage collapse points, and determining the reactive power margin of the weakest node;

[0009] The key node set of offshore wind power grid strength is constructed, the mutual impedance matrix is generated online based on the preset strength standard, the new energy multi-station short circuit ratio of the offshore wind farm is analyzed, the short circuit capacity and voltage interaction influence coefficient of each node are calculated, and the grid strength is evaluated.

[0010] The voltage static stability margin of the key node of the offshore wind farm is sorted and analyzed, the weakest node is determined, the change trend is analyzed, the grid strength of the key node of the offshore wind power grid strength is sorted and analyzed, the weakest node is determined, the change trend is analyzed, the comprehensive operation risk is evaluated based on the early warning threshold, and the risk node is warned.

[0011] Further, the virtual phase modifier is put into the key node, and the real-time / predicted node voltage-virtual phase modifier reactive power output curve of each key node is obtained, including:

[0012] The output voltage of the virtual phase modifier is changed by a set step, the power flow is solved, and the node voltage and the reactive power output of the virtual phase modifier are recorded;

[0013] The output voltage of the virtual phase modifier is repeatedly changed and the power flow is calculated until enough points are collected, and the real-time / predicted node voltage-virtual phase modifier reactive power output curve of each key node is formed.

[0014] Further, the voltage collapse node is analyzed, and the reactive power margin of the weakest node is determined, including:

[0015] The lowest point in the node voltage-virtual phase modifier reactive power output curve is the voltage collapse point of the current node;

[0016] The reactive power margin is the vertical distance between the current operating point of the node and the voltage collapse point, and the vertical distance of the weakest node is the smallest.

[0017] Further, the mutual impedance matrix is generated online, including:

[0018] The physical parameter information of the grid framework topology and device elements and the dynamic model parameters of the grid equipment are obtained;

[0019] The admittance matrix suitable for short circuit ratio current calculation is formed;

[0020] The admittance matrix is inverted to obtain the mutual impedance matrix reflecting the current operating state of the grid.

[0021] Further, the new energy multi-station short circuit ratio of the offshore wind farm is analyzed, the short circuit capacity and voltage interaction influence coefficient of each node are calculated, including:

[0022] The new energy multi-station short-circuit ratio of the offshore wind farm group is analyzed based on real-time and predicted data, and the calculation method of the new energy multi-station short-circuit ratio index is as follows:

[0023]

[0024] In the formula, S ki is the short-circuit capacity of node i, S ki is the product of the maximum short-circuit current I Bi of the wind farm i and the short-circuit point, S i is the complex power form when the node has active power P i and reactive power Q i ; when the active power and the reactive power take the current real-time value, the obtained short-circuit ratio is the real-time short-circuit ratio; when the active power and the reactive power take the current short-term / ultra-short-term value, the obtained short-circuit ratio is the short-term / ultra-short-term short-circuit ratio; r ji is the voltage interaction coefficient between the wind farm j and the wind farm i, ΔV j and ΔV i are the change values of the voltage of the wind farm j and the wind farm i respectively; Z ii is the self-impedance of the wind farm i; and Z ji is the mutual impedance between the wind farm j and the wind farm i.

[0025] Further, the voltage static stability margin of the key node of the offshore wind farm group is sorted and analyzed to determine the weakest node, including:

[0026] When the voltage collapse point is analyzed to determine the reactive power margin of the weakest node, the voltage static stability margin of each access point of the onshore collection station is sorted and analyzed, the weakest node i is determined according to the minimum reactive power margin, the change trend of the voltage static stability margin of the weakest node i is analyzed, the reactive power margin change index of the weakest node i is obtained, and the calculation method is as follows:

[0027]

[0028] In the formula, Q r is the voltage static stability margin of the weakest node under the current output working condition; and Q f is the voltage static stability margin of the weakest node based on the predicted power output working condition.

[0029] Further, the grid strength of the key node of the offshore wind power grid is sorted and analyzed to determine the weakest node, including:

[0030] The new energy multi-station short-circuit ratio of the offshore wind power group is analyzed, the grid strength of each access point of the offshore wind farm is sorted and analyzed, the weakest node j is determined according to the minimum grid strength, the change trend of the grid strength of the weakest node j is analyzed, the grid strength change index of the weakest node j is obtained, and the calculation mode is as follows:

[0031]

[0032] In the formula: SCR r is the grid strength of the weakest node under the current output working condition; SCR f is the grid strength of the weakest node based on the predicted power output working condition.

[0033] Further, the comprehensive operation risk assessment based on the early warning threshold value comprises:

[0034] For the weak node i, when k Q is a negative value, and |k Q |>k Q_min , it is determined that the node i is a risk node; for the weak node j, when k S is a negative value, and |k S |>k S_min , it is determined that the node j is a risk node, for all analyzed grid strength nodes m, when SCR f <1.5, it is determined that the node m is a risk node, and the key nodes determined as risk nodes are classified into a risk area; k Q_min , k S_min is the early warning threshold value.

[0035] The application also comprises an offshore wind power analysis system considering prediction information, using the method as described above, comprising:

[0036] A voltage static security analysis module is used to construct a key node set of the offshore wind power group, adjust the active power output of each power source involved in the power flow calculation, input a virtual phase modifier at the key node, obtain real-time / predicted node voltage-virtual phase modifier reactive power output curves of each key node, analyze voltage collapse points, and determine the reactive power margin of the weakest node;

[0037] A grid strength online analysis module is used to construct a key node set of the offshore wind power grid strength, generate an interimpedance matrix online based on a preset strength standard, analyze the new energy multi-station short-circuit ratio of the offshore wind power group, calculate the short-circuit capacity and voltage interaction influence coefficient of each node, and evaluate the grid strength;

[0038] The operation risk analysis module is used for sorting analysis on voltage static stability margin of key nodes of the offshore wind power group, determining the weakest node, analyzing the change trend, sorting analysis on grid strength of key nodes of the offshore wind power grid, determining the weakest node, analyzing the change trend, and comprehensively evaluating the operation risk based on the early warning threshold value to early warn the risk nodes.

[0039] The application further comprises a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method as described above when executing the computer program.

[0040] The application further comprises a storage medium, which stores a computer program executable on a processor to implement the method as described above.

[0041] The application has the following beneficial effects: the offshore wind power operation risk analysis method suitable for guiding production and combining real-time and prediction is proposed for the problem of insufficient offshore wind power grid strength, the topological structure of the grid under a typical operation mode, voltage static stability safety index, real-time power of the wind farm, ultra-short-term / short-term power prediction and other data are fully considered, the comprehensive operation risk prediction accuracy of the offshore wind power is effectively improved through fusion analysis of multiple data sources, the method is suitable for weak node and operation risk evaluation when large-scale wind farms are connected to the grid, has theoretical guiding significance for avoiding oscillation risk of offshore wind power clusters and improving voltage stability margin, and the method can be easily implemented in a dispatch automation system, has relatively simple application steps and certain engineering value. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0043] Figure 1 The flow chart of the method of the present application;

[0044] Figure 2 The structural schematic diagram of the system of the present application;

[0045] Figure 3 The flow chart of the offshore wind power operation risk online analysis;

[0046] Figure 4 The typical grid frame of large-scale offshore wind power concentrated access;

[0047] Figure 5 Flowchart of the algorithm for the voltage static security analysis module of offshore wind farms;

[0048] Figure 6(a) shows the UQ curves of the onshore grid connection points of each wind farm under light load conditions (busbar segment connection), Figure 6(b) shows the UQ curves of the onshore grid connection points of each wind farm under heavy load conditions (busbar segment connection), and Figure 6(c) shows the UQ curves of the onshore grid connection points of each wind farm under heavy load conditions (busbar segment disconnection).

[0049] Figure 7 Flowchart of the algorithm for the online grid intensity analysis module for offshore wind farms;

[0050] Figure 8 Here is the algorithm flowchart for the offshore wind power operation risk analysis module;

[0051] Figure 9 This is a schematic diagram of the structure of the computer device of the present invention. Detailed Implementation

[0052] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0053] Example 1:

[0054] like Figure 1 As shown: A method for analyzing offshore wind power that takes into account forecast information, comprising the following steps:

[0055] Construct a set of key nodes for offshore wind farms, adjust the active power output of each power source involved in power flow calculation, deploy virtual synchronous condensers at key nodes, obtain the real-time / predicted node voltage-virtual synchronous condenser reactive power output curves of each key node, analyze voltage collapse points, and determine the reactive power margin of the weakest node.

[0056] Construct a set of key nodes for the strength of offshore wind power grids, generate a mutual impedance matrix online based on a preset strength standard, analyze the short-circuit ratio of new energy multi-sites in offshore wind power clusters, calculate the short-circuit capacity and voltage interaction coefficient of each node, and evaluate the grid strength.

[0057] The voltage static stability margin of key nodes in offshore wind farms is ranked and analyzed to identify the weakest node and analyze its changing trend. The grid strength of key nodes in offshore wind farms is ranked and analyzed to identify the weakest node and analyze its changing trend. Based on the early warning threshold, a comprehensive operational risk assessment is conducted, and early warning is issued for risky nodes.

[0058] In view of the problem of insufficient offshore wind power grid strength, an offshore wind power operation risk analysis method suitable for guiding dispatching production and combining real-time and prediction is provided, which fully considers the topological structure of the power grid under the typical operation mode, the voltage static stability safety index, the real-time power of the wind farm, the ultra-short-term / short-term power prediction and other data, and through the fusion analysis of multiple data sources, the comprehensive operation risk prediction accuracy of the offshore wind power is effectively improved, which is suitable for the weak node and operation risk assessment of the large-scale wind farm concentratedly connected to the power grid, has theoretical guiding significance for avoiding the oscillation risk of the offshore wind power cluster and improving the voltage stability margin. In addition, the method can be conveniently implemented in the dispatching automation system, the application steps are relatively simple, and has certain engineering value.

[0059] In the embodiment, the virtual phase modifier is put into the key node to obtain the real-time / predicted node voltage-virtual phase modifier reactive power output curve of each key node, including:

[0060] The output voltage of the virtual phase modifier is changed by a set step, the power flow is solved, and the node voltage and the reactive power output of the virtual phase modifier are recorded;

[0061] The output voltage of the virtual phase modifier is repeatedly changed and the power flow is calculated until enough points are collected to form the real-time / predicted node voltage-virtual phase modifier reactive power output curve of each key node.

[0062] The voltage collapse node is analyzed to determine the reactive power margin of the weakest node, including:

[0063] The lowest point in the node voltage-virtual phase modifier reactive power output curve is the voltage collapse point of the current node;

[0064] The reactive power margin is the vertical distance between the current operating point and the voltage collapse point of the node, and the weakest node has the minimum vertical distance.

[0065] Wherein, the mutual impedance matrix is generated online, including:

[0066] The topological structure of the power grid and the physical parameter information of the device elements and the dynamic model parameters of the power grid devices are obtained;

[0067] The admittance matrix suitable for short-circuit ratio current calculation is formed;

[0068] The inverse of the admittance matrix is obtained to obtain the mutual impedance matrix reflecting the current operating state of the power grid.

[0069] The new energy multi-station short-circuit ratio of the offshore wind power cluster is analyzed, the short-circuit capacity and the voltage interaction coefficient of each node are calculated, including:

[0070] Based on real-time and prediction data analysis of offshore wind power group new energy multi-station short circuit ratio, the calculation method of new energy multi-station short circuit ratio index is as follows:

[0071]

[0072] In the formula: S ki is the short-circuit capacity of node i, S ki is the product of the maximum short-circuit current I Bi of the wind farm i and the short-circuit current of the grid connection point, S i is the complex power form when the node has active power P i and reactive power Q i ; when the active and reactive power takes the current real-time value, the short-circuit ratio obtained is the real-time short-circuit ratio; when the active and reactive power takes the current short-term / ultra-short-term value, the short-circuit ratio obtained is the short-term / ultra-short-term short-circuit ratio; r ji is the voltage interaction coefficient between wind farm j and wind farm i, ΔV j and ΔV i are the change values of the voltages of wind farm j and wind farm i respectively; Z ii is the self-impedance of wind farm i; Z ji is the mutual impedance between wind farm j and wind farm i.

[0073] In this embodiment, the voltage static stability margin of the key node of the offshore wind power group is sorted and analyzed to determine the weakest node, including:

[0074] When analyzing the voltage collapse point and determining the reactive power margin of the weakest node, the voltage static stability margin of each access point of the onshore collection station is sorted and analyzed, and the weakest node i is determined according to the minimum reactive power margin. The change trend of the voltage static stability margin of the weakest node i is analyzed to obtain the reactive power margin change index of the weakest node i, and the calculation method is as follows:

[0075]

[0076] In the formula: Q r is the voltage static stability margin of the weakest node under the current output working condition; Q f is the voltage static stability margin of the weakest node based on the predicted power output working condition.

[0077] The grid strength of the key node of the offshore wind power grid is sorted and analyzed to determine the weakest node, including:

[0078] When analyzing the short-circuit ratio of the new energy multi-station of the offshore wind farm group, the grid strength of each access point of the offshore wind farm is sorted and analyzed, the weakest node j is determined according to the minimum grid strength, the change trend of the grid strength of the weakest node j is analyzed, the grid strength change index of the weakest node j is obtained, and the calculation method is as follows:

[0079]

[0080] In the formula: SCR r is the grid strength of the weakest node under the current output working condition; SCR f is the grid strength of the weakest node based on the predicted power output working condition.

[0081] Among them, the comprehensive operation risk assessment based on the early warning threshold value includes:

[0082] For the weak node i, when k Q is negative, and |k Q |>k Q_min , it is determined that the node i is a risk node; for the weak node j, when k S is negative, and |k S |>k S_min , it is determined that the node j is a risk node, for all analyzed grid strength nodes m, when SCR f <1.5, it is determined that the node m is a risk node, and the key nodes determined as risk nodes are classified into a risk area; k Q_min , k S_min are early warning threshold values.

[0083] As shown in Figure 2 , the embodiment further includes an offshore wind power analysis system considering prediction information, using the method as described above, including:

[0084] The voltage static security analysis module is configured to construct a key node set of the offshore wind farm group, adjust active power output of each power source involved in the power flow calculation, input a virtual phase modifier at the key node, obtain real-time / predicted node voltage-virtual phase modifier reactive power output curves of each key node, analyze voltage collapse points, and determine a reactive power margin of the weakest node;

[0085] The grid strength online analysis module is configured to construct a key node set of the offshore wind farm grid strength, generate an interimpedance matrix online based on a preset strength standard, analyze the short-circuit ratio of the new energy multi-station of the offshore wind farm group, calculate the short-circuit capacity and voltage interaction influence coefficient of each node, and evaluate the grid strength;

[0086] The running risk analysis module is used for sorting analysis on voltage static stability margin of key nodes of the offshore wind power group, determining the weakest node, analyzing the change trend, sorting analysis on grid strength of key nodes of the offshore wind power grid, determining the weakest node, analyzing the change trend, and comprehensively evaluating the running risk based on the early warning threshold value, and early warning on the risk node.

[0087] Embodiment 2

[0088] The offshore wind power running risk analysis system considering prediction information provided by the embodiment of the application is described below with reference to the accompanying drawings.

[0089] Figure 3 For the online analysis flowchart of the offshore wind power running risk in the embodiment of the application, as shown in Figure 3 , the specific steps of the offshore wind power running risk online analysis system considering prediction information in the embodiment are as follows:

[0090] Step S1 considers the offshore wind power group voltage static security analysis module of the grid running mode.

[0091] Firstly, based on the grid structure of the offshore wind power centralized access, the key node set of the offshore wind power group to be analyzed is constructed. Taking the typical network frame of the large-scale offshore wind power centralized access as shown in Figure 4 , the offshore wind farm generally boosts the wind power generation unit terminal voltage 0.69kV to the wind power transformer substation 35kV, and further boosts to the grid connection point 220kV, and is connected to the onshore collection station through the submarine cable. The 220kV access point of each onshore wind farm is taken as the key node to be analyzed, that is, the set composed of the BusA of each wind farm in Figure 4 , which is generally the control node of the automatic voltage control system AVC substation of the onshore centralized control station.

[0092] As shown in Figure 5 , each key node of the offshore wind power group is traversed, the active power P g of each power source involved in the power flow calculation is adjusted, the real-time / predicted output of the current wind farm is adjusted, the virtual phase modifier is put into the key node, the output voltage of the phase modifier is changed by a certain step, usually 0.01 per unit value, the power flow is solved, and the node voltage (U) and the reactive power output (Q) of the phase modifier are recorded.

[0093] Finally, by repeatedly changing the output voltage of the virtual phase modifier and executing the power flow calculation, enough points are collected to form the real-time / predicted U-Q curve of each key node. The lowest point (dQ / dU=0) of the U-Q curve of each node of the offshore wind power is the critical point of voltage collapse, all points on the left of the lowest point are considered to be voltage unstable, and points on the right are voltage stable. On the U-Q curve cluster, the collapse point of the weakest node has the lowest reactive power margin, and the voltage change has the largest amplitude.

[0094] Figure 6 shows the static safety analysis results of offshore wind power under specific output levels (light load, heavy load) and operating modes (busbar segmented connection, busbar segmented opening). Figure 6(a) shows the UQ curves of the onshore grid-connected points of each wind farm under light load conditions (busbar segmented connection), Figure 6(b) shows the UQ curves of the onshore grid-connected points of each wind farm under heavy load conditions (busbar segmented connection), and Figure 6(c) shows the UQ curves of the onshore grid-connected points of each wind farm under heavy load conditions (busbar segmented opening). Each curve reflects the voltage static stability safety status of each key node.

[0095] The reactive power margin index of a node is defined as the vertical distance between the current operating point A and the voltage collapse point B, as shown in Figure 6(a). This value reflects the voltage static stability safety margin of weak nodes in the power grid. Comparative Analysis Figure 6(a) and 6(b) The heavier the load, the higher the critical voltage, the smaller the reactive power margin, and the worse the voltage stability. Conversely, the lighter the load, the lower the critical voltage, the larger the reactive power margin, and the better the voltage stability.

[0096] Step S2 is based on the online analysis module of the grid intensity of offshore wind farms using real-time and predicted data.

[0097] like Figure 7 As shown, firstly, based on the grid structure of centralized offshore wind power grid integration, a set of key nodes for analyzing the grid intensity of offshore wind power is constructed. The high-voltage busbars of the wind turbines in each wind farm are taken as the key nodes to be analyzed, i.e. Figure 4 This is a collection of BusBs from various wind farms in China, and this node is generally the marine access point for submarine cables.

[0098] The grid strength described in this invention adopts the short-circuit ratio index for multiple renewable energy power plants proposed in the State Grid enterprise standard "Calculation Specification for Short-Circuit Ratio of Multiple Renewable Energy Power Plants Q / GDW12290-2023" as the evaluation criterion. This index can consider the voltage interaction effects between different renewable energy power plants and, to a certain extent, reflect the operational risks when offshore wind farms are centrally connected to the grid. This invention proposes an online calculation method for the short-circuit ratio index of multiple renewable energy power plants, which generates the mutual impedance matrix Z reflecting the current operating state of the grid online, and analyzes the short-circuit ratio of multiple renewable energy power plants in offshore wind farms based on real-time and predicted data.

[0099] Establishing the node mutual impedance matrix is ​​much more difficult than establishing the node admittance matrix. The node impedance matrix is ​​usually derived indirectly from the node admittance matrix. By inverting the admittance matrix Y, which is applicable to short-circuit ratio current calculations, the mutual impedance matrix Z, reflecting the current operating state of the power grid, is obtained.

[0100] The node admittance matrix with ground as reference node is Y, which is a N*N sparse matrix. If there is a ground branch in the network, Y is non-singular, and its inverse matrix is the node impedance matrix, that is, Z=Y -1 .

[0101] The diagonal element of the conventional node admittance matrix Y ii (i=1, 2, …, n) is called self-admittance. The self-admittance Y ii is numerically equal to the current injected into the network through node i when a unit voltage is applied to node i and all other nodes are grounded. The non-diagonal element of the node admittance matrix Y ji (j=1, 2, …, n, i=1, 2, …, n; j≠i) is called mutual admittance. The mutual admittance Y ji is numerically equal to the current injected into the network through node j when a unit voltage is applied to node i and all other nodes are grounded. Therefore, the diagonal element of the node admittance matrix is equal to the sum of the admittance connected to the node. The non-diagonal element of the node admittance matrix is equal to the negative value of the admittance connected between nodes i and j.

[0102] In the calculation of short-circuit current, the sub-transient reactance of the generator is needed, so the generator node of the conventional network node admittance matrix is modified, and the value of the corresponding admittance branch of the sub-transient reactance of the generator of the node is added to the self-admittance to form a contracted node admittance matrix. Therefore, two aspects of input information are needed to form the admittance matrix Y. On the one hand, the physical parameter information of the grid topology structure and the equipment elements such as transformers and lines is needed, which can be obtained by arranging the state estimation results of the current power grid, and the state estimation results can be obtained by the QS file service provided by the dispatching automation system; on the other hand, the dynamic model parameters (for example: sub-transient reactance X d ”, X q ”, winding resistance R dynamic model parameters) of the power grid equipment are needed, which can be obtained by arranging the data packet provided by the operation mode calculation software (such as PSASP\BPA).

[0103] The added value of the generator node admittance element is shown in formula (1).

[0104]

[0105] In the formula: Y re is the real part of the admittance; Y im is the real part of the admittance.

[0106] Then, based on real-time and predicted data, the new energy multi-station short-circuit ratio of the offshore wind farm is analyzed. The calculation method of the new energy multi-station short-circuit ratio index is shown in formula (2).

[0107]

[0108] In the formula: S ki Let be the short-circuit capacity of node i, and let be the product of the maximum short-circuit current and voltage when the grid connection point of wind farm i is short-circuited. It can be estimated according to equation (3).

[0109]

[0110] S i When the node has active power P i Reactive power Q i The power prediction system calculates the short-circuit ratio in complex form. When the active and reactive power values ​​are taken as the current real-time values, the short-circuit ratio is the real-time short-circuit ratio. When the active and reactive power values ​​are taken as the current short-term / ultra-short-term values, the short-circuit ratio is the short-term / ultra-short-term short-circuit ratio. Since the power prediction system only provides active power prediction values, the reactive power prediction values ​​at the wind farm grid connection point can be estimated proportionally based on the current wind farm power factor.

[0111] r ji Let be the voltage interaction coefficient between wind farm j and wind farm i, and its specific expression is shown in equation (4).

[0112]

[0113] Where: ΔV j and ΔV i These represent the voltage changes at wind farm j and wind farm i, respectively; Z ii Z is the self-impedance of wind farm i; ji Let be the mutual impedance between wind farm j and wind farm i.

[0114] Step S3 considers the operational risk analysis module of the voltage static stability limit of offshore wind farms and grid strength.

[0115] like Figure 8 As shown, firstly, the voltage static stability margin of each access point of the land-based collection station obtained by S1 is sorted and analyzed. The weakest node i is determined according to the minimum reactive power margin. Then, the voltage static stability margin of the weakest node i is analyzed to obtain the reactive power margin change index of the weakest node i. The specific expression is shown in Equation (5).

[0116]

[0117] In the formula: Q r Q represents the voltage static stability margin of the weakest node under the current power output condition; f This is the voltage static stability margin based on the weakest node under predicted power output conditions.

[0118] Then, the grid strength of each access point of the offshore wind farm obtained for S2 is sorted and analyzed, the weakest node j is determined according to the minimum grid strength, and then the change trend of the grid strength of the weakest node j is analyzed to obtain the grid strength change index of the weakest node j, and the specific expression is shown as formula (6).

[0119]

[0120] In the formula, SCR r is the grid strength of the weakest node under the current power output condition; SCR f is the grid strength of the weakest node based on the predicted power output condition.

[0121] Finally, the comprehensive operation risk assessment is carried out based on the early warning threshold value, whether each key node is classified into the risk area is judged, and the nodes in the risk area are warned. For the weak node i, when k Q is negative, and |k Q |>k Q_min , it is determined that the node i is a risk node; for the weak node j, when k S is negative, and |k S |>k S_min , it is determined that the node j is a risk node; for all analyzed grid strength nodes m, when SCR f <1.5, it is determined that the node m is a risk node, wherein the early warning threshold value k Q_min , k S_min is set according to experience, for example, 20% to 50%.

[0122] In the embodiment, an offshore wind power operation risk analysis system considering prediction information is provided, which can effectively solve the practical engineering problem of how to quickly and accurately analyze the offshore wind power operation risk based on the grid strength index under the large-scale new energy grid connection scene. The operation risk of offshore wind power can be evaluated from multiple dimensions, including voltage stability, frequency stability, wideband oscillation, etc., among which the grid strength is an index that has been widely concerned in recent years, its physical meaning is relatively clear, and offline analysis and calculation is relatively easy, but how to apply it to online systems has always lacked relevant practices. Due to the connection of large-scale new energy, the grid structure becomes more and more complex, which poses a challenge to online calculation of grid strength. In the embodiment, an online analysis system of offshore wind power operation risk considering prediction information is designed, which combines offline impedance matrix training and scene online matching, and the feasibility of the method is verified through practice. The method can provide technical support for offshore wind power operation analysis, weak point judgment of the grid and design of the regulation scheme after large-scale new energy is connected to the grid.

[0123] Please refer to Figure 9The structural schematic diagram of the computer device provided by the embodiment of the application is shown. The computer device 400 provided by the embodiment of the application comprises a processor 410 and a memory 420, the memory 420 stores a computer program executable by the processor 410, and the computer program is executed by the processor 410 to perform the method as above.

[0124] The embodiment of the application further provides a storage medium 430, the storage medium 430 stores a computer program, and the computer program is executed by the processor 410 to perform the method as above.

[0125] The storage medium 430 can be implemented by any type of volatile or nonvolatile storage device or combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0126] In the description of the application, the terms "first", "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first", "second" can be explicitly or implicitly included one or more of the features. The meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0127] In the application, unless otherwise specifically defined and limited, the terms "mounting", "connection", "connection", "fixing" and the like should be understood in a broad sense, for example, it can be fixed connection, or detachable connection, or integral; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through intermediate medium, it can be the internal communication of two elements or the interaction relationship of two elements. For ordinary skilled in the art, the specific meaning of the above terms in the application can be understood according to the specific circumstances.

[0128] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions 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 suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0129] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0130] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0131] It should be understood that portions of the present application can be implemented with hardware, software, firmware or a combination thereof. In the above embodiments, a plurality of steps or methods can be implemented with software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, known in the art, or their combinations can be used: discrete logic circuitry having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

[0132] Those skilled in the art of the present technology can understand that all or part of the steps carried out by the above-mentioned embodiments can be completed by a program instructing the relevant hardware, and the program can be stored in a computer readable storage medium. When the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0133] The storage medium mentioned above can 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 should be understood that the above-mentioned embodiments are exemplary and cannot be understood as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above-mentioned embodiments within the scope of the present application.

Claims

1. A method of offshore wind power analysis taking into account prediction information, characterized in that, The method comprises the following steps: A set of key nodes of the offshore wind farm is constructed, active power output of each power source involved in the power flow calculation is adjusted, a virtual phase modifier is put into each key node, real-time / forecast node voltage-virtual phase modifier reactive power output curves of each key node are obtained, a voltage collapse point is analyzed, and a reactive power margin of the weakest node is determined; A set of key nodes of the offshore wind farm strength is constructed, an interimpedance matrix is generated online based on a preset strength criterion, new energy multi-station short-circuit ratios of the offshore wind farm are analyzed, short-circuit capacities and voltage interaction influence coefficients of each node are calculated, and the strength of the offshore wind farm is evaluated; The voltage static stability margin of the key nodes of the offshore wind farm is sorted and analyzed, the weakest node is determined according to the smallest reactive power margin, the trend of the weakest node is analyzed, the strength of the key nodes of the offshore wind farm strength is sorted and analyzed, the weakest node is determined, the trend of the weakest node is analyzed, and the comprehensive operation risk is evaluated based on an early warning threshold value, and the risk node is warned.

2. The offshore wind power analysis method considering prediction information according to claim 1, characterized in that, The virtual phase modifier is put into each key node, and real-time / forecast node voltage-virtual phase modifier reactive power output curves of each key node are obtained, which comprises: The output voltage of the virtual phase modifier is changed by a set step, power flow is solved, and node voltage and reactive power output of the virtual phase modifier are recorded; The output voltage of the virtual phase modifier is repeatedly changed and power flow is calculated until enough points are collected, and real-time / forecast node voltage-virtual phase modifier reactive power output curves of each key node are formed.

3. The offshore wind farm analysis method considering prediction information according to claim 2, characterized in that, The voltage collapse point is analyzed, and the reactive power margin of the weakest node is determined, which comprises: The lowest point in the node voltage-virtual phase modifier reactive power output curve is the voltage collapse point of the current node; The reactive power margin is the vertical distance between the current operating point of the node and the voltage collapse point, and the vertical distance with the smallest value is the reactive power margin of the weakest node.

4. The offshore wind power analysis method considering prediction information according to claim 1, characterized in that, The interimpedance matrix is generated online, which comprises: The topological structure of the grid, physical parameter information of equipment elements, and dynamic model parameters of grid equipment are obtained; An admittance matrix suitable for short-circuit current calculation is formed; The admittance matrix is inverted to obtain the interimpedance matrix reflecting the current operating state of the grid.

5. The offshore wind power analysis method considering prediction information according to claim 4, characterized in that, The new energy multi-station short-circuit ratios of the offshore wind farm are analyzed, and short-circuit capacities and voltage interaction influence coefficients of each node are calculated, which comprises: The new energy multi-station short-circuit ratios of the offshore wind farm are analyzed based on real-time and forecast data, and the calculation method of the new energy multi-station short-circuit ratio index is as shown in the following formula: ； ； ； In the formula: S ki is the short-circuit capacity of node i, and is the maximum short-circuit current I of the wind farm i when the wind farm i is short-circuited ki and the product of the voltage U Bi ; S i is the complex power form of the active power P i and the reactive power Q i of node i; when the active power and the reactive power take the current real-time values, the obtained short-circuit ratio is the real-time short-circuit ratio; when the active power and the reactive power take the current short-term / ultra-short-term values, the obtained short-circuit ratio is the short-term / ultra-short-term short-circuit ratio; r ji is the voltage interaction coefficient between the wind farm j and the wind farm i, ΔV j and ΔV i are the change values of the voltages of the wind farm j and the wind farm i respectively; Z ii is the self-impedance of the wind farm i; and Z ji is the mutual impedance between the wind farm j and the wind farm i.

6. The offshore wind power analysis method considering prediction information according to claim 1, wherein, The voltage static stability margin of the key nodes of the offshore wind farm is sorted and analyzed, and the weakest node is determined, which comprises: When the voltage collapse point is analyzed and the reactive power margin of the weakest node is determined, the voltage static stability margins of each access point of the onshore collection station are sorted and analyzed, the weakest node i is determined according to the smallest reactive power margin, the trend of the voltage static stability margin of the weakest node i is analyzed, the reactive power margin change index of the weakest node i is obtained, and the calculation method is as follows: ； In the formula, Q r is the voltage static stability margin of the weakest node under the current power output condition; Q f is the voltage static stability margin of the weakest node based on the predicted power output condition.

7. The offshore wind power analysis method considering prediction information according to claim 6, characterized in that, The strength of the key nodes of the offshore wind farm strength is sorted and analyzed, and the weakest node is determined, which comprises: The new energy multi-station short-circuit ratio of the offshore wind power group is analyzed, the grid strength of each access point of the offshore wind farm is sorted and analyzed, the weakest node j is determined according to the minimum grid strength, the change trend of the grid strength of the weakest node j is analyzed, the grid strength change index of the weakest node j is obtained, and the calculation method is as follows: ； where: SCR r is the grid strength of the weakest node under the current power output condition; SCR f is the grid strength of the weakest node under the predicted power output condition.

8. The offshore wind power analysis method considering prediction information according to claim 7, characterized in that, The comprehensive operation risk assessment based on the early warning threshold value comprises: For weak node i, when k Q is negative, and |k Q |> k Q_min , it is determined that node i is a risk node; for weak node j, when k S is negative, and |k S |> k S_min , it is determined that node j is a risk node, for all analyzed power grid strength nodes m, when SCR f <1.5, it is determined that node m is a risk node, and the key nodes determined as risk nodes are classified into a risk area; k Q_min , k S_min are pre-warning threshold values.

9. A system for offshore wind power analysis taking into account prediction information, characterized in that The method according to any one of claims 1 to 8 is used, comprising: The voltage static security analysis module is used to construct a key node set of the offshore wind power group, adjust active power output of each power source involved in the power flow calculation, input a virtual phase modifier at the key node, obtain a real-time / predicted node voltage-virtual phase modifier reactive power output curve of each key node, analyze a voltage collapse point, and determine a reactive power margin of the weakest node; The grid strength online analysis module is used to construct a key node set of the offshore wind power grid strength, generate an interimpedance matrix online based on a preset strength standard, analyze the new energy multi-station short-circuit ratio of the offshore wind power group, calculate the short-circuit capacity and voltage interaction influence coefficient of each node, and evaluate the grid strength; The operation risk analysis module is used to sort and analyze the voltage static stability margin of the key node of the offshore wind power group, determine the weakest node according to the minimum reactive power margin, analyze the change trend, sort and analyze the grid strength of the key node of the offshore wind power grid strength, determine the weakest node, analyze the change trend, and comprehensively evaluate the operation risk based on the early warning threshold value, and early warn the risk node.

10. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the method according to any one of claims 1 to 8.

11. A storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to realize the method according to any one of claims 1 to 8.

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