Wind power integration stability control monitoring method

Abstract

The invention relates to the technical field of wind power integration monitoring, and discloses a wind power integration stability monitoring control method comprising the steps that a power grid monitoring model is constructed based on the distribution condition of a plurality of generator sets, and the power grid monitoring model comprises a plurality of power grid monitoring sub-models; the power grid monitoring sub-model comprises a plurality of nodes and branches connected among the nodes; calculating a stable value of each node in the power grid monitoring sub-model, and judging the stability of the current power grid monitoring sub-model based on the node stable value; and generating a wind power integration control strategy of the power grid monitoring sub-model based on the stability of the current power grid monitoring sub-model. Through the accurate generator set output model and the power grid monitoring model, potential unstable factors in the wind power integration process can be found in time, and measures are taken in advance for correction, so that the stability of wind power integration is improved, and the impact on the power grid is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power grid-connected monitoring, and in particular to a wind power grid-connected stability control and monitoring method. Background Art

[0002] At present, with the large-scale development of wind energy resources and the continuous advancement of wind power grid-connected technology, wind power has become an important part of the global energy structure transformation. However, the intermittent and volatile nature of wind power has brought challenges to the stability of the power grid.

[0003] The access of distributed renewable energy increases the complexity of the power grid, and the low accuracy of renewable energy prediction may affect the stable operation of the power grid and the balance of power and electricity. Traditional power grid analysis and control methods are difficult to comprehensively and systematically evaluate the stability of the power grid, which provides an effective means to accurately evaluate the stability of the power grid during the process of wind power grid connection. Summary of the invention

[0004] The purpose of the present invention is to construct a wind power grid-connected stability monitoring model based on the historical data of the current generator set, generate the generator set output stability value and the power grid stability value respectively, combine the two to obtain the generator set grid-connected stability evaluation result, and finally judge whether the wind power grid-connected process needs to be corrected based on the evaluation result, and generate corresponding control instructions.

[0005] In order to achieve the above object, the present invention provides a wind power grid-connected stability monitoring and control method, comprising:

[0006] Building a power grid monitoring model based on the distribution of multiple generator sets, the power grid monitoring model includes multiple power grid monitoring sub-models;

[0007] The power grid monitoring sub-model includes multiple nodes and branches connecting the nodes;

[0008] Calculate the stability value of each node in the power grid monitoring sub-model, and judge the stability of the current power grid monitoring sub-model based on the node stability value;

[0009] Generate a wind power grid connection control strategy for the grid monitoring sub-model based on the stability of the current grid monitoring sub-model;

[0010] Among them, the nodes of the power grid monitoring sub-model are the generator sets, and the branches of the power grid monitoring sub-model are the transmission lines;

[0011] A generator set output model is constructed based on the historical data of the generator set. The output stability value of the generator set in the power grid monitoring sub-model is calculated through the generator set output model, and the output stability value of the generator set is taken as the node stability value.

[0012] In some embodiments of the present invention, the construction of the power grid monitoring model includes:

[0013] A power grid monitoring model is constructed with generators as nodes and transmission lines as branches;

[0014] Count the number of branches connected to all nodes in the power grid monitoring model, set the level of the node according to the number of branches connected to the node, and generate multiple nodes of different levels;

[0015] Generate a power grid monitoring sub-model based on nodes of the same level and branches between nodes of the same level;

[0016] The level of the power grid monitoring sub-model is set based on the level of the node, and a plurality of power grid monitoring sub-models of different levels are generated.

[0017] In some embodiments of the present invention, the calculation of the stability value of each node in the power grid monitoring sub-model includes:

[0018] Obtain the historical environmental data and historical power data of the nodes in the current power grid monitoring sub-model;

[0019] Among them, historical environmental data include: primary environmental data and secondary environmental data;

[0020] Establish a preliminary model of generator set output based on primary environmental data and historical power data;

[0021] The preliminary model of the generator set output is modified through the secondary environmental data to generate the generator set output model;

[0022] Setting the monitoring period of the current power grid monitoring sub-model based on the power grid monitoring sub-model level;

[0023] Obtain the environmental prediction data of the current monitoring period, and generate the node power prediction curve of the power grid monitoring sub-model through the generator output model;

[0024] A first stable value of the node and a second stable value of the node are generated based on the node power prediction curve.

[0025] In some embodiments of the present invention, the step of establishing a preliminary model of the generator set output includes:

[0026] Obtain primary environmental data, including historical wind speed data;

[0027] Gradient-dividing the acquired historical wind speed data, and evaluating the generated multiple wind speed gradients to generate multiple wind speed evaluation values;

[0028] Obtain historical power data under the corresponding wind speed evaluation value, remove abnormal data, and generate the generator set output evaluation value P;

[0029] Based on the wind speed evaluation value and the generator output evaluation value P, a preliminary generator output model is generated:

[0030] P=a0+a1v+a2v 2 +…a i v i …+a n v n ;

[0031] Calculate the initial model coefficient a of the generator set output i The value of , where v represents the wind speed evaluation value, n is a constant determined according to the accuracy requirements, and p represents the generator set output evaluation value.

[0032] In some embodiments of the present invention, the calculation of the preliminary model coefficient a of the generator set output i , including:

[0033] Get the wind speed evaluation value V, V = {v1, v2…v j …v m};

[0034] Generator output evaluation value P, P = {p1, p2…p j …p m};

[0035] Among them, v j represents the wind speed evaluation value of the jth interval, m represents the number of intervals, and p j represents the output evaluation value of the generator set in the jth interval;

[0036] Generate error function

[0037] For a0, a1, ... a n Calculate the partial derivative and set it equal to 0 to obtain a set of linear equations. By solving this set of linear equations, determine the initial model coefficient a of the generator output i The value of .

[0038] In some embodiments of the present invention, the correction of the preliminary model of the generator set output by the secondary environmental data includes:

[0039] Obtain secondary environmental data, including: historical air density data and historical wind direction data;

[0040] Divide the historical air density data into gradient intervals and generate an air density evaluation value pa for each gradient interval of the historical air density data;

[0041] A wind direction deviation angle threshold a is set. If the wind direction deviation angle exceeds the wind direction deviation angle threshold a within the time period t, it is determined that the wind direction has changed.

[0042] Count the number of time periods when the wind direction has not changed, and generate a wind direction stability evaluation value pf;

[0043] Among them, the air density evaluation value pa and the wind direction stability evaluation value pf have the same value range;

[0044] Generate environmental impact data correction value A1 based on air density evaluation value and wind direction stability evaluation value;

[0045] A1=k1*pa+k2*pf;

[0046] Among them, k1 represents the weight of the air density evaluation value pa, and k2 represents the weight of the wind direction stability evaluation value pf, which is obtained by calculating the increment of the air density evaluation value pa and the wind direction stability evaluation value pf to the generator output in the historical data;

[0047] The environmental impact data evaluation value A1 is generated through experiments to modify the preliminary model of the generator set output;

[0048] Generate generator output model:

[0049] P'=a0+a1v+a2v 2 +…a i v i …+a n v n +A1;

[0050] Wherein, P' represents the generator set output correction value.

[0051] In some embodiments of the present invention, the generating of the first stable value of the node based on the node power prediction curve includes:

[0052] Based on the generator set output power prediction curve obtained from the generator set output model, the average value of the generator set output power prediction value is calculated as the first stable value EP of the generator set output.

[0053] In some embodiments of the present invention, the generating of the second stable value of the node based on the node power prediction curve includes:

[0054] Count the size and frequency of the peak and valley values ​​of the power prediction curve of the generator set output;

[0055] Calculate the deviation degree E of the peak value and the valley value from the average value of the output power prediction curve of the generator set; set the first gradient E1, the second gradient E2, and the third gradient E3 of the deviation degree E according to historical data;

[0056] Among them, E2=2*E1, E3=3*E1;

[0057] Count the frequency H1 of the peak and valley values ​​of E>E1;

[0058] Count the frequency H2 of the peaks and valleys where E3 > E > E2;

[0059] Count the frequency H3 of the peaks and valleys where E > E3;

[0060] Set the weight of the second stable value of the generator set output force according to the multiple relationship between the first gradient E1, the second gradient E2, and the third gradient E3, and generate the second stable value DE of the generator set output force, DE = H1 + 2 * H2 + 3 * H3.

[0061] In some embodiments of the present invention, when generating the wind power grid connection control strategy, it includes:

[0062] Calculate the difference between the first stable value EP of the generator set output force and the branch collapse value Z of the power grid monitoring sub-model, and set the preset value DE1 of the second stable value DE of the generator set output force;

[0063] If EP - Z > 0 or DE > DE1, execute the first-level wind power grid connection control instruction;

[0064] If EP - Z < 0 and DE < DE1, obtain whether there is a time node when the peak value of the predicted curve of the generator set output power exceeds the branch collapse value Z;

[0065] If so, execute the second-level wind power grid connection control instruction;

[0066] If not, control the wind power grid connection.

[0067] In some embodiments of the present invention, when executing the second-level wind power grid connection control instruction, it includes:

[0068] Based on the second stable value DE of the generator set output force, set the energy storage system to absorb the excess energy and correct the generator set output force when the peak value exceeds the branch collapse value Z.

[0069] Compared with the prior art, the beneficial effects of a wind power grid connection stability monitoring and control method according to an embodiment of the present invention are as follows:

[0070] By constructing a preliminary model of the generator set output force, the relationship between factors such as wind speed and the generator set output force can be accurately reflected, and the output situation of the generator set can be accurately estimated under different wind speeds.

[0071] By correcting the preliminary model with secondary environmental data and adding the influence of the air density evaluation value and the wind direction stability evaluation value, the generated generator set output force model improves the accuracy of the generator set output force prediction.

[0072] By calculating the second stable value DE of the power output of the generator set, the peak and valley values ​​of the power output prediction curve of the generator set are statistically analyzed in many aspects to obtain DE, which can comprehensively evaluate the fluctuation of the power output of the generator set.

[0073] By judging the DE value, grid operators can take measures in advance, such as adjusting the charging and discharging strategies of the energy storage system and the output of other power generation equipment, to cope with the unstable effects that may be caused by the generator sets.

[0074] By constructing a power grid monitoring model, the topological structure of the power grid can be presented intuitively, which will help power system planners, operators and researchers to better understand the layout of generators in the power grid and the transmission path of electric energy.

[0075] By generating sub-models of different levels, these key nodes can be monitored more carefully and frequently, while relatively loose monitoring strategies can be adopted for relatively unimportant nodes. This can ensure effective monitoring of important parts of the power grid and rationally allocate monitoring resources.

[0076] Obtaining branch collapse values ​​and generating wind power grid-connected control strategies can effectively control wind power access under different working conditions (such as different wind speeds, grid loads, etc.) to ensure the safe and stable operation of the grid.

[0077] Multi-level grid monitoring sub-models can facilitate fault location, because faults are more likely to occur near nodes or branches of a specific level. By analyzing the sub-models of the corresponding level, the approximate scope of the fault can be quickly determined, thereby improving the efficiency of fault repair and reducing power outage time and losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 It is a flow chart of a wind power grid-connected stability monitoring and control method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0079] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0080] In the description of the present invention, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0081] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0082] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0083] Embodiment 1:

[0084] The embodiment of the present invention preferably discloses a wind power grid-connected stability monitoring and control method, such as Figure 1 As shown, including:

[0085] Building a power grid monitoring model based on the distribution of multiple generator sets, the power grid monitoring model includes multiple power grid monitoring sub-models;

[0086] The power grid monitoring sub-model includes multiple nodes and branches connecting the nodes;

[0087] Calculate the stability value of each node in the power grid monitoring sub-model, and judge the stability of the current power grid monitoring sub-model based on the node stability value;

[0088] Generate a wind power grid connection control strategy for the grid monitoring sub-model based on the stability of the current grid monitoring sub-model;

[0089] Among them, the nodes of the power grid monitoring sub-model are the generator sets, and the branches of the power grid monitoring sub-model are the transmission lines;

[0090] A generator set output model is constructed based on the historical data of the generator set. The output stability value of the generator set in the power grid monitoring sub-model is calculated through the generator set output model, and the output stability value of the generator set is taken as the node stability value.

[0091] Embodiment 2:

[0092] The construction of the power grid monitoring model includes:

[0093] A power grid monitoring model is constructed with generators as nodes and transmission lines as branches;

[0094] Count the number of branches connected to all nodes in the power grid monitoring model, set the level of the node according to the number of branches connected to the node, and generate multiple nodes of different levels;

[0095] Generate a power grid monitoring sub-model based on nodes of the same level and branches between nodes of the same level;

[0096] The level of the power grid monitoring sub-model is set based on the level of the node, and a plurality of power grid monitoring sub-models of different levels are generated.

[0097] In this embodiment, the power grid monitoring model is constructed by treating the generator set as a node and the transmission line as a branch. This construction method is similar to the graph structure in graph theory, where the generator set node represents the source of electricity and the transmission line branch represents the path of power transmission. Such a model can intuitively reflect the connection relationship between the generator set and the transmission network, providing a basic structure for subsequent analysis and monitoring.

[0098] Set node level based on number of branches

[0099] The node level is determined based on the number of branches connected to each node (generator set). A node with a large number of connected branches may be in a more critical position in the power grid because it is connected to more transmission lines and its operating status may have a greater impact on the overall power grid. For example, if a generator set node is connected to 5 transmission lines and another generator set node is only connected to 2 transmission lines, the node connected to 5 branches may be set to a higher level.

[0100] Generate power grid monitoring sub-model based on node level

[0101] Generate a power grid monitoring sub-model based on the node level. Nodes of different levels may have different characteristics and impacts on the stability of the power grid. By generating monitoring sub-models for nodes of different levels, the power grid can be monitored more accurately. For example, for high-level nodes, their monitoring sub-models may need to collect data more frequently and analyze power parameters more carefully.

[0102] In this embodiment, the number of branches connected to all nodes (here, nodes represent generators) in the power grid monitoring model is first counted. This step is to obtain the connection characteristics of each node in the power grid structure, because the number of branches connected to the node can reflect the importance of the node in the power grid or its impact on the overall structure of the power grid.

[0103] Set the node level according to the number of branches connected to the node. For example, if the node with the most connected branches is set as the highest level, then different levels can be divided in descending order according to the number of connected branches. Assume that node A is connected to 8 branches, node B is connected to 5 branches, and node C is connected to 3 branches. If the number of connected branches is greater than or equal to 6, the highest level, 3-5 is medium, and less than 3 is low, then node A is the highest level, node B is medium, and node C is low, thus generating multi-level nodes.

[0104] The branches between nodes of the same level are marked. This is to associate the branches with the level of the nodes so as to build a power grid monitoring sub-model with a hierarchical structure later. For example, for the branch between a node B of the intermediate level and another node D of the intermediate level, this branch is marked as the intermediate level.

[0105] The marked branches are divided into the same level as the nodes, and then the nodes of the same level are connected to the branches. In this way, a power grid monitoring sub-model is constructed. For example, all the intermediate-level nodes and intermediate-level branches are connected according to the actual connection relationship of the power grid to form a sub-model that reflects the structure of the intermediate-level power grid. Similarly, the same operation is performed on other levels, and finally multiple power grid monitoring sub-models of different levels are generated. These sub-models together constitute a multi-level power grid monitoring sub-model.

[0106] Embodiment 3:

[0107] The calculation of the stability value of each node in the power grid monitoring sub-model includes:

[0108] Obtain the historical environmental data and historical power data of the nodes in the current power grid monitoring sub-model;

[0109] Among them, historical environmental data include: primary environmental data and secondary environmental data;

[0110] Establish a preliminary model of generator set output based on primary environmental data and historical power data;

[0111] The preliminary model of the generator set output is modified through the secondary environmental data to generate the generator set output model;

[0112] Setting the monitoring period of the current power grid monitoring sub-model based on the power grid monitoring sub-model level;

[0113] Obtain the environmental prediction data of the current monitoring period, and generate the node power prediction curve of the power grid monitoring sub-model through the generator output model;

[0114] A first stable value of the node and a second stable value of the node are generated based on the node power prediction curve.

[0115] In this embodiment, by analyzing factors such as wind speed, wind direction, temperature, air pressure, etc. in historical environmental data and the corresponding historical power data, the power generation efficiency range of the generator set under different environmental conditions can be determined, avoiding incorrect assessment of the generator set output due to single data.

[0116] Generating the stable output value of the generator set based on the output power prediction curve of the generator set can quantify the stability of the generator set output. The stable value can intuitively reflect the power generation stability of the generator set in the future, which is of great significance to both the generator set operator and the power system dispatching department.

[0117] For generator operators, the stability value can help evaluate the operating performance of the generator and promptly identify factors that may affect the stability of power generation, such as hidden equipment failures and abnormal environmental factors, so that corresponding maintenance and adjustment measures can be taken. For the power system dispatching department, the generator output stability value is an important reference for arranging the grid dispatching plan, ensuring the balance of power supply and demand, and the stable operation of the grid. For example, if the output stability value of the generator is low, the power system dispatching department can adjust the dispatching plan of other power generation resources in advance to compensate for possible fluctuations in wind power output.

[0118] Embodiment 4:

[0119] The establishment of the preliminary model of the generator set output includes:

[0120] Obtain primary environmental data, including historical wind speed data, etc.;

[0121] Gradient-dividing the acquired historical wind speed data, and evaluating the generated multiple wind speed gradients to generate multiple wind speed evaluation values;

[0122] Obtain historical power data under the corresponding wind speed evaluation value, remove abnormal data, and generate the generator set output evaluation value P;

[0123] Based on the wind speed evaluation value and the generator output evaluation value P, a preliminary generator output model is generated:

[0124] P=a0+a1v+a2v 2 +…a i v i …+a n v n ;

[0125] Among them, a i is the preliminary model coefficient of the generator set output, v represents the wind speed evaluation value, n is a constant determined according to the accuracy requirements, and p represents the generator set output evaluation value.

[0126] Embodiment 5:

[0127] Preliminary model coefficient a of generator set output i The calculation is as follows:

[0128] Get the wind speed evaluation value V, V = {v1, v2…v j …v m};

[0129] Generator output evaluation value P, P = {p1, p2…p j …p m};

[0130] Among them, v j represents the wind speed evaluation value of the jth interval, m represents the number of intervals, and p j represents the output evaluation value of the generator set in the jth interval;

[0131] Generate error function

[0132] For a0, a1, ... a n Calculate the partial derivative and set it equal to 0 to obtain a set of linear equations. By solving this set of linear equations, determine the initial model coefficient a of the generator output i The value of .

[0133] In this embodiment, when the wind speed is low, the low-order terms in the model (such as a1v) may play a dominant role; when the wind speed is high, the high-order terms (such as a n v n ) will have a greater impact on the output. This helps to rationally plan the operation strategy of the generator set in different wind speed ranges.

[0134] Obtain historical wind speed data. The wind speed data is collected at multiple measurement points of the generator set, and the collection frequency may be once a minute or once every 5 minutes. When the historical wind speed data is divided into gradients, the climate characteristics of the area where the generator set is located and the performance parameters of the wind turbine should be comprehensively considered. For example, in areas with relatively rich wind resources, the wind speed gradient may be divided more finely, such as 0-1m / s, 1-3m / s, 3-5m / s, 5-8m / s, 8-12m / s, 12-15m / s, etc. The evaluation of each wind speed gradient is not only based on the size of the wind speed, but also considers factors such as the power generation efficiency and stability of the wind turbine under the wind speed. For example, a wind speed of 0-1m / s may be evaluated as a wind speed evaluation value of 0 because it is lower than the starting wind speed of the wind turbine; at a wind speed of 1-3m / s, the wind turbine starts to start but the power generation efficiency is low, and the wind speed evaluation value is set to 1; as the wind speed increases, the power generation efficiency improves, and the wind speed evaluation value increases accordingly.

[0135] Historical power data matching and exception handling

[0136] Obtain historical power data under the corresponding wind speed evaluation value to ensure that the wind speed data and power data are strictly corresponding in time. When eliminating abnormal data, a combination of multiple methods is used. In addition to the conventional 3σ principle, the actual operating experience of the generator set is also combined. For example, if the power suddenly drops sharply to close to 0 or rises sharply to exceed the rated power of the wind turbine when the wind speed is stable, it is judged as abnormal data. Through such processing, the generator set output evaluation value P is generated.

[0137]

[0138] make but

[0139] Right now:

[0140] make but

[0141] Right now:

[0142] And so on, a set of linear equations is generated:

[0143]

[0144] Use matrix operations or numerical calculation methods (such as Gaussian elimination method, etc.) to solve this linear equation system to determine the initial model coefficient a of the generator output i The value of .

[0145] Embodiment 6:

[0146] The correction of the preliminary model of the generator set output by the secondary environmental data includes:

[0147] Obtain secondary environmental data, including: historical air density data and historical wind direction data;

[0148] Divide the historical air density data into gradient intervals and generate an air density evaluation value pa for each gradient interval of the historical air density data;

[0149] A wind direction deviation angle threshold a is set. If the wind direction deviation angle exceeds the wind direction deviation angle threshold a within the time period t, it is determined that the wind direction has changed.

[0150] Count the number of time periods when the wind direction has not changed, and generate a wind direction stability evaluation value pf;

[0151] Among them, the air density evaluation value pa and the wind direction stability evaluation value pf have the same value range;

[0152] Generate environmental impact data correction value A1 based on air density evaluation value and wind direction stability evaluation value;

[0153] A1=k1*pa+k2*pf;

[0154] Among them, k1 represents the weight of the air density evaluation value pa, and k2 represents the weight of the wind direction stability evaluation value pf, which is obtained by calculating the increment of the air density evaluation value pa and the wind direction stability evaluation value pf to the generator output in the historical data;

[0155] The environmental impact data evaluation value A1 is generated through experiments to modify the preliminary model of the generator set output;

[0156] Generate generator output model:

[0157] P'=a0+a1v+a2v 2 +…a i v i …+a n v n +A1;

[0158] Wherein, P' represents the generator set output correction value.

[0159] In this embodiment, the accuracy and adaptability of the model can be improved by correcting the preliminary model of the generator set output through the secondary environmental data. The secondary environmental data, such as detailed information on air density and wind direction, can take into account more complex factors in actual operation.

[0160] As for air density, different air densities will affect the force and aerodynamic performance of the wind turbine blades. The revised model can more accurately reflect this influence, making the output prediction of the generator set closer to the actual situation. In terms of wind direction, the stability and change trend of wind direction have an important impact on the overall output of the generator set. By incorporating the wind direction factor into the revised model, we can better cope with the changing wind direction and reduce the output fluctuation prediction error caused by wind direction changes.

[0161] Obtained from the meteorological sensor of the meteorological station or generator set, the time interval is consistent with the wind speed data. When dividing the historical air density data into gradient intervals, the impact of air density on fan performance should be considered. For example, the air density is divided into three intervals: low, medium, and high, and the boundary value of each interval is determined based on theoretical calculations and practical experience. For each interval, a corresponding air density evaluation value pa is given according to the degree of influence of air density on the fan output. For example, when the air density is low, the fan output will be affected to a certain extent, and the evaluation value is set to 0.8; when the air density is moderate, the evaluation value is set to 1; when the air density is high, the evaluation value is set to 1.2.

[0162] Get historical wind direction data and set the wind direction deviation angle threshold a, for example a = 30 ° In a specific period of time t (such as one day), the wind direction data is analyzed. If the wind direction deviation angle exceeds the wind direction deviation angle threshold a in the period of time t, it is judged that the wind direction has changed. The number of time periods in which the wind direction has not changed is counted, and the wind direction stability evaluation value pf is generated according to the impact of wind direction stability on the output of the generator set. For example, if the wind direction stability is good, that is, the period in which the wind direction has not changed is long, the value of pf is set to a higher value, such as 0.8; if the wind direction stability is poor, the value of pf is set to a lower value, such as 0.4.

[0163] Based on the air density evaluation value and the wind direction stability evaluation value, the environmental impact data correction value A1 is generated. The determination of k1 and k2 is obtained by analyzing in detail the increment of the air density evaluation value pa and the wind direction stability evaluation value pf in the historical data on the output of the generator set.

[0164] Embodiment 7:

[0165] The first stable value of a node is calculated as follows:

[0166] Based on the generator set output power prediction curve obtained by the generator set output model, the average value of the generator set output power prediction value is calculated as the first stable value EP of the generator set output;

[0167] Embodiment 8:

[0168] The second stable value of a node is calculated as follows:

[0169] Count the size and frequency of the peak and valley values ​​of the power prediction curve of the generator set output;

[0170] Calculate the deviation degree E of the peak value and the valley value from the average value of the output power prediction curve of the generator set; set the first gradient E1, the second gradient E2, and the third gradient E3 of the deviation degree E according to historical data;

[0171] Count the frequency H1 of the peak and valley values ​​of E>E1;

[0172] Count the frequency H2 of peak and valley values ​​of E>E2;

[0173] Count the frequency of peak and valley values ​​of E>E3 H3;

[0174] Generate a second stable value DE of the power output by the generator set, DE=H1+2*H2+3*H3.

[0175] In this embodiment, the peak value and the valley value are statistically analyzed: the predicted curve of the output power of the generator set is analyzed to find the peak value (local maximum value) and the valley value (local minimum value) on the curve. At the same time, record the magnitudes of these peak values and valley values and the frequencies at which they occur.

[0176] Calculate the deviation degree E: for each peak value and valley value, calculate the difference between it and the average value EP. The absolute value of this difference represents the deviation degree of this point relative to the average value. Based on the influence of the magnitude of the deviation degree E in historical data on the wind power output, generate the first gradient E1, the second gradient E2, and the third gradient E3.

[0177] Points in different deviation degree ranges are given different weights. The greater the deviation degree, the higher the weight. In this way, the influence of peak values and valley values with different deviation degrees on the output stability of the generator set is comprehensively considered.

[0178] Embodiment 9:

[0179] When generating the wind power grid connection control strategy, it includes:

[0180] Calculate the difference between the first stability value EP of the output of the generator set and the branch collapse value Z of the power grid monitoring sub-model, and set the preset value DE1 of the second stability value DE of the output of the generator set;

[0181] If EP - Z > 0 or DE > DE1, then execute the first-level wind power grid connection control instruction;

[0182] If EP - Z < 0 and DE < DE1, then obtain whether there is a time node on the predicted curve of the output power of the generator set where the peak value exceeds the branch collapse value Z;

[0183] If so, then execute the second-level wind power grid connection control instruction;

[0184] If not, then control the wind power grid connection.

[0185] In this embodiment, set the time interval of the generator set monitoring node

[0186] Determine the time interval of the generator set monitoring node according to the level of the power grid monitoring sub-model. The generator set corresponding to the high-level power grid monitoring sub-model may require a shorter monitoring node time interval to more timely capture the changes in its operating state. For example, the generator set corresponding to the high-level sub-model may be monitored every 10 minutes, while the generator set corresponding to the low-level sub-model can be monitored every 30 minutes. Such a setting can reasonably allocate monitoring resources while ensuring the effectiveness of the power grid stability monitoring.

[0187] Obtain the branch collapse value and generate the wind power grid connection control strategy

[0188] Within the current monitoring node, obtain the branch collapse value Z of the power grid monitoring sub-model. The branch collapse value Z reflects the critical power parameter value at which a transmission line (branch) may collapse under various operating conditions (such as overload, fault, etc.). This value can be obtained through methods such as analyzing the electrical characteristics of the transmission line and statistical analysis of historical fault data.

[0189] Based on the branch voltage collapse value Z of the power grid monitoring sub-model and the generator output stability value, formulate a wind power grid connection control strategy. If the generator output stability value is close to or exceeds the branch collapse value Z, corresponding control measures may need to be taken, such as adjusting the output of the generator, switching the transmission line, etc., to ensure the stability of the power grid during the wind power grid connection process. For example, if the generator output stability value is higher than the branch collapse value Z, it may be necessary to reduce the output of the generator to avoid the collapse of the transmission line.

[0190] If EP - Z > 0 or DE > DE1, this indicates that the output of the generator exceeds the tolerance of the power grid to some extent or the stability of the generator is poor (judged according to the second stability value). In this case, execute the first-level wind power grid connection control instruction. For example, when EP - Z = 20MW > 0 or DE = 8 > DE1 = 5, a more stringent first-level control instruction needs to be executed to ensure the stability of the power grid.

[0191] Executing the first-level control instruction includes measures such as reducing the output of the generator, adjusting the operating mode of the generator, and temporarily disconnecting the grid connection of some wind turbines.

[0192] If EP - Z < 0 and DE < DE1, this means that the average output of the generator is lower than the collapse value of the transmission line and the stability of the generator is good (judged according to the second stability value). However, at this time, it is still necessary to further check whether there are time nodes on the predicted curve of the generator output power where the peak exceeds the branch collapse value Z. For example, although the average output is low, the output of the generator may suddenly increase and exceed the tolerance of the transmission line at certain moments.

[0193] If there are time nodes where the peak exceeds the branch collapse value Z, then execute the second-level wind power grid connection control instruction. For example, if it is found in the predicted curve that the output peak of the generator at a certain moment is 120MW and Z = 100MW, then the second-level instruction needs to be executed at this time.

[0194] Control the wind power grid connection

[0195] If there are no time nodes where the peak exceeds the branch collapse value Z, then the wind power grid connection can be directly controlled because at this time, both the average value and the peak value of the generator output are within the tolerance range of the power grid.

[0196] Example 10:

[0197] The execution of the secondary wind power grid connection control instruction includes:

[0198] Based on the second stable value DE of the power output of the generator set, an energy storage system is set to absorb excess energy and correct the power output of the generator set when the peak value exceeds the branch collapse value Z.

[0199] In this embodiment, when executing the secondary wind power grid connection control instruction, based on the second stable value DE of the power output of the generator set, the energy storage system is set to absorb excess energy, and the output of the generator set when the peak value exceeds the branch collapse value Z is corrected. For example, if DE=3, the amount of energy absorbed by the energy storage system or the control strategy is determined according to the value of DE. When the peak output of the generator set exceeds the branch collapse value Z, the energy storage system is started to store the excess energy, so that the actual output value of the generator set is within the range that the transmission line can bear, ensuring the stability of the power grid during the wind power grid connection process.

[0200] Finally, it should be noted that it is obvious that those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technology, the present invention is also intended to include these modifications and variations.

[0201] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A wind power grid-connected stability monitoring and control method, characterized in that: include: Building a power grid monitoring model based on the distribution of multiple generator sets, the power grid monitoring model includes multiple power grid monitoring sub-models; The power grid monitoring sub-model includes multiple nodes and branches connecting the nodes; Calculate the stability value of each node in the power grid monitoring sub-model, and judge the stability of the current power grid monitoring sub-model based on the node stability value; Generate a wind power grid connection control strategy for the grid monitoring sub-model based on the stability of the current grid monitoring sub-model; Among them, the nodes of the power grid monitoring sub-model are the generator sets, and the branches of the power grid monitoring sub-model are the transmission lines; A generator set output model is constructed based on the historical data of the generator set. The output stability value of the generator set in the power grid monitoring sub-model is calculated through the generator set output model, and the output stability value of the generator set is taken as the node stability value.

2. The wind power grid-connected stability monitoring and control method according to claim 1, characterized in that: The construction of the power grid monitoring model includes: A power grid monitoring model is constructed with generators as nodes and transmission lines as branches; Count the number of branches connected to all nodes in the power grid monitoring model, set the level of the node according to the number of branches connected to the node, and generate multiple nodes of different levels; Generate a power grid monitoring sub-model based on nodes of the same level and branches between nodes of the same level; The level of the power grid monitoring sub-model is set based on the level of the node, and a plurality of power grid monitoring sub-models of different levels are generated.

3. The wind power grid-connected stability monitoring and control method according to claim 2, characterized in that: The calculation of the stability value of each node in the power grid monitoring sub-model includes: Obtain the historical environmental data and historical power data of the nodes in the current power grid monitoring sub-model; Among them, historical environmental data include: primary environmental data and secondary environmental data; Establish a preliminary model of generator set output based on primary environmental data and historical power data; The preliminary model of the generator set output is modified through the secondary environmental data to generate the generator set output model; Setting the monitoring period of the current power grid monitoring sub-model based on the power grid monitoring sub-model level; Obtain the environmental prediction data of the current monitoring period, and generate the node power prediction curve of the power grid monitoring sub-model through the generator output model; A first stable value of the node and a second stable value of the node are generated based on the node power prediction curve.

4. The wind power grid-connected stability monitoring and control method according to claim 3, characterized in that: The establishment of the preliminary model of the generator set output includes: Obtain primary environmental data, including historical wind speed data; Gradient-dividing the acquired historical wind speed data, and evaluating the generated multiple wind speed gradients to generate multiple wind speed evaluation values; Obtain historical power data under the corresponding wind speed evaluation value, remove abnormal data, and generate the generator set output evaluation value P; Based on the wind speed evaluation value and the generator output evaluation value P, a preliminary generator output model is generated: P=a0+a1v+a2v 2 +…and i in i …+a n in n ; Calculate the initial model coefficient a of the generator set output i The value of , where v represents the wind speed evaluation value, n is a constant determined according to the accuracy requirements, and p represents the generator set output evaluation value.

5. The wind power grid-connected stability monitoring and control method according to claim 4, characterized in that: The calculation of the initial model coefficient a of the generator set output i , including: Get the wind speed evaluation value V, V = {v1, v2…v j …v m }; Generator output evaluation value P, P = {p1, p2…p j …p m }; Among them, v j represents the wind speed evaluation value of the jth interval, m represents the number of intervals, and p j represents the output evaluation value of the generator set in the jth interval; Generate error function For a0, a1, ... a n Calculate the partial derivative and set it equal to 0 to obtain a set of linear equations. By solving this set of linear equations, determine the initial model coefficient a of the generator output i The value of .

6. The wind power grid-connected stability monitoring and control method according to claim 5, characterized in that: The correction of the preliminary model of the generator set output by the secondary environmental data includes: Obtain secondary environmental data, including: historical air density data and historical wind direction data; Divide the historical air density data into gradient intervals and generate an air density evaluation value pa for each gradient interval of the historical air density data; A wind direction deviation angle threshold a is set. If the wind direction deviation angle exceeds the wind direction deviation angle threshold a within the time period t, it is determined that the wind direction has changed. Count the number of time periods when the wind direction has not changed, and generate a wind direction stability evaluation value pf; Among them, the evaluation value of air density $p_a$ and the evaluation value of wind direction stability $p_f$ have the same value range; Based on the evaluation value of air density and the evaluation value of wind direction stability, an environmental impact data correction value $A_1$ is generated; $A_1 = k_1\times p_a + k_2\times p_f$; Among them, $k_1$ represents the weight of the air density evaluation value $p_a$, and $k_2$ represents the weight of the wind direction stability evaluation value $p_f$, which is obtained by calculating the increment of the air density evaluation value $p_a$ and the wind direction stability evaluation value $p_f$ on the generator set output in historical data; Generate an initial model for correcting the generator set output by experimentally generating the environmental impact data evaluation value $A_1$; Generate a generator set output model: P'=a0+a1v+a2v 2 +…and i in i …+a n in n +A1; Among them, $P'$ represents the correction value of the generator set output.

7. The wind power grid-connected stability monitoring and control method according to claim 6, characterized in that: When generating the first stability value of the node based on the node power prediction curve, it includes: Based on the generator set output power prediction curve obtained from the generator set output model, calculate the average value of the generator set output power prediction value as the first stability value $E_P$ of the generator set output.

8. The wind power grid-connected stability monitoring and control method according to claim 7, characterized in that: When generating the second stability value of the node based on the node power prediction curve, it includes: Statistically analyze the magnitudes and occurrence frequencies of the peaks and valleys of the generator set output power prediction curve; Calculate the deviation degree $E$ of the peaks and valleys of the generator set output power prediction curve from the average value; set the first gradient $E_1$, the second gradient $E_2$, and the third gradient $E_3$ of the deviation degree $E$ through historical data; Among them, $E_2 = 2\times E_1$, $E_3 = 3\times E_1$; Statistically analyze the frequencies $H_1$ of the peaks and valleys with $E > E_1$; Statistically analyze the frequencies $H_2$ of the peaks and valleys with $E_3 > E > E_2$; Statistically analyze the frequencies $H_3$ of the peaks and valleys with $E > E_3$; Set the weight of the second stability value of the generator set output according to the multiple relationship between the first gradient $E_1$, the second gradient $E_2$, and the third gradient $E_3$, and generate the second stability value $D_E$ of the generator set output, $D_E = H_1 + 2\times H_2 + 3\times H_3$.

9. The wind power grid-connected stability monitoring and control method according to claim 8, characterized in that: When generating the wind power grid connection control strategy, it includes: Calculate the difference between the first stability value $E_P$ of the generator set output and the branch collapse value $Z$ of the power grid monitoring sub-model, and set the preset value $D_{E1}$ of the second stability value $D_E$ of the generator set output; If $E_P - Z > 0$ or $D_E > D_{E1}$, then execute the first-level wind power grid connection control instruction; If $E_P - Z < 0$ and $D_E < D_{E1}$, then check whether there is a time node on the generator set output power prediction curve where the peak exceeds the branch collapse value $Z$; If so, then execute the second-level wind power grid connection control instruction; If not, then control the wind power grid connection.

10. The wind power grid-connected stability monitoring and control method according to claim 9, characterized in that: When executing the second-level wind power grid connection control instruction, it includes: Based on the second stability value $D_E$ of the generator set output, set the energy storage system to absorb the excess energy and correct the generator set output when the peak exceeds the branch collapse value $Z$.

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