Electric power system safety and stability margin prediction method and device based on graph neural network

Abstract

The invention provides an electric power system safety and stability margin prediction method and device based on a graph neural network, and the method comprises the following steps: constructing an analysis model according to an electric power system, obtaining a key power transmission line according to the impedance of the power transmission line and the voltage level of the power transmission line, initializing the analysis model, obtaining the current active transmission power of the key power transmission lines under different operation conditions, carrying out fault presetting on each power transmission line to obtain the limit transmission power according to a stability criterion, and obtaining the safety and stability margin of the operation conditions according to the limit transmission power and the current active transmission power; and forming a graph data set based on the analysis model and the safety and stability margin value set, performing graph neural network training to obtain a graph neural network model, and analyzing the power system to obtain a safety and stability margin predicted value. The invention provides a rapid and accurate method for analyzing the state of the power system in real time, and provides a visual safety and stability index for dispatching control of the power system.

Description

Technical Field

[0001] The present invention relates to the field of power system safety technology, and in particular to a method and device for predicting power system safety and stability margin based on graph neural network. Background Art

[0002] The access of a high proportion of power electronic equipment to the power grid and the increase in the penetration rate of new energy, as well as the random diversity of load behavior, have led to the continuous expansion of the scale of the power grid and the complexity of the operation mode. The safety and stability boundaries of the power system are constantly changing, and the safety and stability mechanism of the modern power system is becoming more and more complex. The access of a high proportion of new energy and the increase of power electronic dynamic response equipment have led to the system facing problems such as flow reversal, increased source-load uncertainty, and reduced system inertia, making the frequency safety, voltage stability and power angle stability problems present a more complex interweaving pattern, and the online evaluation and operation control of the safety and stability of the power grid face serious challenges. With the continuous increase in the proportion of power electronic equipment such as new energy, the model based on the time domain simulation method is becoming more and more complex and the calculation complexity is high, which reduces the timeliness of the time domain simulation method. When the power of new energy or load changes rapidly, and the actual network-related performance of the computer group and the offline parameters are also different, it greatly affects the accuracy of the time domain simulation results.

[0003] At present, the transient stability methods of power systems are divided into time domain simulation method, direct method and data-driven method. As the mainstream method for transient stability assessment of power systems, the time domain simulation method is generally used for offline calculations. Its modeling accuracy is proportional to the calculation accuracy and time consumption. It faces accuracy and speed problems in actual online applications. The direct method is a method of determining whether the system is unstable by comparing the energy value at the time of fault removal with the critical energy value after establishing an energy function. However, it is difficult to establish a suitable energy function for a specific power grid, and the results obtained by using a simplified physical model in the application process are relatively conservative. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention provides a method for predicting the safety and stability margin of a power system based on a graph neural network, which can quickly provide intuitive safety and stability indicators and make accurate predictions.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows: A method for predicting the safety and stability margin of a power system based on a graph neural network, comprising the following steps:

[0006] Constructing an analysis model according to the power system, wherein the analysis model includes a generator, a busbar, and a transmission line;

[0007] Acquiring historical data sets of the power system, transmission line impedance, and transmission line voltage levels according to the analysis model;

[0008] Obtaining a key transmission line according to the transmission line impedance and the transmission line voltage level, wherein the key transmission line is the transmission line with the lowest transmission line impedance and the highest transmission line voltage level;

[0009] Obtain load variation range, preset variation step, generator output range, preset fault and stability criteria;

[0010] Initialize the analysis model according to the load variation range, the preset variation step and the generator output interval;

[0011] In the generator output range, changing the load of the analysis model within the load variation range with the preset variation step to obtain the operating condition;

[0012] Based on the operating conditions, obtaining the current active transmission power of the key transmission line according to the analysis model;

[0013] Under the operating conditions, respectively presetting faults on each of the transmission lines;

[0014] Based on the preset fault, a limit transmission power is obtained according to the stability criterion, wherein the limit transmission power is the current active transmission power of the key transmission line corresponding to the extreme stable operating condition; the load corresponding to the extreme stable operating condition is increased by the preset change step length to the load corresponding to the first unstable operating condition;

[0015] Obtaining a safety and stability margin of the operating condition according to the limit transmission power and the current active transmission power;

[0016] Obtaining a safety and stability margin value set according to the safety and stability margin;

[0017] Forming a graph data set and constructing an initial graph neural network model according to the analysis model, the historical data set, the transmission line impedance and the safety stability margin value set;

[0018] The initial graph neural network model is trained based on the graph data set, and the parameters of the initial graph neural network model are optimized using the Adam algorithm to obtain a graph neural network model;

[0019] The power system is analyzed according to the graph neural network model to obtain a safety and stability margin prediction value.

[0020] Furthermore, the step of obtaining the limit transmission power is:

[0021] Step S101: Initializing the analysis model so that the load level of the analysis model reaches the preset load level, and when the load level is changed with the preset change step, the generator output matches the current load and is within the generator output range;

[0022] Step S102: setting the current load as the initial load, where the initial load is the lower limit of the load variation range;

[0023] Step S103: obtaining the operating condition according to the current load, and obtaining the current active transmission power of the key transmission line based on the operating condition;

[0024] Step S104: Under the operating condition, performing the preset fault on each of the transmission lines respectively;

[0025] Step S105: Based on the preset fault, a stability mark is obtained according to the stability criterion, wherein the stability mark indicates whether each of the transmission lines meets the stability criterion under the operating condition;

[0026] Step S106: increasing the load according to the preset change step to obtain a current load update value:

[0027] If the current load update value is not greater than the upper limit of the load variation range, the current load update value is set equal to the current load, and steps S103 to S106 are repeated;

[0028] If the current load update value is greater than the upper limit of the load variation range, obtaining a working condition stability table according to the stability mark;

[0029] Step S107: obtaining the extreme stable operating condition according to the operating condition stability table, and obtaining the extreme transmission power according to the extreme stable operating condition.

[0030] Furthermore, the preset fault is to cut off the corresponding transmission line after a three-phase short circuit fault occurs in each transmission line and lasts for a preset period of time; the stability criterion is:

[0031] During the three-phase short circuit fault, the maximum power angle difference of the generator cannot exceed 360°;

[0032] Furthermore, starting from the time when the three-phase short circuit fault occurs, the minimum voltage of the generator is not lower than a first preset value and the duration does not exceed the first preset time, and the minimum frequency of the generator is not lower than a second preset value and the duration does not exceed the second preset time.

[0033] Further, the stability marker includes 1 or 0;

[0034] If, under the operating condition, each of the transmission lines satisfies the stability criterion, the stability mark of the operating condition is 1;

[0035] Otherwise, the stability of the operating condition is marked as 0.

[0036] Furthermore, the step of obtaining the limit transmission power is:

[0037] When the load is increased within the load variation range with the preset variation step length, the first unstable operating condition is obtained according to the operating condition corresponding to the first appearance of the stability mark 0;

[0038] Obtaining a first instability load according to the first instability operating condition;

[0039] According to the first unstable load, the preset change step is reduced to obtain the ultimate stable operating load;

[0040] The limit transmission power is obtained according to the limit stable operation load.

[0041] Furthermore, the load variation range is 0.9-1.1, and the preset variation step is 0.01.

[0042] Furthermore, the safety stability margin is:

[0043]

[0044] Where M i is the safety and stability margin of the ith operating condition, P max is the limit transmission power, P i is the current active transmission power under the i-th operating condition, and i is the operating condition number.

[0045] Furthermore, the historical data set includes a bus power characteristic value set; the steps of forming the graph data set are:

[0046] constructing an adjacency matrix of a graph according to the analysis model, wherein the nodes of the graph are the buses and the edges of the graph are the transmission lines;

[0047] A node characteristic matrix is ​​obtained according to the bus power characteristic value set, and an edge characteristic matrix is ​​obtained according to the inverse of the transmission line impedance and the bus power characteristic value set;

[0048] Based on the adjacency matrix, the node feature matrix and the edge feature matrix, the graph data set is obtained with the safety stability margin value set as a label.

[0049] Furthermore, the bus electricity characteristic value set includes bus voltage amplitude, bus voltage phase angle, active power flowing into the bus and reactive power flowing into the bus.

[0050] A device for implementing the power system safety and stability margin prediction method based on graph neural network, comprising a first modeling unit, a calculation unit and a second modeling unit;

[0051] The first modeling unit:

[0052] for constructing an analysis model according to the power system;

[0053] for obtaining historical data sets, transmission line impedances, and transmission line voltage levels of the power system according to the analysis model;

[0054] Used to obtain key transmission lines according to the transmission line impedance and the transmission line voltage level; used to obtain load change range, preset change step, generator output range, fault duration and stability criterion;

[0055] Used to initialize the analysis model according to the load variation range, the preset variation step and the generator output interval;

[0056] Used to change the load of the analysis model within the load variation range with the preset variation step length within the generator output range to obtain the operating condition;

[0057] Used to obtain the current active transmission power of the transmission line according to the operating condition;

[0058] Used to, under the operating condition, cut off the corresponding transmission line after a three-phase short circuit fault occurs in each of the transmission lines and lasts for the fault duration;

[0059] Used to obtain the limit transmission power according to the stability criterion based on the three-phase short circuit fault;

[0060] The computing unit:

[0061] Used to obtain the safety and stability margin of the power transmission line according to the limit transmission power and the current active transmission power;

[0062] Used to obtain a safety stability margin value set according to the safety stability margin;

[0063] The second modeling unit:

[0064] Used to form a graph data set and construct an initial graph neural network model according to the analysis model, the historical data set, the transmission line impedance and the safety stability margin value set;

[0065] Used to train the graph data set based on a graph neural network, and use the Adam algorithm to optimize the parameters of the graph neural network to obtain a graph neural network model;

[0066] Used to analyze the power system according to the graph neural network model to obtain a safety and stability margin prediction value.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] (1) The present invention adopts artificial intelligence method to evaluate the safety and stability of the power system and quickly gives the safety and stability margin, which provides a fast and accurate method for real-time analysis of the power system status and provides a new technical means for the dispatching and operation of the power grid.

[0069] (2) The present invention defines the safety and stability margin by the current active transmission power of the key transmission lines, thereby providing an intuitive safety and stability indicator for power system dispatching and control.

[0070] (3) The present invention converts the power system operating parameter information and topology information into graph data, thereby enhancing the interpretability of the application of artificial intelligence algorithms in the power system field. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a flow chart of a method for predicting the safety and stability margin of a power system based on a graph neural network according to the present invention;

[0072] Figure 2 This is a topology diagram of a 39-node standard example system in an embodiment of the present invention;

[0073] Figure 3 This is an architecture diagram of the initial graph neural network model in an embodiment of the present invention;

[0074] Figure 4 This is a graph showing the prediction effect of the graph neural network model based on the training set in the implementation of the present invention;

[0075] Figure 5 This is a diagram showing the prediction effect of the graph neural network model based on the test set in the implementation of the present invention. DETAILED DESCRIPTION

[0076] Data-driven artificial intelligence technology provides a new solution and method for transient stability analysis and decision-making of power systems. Compared with traditional methods, this data-driven model is highly efficient and fast when put into practical online application. It can achieve prediction within milliseconds and effectively meet the requirements of online safety assessment for speed and accuracy.

[0077] In order to clearly illustrate the technical features of this solution, this solution is described below through a specific implementation method.

[0078] A method for predicting power system security and stability margin based on graph neural network includes the following steps:

[0079] Constructing an analysis model according to the power system, the analysis model including generators, busbars and transmission lines; preferably, using PSASP to construct the analysis model;

[0080] According to the analysis model, the historical data set, transmission line impedance and transmission line voltage level of the power system are obtained; the historical data set includes a bus power characteristic value set, and the bus power characteristic value set includes bus voltage amplitude, bus voltage phase angle, active power flowing into the bus and reactive power flowing into the bus; wherein, the historical data set is obtained according to the PSASP example temp file;

[0081] Obtain the key transmission line according to the transmission line impedance and the transmission line voltage level. The key transmission line is the transmission line with the lowest transmission line impedance and the highest transmission line voltage level; obtain the load change range, preset change step, generator output range, preset fault and stability criterion; the preset fault is an "N-1" fault, that is, the preset fault is to cut off the corresponding transmission line after a three-phase short circuit fault occurs on each transmission line and lasts for a preset period of time;

[0082] Preferably, the load variation range is 0.9-1.1, the preset variation step is 0.01; the preset duration is 100ms; the stability criterion is: during the three-phase short circuit fault, the maximum power angle difference of the generator cannot exceed 360°; and, starting from the three-phase short circuit fault, the minimum voltage of the generator is not less than the first preset value and the duration does not exceed the first preset time, and the minimum frequency of the generator is not less than the second preset value and the duration does not exceed the second preset time; preferably, the first preset value is 0.7pu, the first preset time is 1s, the second preset value is 47Hz, and the second preset time is 1s, that is, starting from the three-phase short circuit fault, the minimum voltage of the generator is not less than 0.7 and the duration does not exceed 1s, and the minimum frequency of the generator is not less than 47Hz and the duration does not exceed 1s;

[0083] Initialize the analysis model according to the load variation range, the preset variation step and the generator output range;

[0084] Before obtaining the safety and stability margin, the analysis model is initialized, that is, the load level of the analysis model is adjusted and the generator output is matched, that is, the load level of the analysis model is within the load variation range and changes with a preset variation step size, and the generator output matches the load level within the generator output range; specifically:

[0085] The analysis model is divided according to the key transmission line to obtain the sending end and the receiving end. In the analysis model, the power is transmitted from the sending end to the receiving end after passing through the key transmission line;

[0086] Adjust the sending end and the receiving end so that the load level of the sending end and the load level of the receiving end are respectively within the load variation range and change with a preset variation step size, and the generator output matches the load level within the generator output interval. Preferably, for each load level, the sending end generator output is changed first, and when the sending end generator output cannot meet the load demand, the receiving end generator output is changed;

[0087] Within the generator output range, the load of the analysis model is changed within the load variation range with a preset variation step to obtain the operating condition;

[0088] Based on the operating conditions, the current active transmission power of the key transmission lines is obtained according to the analytical model;

[0089] Under the operating conditions, a preset fault is set on each transmission line, that is, a three-phase short circuit fault is set on each transmission line and the corresponding transmission line is cut off after the fault lasts for a preset period of time;

[0090] Based on the preset fault, the limit transmission power is obtained according to the stability criterion. The limit transmission power is the current active transmission power of the key transmission line corresponding to the limit stable operation condition. The load corresponding to the limit stable operation condition increases by a preset change step size equal to the load corresponding to the first unstable operation condition. The steps for obtaining the limit transmission power are:

[0091] Step S101: Initializing the analysis model so that the load level of the analysis model reaches a preset load level, and when the load level is changed with a preset change step, the generator output matches the current load and is within the generator output range;

[0092] Step S102: Let the current load be the initial load, and the initial load be the lower limit of the load variation range;

[0093] Step S103: obtaining an operating condition according to the current load, and obtaining a current active transmission power of a key transmission line based on the operating condition;

[0094] Step S104: Under the operating condition, a three-phase short circuit fault is respectively generated on each transmission line and the corresponding transmission line is cut off after the fault lasts for a preset period of time;

[0095] Step S105: Based on the three-phase short circuit fault, a stability mark is obtained according to the stability criterion, where the stability mark indicates whether each transmission line meets the stability criterion under the operating condition; the stability mark includes 1 or 0;

[0096] If each transmission line meets the stability criterion under the operating condition, the stability mark of the operating condition is 1;

[0097] Otherwise, the stability of the operating condition is marked as 0;

[0098] Step S106: Increase the load according to the preset change step to obtain the current load update value:

[0099] If the current load update value is not greater than the upper limit of the load variation range, the current load update value is set equal to the current load, and steps S103 to S106 are repeated;

[0100] If the current load update value is greater than the upper limit of the load variation range, the working condition stability table is obtained according to the stability mark;

[0101] Step S107: Obtain the extreme stable operating condition according to the operating condition stability table, and obtain the extreme transmission power according to the extreme stable operating condition; the steps for obtaining the extreme transmission power are:

[0102] When the load is increased with a preset change step within the load change range, the first unstable operating condition is obtained according to the operating condition corresponding to the first appearance of the stability mark 0;

[0103] Obtain the first instability load according to the first instability operating condition;

[0104] According to the first unstable load, the preset change step is reduced to obtain the ultimate stable operating load;

[0105] The ultimate transmission power is obtained according to the ultimate stable operating load.

[0106] The safety and stability margin of the operating condition is obtained according to the limit transmission power and the current active transmission power; the safety and stability margin is:

[0107]

[0108] Where M i is the safety and stability margin of the ith operating condition, P max is the limit transmission power, P i is the current active transmission power under the i-th operating condition, i is the operating condition number;

[0109] Obtaining a safety stability margin value set according to the safety stability margin;

[0110] A graph data set is formed based on the analysis model, the historical data set, the transmission line impedance and the safety and stability margin value set, and an initial graph neural network model is constructed; the steps for forming the graph data set are:

[0111] According to the analytical model, the adjacency matrix of the graph is constructed, the nodes of the graph are buses, and the edges of the graph are transmission lines;

[0112] The node characteristic matrix is ​​obtained according to the busbar power characteristic value set, and the edge characteristic matrix is ​​obtained according to the inverse of the transmission line impedance, the active power flowing into the busbar, and the reactive power flowing into the busbar;

[0113] Based on the adjacency matrix, node feature matrix and edge feature matrix, a graph dataset is obtained with the safety and stability margin value set as the label.

[0114] Train the initial graph neural network model based on the graph dataset, and use the Adam algorithm to optimize the parameters of the initial graph neural network model to obtain the graph neural network model;

[0115] The power system is analyzed based on the graph neural network model to obtain the predicted value of safety and stability margin.

[0116] See also Figure 2 , the initial graph neural network model includes input layer, normalization layer, convolution layer, activation function, regularization layer and loss function, where:

[0117] The input layer is a graph dataset:

[0118] In the normalization layer, first calculate the degree of the first node in the original adjacency matrix A to form the degree matrix D, which is a diagonal matrix. Then calculate the inverse square root of the degree matrix D, and finally obtain the normalized adjacency matrix through the normalization formula. The normalization formula is:

[0119]

[0120] There are 3 convolutional layers in total, the activation function is the RELU function, the regularization layer is Dropout, and the loss function is the root mean square function.

[0121] Calculation example:

[0122] Take the 39-node standard example system as an example. The topology diagram of the 39-node standard example system is as follows: Figure 2 As shown;

[0123] Since the impedance value of branch 4-14 is the lowest and the voltage level is 220kV (the highest voltage level in this example), branch 4-14 is the key transmission line. Area 1, i.e. the sending end, includes generators 30, 37 and 39, and area 2, i.e. the receiving end, includes generators 31, 32, 33, 34, 35, 36 and 38;

[0124] The sending-end load level and the receiving-end load level k are changed within the range of 0.9 to 1.1, respectively, and the preset change step is 0.01. For each load level, the sending-end generator output is changed first. When the power generated by the sending-end generator cannot meet the load demand, the receiving-end generator output is changed. The change in generator output also matches the change in load. In the process of generator output matching, it is ensured that the generator output does not exceed the generator output range, that is, the upper and lower limits of the generator output.

[0125] Perform power flow calculation to obtain the current active transmission power under each operating condition;

[0126] Under each operating condition, the safety and stability margin is calculated, and three-phase short circuit faults are set for all transmission lines (33) of the 39-node standard example system. After 100ms, the corresponding transmission lines are cut off. According to the stability criterion, if the system meets the transient stability and the stability criterion after the three-phase short circuit fault is set for the 33 transmission lines, the stability mark of this operating condition is 1, otherwise it is 0. Thus, a total of (21*21) different operating conditions are generated, and Table 1 shows the stability of the work unit of 9 of them.

[0127] According to Table 1, the active power of the 4-14 branches, i.e., the key transmission lines, corresponding to the operating conditions before the first unstable operating condition is used as the limit transmission power, i.e., 2.7595 is used as the limit transmission power. Further, the safety and stability margin of each operating condition is obtained according to the formula of the safety and stability margin, and a safety and stability margin value set is obtained. The safety and stability margin value set includes 441 safety and stability margins.

[0128] Table 1 Working condition stability table

[0129]

[0130] The historical data set of the 39-node standard example system is converted into a graph data set of the graph neural network. All buses are used as nodes of the graph and all transmission lines are used as edges of the graph to construct the adjacency matrix A. Since the system has a total of 33 transmission lines, A is a matrix with 33 rows and 33 columns. The adjacency matrix is ​​converted into a sparse form as shown in Table 2:

[0131] Table 2 Sparse form of adjacency matrix

[0132]

[0133]

[0134] The node characteristics in the node characteristic matrix are the bus voltage amplitude and bus voltage phase angle of 28 buses, see Table 3;

[0135] Table 3 Node feature table

[0136]

[0137] The edge features in the edge feature matrix are the inverse of the transmission line impedance of the 33 transmission lines, the active power flowing into the bus, and the reactive power flowing into the bus, see Table 4;

[0138] Table 4 Edge feature table

[0139]

[0140] The historical data sets under each operating condition are converted into the data in Table 2, Table 3 and Table 4 respectively, and finally 441 such graph data are obtained. The 441 safety and stability margins are used as the labels corresponding to each graph data in the graph data set;

[0141] Train the graph neural network on the graph data in the graph dataset: first preprocess and normalize each graph data;

[0142] There are 441 data sets in total. The graph data sets are randomly divided into training sets and test sets in a ratio of 8:2. The number of training sets is 350 and the number of test sets is 91.

[0143] Build an initial graph neural network model, which has three convolutional layers, which are used for feature aggregation, information propagation, and graph structure learning:

[0144] Feature aggregation: Update the feature representation of a node by aggregating the features of its neighboring nodes, so that the node features contain local structural information;

[0145] Propagating information: The convolutional layers of GCN propagate information in the network; the features of the nodes in each convolutional layer are updated, and the updated features are used for the calculation of the next layer, so that the representation of the node can include more distant neighbor information;

[0146] Learning graph structure: The convolutional layer learns the structural information of the graph through weights, which represent the influence of different adjacent nodes on the characteristics of the central node.

[0147] Through training, the initial graph neural network model can learn the importance of different relationships in the graph data; finally, through a layer of RELU activation function, the nonlinear expression ability of the initial graph neural network model is further improved.

[0148] The specific initial graph neural network model architecture is as follows Figure 3 As shown;

[0149] The node features and adjacency matrix in the graph data reflect the steady-state information and topological relationship of the 39-node standard example system. The safety and stability margin of the graph label already contains fault information. During the training process, the initial graph neural network model learns the information of the graph data through the convolution layer, continuously performs forward propagation and back propagation, and uses the Adam algorithm to optimize the update of parameters, and finally obtains the trained graph neural network model. The prediction effect of the graph neural network model based on the training set and the prediction effect of the graph neural network model based on the test set are respectively shown in Figure 4 and Figure 5 .

[0150] It can be seen that in the prediction results of the test set, the prediction errors of 91% of the total prediction samples of the graph neural network model are less than 10%. Therefore, the prediction results of the graph neural network model can be used as the prediction results of the safety stability margin.

[0151] A device for implementing a power system safety and stability margin prediction method based on a graph neural network, comprising a first modeling unit, a calculation unit, and a second modeling unit;

[0152] First Modeling Unit:

[0153] Used to build analytical models based on power systems;

[0154] Used to obtain historical data sets of power systems, transmission line impedances, and transmission line voltage levels according to analytical models;

[0155] Used to obtain key transmission lines according to transmission line impedance and transmission line voltage level; used to obtain load change range, preset change step, generator output range, preset fault and stability criteria;

[0156] Used to initialize the analysis model according to the load variation range, preset variation step and generator output range;

[0157] Used to change the load of the analysis model within the load variation range with a preset variation step within the generator output range to obtain the operating condition;

[0158] Used to obtain the current active transmission power of key transmission lines based on operating conditions and analysis models;

[0159] Used to preset faults on each transmission line under operating conditions;

[0160] Used to obtain the limit transmission power according to the stability criterion based on the preset fault;

[0161] Computational Unit:

[0162] Used to obtain the safety and stability margin of the operating conditions for the limit transmission power and the current active transmission power;

[0163] Used to obtain a safety stability margin value set according to the safety stability margin;

[0164] Second modeling unit:

[0165] Used to form a graph data set and construct an initial graph neural network model based on the analysis model, historical data set, transmission line impedance and safety stability margin value set;

[0166] Used to train the initial graph neural network model based on the graph dataset, and use the Adam algorithm to optimize the parameters of the initial graph neural network model to obtain the graph neural network model;

[0167] It is used to analyze the power system based on the graph neural network model to obtain the safety and stability margin prediction value.

[0168] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention by ordinary technicians in this field do not deviate from the essence and scope of the technical solution of the present invention.

Claims

1. A method for predicting power system security and stability margin based on graph neural network, characterized by: The following steps are involved: Constructing an analysis model according to the power system, wherein the analysis model includes a generator, a busbar, and a transmission line; Acquiring historical data sets of the power system, transmission line impedance, and transmission line voltage levels according to the analysis model; Obtaining a key transmission line according to the transmission line impedance and the transmission line voltage level, wherein the key transmission line is the transmission line with the lowest transmission line impedance and the highest transmission line voltage level; Obtain load variation range, preset variation step, generator output range, preset fault and stability criteria; Initialize the analysis model according to the load variation range, the preset variation step and the generator output interval; In the generator output range, changing the load of the analysis model within the load variation range with the preset variation step to obtain the operating condition; Based on the operating conditions, obtaining the current active transmission power of the key transmission line according to the analysis model; Under the operating conditions, respectively presetting faults on each of the transmission lines; Based on the preset fault, a limit transmission power is obtained according to the stability criterion, wherein the limit transmission power is the current active transmission power of the key transmission line corresponding to the extreme stable operating condition; the load corresponding to the extreme stable operating condition is increased by the preset change step length to the load corresponding to the first unstable operating condition; Obtaining a safety and stability margin of the operating condition according to the limit transmission power and the current active transmission power; Obtaining a safety and stability margin value set according to the safety and stability margin; Forming a graph data set and constructing an initial graph neural network model according to the analysis model, the historical data set, the transmission line impedance and the safety stability margin value set; The initial graph neural network model is trained based on the graph data set, and the parameters of the initial graph neural network model are optimized using the Adam algorithm to obtain a graph neural network model; The power system is analyzed according to the graph neural network model to obtain a safety and stability margin prediction value.

2. The method for predicting power system security and stability margin based on graph neural network according to claim 1 is characterized in that: The steps of obtaining the limit transmission power are: Step S101: Initializing the analysis model so that the load level of the analysis model reaches the preset load level, and when the load level is changed with the preset change step, the generator output matches the current load and is within the generator output range; Step S102: setting the current load as the initial load, where the initial load is the lower limit of the load variation range; Step S103: obtaining the operating condition according to the current load, and obtaining the current active transmission power of the key transmission line based on the operating condition; Step S104: Under the operating condition, performing the preset fault on each of the transmission lines respectively; Step S105: Based on the preset fault, a stability mark is obtained according to the stability criterion, wherein the stability mark indicates whether each of the transmission lines meets the stability criterion under the operating condition; Step S106: increasing the load according to the preset change step to obtain a current load update value: If the current load update value is not greater than the upper limit of the load variation range, the current load update value is set equal to the current load, and steps S103 to S106 are repeated; If the current load update value is greater than the upper limit of the load variation range, obtaining a working condition stability table according to the stability mark; Step S107: obtaining the extreme stable operating condition according to the operating condition stability table, and obtaining the extreme transmission power according to the extreme stable operating condition.

3. The method for predicting power system security and stability margin based on graph neural network according to claim 2 is characterized in that: The preset fault is to cut off the corresponding transmission line after a three-phase short circuit fault occurs in each transmission line and lasts for a preset period of time; the stability criterion is: During the three-phase short circuit fault, the maximum power angle difference of the generator cannot exceed 360°; Furthermore, starting from the time when the three-phase short circuit fault occurs, the minimum voltage of the generator is not lower than a first preset value and the duration does not exceed the first preset time, and the minimum frequency of the generator is not lower than a second preset value and the duration does not exceed the second preset time.

4. The method for predicting power system security and stability margin based on graph neural network according to claim 3 is characterized in that: The stability marker includes 1 or 0; If, under the operating condition, each of the transmission lines satisfies the stability criterion, the stability mark of the operating condition is 1; Otherwise, the stability of the operating condition is marked as 0.

5. The method for predicting power system security and stability margin based on graph neural network according to claim 4 is characterized in that: The steps of obtaining the limit transmission power are: When the load is increased within the load variation range with the preset variation step length, the first unstable operating condition is obtained according to the operating condition corresponding to the first appearance of the stability mark 0; Obtaining a first instability load according to the first instability operating condition; According to the first unstable load, the preset change step is reduced to obtain the ultimate stable operating load; The limit transmission power is obtained according to the limit stable operation load.

6. The method for predicting power system security and stability margin based on graph neural network according to claim 5 is characterized in that: The load variation range is 0.9-1.1, and the preset variation step is 0.

01.

7. The method for predicting power system security and stability margin based on graph neural network according to any one of claims 1 to 6, characterized in that: The safety stability margin is: Where M i is the safety and stability margin of the ith operating condition, P max is the limit transmission power, P i is the current active transmission power under the i-th operating condition, and i is the operating condition number.

8. The method for predicting power system security and stability margin based on graph neural network according to claim 1 is characterized in that: The historical data set includes a bus power characteristic value set; the steps of forming the graph data set are: constructing an adjacency matrix of a graph according to the analysis model, wherein the nodes of the graph are the buses and the edges of the graph are the transmission lines; A node characteristic matrix is ​​obtained according to the bus power characteristic value set, and an edge characteristic matrix is ​​obtained according to the inverse of the transmission line impedance and the bus power characteristic value set; Based on the adjacency matrix, the node feature matrix and the edge feature matrix, the graph data set is obtained with the safety stability margin value set as a label.

9. The method for predicting power system security and stability margin based on graph neural network according to claim 8 is characterized in that: The bus electrical quantity characteristic value set includes bus voltage amplitude, bus voltage phase angle, active power flowing into the bus and reactive power flowing into the bus.

10. A device for implementing the method for predicting the safety and stability margin of a power system based on a graph neural network according to any one of claims 1 to 9, characterized in that: comprising a first modeling unit, a calculation unit and a second modeling unit; The first modeling unit: for constructing an analysis model according to the power system; for obtaining historical data sets, transmission line impedances, and transmission line voltage levels of the power system according to the analysis model; for obtaining a key transmission line according to the transmission line impedance and the transmission line voltage level; Used to obtain load variation range, preset variation step, generator output range, preset fault and stability criteria; Used to initialize the analysis model according to the load variation range, the preset variation step and the generator output interval; Used to change the load of the analysis model within the load variation range with the preset variation step length within the generator output range to obtain the operating condition; Used to obtain the current active transmission power of the key transmission line based on the operating condition and according to the analysis model; Used to perform the preset fault on each of the transmission lines under the operating conditions; Used to obtain the limit transmission power according to the stability criterion based on the preset fault; The computing unit: A safety and stability margin for obtaining the operating condition using the limit transmission power and the current active transmission power; Used to obtain a safety stability margin value set according to the safety stability margin; The second modeling unit: Used to form a graph data set and construct an initial graph neural network model according to the analysis model, the historical data set, the transmission line impedance and the safety stability margin value set; Used to train the initial graph neural network model based on the graph data set, and use the Adam algorithm to optimize the parameters of the initial graph neural network model to obtain the graph neural network model; Used to analyze the power system according to the graph neural network model to obtain a safety and stability margin prediction value.

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