A power grid system control method and device considering deterministic network communication attacks

Abstract

The embodiments of this specification provide a power grid system control method and device that considers deterministic network communication attacks, wherein the method includes: establishing a control model for a DC microgrid system; establishing an RBF neural network DC microgrid prediction model; calculating the deviation between the monitoring value of the DC microgrid system obtained by sensor monitoring and the predicted value of the RBF neural network DC microgrid prediction model; based on the deviation value and a preset threshold, determining whether a preset event trigger mechanism is triggered; if so, using the predicted value of the RBF neural network DC microgrid prediction model as an input parameter of a super-helical sliding mode controller; if not, using the monitored value as an input parameter of the super-helical sliding mode controller; and setting a super-helical sliding mode controller for converging the output value of the DC microgrid system based on the input parameter. The embodiments of this specification can solve the problem of system instability when a DC microgrid system is attacked by network communication.

Description

Technical Field

[0001] The embodiments of this specification relate to the field of network communications, and in particular, to a power grid system control method and apparatus that considers deterministic network communication attacks. Background Art

[0002] As an emerging network communication architecture, deterministic networking, leveraging its core technologies of resource reservation, service assurance, and explicit routing, places high demands on data transmission performance. Within this framework, information exchange between controllers in distributed DC microgrid systems relies heavily on the communication network, making them more vulnerable to network communication attacks. These attacks can cause the microgrid to deviate from its rated operating point, or even directly cause power outages. To improve system stability, a series of measures are needed to address these challenges and ensure the safe and stable operation of the grid system despite network communication attacks.

[0003] In the prior art, Chinese invention patent application number 202110209790.4 uses a univariate linear regression model to perform linear fitting of voltage differentials and current changes, and then detects operational data. However, nonlinear factors such as equipment aging, ambient temperature fluctuations, and sudden load changes are common in real power grid systems, which can prevent this linear model from accurately reflecting the system state. Furthermore, this method fails to account for system steady-state deviations caused by network communication attacks. Chinese invention patent application number 202210272522.1 constructs a controller based on a distributed sliding mode observer. This controller uses the distributed sliding mode observer to estimate attack signals and uses the compensation output by the controller to supplement the distributed two-layer control, thereby achieving bus voltage recovery and current output distribution. However, the performance of the distributed sliding mode observer is highly dependent on its parameter settings. Improper parameter settings can weaken the observer's detection capability, leading to false or missed attack signals and exacerbating controller chattering under FDI attacks. Furthermore, this method fails to isolate detected network communication attacks, posing a potential threat to the overall stability and reliability of the system.

[0004] Therefore, there is an urgent need for a power grid system control method that takes into account deterministic network communication attacks, which can easily and cost-effectively solve the problem of system instability when the distributed controlled DC microgrid system is attacked by network communication. Summary of the Invention

[0005] The purpose of the embodiments of this specification is to provide a power grid system control method and device that takes into account deterministic network communication attacks, so as to solve the problem of system instability when a distributed controlled DC microgrid system is attacked by network communication with low difficulty and low cost.

[0006] To achieve the above objectives, on the one hand, embodiments of this specification provide a power grid system control method considering deterministic network communication attacks, including:

[0007] Establish a control model for the DC microgrid system;

[0008] Based on the control model of the DC microgrid system, an RBF neural network DC microgrid prediction model is established;

[0009] Calculating a deviation between a monitoring value of the DC microgrid system obtained by sensor monitoring and a predicted value of the RBF neural network DC microgrid prediction model;

[0010] Based on the deviation value and a preset threshold, determining whether a preset event triggering mechanism is triggered, wherein the preset event triggering mechanism indicates that the DC microgrid system is subject to a network communication attack;

[0011] If yes, the predicted value of the RBF neural network DC microgrid prediction model is used as the input parameter of the super spiral sliding mode controller;

[0012] If not, the monitored value is used as an input parameter of the super-helical sliding mode controller;

[0013] According to the input parameters, a super-helical sliding mode controller is set for converging the output value of the DC microgrid system.

[0014] Preferably, the establishing of the control model of the DC microgrid system further comprises:

[0015] Set up multiple distributed generator sets;

[0016] Any distributed generator set is connected to a busbar through a boost converter, and each busbar is connected through resistance and inductance to form a DC microgrid system;

[0017] A mathematical model of the boost converter in one switching cycle is established as the control model of the DC microgrid system.

[0018] Preferably, the mathematical model of the boost converter in one switching cycle includes:

[0019]

[0020]

[0021]

[0022] Among them, L i,f is the inductance of the i-th boost converter, C i,f is the capacitance of the i-th boost converter, For the i-th boost converter through the inductor L f The current, i i,o 、v i,o are the output current and output voltage of the i-th boost converter in the monitoring values monitored by the sensor, d i is the duty cycle of the i-th boost converter switch, r i,d is the equivalent resistance of the i-th boost converter, V i,n is the output voltage of the distributed generator set corresponding to the i-th boost converter, subscript i = 1, 2, ..., N, N is the number of boost converters, and t is a discrete time point.

[0023] Preferably, the establishing of the RBF neural network DC microgrid prediction model based on the control model of the DC microgrid system further comprises:

[0024] Establish the RBF neural network DC microgrid voltage initial model and the RBF neural network DC microgrid current initial model respectively;

[0025] Actual inputs of a mathematical model of a boost converter in one switching cycle under different load changes are used as input data for the RBF neural network DC microgrid voltage initial model and the RBF neural network DC microgrid current initial model respectively;

[0026] Inputting the input data into the RBF neural network DC microgrid voltage initial model and the RBF neural network DC microgrid current initial model respectively to obtain output data of the two initial models;

[0027] Based on the actual output of the mathematical model of the boost converter within one switching cycle under different load changes and the output data of the two initial models, the RBF neural network DC microgrid voltage initial model and the RBF neural network DC microgrid current initial model are optimized respectively to obtain the RBF neural network DC microgrid voltage prediction model and the RBF neural network DC microgrid current prediction model.

[0028] Preferably, the input data is:

[0029]

[0030] in, is the input data of the RBF neural network DC microgrid voltage initial model, or the input data of the RBF neural network DC microgrid current initial model, For the i-th boost converter through the inductor L f The current, d iis the duty cycle of the i-th boost converter switch, with subscript i = 1, 2, …, N, where N is the number of boost converters;

[0031] The output data is:

[0032] y i,k =w i,k,1 h i,k,1 +w i,k,2 h i,k,2 +…+w i,k,j h i,k,j +…+w i,k,l h i,k,l ;

[0033]

[0034] Among them, when k=1, y i,k is the output data of the initial model of DC microgrid voltage of RBF neural network, w i,k,j is the weight value of the jth hidden layer neuron in the RBF neural network DC microgrid voltage initial model, h i,k,j is the output of the jth hidden layer neuron in the RBF neural network DC microgrid voltage initial model, b i,k,j is the width of the Gaussian basis function of the jth hidden layer neuron in the RBF neural network DC microgrid voltage initial model, and b i,k,j >0,c i,k,j =[c i,k,j,1 c i,k,j,2 ] T , c i,k,j is the center point vector value of the jth hidden layer neuron, c i,k,j,m is the mth component of the center point vector value of the jth hidden layer neuron, m = 1, 2, j = 1, 2, ..., l, l is the number of hidden layer neurons;

[0035] Among them, when k = 2, y i,k is the output data of the initial model of the DC microgrid current using the RBF neural network, w i,k,j is the weight value of the jth hidden layer neuron in the RBF neural network DC microgrid current initial model, h i,k,j is the output of the jth hidden layer neuron in the initial DC microgrid current model of the RBF neural network, b i,k,j is the width of the Gaussian basis function of the jth hidden layer neuron in the initial model of the DC microgrid current of the RBF neural network, and b i,k,j >0,c i,k,j =[c i,k,j,1 c i,k,j,2 ] T , c i,k,jis the center point vector value of the jth hidden layer neuron, c i,k,j,m is the mth component of the center point vector value of the jth hidden layer neuron, m = 1, 2, j = 1, 2, …, l, and l is the number of hidden layer neurons.

[0036] Preferably, the RBF neural network DC microgrid voltage initial model and the RBF neural network DC microgrid current initial model are optimized based on the actual output of the mathematical model of the boost converter in one switching cycle under different load changes and the output data of the two initial models, respectively, to obtain the RBF neural network DC microgrid voltage prediction model and the RBF neural network DC microgrid current prediction model further comprising:

[0037] Calculating an error value between the actual output and the output data based on the actual output of the mathematical model of the boost converter in one switching cycle under different load changes and the output data of the two initial models;

[0038] The Adam gradient descent method is used to optimize the model parameters of the two initial models respectively, and the RBF neural network DC microgrid voltage prediction model and the RBF neural network DC microgrid current prediction model corresponding to the minimized error value are obtained.

[0039] Preferably, the calculating the error value between the actual output and the output data based on the actual output of the mathematical model of the boost converter in one switching cycle under different load changes and the output data of the two initial models further comprises:

[0040] The error between the actual output and the output data is calculated using the following formula:

[0041]

[0042] Among them, when k=1, y i,k is the output data of the initial model of DC microgrid voltage using RBF neural network, is the actual output voltage in the actual output, E i,k is the error value between the actual output voltage and the output data of the RBF neural network DC microgrid voltage initial model; when k = 2, y i,k is the output data of the initial model of the DC microgrid current using the RBF neural network, is the actual output current in the actual output, E i,k is the error value between the actual output current and the output data of the RBF neural network DC microgrid current initial model.

[0043] Preferably, the preset event trigger mechanism includes: a preset event trigger mechanism for current network communication attacks, and a preset event trigger mechanism for voltage network communication attacks;

[0044] The preset event trigger mechanism for current network communication attacks is characterized by the following formula:

[0045]

[0046] Among them, Θ i,I is the nonlinear time-varying trigger function for current network communication attacks, κ i,1 , κ i,2 , κ i,3 , κ i,4 、 are all constants, e i,I is the deviation value corresponding to the current when the current network communication is attacked, i i,o is the current output by the i-th boost converter in the monitoring value obtained by the sensor, is the predicted current value of the RBF neural network DC microgrid prediction model, subscript i=1,2,…,N, N is the number of boost converters, is the preset threshold for current network communication attacks, is the kth triggering moment of the i-th boost converter for the current network communication attack, is the k+1th triggering moment of the ith boost converter for the current network communication attack, inf{·} is the infimum of the set, and t is a discrete time point;

[0047] The preset event trigger mechanism for voltage network communication attacks is characterized by the following formula:

[0048]

[0049] Among them, Θ i,V is the nonlinear time-varying trigger function for voltage network communication attacks, κ i,5 , κ i,6 , κ i,7 , κ i,8 、 is a constant, e i,V is the voltage deviation value corresponding to the voltage network communication attack, v i,o is the voltage output by the i-th boost converter in the monitoring value obtained by the sensor, is the predicted voltage value of the RBF neural network DC microgrid prediction model, subscript i=1,2,…,N, N is the number of boost converters, is the preset threshold for voltage network communication attacks, is the kth triggering moment of the i-th boost converter for voltage network communication attack, is the k+1th triggering moment of the ith boost converter against the voltage network communication attack, inf{·} is the infimum of the set, and t is a discrete time point.

[0050] Preferably, the super spiral sliding mode controller configured to converge the output value of the DC microgrid system according to the input parameters further comprises:

[0051] Based on the input parameters, calculating the average voltage and current coordination errors of any distributed generator set in the DC microgrid system;

[0052] Simulate network communication attacks and generate attack models;

[0053] Based on the attack model, determining the average voltage under attack and the coordinated current under attack of any distributed generator set in the DC microgrid system under a network communication attack;

[0054] Calculating a steady-state voltage average value of the DC microgrid system based on the average voltage under the attack;

[0055] Analyze the dynamic change of the current coordination error under the attack to obtain a dynamic change model;

[0056] Substituting the steady-state voltage average value into the dynamic change model to obtain an updated dynamic change model;

[0057] Based on the updated dynamic change model, under the condition that the current cooperation error is zero, calculating the steady-state voltage deviation value of the DC microgrid system;

[0058] Substituting the steady-state voltage deviation value into the updated dynamic change model to obtain a steady-state current deviation value of the DC microgrid system;

[0059] Obtaining a first control variable of the boost converter according to a reference voltage of the boost converter, a voltage output by the boost converter, and a steady-state voltage deviation value;

[0060] Obtaining a second controlled variable of the boost converter according to a reference current of the current coordination error, a current output by the boost converter, and a steady-state current deviation value;

[0061] A super-helical sliding mode controller is set based on the first control variable, the second control variable, the sliding surface function, the reaching law and the nonlinear sigmoid function.

[0062] On the other hand, an embodiment of this specification provides a power grid system control device that considers deterministic network communication attacks, the device comprising:

[0063] A control model building module is used to build a control model for the DC microgrid system;

[0064] A prediction model establishment module is used to establish an RBF neural network DC microgrid prediction model based on the control model of the DC microgrid system;

[0065] A deviation value calculation module is used to calculate the deviation value between the monitoring value of the DC microgrid system obtained by sensor monitoring and the predicted value of the RBF neural network DC microgrid prediction model;

[0066] a judgment module, configured to judge whether a preset event trigger mechanism is triggered based on the deviation value and a preset threshold value, wherein the preset event trigger mechanism indicates that the DC microgrid system is subjected to a network communication attack; if so, using the predicted value of the RBF neural network DC microgrid prediction model as an input parameter of the super spiral sliding mode controller; if not, using the monitored value as the input parameter of the super spiral sliding mode controller;

[0067] The setting module is used to set a super spiral sliding mode controller for converging the output value of the DC microgrid system according to the input parameters.

[0068] On the other hand, an embodiment of this specification further provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein when the computer program is run by the processor, the computer program executes instructions of any one of the above methods.

[0069] On the other hand, an embodiment of the present specification further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor of a computer device, executes instructions of any one of the above methods.

[0070] On the other hand, the embodiments of this specification further provide a computer program product, which, when executed by a processor of a computer device, executes instructions of any one of the above methods.

[0071] It can be seen from the technical solutions provided in the above embodiments of this specification that, by using the method of the embodiments of this specification, a control model of a DC microgrid system is constructed, and an RBF neural network is introduced for prediction, which can effectively improve the operation effect of the system. The deviation between the sensor monitoring value and the predicted value of the RBF neural network prediction model is used to determine whether to trigger a preset event mechanism, such as a network communication attack. If an attack is detected, the system will use the predicted value as the input of the super-helical sliding mode controller to ensure that the system can make reasonable adjustments in the face of anomalies to prevent performance degradation; if no attack is detected, the actual monitoring value is used for control to ensure that the system still maintains efficient operation under normal circumstances. The super-helical sliding mode controller can dynamically adjust the system output according to the input parameters so that it converges within the expected range, thereby improving the stability and response speed of the system. Overall, this process not only improves the stability and robustness of the system, but also optimizes the control effect through real-time monitoring and adjustment, ensuring that the system can operate efficiently and stably under various working conditions.

[0072] In order to make the above and other purposes, features and advantages of this specification more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0074] Figure 1 A schematic flow chart of a power grid system control method considering deterministic network communication attacks provided in an embodiment of this specification is shown;

[0075] Figure 2 A schematic diagram of a process for establishing a control model for a DC microgrid system according to an embodiment of this specification is shown;

[0076] Figure 3 A schematic diagram of a process for establishing an RBF neural network DC microgrid prediction model provided in an embodiment of this specification is shown;

[0077] Figure 4 A schematic diagram of the process of optimizing the initial model provided in the embodiment of this specification is shown;

[0078] Figure 5 A schematic diagram of a flow chart of a super spiral sliding mode controller provided in an embodiment of this specification for converging the output value of a DC microgrid system is shown;

[0079] Figure 6 A schematic diagram of the module structure of a power grid system control device considering deterministic network communication attacks provided by an embodiment of this specification is shown;

[0080] Figure 7 A schematic diagram of the structure of a computer device provided in an embodiment of this specification is shown.

[0081] Description of the accompanying symbols:

[0082] 100. Control model building module;

[0083] 200. Prediction model building module;

[0084] 300. Deviation value calculation module;

[0085] 400. Judgment module;

[0086] 500, setting module;

[0087] 702. Computer equipment;

[0088] 704, processor;

[0089] 706. Memory;

[0090] 708, driving mechanism;

[0091] 710, input / output module;

[0092] 712. Input devices;

[0093] 714. Output device;

[0094] 716. Presentation equipment;

[0095] 718. Graphical User Interface;

[0096] 720, network interface;

[0097] 722, communication link;

[0098] 724. Communication bus. DETAILED DESCRIPTION

[0099] The following will clearly and completely describe the technical solutions in the embodiments of this specification in conjunction with the drawings in the embodiments of this specification. Obviously, the embodiments described are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the embodiments of this specification.

[0100] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0101] As an emerging network communication architecture, deterministic networking, leveraging its core technologies of resource reservation, service assurance, and explicit routing, places high demands on data transmission performance. Within this framework, information exchange between controllers in distributed DC microgrid systems relies heavily on the communication network, making them more vulnerable to network communication attacks. These attacks can cause the microgrid to deviate from its rated operating point, or even directly cause power outages. To improve system stability, a series of measures are needed to address these challenges and ensure the safe and stable operation of the grid system despite network communication attacks.

[0102] In order to solve the above problems, the embodiments of this specification provide a power grid system control method considering deterministic network communication attacks. Figure 1 This is a flowchart of a power grid system control method that takes into account deterministic network communication attacks, provided in an embodiment of this specification. This specification provides method operation steps as described in the embodiment or flowchart, but may include more or fewer operation steps based on conventional or non-creative work. The order of steps listed in the embodiment is only one way of executing the steps among many steps and does not represent the only execution order. When the actual system or device product is executed, it can be executed in the order or in parallel according to the method shown in the embodiment or the accompanying drawings.

[0103] It should be noted that the terms "first", "second", etc. in the description and claims of the embodiments of this specification and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of this specification described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0104] Reference Figure 1, an embodiment of this specification provides a power grid system control method considering deterministic network communication attacks, including:

[0105] S101: Establish a control model for the DC microgrid system;

[0106] S102: Establishing an RBF neural network DC microgrid prediction model based on the control model of the DC microgrid system;

[0107] S103: Calculating a deviation between a monitoring value of the DC microgrid system obtained by monitoring by sensors and a predicted value of the RBF neural network DC microgrid prediction model;

[0108] S104: Based on the deviation value and a preset threshold, determining whether a preset event triggering mechanism is triggered, wherein the preset event triggering mechanism indicates that the DC microgrid system is subject to a network communication attack;

[0109] S105: If yes, use the predicted value of the RBF neural network DC microgrid prediction model as an input parameter of the super spiral sliding mode controller;

[0110] S106: If not, use the monitoring value as an input parameter of the super-helical sliding mode controller;

[0111] S107: Setting a super-helical sliding mode controller for converging an output value of the DC microgrid system according to the input parameters.

[0112] By using the method of the embodiment of this specification, a control model of a DC microgrid system is constructed, and an RBF neural network is introduced for prediction, which can effectively improve the operation effect of the system. The deviation between the sensor monitoring value and the predicted value of the RBF neural network prediction model is used to determine whether to trigger a preset event mechanism, such as a network communication attack. If an attack is detected, the system will use the predicted value as the input of the super-helical sliding mode controller to ensure that the system can make reasonable adjustments in the face of anomalies to prevent performance degradation; if no attack is detected, the actual monitoring value is used for control to ensure that the system still maintains efficient operation under normal circumstances. The super-helical sliding mode controller can dynamically adjust the system output according to the input parameters so that it converges within the expected range, thereby improving the stability and response speed of the system. Overall, this process not only improves the stability and robustness of the system, but also optimizes the control effect through real-time monitoring and adjustment, ensuring that the system can operate efficiently and stably under various working conditions.

[0113] In order to prevent the deviation between the sensor-based monitoring value and the predicted value of the RBF neural network DC microgrid prediction model from exceeding a preset threshold during load changes, thereby causing false alarms of network communication attacks, the embodiment of this specification designs a nonlinear time-varying trigger function that incorporates a nonlinear function and a network communication attack error. When the nonlinear time-varying trigger function exceeds the preset threshold, the system is deemed to have suffered a network communication attack, and the predicted value of the RBF neural network DC microgrid prediction model replaces the sensor's monitoring value as the input of the super-helical sliding mode controller, thereby effectively isolating the impact of the network communication attack on the power grid system.

[0114] By analyzing the steady-state voltage deviation and steady-state current deviation caused by voltage network communication attacks and current network communication attacks respectively, and further integrating the output voltage and output current of the boost converter to construct the sliding mode surface, a super-helical sliding mode controller is designed. At the same time, a smooth nonlinear sigmoid function is introduced, so that the actual operating state of the power grid system can converge to the target state quickly and smoothly, effectively curbing the impact of network communication attacks on the stability of the power grid system.

[0115] In the embodiments of this specification, refer to Figure 2 , the control model of the DC microgrid system further includes:

[0116] S201: Setting up multiple distributed generator sets;

[0117] S202: Each distributed generator set is connected to a busbar via a boost converter, and each busbar is connected via resistors and inductors to form a DC microgrid system;

[0118] S203: Establish a mathematical model of the boost converter in one switching cycle as a control model of the DC microgrid system.

[0119] The mathematical model of the boost converter in one switching cycle includes:

[0120]

[0121]

[0122]

[0123] Among them, L i,f is the inductance of the i-th boost converter, C i,f is the capacitance of the i-th boost converter, For the i-th boost converter through the inductor L f The current, i i,o 、v i,oare the output current and output voltage of the i-th boost converter in the monitoring values monitored by the sensor, d i is the duty cycle of the i-th boost converter switch, r i,d is the equivalent resistance of the i-th boost converter, V i,n is the output voltage of the distributed generator set corresponding to the i-th boost converter, subscript i = 1, 2, ..., N, N is the number of boost converters, and t is a discrete time point.

[0124] In the embodiments of this specification, refer to Figure 3 , the control model based on the DC microgrid system, establishing the RBF neural network DC microgrid prediction model further includes:

[0125] S301: establishing an initial voltage model of a RBF neural network DC microgrid and an initial current model of a RBF neural network DC microgrid respectively;

[0126] S302: Actual inputs of a mathematical model of a boost converter in one switching cycle under different load changes are used as input data for the RBF neural network DC microgrid voltage initial model and the RBF neural network DC microgrid current initial model respectively;

[0127] S303: Inputting the input data into the RBF neural network DC microgrid voltage initial model and the RBF neural network DC microgrid current initial model respectively to obtain output data of the two initial models;

[0128] S304: Based on the actual output of the mathematical model of the boost converter within one switching cycle under different load changes and the output data of the two initial models, the RBF neural network DC microgrid voltage initial model and the RBF neural network DC microgrid current initial model are optimized respectively to obtain the RBF neural network DC microgrid voltage prediction model and the RBF neural network DC microgrid current prediction model.

[0129] The actual inputs of the mathematical model of the boost converter in one switching cycle under different load changes include duty cycle, inductance, and current. The actual inputs are used as input data for the RBF neural network DC microgrid voltage initial model and the RBF neural network DC microgrid current initial model, respectively. The input data are:

[0130]

[0131] in, is the input data of the RBF neural network DC microgrid voltage initial model, or the input data of the RBF neural network DC microgrid current initial model, that is, the input feature data set of the i-th boost converter under different load changes; For the i-th boost converter through the inductor L f The current, d i is the duty cycle of the i-th boost converter switch, with subscript i = 1, 2, …, N, where N is the number of boost converters;

[0132] The output data of the RBF neural network DC microgrid voltage initial model and the RBF neural network DC microgrid current initial model are:

[0133] y i,k =w i,k,1 h i,k,1 +w i,k,2 h i,k,2 +…+w i,k,j h i,k,j +…+w i,k,l h i,k,l (5)

[0134]

[0135] Among them, when k=1, y i,k is the output data of the initial model of DC microgrid voltage of RBF neural network, w i,k,j is the weight value of the jth hidden layer neuron in the RBF neural network DC microgrid voltage initial model, h i,k,j is the output of the jth hidden layer neuron in the RBF neural network DC microgrid voltage initial model, b i,k,j is the width of the Gaussian basis function of the jth hidden layer neuron in the RBF neural network DC microgrid voltage initial model, and b i,k,j >0,c i,k,j =[c i,k,j,1 c i,k,j,2 ] T , c i,k,j is the center point vector value of the jth hidden layer neuron, c i,k,j,m is the mth component of the center point vector value of the jth hidden layer neuron, m = 1, 2, j = 1, 2, ..., l, l is the number of hidden layer neurons;

[0136] Among them, when k = 2, y i,k is the output data of the initial model of the DC microgrid current using the RBF neural network, w i,k,j is the weight value of the jth hidden layer neuron in the RBF neural network DC microgrid current initial model, h i,k,jis the output of the jth hidden layer neuron in the initial DC microgrid current model of the RBF neural network, b i,k,j is the width of the Gaussian basis function of the jth hidden layer neuron in the initial model of the DC microgrid current of the RBF neural network, and b i,k,j >0,c i,k,j =[c i,k,j,1 c i,k,j,2 ] T , c i,k,j is the center point vector value of the jth hidden layer neuron, c i,k,j,m is the mth component of the center point vector value of the jth hidden layer neuron, m = 1, 2, j = 1, 2, ..., l, l is the number of hidden layer neurons;

[0137] Reference Figure 4 , based on the actual output of the mathematical model of the boost converter in one switching cycle under different load changes and the output data of the two initial models, respectively optimizing the RBF neural network DC microgrid voltage initial model and the RBF neural network DC microgrid current initial model to obtain the RBF neural network DC microgrid voltage prediction model and the RBF neural network DC microgrid current prediction model further include:

[0138] S401: Calculating an error value between the actual output and the output data based on the actual output of the mathematical model of the boost converter in one switching cycle under different load changes and the output data of the two initial models;

[0139] S402: Adopting the Adam gradient descent method to optimize the model parameters of the two initial models respectively, and obtaining the RBF neural network DC microgrid voltage prediction model and the RBF neural network DC microgrid current prediction model corresponding to the minimized error value.

[0140] The actual output of the mathematical model of the boost converter in one switching cycle under different load changes includes current and voltage.

[0141] The calculating the error value between the actual output and the output data based on the actual output of the mathematical model of the boost converter in one switching cycle under different load changes and the output data of the two initial models further includes:

[0142] The error between the actual output and the output data is calculated using the following formula:

[0143]

[0144] Among them, when k=1, y i,k is the output data of the initial model of DC microgrid voltage using RBF neural network, is the actual output voltage in the actual output, E i,k is the error value between the actual output voltage and the output data of the RBF neural network DC microgrid voltage initial model; when k = 2, y i,k is the output data of the initial model of the DC microgrid current using the RBF neural network, is the actual output current in the actual output, E i,k is the error value between the actual output current and the output data of the RBF neural network DC microgrid current initial model.

[0145] The Adam gradient descent method is used to optimize the model parameters of the two initial models respectively, and the weight value w i,k,j , the center point c of the hidden layer neuron i,k,j,m and the width b of the Gaussian basis function i,k,j The gradient of is:

[0146]

[0147] Where Δw i,k,j is the gradient of the weight value, Δb i,k,j is the gradient of the width of the Gaussian basis function, Δc i,k,j,m is the gradient of the center point of the hidden layer neuron. When m=1, When m=2

[0148] When k=1, w i,k,j is the weight value of the jth hidden layer neuron in the RBF neural network DC microgrid voltage initial model, is the input data of the RBF neural network DC microgrid voltage initial model, y i,k is the output data of the initial model of DC microgrid voltage using RBF neural network, is the actual output voltage in the actual output, E i,k is the error between the actual output voltage and the output data of the RBF neural network DC microgrid voltage initial model, h i,k,j is the output of the jth hidden layer neuron in the RBF neural network DC microgrid voltage initial model;

[0149] When k=2, w i,k,j is the weight value of the jth hidden layer neuron in the RBF neural network DC microgrid current initial model, is the input data of the initial model of RBF neural network DC microgrid current, y i,k is the output data of the initial model of the DC microgrid current using the RBF neural network, is the actual output current in the actual output, E i,kis the error between the actual output current and the output data of the RBF neural network DC microgrid current initial model, h i,k,j is the output of the jth hidden layer neuron in the initial DC microgrid current model of the RBF neural network;

[0150] b i,k,j is the width of the Gaussian basis function of the jth hidden layer neuron.

[0151] According to formula (8), the Adam gradient descent method is used as the parameter w i,k,j Design an adaptive learning rate to update it:

[0152]

[0153] Among them, m a w is the initial model in the current cycle i,k,j The first moment estimate of the gradient, m a t-T w is the initial model in the previous cycle i,k,j The first moment estimate of the gradient, v a w i,k,j The initial model's second-order moment estimate of the gradient in the current cycle, v a t-T w is the initial model in the previous cycle i,k,j The second moment estimate of the gradient, is the first-order moment estimate of the error correction, is the decay rate of the first-order moment estimate, is the decay rate of the second-order moment estimate, is the error-corrected second-order moment estimate, w i,k,j is the weight value of the jth hidden layer neuron in the initial model in the current cycle, w i，k，j t-T is the weight value of the jth hidden layer neuron in the previous cycle of the initial model, η a w i,k,j Adaptive learning rate, Λ a , β a1 , β a2 ,∈ a is a constant.

[0154] According to formula (8), Adam gradient descent method is used as parameter b i,k,j Design an adaptive learning rate to update it:

[0155]

[0156] Among them, m b b is the initial model in the current cycle i,k,jThe first moment estimate of the gradient, m b t-T b is the initial model in the previous cycle i,k,j The first moment estimate of the gradient, v b b is the initial model in the current cycle i,k,j The second moment estimate of the gradient, v b t-T b is the initial model in the previous cycle i,k,j The second moment estimate of the gradient, is the first-order moment estimate of the error correction, is the error-corrected second-order moment estimate, is the decay rate of the first-order moment estimate, is the decay rate of the second-order moment estimate, b i,k,j is the width of the Gaussian basis function of the jth hidden layer neuron in the initial model in the current cycle, b i，k，j t-T is the width of the Gaussian basis function of the jth hidden layer neuron in the initial model in the previous cycle, η b for b i,k,j Adaptive learning rate, Λ b , β b1 , β b2 ,∈ b is a constant;

[0157] According to formula (8), Adam gradient descent method is used as parameter c i,k,j,m Design an adaptive learning rate to update it:

[0158]

[0159] Among them, m c c is the initial model in the current cycle i,k,j,m The first moment estimate of the gradient, m c t-T c is the initial model in the previous cycle i,k,j,m The first moment estimate of the gradient, v c c is the initial model in the current cycle i,k,j,m The second moment estimate of the gradient, v c t-T c is the initial model in the previous cycle i,k,j,m The second moment estimate of the gradient, is the first-order moment estimate of the error correction, is the error-corrected second-order moment estimate, is the decay rate of the first-order moment estimate, is the decay rate of the second-order moment estimate, η c is parameter c i,k,j,mAdaptive learning rate, Λ c , β c1 , β c2 ,∈ c is a constant, c i,k,j,m is the mth component of the center point vector value of the jth hidden layer neuron in the current cycle, c i，k，j，m t-T is the mth component of the center point vector value of the jth hidden layer neuron in the previous cycle, where m is 1 or 2.

[0160] In the embodiment of this specification, the deviation value between the monitoring value of the DC microgrid system obtained by the sensor monitoring and the predicted value of the RBF neural network DC microgrid prediction model is calculated.

[0161] Specifically:

[0162]

[0163] Among them, e i,I is the deviation value corresponding to the current when the current network communication is attacked, i i,o is the current output by the i-th boost converter in the monitoring value obtained by the sensor, is the predicted current value of the RBF neural network DC microgrid prediction model.

[0164]

[0165] Among them, e i,V is the voltage deviation value corresponding to the voltage network communication attack, v i,o is the voltage output by the i-th boost converter in the monitoring value obtained by the sensor, is the predicted voltage value of the RBF neural network DC microgrid prediction model.

[0166] Based on the deviation value and the preset threshold, it is determined whether the preset event trigger mechanism is triggered. The preset event trigger mechanism indicates that the DC microgrid system is under a network communication attack.

[0167] The preset event trigger mechanism includes: a preset event trigger mechanism for current network communication attacks, and a preset event trigger mechanism for voltage network communication attacks;

[0168] The preset event trigger mechanism for current network communication attacks is characterized by the following formula:

[0169]

[0170] Among them, Θ i,I is the nonlinear time-varying trigger function for current network communication attacks, κ i,1 , κi,2 , κ i,3 , κ i,4 、 are all constants, e i,I is the deviation value corresponding to the current during the current network communication attack, i i,o is the current output by the i-th boost converter in the monitoring value obtained by the sensor, is the predicted current value of the RBF neural network DC microgrid prediction model, i.e. i,2 , subscript i=1,2,…,N, N is the number of boost converters, is the preset threshold for current network communication attacks, is the kth triggering moment of the i-th boost converter for the current network communication attack, is the k+1th triggering moment of the ith boost converter for the current network communication attack, inf{·} is the infimum of the set, and t is a discrete time point;

[0171] The preset event trigger mechanism for voltage network communication attacks is characterized by the following formula:

[0172]

[0173] Among them, Θ i,V is the nonlinear time-varying trigger function for voltage network communication attacks, κ i,5 , κ i,6 , κ i,7 , κ i,8 、 is a constant, e i,V is the voltage deviation value corresponding to the voltage network communication attack, v i,o is the voltage output by the i-th boost converter in the monitoring value obtained by the sensor, is the predicted voltage value of the RBF neural network DC microgrid prediction model, i.e., y i,1 , subscript i=1,2,…,N, N is the number of boost converters, is the preset threshold for voltage network communication attacks, is the kth triggering moment of the i-th boost converter for voltage network communication attack, is the k+1th triggering moment of the ith boost converter against the voltage network communication attack, inf{·} is the infimum of the set, and t is a discrete time point.

[0174] If the preset event trigger mechanism is triggered, the predicted value of the RBF neural network DC microgrid prediction model is used as the input parameter of the super-helical sliding mode controller:

[0175]

[0176]

[0177] in, is the current parameter in the input parameters, is the voltage parameter in the input parameters.

[0178] If the preset event trigger mechanism is not triggered, the monitored value is used as the input parameter of the super-helical sliding mode controller:

[0179]

[0180]

[0181] in, is the current parameter in the input parameters, is the voltage parameter in the input parameters.

[0182] In the embodiments of this specification, refer to Figure 5 , the super spiral sliding mode controller configured to converge the output value of the DC microgrid system according to the input parameters further comprises:

[0183] S501: Calculating average voltage and current coordination errors of any distributed generator set in the DC microgrid system based on the input parameters;

[0184] S502: Simulate network communication attacks and generate attack models;

[0185] S503: Based on the attack model, determining an average voltage and current coordination error under attack of any distributed generator set in the DC microgrid system under a network communication attack;

[0186] S504: Calculating an average steady-state voltage of the DC microgrid system based on the average voltage under the attack;

[0187] S505: Analyze the dynamic change of the current coordination error under the attack to obtain a dynamic change model;

[0188] S506: Substituting the steady-state voltage average value into the dynamic change model to obtain an updated dynamic change model;

[0189] S507: Based on the updated dynamic change model, under the condition that the current cooperation error is zero, calculating the steady-state voltage deviation value of the DC microgrid system;

[0190] S508: Substituting the steady-state voltage deviation value into the updated dynamic change model to obtain a steady-state current deviation value of the DC microgrid system;

[0191] S509: Obtaining a first control variable of the boost converter according to the reference voltage of the boost converter, the voltage output by the boost converter, and the steady-state voltage deviation value;

[0192] S510: Obtaining a second controlled variable of the boost converter according to a reference current of the current coordination error, a current output by the boost converter, and a steady-state current deviation value;

[0193] S511: Setting a super spiral sliding mode controller based on the first control variable, the second control variable, the sliding surface function, the reaching law and the nonlinear sigmoid function.

[0194] Based on the input parameters, the average voltage and current coordination error of any distributed generator set in the DC microgrid system is calculated as:

[0195]

[0196]

[0197] in, is the average voltage of the i-th distributed generator set, is the average voltage of the jth distributed generator set, is the voltage parameter of the i-th distributed generator set in the input parameters, a ij is the information exchange value between the i-th distributed generator set and the j-th distributed generator set, which is 1 if there is information exchange and 0 if there is no information exchange. N is the number of boost converters.

[0198] ω i is the current coordination error of the i-th distributed generator set, is the current parameter of the i-th distributed generator set in the input parameters, is the current parameter of the jth distributed generator set in the input parameters.

[0199] Rewrite formulas (20) and (21) into matrix form:

[0200]

[0201] W i =-LI o (twenty three)

[0202] in,

[0203]

[0204]

[0205] , W i =[ω1 ω2 … ω N ] T .

[0206] The reference voltage of the i-th boost converter is further obtained as:

[0207]

[0208] in, is the reference voltage of the i-th boost converter, is the voltage correction term, is the current correction term, are the proportional coefficient and integral coefficient of the voltage controller, are the proportional coefficient and integral coefficient of the current controller respectively, s is the Laplace operator, is the average voltage of the i-th distributed generator set, ω i is the current coordination error of the i-th distributed generator set, The reference current of the current coordination error is usually 0, v nom is the nominal voltage value, is the current parameter of the i-th distributed generator set in the input parameters, r i,d is the equivalent resistance of the i-th boost converter, and N is the number of boost converters.

[0209] Furthermore, the network communication attack is simulated and the attack model is generated as follows:

[0210]

[0211] in, is the attack data value, xi is the monitoring value of the DC microgrid system obtained by the sensor, and the monitoring value includes voltage or current, is the amplitude of false data, δ i Indicates whether there is an attack. If there is an attack, then δ i is 1, if there is no attack, then δ i is 0.

[0212] Based on the attack model formulas (22) and (23), the average voltage and current coordination errors under attack for any distributed generator set in the DC microgrid system under network communication attack are determined as follows:

[0213]

[0214]

[0215] in, is the average voltage under attack, is the current coordination error under attack,

[0216] α=[α1 α2 … α N ] T , β=[β1 β2 … β N ] T ,

[0217]

[0218]

[0219] α i Indicates whether there is a voltage network communication attack. If there is an attack, then α i is 1, if there is no attack, then α i is 0, β i Indicates whether there is a current network communication attack. If there is an attack, β i is 1, if there is no attack, then β i is 0, The amplitude of the false data injected for voltage network communication attack, The amplitude of the false data injected into the current network communication attack, is the final average voltage value of the current network communication attack, is the final current coordination error value after the voltage network communication attack, i = 1, 2, ..., N, where N is the number of boost converters.

[0220] Based on the formula (25) of the average voltage under attack, the average steady-state voltage of the DC microgrid system is calculated as:

[0221]

[0222] in, is the average steady-state voltage, is the average voltage value when there is no network communication attack.

[0223]

[0224] Derivative formula (26) and combined with formulas (20), (21), (22), and (23), in steady state, All are 0. The dynamic change of the current coordination error under attack is analyzed to obtain the dynamic change model:

[0225]

[0226] Among them, V nom =[v nom ,v nom ,…,v nom ] T ∈R n×1 ,

[0227] The steady-state voltage average Substituting into the dynamic change model formula (28), we obtain the updated dynamic change model:

[0228]

[0229] Based on the updated dynamic change model formula (29), under the condition that the current cooperation error is zero, that is, Calculate the steady-state voltage deviation of the DC microgrid system:

[0230]

[0231] Among them, v is the steady-state voltage deviation value of the DC microgrid system,

[0232]

[0233] N is the number of boost converters.

[0234] Substituting the steady-state voltage deviation formula (30) into the updated dynamic change model formula (29), the steady-state current deviation of the DC microgrid system is obtained:

[0235]

[0236] Among them, I is the steady-state current deviation value of the DC microgrid system.

[0237] According to the reference voltage of the boost converter, the voltage output by the boost converter, and the steady-state voltage deviation value, the first control variable of the boost converter is obtained:

[0238]

[0239] Among them, x i,1 is the first control variable of the i-th boost converter, is the reference voltage of the i-th boost converter, v i,o is the voltage output by the i-th boost converter in the monitoring value obtained by the sensor, i,V is the steady-state voltage deviation value of the i-th boost converter, is the predicted voltage value of the RBF neural network DC microgrid prediction model, and N is the number of boost converters.

[0240] According to the reference current of the current coordination error, the current output by the boost converter, and the steady-state current deviation value, the second control variable of the boost converter is obtained:

[0241]

[0242] Among them, x i,2 is the second control variable of the i-th boost converter, is the reference current of the current coordination error, i i,o is the current output by the i-th boost converter in the monitoring value obtained by the sensor, i,I is the steady-state current deviation value of the i-th boost converter, is the current parameter in the input parameters, is the predicted current value of the RBF neural network DC microgrid prediction model.

[0243] In the embodiment of this specification, the sliding surface function s i for:

[0244] s i =c i,1 x i,1 +c i,2 x i,2 (34)

[0245] Among them, s i is the sliding surface function, c i,1 、c i,2 is a constant.

[0246] reaching law i for:

[0247]

[0248] Among them, u i is the reaching law, sign(g) is the sign function, λ i,1 ,λ i,2 is a constant and satisfies λ i,1 >0,λ i,2 >0.

[0249] In order to reduce the chattering phenomenon that may occur in the DC microgrid system under the control of the super-helical sliding mode controller, a smoother nonlinear sigmoid function is introduced. It can effectively alleviate the sudden changes and high-frequency oscillations in the control signal while maintaining the control accuracy, significantly reduce the occurrence of chattering, and improve the stability and control performance of the system.

[0250] A superhelical sliding mode controller is obtained based on the following elements: the first and second control variables are used to adjust the voltage and current to maintain the stability of the system, the sliding surface function is used to define the target position of the system state, and the reaching law and nonlinear Sigmoid function are used to improve the control performance to ensure that the system can remain stable in the presence of disturbances.

[0251] This application provides users with corresponding big data analysis (such as personal biometrics, identity data, consumption data, asset data, electronic terminal operation data, etc.) operation entrances for users to choose to agree or reject automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered.

[0252] Based on the above-mentioned power grid system control method considering deterministic network communication attacks, the embodiments of this specification also provide a power grid system control device considering deterministic network communication attacks.

[0253] The apparatus may include a system (including a distributed system), software (application), module, component, server, client, etc., using the methods described in the embodiments of this specification, combined with the necessary implementation hardware. Based on the same innovative concept, the apparatus in one or more embodiments provided in the embodiments of this specification are described in the following embodiments.

[0254] Since the implementation scheme of the device to solve the problem is similar to the method, the implementation of the specific device in the embodiments of this specification can be referred to the implementation of the aforementioned method, and the repeated parts will not be repeated. As used below, the term "unit" or "module" can be a combination of software and / or hardware that implements the predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0255] Specifically, Figure 6 This is a schematic diagram of the module structure of an embodiment of a power grid system control device considering deterministic network communication attacks provided by the embodiment of this specification, with reference to Figure 6 As shown, an embodiment of this specification provides a power grid system control device that takes into account deterministic network communication attacks, including: a control model establishment module 100, a prediction model establishment module 200, a deviation value calculation module 300, a judgment module 400, and a setting module 500.

[0256] A control model building module 100 is used to build a control model of a DC microgrid system;

[0257] A prediction model building module 200 is used to build an RBF neural network DC microgrid prediction model based on the control model of the DC microgrid system;

[0258] The deviation value calculation module 300 is used to calculate the deviation value between the monitoring value of the DC microgrid system obtained by the sensor monitoring and the predicted value of the RBF neural network DC microgrid prediction model;

[0259] A judgment module 400 is configured to determine whether a preset event trigger mechanism is triggered based on the deviation value and a preset threshold value, wherein the preset event trigger mechanism indicates that the DC microgrid system is subjected to a network communication attack; if so, using the predicted value of the RBF neural network DC microgrid prediction model as an input parameter of a super spiral sliding mode controller; if not, using the monitored value as an input parameter of the super spiral sliding mode controller;

[0260] The setting module 500 is used to set a super spiral sliding mode controller for converging the output value of the DC microgrid system according to the input parameters.

[0261] Reference Figure 7 As shown, based on the above-mentioned power grid system control method considering deterministic network communication attacks, an embodiment of this specification further provides a computer device 702, wherein the above-mentioned method runs on the computer device 702.

[0262] Computer device 702 may include one or more processors 704, such as one or more central processing units (CPUs) or graphics processing units (GPUs), each of which may implement one or more hardware threads. Computer device 702 may also include any memory 706 for storing any type of information, such as code, settings, data, and the like. In one embodiment, a computer program is stored on memory 706 and is executable by processor 704. When executed by processor 704, the computer program may perform instructions according to the above-described method. For example, and without limitation, memory 706 may include any one or more combinations of the following: any type of RAM, any type of ROM, a flash memory device, a hard disk, an optical disk, and the like. More generally, any memory may use any technology to store information. Furthermore, any memory may provide volatile or non-volatile retention of information. Furthermore, any memory may represent a fixed or removable component of computer device 702. In one embodiment, when processor 704 executes associated instructions stored in any memory or combination of memories, computer device 702 may perform any operation of the associated instructions. The computer device 702 also includes one or more drive mechanisms 708 for interacting with any storage, such as a hard disk drive mechanism, an optical disk drive mechanism, and the like.

[0263] The computer device 702 may also include an input / output module 710 (I / O) for receiving various inputs (via input devices 712) and for providing various outputs (via output devices 714). A specific output mechanism may include a presentation device 716 and an associated graphical user interface 718 (GUI). In other embodiments, the input / output module 710 (I / O), input devices 712, and output devices 714 may not be included, and the computer device 702 may simply function as a computer device in a network. The computer device 702 may also include one or more network interfaces 720 for exchanging data with other devices via one or more communication links 722. One or more communication buses 724 couple the components described above together.

[0264] The communication link 722 may be implemented in any manner, for example, via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication link 722 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0265] Corresponding to Figure 1-Figure 5 The method in this specification also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the above method are executed.

[0266] The embodiment of this specification also provides a computer-readable instruction, wherein when the processor executes the instruction, the program therein causes the processor to execute the following Figures 1 to 5 The method shown.

[0267] The embodiment of the present specification also provides a computer program product. When the computer program product is executed by a processor of a computer device, the computer program product performs the following steps: Figures 1 to 5 The method shown.

[0268] The computer program product described in this specification is a software product that mainly implements the method described in this specification through a computer program.

[0269] It should be understood that in the various embodiments of this specification, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this specification.

[0270] It should also be understood that in the embodiments of this specification, the term "and / or" is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent three situations: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, in the embodiments of this specification, the character " / " generally indicates that the associated objects are in an "or" relationship.

[0271] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the embodiments of this specification can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of this specification.

[0272] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0273] In the several embodiments provided in this specification, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or can be an electrical, mechanical or other form of connection.

[0274] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of this specification.

[0275] In addition, the functional units in the various embodiments of this specification may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0276] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0277] Based on this understanding, the technical solutions of the embodiments of this specification, or the portion that contributes to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in each embodiment of this specification. The aforementioned storage medium includes: a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., various media that can store program code.

[0278] Specific embodiments are used in this specification to illustrate the principles and implementation methods of the embodiments of this specification. The description of the above embodiments is only used to help understand the methods and core ideas of the embodiments of this specification. At the same time, for those skilled in the art, based on the ideas of the embodiments of this specification, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the embodiments of this specification.

Claims

1. A power grid system control method considering deterministic network communication attacks, characterized in that: include: Establish a control model for the DC microgrid system; Based on the control model of the DC microgrid system, an RBF neural network DC microgrid prediction model is established; Calculating a deviation between a monitoring value of the DC microgrid system obtained by sensor monitoring and a predicted value of the RBF neural network DC microgrid prediction model; Based on the deviation value and a preset threshold, determining whether a preset event triggering mechanism is triggered, wherein the preset event triggering mechanism indicates that the DC microgrid system is subject to a network communication attack; If yes, the predicted value of the RBF neural network DC microgrid prediction model is used as the input parameter of the super spiral sliding mode controller; If not, the monitored value is used as an input parameter of the super-helical sliding mode controller; According to the input parameters, a super-helical sliding mode controller is set to converge the output value of the DC microgrid system; The preset event trigger mechanism includes: a preset event trigger mechanism for current network communication attacks, and a preset event trigger mechanism for voltage network communication attacks; The preset event trigger mechanism for current network communication attacks is characterized by the following formula: Among them, Θ i,I is the nonlinear time-varying trigger function for current network communication attacks, κ i,1 , κ i,2 , κ i,3 , κ i,4 、 are all constants, e i,I is the deviation value corresponding to the current when the current network communication is attacked, i i,o is the current output by the i-th boost converter in the monitoring value obtained by the sensor, is the predicted current value of the RBF neural network DC microgrid prediction model, subscript i=1,2,L,N, N is the number of boost converters, is the preset threshold for current network communication attacks, is the kth triggering moment of the i-th boost converter for the current network communication attack, is the k+1th triggering moment of the ith boost converter for the current network communication attack, inf{·} is the infimum of the set, and t is a discrete time point; The preset event trigger mechanism for voltage network communication attacks is characterized by the following formula: Among them, Θ i,V is the nonlinear time-varying trigger function for voltage network communication attacks, κ i,5 , κ i,6 , κ i,7 , κ i,8 、 is a constant, e i,V is the voltage deviation value corresponding to the voltage network communication attack, v i,o is the voltage output by the i-th boost converter in the monitoring value obtained by the sensor, is the predicted voltage value of the RBF neural network DC microgrid prediction model, subscript i=1,2,L,N, N is the number of boost converters, is the preset threshold for voltage network communication attacks, is the kth triggering moment of the i-th boost converter for voltage network communication attack, is the k+1th triggering moment of the ith boost converter against the voltage network communication attack, inf{·} is the infimum of the set, and t is a discrete time point.

2. The method according to claim 1, characterized in that The control model of the DC microgrid system further includes: Set up multiple distributed generator sets; Any distributed generator set is connected to a busbar through a boost converter, and each busbar is connected through resistance and inductance to form a DC microgrid system; A mathematical model of the boost converter in one switching cycle is established as the control model of the DC microgrid system.

3. The method according to claim 2, characterized in that The mathematical model of the boost converter in one switching cycle includes: Among them, L i,f is the inductance of the i-th boost converter, C i,f is the capacitance of the i-th boost converter, For the i-th boost converter through the inductor L f The current, i i,o 、v i,o are the output current and output voltage of the i-th boost converter in the monitoring values monitored by the sensor, d i is the duty cycle of the i-th boost converter switch, r i,d is the equivalent resistance of the i-th boost converter, V i,n is the output voltage of the distributed generator set corresponding to the i-th boost converter, subscript i = 1, 2, L, N, N is the number of boost converters, and t is a discrete time point.

4. The method according to claim 2, characterized in that The control model based on the DC microgrid system, establishing the RBF neural network DC microgrid prediction model further includes: Establish the RBF neural network DC microgrid voltage initial model and the RBF neural network DC microgrid current initial model respectively; Actual inputs of a mathematical model of a boost converter in one switching cycle under different load changes are used as input data for the RBF neural network DC microgrid voltage initial model and the RBF neural network DC microgrid current initial model respectively; Inputting the input data into the RBF neural network DC microgrid voltage initial model and the RBF neural network DC microgrid current initial model respectively to obtain output data of the two initial models; Based on the actual output of the mathematical model of the boost converter within one switching cycle under different load changes and the output data of the two initial models, the RBF neural network DC microgrid voltage initial model and the RBF neural network DC microgrid current initial model are optimized respectively to obtain the RBF neural network DC microgrid voltage prediction model and the RBF neural network DC microgrid current prediction model.

5. The method according to claim 4, characterized in that The input data is: in, is the input data of the RBF neural network DC microgrid voltage initial model, or the input data of the RBF neural network DC microgrid current initial model, For the i-th boost converter through the inductor L f The current, d i is the duty cycle of the i-th boost converter switch, subscript i = 1, 2, L, N, N is the number of boost converters; The output data is: y i,k =w i,k,1 h i,k,1 +w i,k,2 h i,k,2 +L+w i,k,j h i,k,j +L+w i,k,l h i,k,l ; Among them, when k=1, y i,k is the output data of the initial model of DC microgrid voltage of RBF neural network, w i,k,j is the weight value of the jth hidden layer neuron in the RBF neural network DC microgrid voltage initial model, h i,k,j is the output of the jth hidden layer neuron in the RBF neural network DC microgrid voltage initial model, b i,k,j is the width of the Gaussian basis function of the jth hidden layer neuron in the RBF neural network DC microgrid voltage initial model, and b i,k,j >0,c i,k,j =[c i,k,j,1 c i,k,j,2 ] T , c i,k,j is the center point vector value of the jth hidden layer neuron, c i,k,j,m is the mth component of the center point vector value of the jth hidden layer neuron, m = 1, 2, j = 1, 2, L, l, l represents the number of hidden layer neurons; Among them, when k = 2, y i,k is the output data of the initial model of the DC microgrid current using the RBF neural network, w i,k,j is the weight value of the jth hidden layer neuron in the RBF neural network DC microgrid current initial model, h i,k,j is the output of the jth hidden layer neuron in the initial DC microgrid current model of the RBF neural network, b i,k,j is the width of the Gaussian basis function of the jth hidden layer neuron in the initial model of the DC microgrid current of the RBF neural network, and b i,k,j >0,c i,k,j =[c i,k,j,1 c i,k,j,2 ] T , c i,k,j is the center point vector value of the jth hidden layer neuron, c i,k,j,m is the mth component of the center point vector value of the jth hidden layer neuron, m = 1, 2, j = 1, 2, L, l, and l is the number of hidden layer neurons.

6. The method according to claim 5, characterized in that The RBF neural network DC microgrid voltage initial model and the RBF neural network DC microgrid current initial model are optimized based on the actual output of the mathematical model of the boost converter in one switching cycle under different load changes and the output data of the two initial models, respectively, to obtain the RBF neural network DC microgrid voltage prediction model and the RBF neural network DC microgrid current prediction model further comprising: Calculating an error value between the actual output and the output data based on the actual output of the mathematical model of the boost converter in one switching cycle under different load changes and the output data of the two initial models; The Adam gradient descent method is used to optimize the model parameters of the two initial models respectively, and the RBF neural network DC microgrid voltage prediction model and the RBF neural network DC microgrid current prediction model corresponding to the minimized error value are obtained.

7. The method according to claim 6, characterized in that The calculating the error value between the actual output and the output data based on the actual output of the mathematical model of the boost converter in one switching cycle under different load changes and the output data of the two initial models further includes: The error between the actual output and the output data is calculated using the following formula: Among them, when k=1, y i,k is the output data of the initial model of DC microgrid voltage using RBF neural network, is the actual output voltage in the actual output, E i,k is the error value between the actual output voltage and the output data of the RBF neural network DC microgrid voltage initial model; when k = 2, y i,k is the output data of the initial model of the DC microgrid current using the RBF neural network, is the actual output current in the actual output, E i,k is the error value between the actual output current and the output data of the RBF neural network DC microgrid current initial model.

8. The method according to claim 1, characterized in that The super spiral sliding mode controller configured to converge the output value of the DC microgrid system according to the input parameters further comprises: Based on the input parameters, calculating the average voltage and current coordination errors of any distributed generator set in the DC microgrid system; Simulate network communication attacks and generate attack models; Based on the attack model, determining the average voltage under attack and the coordinated current under attack of any distributed generator set in the DC microgrid system under a network communication attack; Calculating a steady-state voltage average value of the DC microgrid system based on the average voltage under the attack; Analyze the dynamic change of the current coordination error under the attack to obtain a dynamic change model; Substituting the steady-state voltage average value into the dynamic change model to obtain an updated dynamic change model; Based on the updated dynamic change model, under the condition that the current cooperation error is zero, calculating the steady-state voltage deviation value of the DC microgrid system; Substituting the steady-state voltage deviation value into the updated dynamic change model to obtain a steady-state current deviation value of the DC microgrid system; Obtaining a first control variable of the boost converter according to a reference voltage of the boost converter, a voltage output by the boost converter, and a steady-state voltage deviation value; Obtaining a second controlled variable of the boost converter according to a reference current of the current coordination error, a current output by the boost converter, and a steady-state current deviation value; A super-helical sliding mode controller is set based on the first control variable, the second control variable, the sliding surface function, the reaching law and the nonlinear sigmoid function.

9. A power grid system control device considering deterministic network communication attacks, characterized in that: The device comprises: A control model building module is used to build a control model for the DC microgrid system; A prediction model establishment module is used to establish an RBF neural network DC microgrid prediction model based on the control model of the DC microgrid system; A deviation value calculation module is used to calculate the deviation value between the monitoring value of the DC microgrid system obtained by sensor monitoring and the predicted value of the RBF neural network DC microgrid prediction model; A judgment module is configured to determine whether a preset event trigger mechanism is triggered based on the deviation value and a preset threshold value, wherein the preset event trigger mechanism indicates that the DC microgrid system is subjected to a network communication attack; if so, the predicted value of the RBF neural network DC microgrid prediction model is used as an input parameter of the super spiral sliding mode controller; if not, the monitored value is used as an input parameter of the super spiral sliding mode controller; the preset event trigger mechanism includes: a preset event trigger mechanism for current network communication attacks, and a preset event trigger mechanism for voltage network communication attacks; The preset event trigger mechanism for current network communication attacks is characterized by the following formula: Among them, Θ i,I is the nonlinear time-varying trigger function for current network communication attacks, κ i,1 , κ i,2 , κ i,3 , κ i,4 、 are all constants, e i,I is the deviation value corresponding to the current when the current network communication is attacked, i i,o is the current output by the i-th boost converter in the monitoring value obtained by the sensor, is the predicted current value of the RBF neural network DC microgrid prediction model, subscript i=1,2,L,N, N is the number of boost converters, is the preset threshold for current network communication attacks, is the kth triggering moment of the i-th boost converter for the current network communication attack, is the k+1th triggering moment of the ith boost converter for the current network communication attack, inf{·} is the infimum of the set, and t is a discrete time point; The preset event trigger mechanism for voltage network communication attacks is characterized by the following formula: Among them, Θ i,V is the nonlinear time-varying trigger function for voltage network communication attacks, κ i,5 , κ i,6 , κ i,7 , κ i,8 、 is a constant, e i,V is the voltage deviation value corresponding to the voltage network communication attack, v i,o is the voltage output by the i-th boost converter in the monitoring value obtained by the sensor, is the predicted voltage value of the RBF neural network DC microgrid prediction model, subscript i=1,2,L,N, N is the number of boost converters, is the preset threshold for voltage network communication attacks, is the kth triggering moment of the i-th boost converter for voltage network communication attack, is the k+1th triggering moment of the ith boost converter against the voltage network communication attack, inf{·} is the infimum of the set, and t is a discrete time point; The setting module is used to set a super spiral sliding mode controller for converging the output value of the DC microgrid system according to the input parameters.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that: When the computer program is executed by the processor, the computer program executes the instructions of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor of a computer device, the computer program executes the instructions of the method according to any one of claims 1 to 8.

12. A computer program product, characterized in that When the computer program product is executed by a processor of a computer device, the computer program product executes the instructions of the method according to any one of claims 1 to 8.

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