Frequency response service attack detection method and device, storage medium and equipment

Abstract

The invention provides a frequency response service attack detection method and device, a storage medium and equipment. The method comprises the following steps: acquiring a frequency measurement signal and an area control error measurement signal of a power grid control area; inputting the frequency measurement signal into a trained adaptive neural fuzzy inference model to obtain an area control error prediction signal of a power grid control area; obtaining a root-mean-square error according to the regional control error measurement signal and the regional control error prediction signal; if the root-mean-square error exceeds a preset threshold value, determining that an attack behavior exists in the power grid control region, and setting a region control error as a region control error prediction signal; and repeating the steps until the operation time of the frequency response control system reaches a preset time threshold. And determining whether an attack behavior exists in the power grid control region or not by comparing the region control error prediction signal and the measurement signal of the power grid control region, and making a response to the attack behavior so as to maintain the frequency stability of the power grid control region.

Description

Technical Field

[0001] The present invention relates to the field of power system network security, and in particular to a frequency response service attack detection method, device, storage medium and equipment. Background Art

[0002] Under the architecture of smart grid, the Internet and information and communication technologies are used to build a complete power system enterprise intranet, thereby realizing the master-slave transmission, processing and control of various power information services, marking the mature application of power wide area information systems.

[0003] The frequency of the power system is mainly determined by the active power, and the stability control business between active power and frequency is called frequency control business. Frequency stability and interconnection line power stability are important indicators of power quality in multi-regional power systems. Any sudden change in load may lead to deviations in the power exchange of interconnection lines between systems and fluctuations in system frequency. At the same time, potential network attacks will also have a significant impact on system stability, and even cause system instability in severe cases. Therefore, determining whether the fluctuations in the system are caused by normal source-load fluctuations or network attacks and responding to them is the key to solving the problem of the effectiveness of frequency response commands. Summary of the invention

[0004] Based on this, the present invention provides a frequency response service attack detection method, device, storage medium and equipment, which determines whether there is an attack behavior in the power grid control area by comparing the regional control error prediction signal and the measurement signal of the power grid control area, and responds to it, thereby maintaining the frequency stability of the power grid control area.

[0005] In a first aspect, the present invention provides a frequency response service attack detection method, which is applied to a frequency response control system, comprising:

[0006] Step S1, obtaining a frequency measurement signal and a regional control error measurement signal of a power grid control area;

[0007] Step S2, inputting the frequency measurement signal into the trained adaptive neural fuzzy inference model to obtain a regional control error prediction signal of the power grid control area;

[0008] Step S3, obtaining a root mean square error according to the regional control error measurement signal and the regional control error prediction signal;

[0009] Step S4, if the root mean square error exceeds a preset threshold, there is an attack behavior in the power grid control area, and the regional control error is set as a regional control error prediction signal;

[0010] Step S5, repeating steps S1 to S4 until the operation time of the frequency response control system reaches a preset time threshold.

[0011] Furthermore, step S4 also includes:

[0012] If the root mean square error does not exceed the set threshold, there is no attack behavior in the power grid control area, and the regional control error is set as the regional control error measurement signal.

[0013] Furthermore, the adaptive neuro-fuzzy inference model includes an input layer, a fuzzification layer, a rule layer, a normalization layer and a defuzzification layer;

[0014] The specific expression of the input layer is:

[0015] ,

[0016] in, is the input variable, Frequency measurement signal for the power grid control area;

[0017] The specific expression of the data fuzzification layer is:

[0018] ,

[0019] in, is a fuzzy set, is the membership degree of the input variable to the fuzzy set, is the Gaussian membership function;

[0020] The specific expression of the fuzzy rule of the rule layer is:

[0021] Rule I: If yes ,but ,

[0022] in, is the output variable, The first undetermined parameter is is the second undetermined parameter, and the activation weight of each rule is expressed as , For the The activation weights of the rules;

[0023] The specific expression of the normalization layer is:

[0024] ,

[0025] in, is the normalized rule activation strength, is the total number of rules;

[0026] The specific expression of the defuzzification layer is:

[0027] .

[0028] Furthermore, the root mean square error is obtained according to the regional control error measurement signal and the regional control error prediction signal, and the specific expression is:

[0029] ,

[0030] in, is the root mean square error, is the regional control error prediction signal, is the regional control error measurement signal, is the number of nodes in the power grid control area.

[0031] Furthermore, the training process of the adaptive neuro-fuzzy inference model includes:

[0032] Step S301, obtaining multiple historical frequency measurement signals and multiple historical regional control error measurement signals of a power grid control area, and merging each historical frequency measurement signal and the corresponding historical regional control error measurement signal into one historical data;

[0033] Step S302, dividing the plurality of historical data into a training set and a test set according to a preset ratio;

[0034] Step S303, inputting the training set into the adaptive neural fuzzy inference model to adjust the parameters, and obtaining a preliminary adaptive neural fuzzy inference model;

[0035] Step S304, inputting the verification set into the preliminary adaptive neural fuzzy inference model to obtain a regional control error prediction signal, and obtaining a training error according to the historical regional control error measurement signal and the regional control error prediction signal;

[0036] Step S305, if the training error meets the output condition, outputting the preliminary adaptive neural-fuzzy inference model as the trained adaptive neural-fuzzy inference model;

[0037] Step S306: If the training error does not meet the output condition, return to steps S303-S304 until the training error meets the output condition.

[0038] Furthermore, the expression of the training error is:

[0039] ,

[0040] ,

[0041] in, is the training error, is the regional control error estimation signal, is the historical area control error measurement signal, are the parameters of the adjusted adaptive neuro-fuzzy inference model, are the parameters of the adaptive neuro-fuzzy inference model before adjustment, is the learning rate.

[0042] Furthermore, the attack behaviors in the power grid control area include frequency measurement data attack, regional tie line power measurement data attack and regional ACE signal attack;

[0043] The specific expression of the frequency measurement data attack is:

[0044] ,

[0045] in, For the The grid control area is Frequency measurement data of attack signal at the moment, For the The frequency deviation coefficient of each power grid control area, For the Frequency measurement signals of the power grid control area, is the first attack signal, For the The regional capacity factor of the grid control area, For the The grid control area and The power deviation of the tie lines between the control areas of the power grid;

[0046] The specific expression of the regional tie line power measurement data attack is:

[0047] ,

[0048] in, For the The grid control area and The grid control area is The regional interconnection line power measurement data of the attack signal at the moment, is the second attack signal, For the The regional capacity factor of the grid control area, For the The grid control area and The power deviation of the tie lines between the control areas of the power grid;

[0049] The specific expression of the regional ACE signal attack is:

[0050] ,

[0051] in, For the The grid control area is The ACE signal of the moment is the attack signal. For the The grid control area is Normal ACE signal at all times, This is the third attack signal.

[0052] In a second aspect, the present invention further provides a frequency response service attack detection device, which is applied to a frequency response control system and includes:

[0053] A signal acquisition module, used to acquire a frequency measurement signal and an area control error measurement signal of a power grid control area;

[0054] A signal prediction module, used for inputting the frequency measurement signal into the trained adaptive neural fuzzy inference model to obtain a regional control error prediction signal of a power grid control area;

[0055] A root mean square error calculation module, used for obtaining a root mean square error according to the regional control error measurement signal and the regional control error prediction signal;

[0056] An attack detection module, configured to, if the root mean square error exceeds a preset threshold, indicate that an attack behavior exists in the power grid control area, and set the regional control error as a regional control error prediction signal;

[0057] The running time setting module is used to repeat all the above modules until the running time of the frequency response control system reaches a preset time threshold.

[0058] In a third aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of any one of the frequency response service attack detection methods in the first aspect are implemented.

[0059] In a fourth aspect, the present invention further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it executes any one of the frequency response service attack detection methods in the first aspect.

[0060] The beneficial effects of adopting the above technical solution are as follows: this embodiment obtains the regional control error prediction signal of the power grid control area through the adaptive neural fuzzy inference model, and calculates the root mean square error of the regional control error prediction signal and the actual measurement signal of the power grid control area, and judges whether there is an attack in the power grid control area by whether the root mean square error exceeds the preset threshold, so as to make a response operation to maintain the frequency stability in the power grid control area, ensure the accuracy of the frequency response business, and improve the frequency stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below.

[0062] Figure 1 This is a schematic diagram of a frequency response service attack detection method in one embodiment of the present application;

[0063] Figure 2 This is a flow chart of a frequency response service attack detection method in one embodiment of the present application;

[0064] Figure 3 A schematic diagram of a three-region interconnected power grid system divided by an IEEE 14-node system in one embodiment of the present application;

[0065] Figure 4 A schematic diagram of the frequency control effect of each test method in an embodiment of the present application;

[0066] Figure 5 A schematic diagram of the tie line power control effect of each test method in an embodiment of the present application;

[0067] Figure 6 This is a schematic diagram of a frequency response service attack detection device in an embodiment of the present application. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In order to explain the present invention in more detail, the frequency response service attack detection method, device, storage medium and equipment provided by the present invention are specifically described below in combination with the drawings.

[0069] Unless otherwise defined, the technical terms or scientific terms used in the disclosure of this application should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, similar words such as "one", "one" or "the" do not indicate a quantitative limit, but indicate that there is at least one. Similar words such as "include" or "include" mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Similar words such as "connect" or "connected" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship can also change accordingly.

[0070] The frequency of the power system is mainly determined by the active power. The stability control business between active power and frequency is called frequency control business. Frequency stability and interconnection line power stability are important indicators of power quality in multi-regional power systems. Any sudden change in load may lead to deviations in the exchange power of interconnection lines between systems and fluctuations in system frequency. At the same time, potential network attacks will also have a significant impact on system stability, and in severe cases, even cause system instability.

[0071] In this regard, the present invention provides a frequency response service attack detection method, which determines whether there is an attack behavior in the power grid control area by comparing the regional control error prediction signal and the measurement signal of the power grid control area, and responds to it, thereby maintaining the frequency stability of the power grid control area. Figure 1 A schematic diagram of a frequency response service attack detection method is shown.

[0072] The embodiment of the present application provides an application scenario of the frequency response service attack detection method, which includes the terminal device provided in the embodiment, and the terminal device includes but is not limited to a smart phone and a computer device, wherein the computer device can be at least one of a desktop computer, a portable computer, a laptop computer, a mainframe computer, a tablet computer, etc. The user operates the terminal device to obtain an identification result of whether the charging data is subject to intrusion or tampering. For the specific process, please refer to the embodiment of the method for detecting charging data intrusion characteristics.

[0073] The frequency response service attack detection method of this embodiment is applied to a frequency response control system, which includes multiple power grid control areas, each of which is provided with a controller, and the controller controls and adjusts the output power of the generator in the power grid control area by sending a control signal. When the power grid control area is not attacked, the controller in the automatic generation control system (Automatic Generation Control, AGC) sends a normal area control error (Area Control Error, ACE) signal to maintain the overall balance of the power grid control area. The automatic generation control system can balance the power deviation of the tie lines between different power grid control areas. And make the frequency deviation in the power grid control area Keep it at 0. The automatic power generation control system receives the collected tie line power deviation and the frequency deviation of the grid control area from the distributed sensors, and based on the tie line power deviation Frequency deviation within the grid control area The Area Control Error (ACE) is calculated and sent to the controllers of each grid control area.

[0074] The specific expression of regional control error when the power grid control area is not attacked is:

[0075] ,

[0076] in, For the The grid control area is The regional control error at time, For the The frequency deviation coefficient of each power grid control area, For the The regional capacity factor of the grid control area, For the Frequency measurement signals of the power grid control area, is the total number of grid control areas.

[0077] Based on the received ACE signal, it needs to be converted into a control signal for adjusting the generator power output by the controller, so that the power deviation of the interconnection line in different areas and frequency deviation within the grid control area Keeping within the safe range, the specific expression of the above control signal is:

[0078] ,

[0079] in, It is a control signal used to adjust the power setting value of the generator set; is the proportional gain coefficient, which determines the direct impact of the ACE signal on the control output; is the integral gain coefficient, which ensures that the automatic power generation control system maintains zero steady-state error in the long term; is the differential gain coefficient, which helps to reduce overshoot and oscillation.

[0080] The frequency response service attack detection method of this embodiment specifically includes the following steps:

[0081] Step S1, obtaining a frequency measurement signal and an area control error measurement signal of a power grid control area.

[0082] Step S2, inputting the frequency measurement signal into the trained adaptive neural fuzzy inference model to obtain a regional control error prediction signal of the power grid control area.

[0083] Step S3, obtaining a root mean square error according to the regional control error measurement signal and the regional control error prediction signal.

[0084] In the above step S3, the root mean square error is obtained according to the regional control error measurement signal and the regional control error prediction signal, and the specific expression is:

[0085] ,

[0086] in, is the root mean square error, is the regional control error prediction signal, is the regional control error measurement signal, is the number of nodes in the power grid control area.

[0087] Step S4: if the root mean square error exceeds a preset threshold, there is an attack behavior in the power grid control area, and the regional control error is set as a regional control error prediction signal.

[0088] It should be noted that step S4 also includes:

[0089] If the root mean square error does not exceed the set threshold, there is no attack behavior in the power grid control area, and the regional control error is set as the regional control error measurement signal.

[0090] In this embodiment, the threshold value used to determine whether there is an attack behavior can be set to .

[0091] Step S5, repeating steps S1 to S4 until the operation time of the frequency response control system reaches a preset time threshold.

[0092] The preset time threshold may be a rated operating time of the frequency response control system.

[0093] Through the above steps, it is possible to determine whether the power grid control area is under attack and make response operations to maintain frequency stability in the power grid control area.

[0094] The adaptive neuro-fuzzy inference model (ANFIS) used in the above step S2 includes an input layer, a fuzzification layer, a rule layer, a normalization layer and a defuzzification layer. The adaptive neuro-fuzzy inference model (ANFIS) combines fuzzy logic (FL) with the training capabilities of artificial neural networks (ANNs), and uses membership functions (MFs) to construct input-output pairs through the "if-then" rules of fuzzy logic to provide a suitable input-output mapping relationship. In this embodiment, the adaptive neuro-fuzzy inference model has one input, namely the frequency measurement signal of the power grid control area, and one output, namely the regional control error prediction signal of the power grid control area. The various modules of the adaptive neuro-fuzzy inference model are specifically as follows:

[0095] (1) The specific expression of the input layer is:

[0096] ,

[0097] in, is the input variable, It is the frequency measurement signal of the power grid control area.

[0098] (2) In the fuzzification layer, input variables The membership function is used to map the input variable to the membership degree of the corresponding fuzzy set. The specific expression of the fuzzification layer is:

[0099] ,

[0100] in, is a fuzzy set, is the membership degree of the input variable to the fuzzy set, is the Gaussian membership function.

[0101] (3) In the rule layer, fuzzy rules are generated based on the membership of the input. For a case with only one input variable and one output variable, the specific expression of the fuzzy rule in the rule layer is:

[0102] Rule I: If yes ,but ,

[0103] in, is the output variable, The first undetermined parameter is is the second undetermined parameter, and the activation weight of each rule is expressed as , For the The activation weights of the rules.

[0104] (4) In the normalization layer, the activation strengths of all rules are normalized so that the sum of the activation strengths of all rules is 1. The specific expression of the normalization layer is:

[0105] ,

[0106] in, is the normalized rule activation strength, is the total number of rules.

[0107] (5) In the defuzzification layer, the output of each rule is weighted averaged according to the normalized activation strength to obtain the final output; the specific expression of the defuzzification layer is:

[0108] .

[0109] The training process for the above adaptive neuro-fuzzy inference model includes:

[0110] Step S301 : acquiring a plurality of historical frequency measurement signals and a plurality of historical regional control error measurement signals of a power grid control area, and merging each historical frequency measurement signal and the corresponding historical regional control error measurement signal into one historical data.

[0111] Step S302, divide the plurality of historical data into a training set and a test set according to a preset ratio. In this embodiment, the preset ratio can be set to training set: test set = 80%: 20%.

[0112] Step S303, inputting the training set into the adaptive neural fuzzy inference model to adjust the parameters, and obtaining a preliminary adaptive neural fuzzy inference model.

[0113] In this embodiment, the historical frequency measurement signal of the training set is used as the input signal, and the regional control error prediction signal is obtained as the output signal.

[0114] Step S304: input the verification set into the preliminary adaptive neural fuzzy inference model to obtain a regional control error prediction signal, and obtain a training error according to the historical regional control error measurement signal and the regional control error prediction signal.

[0115] Step S305: if the training error satisfies the output condition, the preliminary adaptive neural-fuzzy inference model is output as the trained adaptive neural-fuzzy inference model.

[0116] Step S306: If the training error does not meet the output condition, return to steps S303-S304 until the training error meets the output condition.

[0117] in,

[0118] ,

[0119] ,

[0120] in, is the training error, is the regional control error estimation signal, is the historical area control error measurement signal, are the parameters of the adjusted adaptive neuro-fuzzy inference model, are the parameters of the adaptive neuro-fuzzy inference model before adjustment, is the learning rate. The output condition of the training error in the above step S306 is The minimum value is reached and it is continuously updated using the gradient descent method.

[0121] It should be noted that, in this embodiment, the attack behaviors in the power grid control area include frequency measurement data attack, regional tie line power measurement data attack and regional ACE signal attack.

[0122] Among them, the specific expression of frequency measurement data attack is:

[0123] ,

[0124] For the The grid control area is Frequency measurement data of attack signal at the moment, For the The frequency deviation coefficient of each power grid control area, For the Frequency measurement signals of the power grid control area, is the first attack signal, For the The regional capacity factor of the grid control area, For the The grid control area and The power deviation of the tie lines between the control areas of the power grid is is the total number of power grid control areas;

[0125] The specific expression of the regional tie line power measurement data attack is:

[0126] ,

[0127] For the The grid control area and The grid control area is The regional interconnection line power measurement data of the attack signal at the moment, is the second attack signal, For the The regional capacity factor of the grid control area, For the The grid control area and The power deviation of the tie lines between the control areas of the power grid;

[0128] The specific expression of regional ACE signal attack is:

[0129] ,

[0130] For the The grid control area is The ACE signal of the moment is the attack signal. For the The grid control area is Normal ACE signal at all times, This is the third attack signal.

[0131] To better illustrate the frequency response service attack detection method of this embodiment, the IEEE 14-node system is taken as an example. Figure 3 As shown, the IEEE 14-bus system is divided into three regional interconnected power grid systems, and the parameters of the generators and tie lines are shown in Table 1:

[0132]

[0133] Table 1 Parameter information of the three-area interconnected power system of the IEEE14-bus system

[0134] From the attacker's perspective, assuming that at t=0s, the frequency signal in area 1 is tampered with by a 10% offset signal, causing a frequency drop. The attack lasts for 3s, and the steady-state frequencies and interconnection line powers in the three areas decrease to a certain extent.

[0135] On this basis, a distributed frequency response strategy based on PID is used, and the error of ACE is used as the input of the controller. The transmission substations of the three regions are nodes 2, 6 and 9, and the control center is located at node 1. The frequency and tie line power information of the three regions are transmitted to the main station through nodes 2, 6 and 9 respectively.

[0136] Conventional frequency response strategies use wrong frequency measurement signals and ACE calculation results, which results in the controlled quantity in the frequency response business not being able to timely reflect the disturbances to the system, resulting in obvious overshoot and poor system stability. Under the detection method of the present invention, the inferred ACE results can be used for compensation control. The simulation results are shown in the attached figure. Figure 4 , Attachment Figure 5 As shown in Table 2, it can be seen that under the action of the compensation detection method, the frequency and the tie line power can recover quickly, and the overshoot and adjustment time are greatly reduced, which greatly improves the control system effect.

[0137]

[0138] Table 2 Comparison of control effects under various detection methods

[0139] After designing an ANFIS-based estimation detection system, it is important to evaluate the effectiveness of the model by comparing the dynamic behavior of the frequency response traffic. and The ANFIS unit was trained using the dataset. The actual data was compared with the training data. After testing ANFIS, the comparison of the actual data with the network data showed that the training process was carried out correctly, with an average RMSE of , capable of maintaining power frequency and power stability in most systems.

[0140] It should be understood that although the Figure 1 The steps in the flowchart are shown in sequence according to the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least part of the steps in the drawings may include multiple sub-steps or sub-stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0141] The above embodiments of the present invention describe in detail the frequency response service attack detection method. The above methods disclosed in the present invention can be implemented by various devices. Therefore, the present invention also discloses a frequency response service attack detection device. Figure 6 , specific embodiments are given below to explain in detail.

[0142] A signal acquisition module 401 is used to acquire a frequency measurement signal and an area control error measurement signal of a power grid control area;

[0143] A signal prediction module 402, configured to input the frequency measurement signal into the trained adaptive neural fuzzy inference model to obtain a regional control error prediction signal of a power grid control area;

[0144] A root mean square error calculation module 403, used to obtain a root mean square error according to the regional control error measurement signal and the regional control error prediction signal;

[0145] An attack detection module 404 is used to set the regional control error as a regional control error prediction signal if the root mean square error exceeds a preset threshold value, indicating that there is an attack behavior in the power grid control area;

[0146] The running time setting module 405 is used to repeat all the above modules until the running time of the frequency response control system reaches a preset time threshold.

[0147] For the frequency response service attack detection device, all the above-mentioned methods can be referred to, and no further details will be given here. Each module in the above-mentioned device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor of the terminal device in the form of hardware, or can be stored in the memory of the terminal device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0148] In one embodiment, the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned frequency response service attack detection method are implemented.

[0149] The computer-readable storage medium may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM (erasable programmable read-only memory), a hard disk or a ROM. Optionally, the computer-readable storage medium includes a non-transitory computer-readable storage medium. The computer-readable storage medium has a storage space for program codes for executing any method step of the above method. These program codes may be read from or written into one or more computer program products, and the program codes may be compressed in an appropriate form.

[0150] In one embodiment, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the above-mentioned frequency response service attack detection method when executing the computer program.

[0151] The computer device includes a memory, a processor, and one or more computer programs, wherein the one or more computer programs can be stored in the memory and configured to be executed by one or more processors, and the one or more application programs are configured to execute the above-mentioned frequency response service attack detection method.

[0152] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect the various parts of the entire computer device, and executes various functions of the computer device and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory. Optionally, the processor can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor can integrate one or more combinations of a central processing unit (Central Processing Unit, CPU), a reporting verifier (Graphics Processing Unit, GPU) for buried data, and a modem. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing display content; and the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor, but may be implemented separately through a communication chip.

[0153] The memory may include a random access memory (RAM) or a read-only memory (ROM). The memory may be used to store instructions, programs, codes, code sets or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc. The data storage area may also store data created by the terminal device during use, etc.

[0154] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A frequency response service attack detection method, the frequency response service attack detection method is applied to a frequency response control system, characterized in that: include: Step S1, obtaining a frequency measurement signal and a regional control error measurement signal of a power grid control area; Step S2, inputting the frequency measurement signal into the trained adaptive neural fuzzy inference model to obtain a regional control error prediction signal of the power grid control area; Step S3, obtaining a root mean square error according to the regional control error measurement signal and the regional control error prediction signal; Step S4, if the root mean square error exceeds a preset threshold, there is an attack behavior in the power grid control area, and the regional control error is set as a regional control error prediction signal; Step S5, repeating steps S1 to S4 until the operation time of the frequency response control system reaches a preset time threshold.

2. The frequency response service attack detection method according to claim 1, characterized in that: Step S4 also includes: If the root mean square error does not exceed the set threshold, there is no attack behavior in the power grid control area, and the regional control error is set as the regional control error measurement signal.

3. The frequency response service attack detection method according to claim 2, characterized in that: The adaptive neuro-fuzzy inference model includes an input layer, a fuzzification layer, a rule layer, a normalization layer and a defuzzification layer; The specific expression of the input layer is: ， in, is the input variable, Frequency measurement signal for the power grid control area; The specific expression of the fuzzy layer is: ， in, is a fuzzy set, is the membership degree of the input variable to the fuzzy set, is the Gaussian membership function; The specific expression of the fuzzy rule of the rule layer is: Rule I: If yes ,but , in, is the output variable, The first undetermined parameter is is the second undetermined parameter, and the activation weight of each rule is expressed as , For the The activation weights of the rules; The specific expression of the normalization layer is: ， in, is the normalized rule activation strength, is the total number of rules; The specific expression of the defuzzification layer is: 。 4. The frequency response service attack detection method according to claim 3, characterized in that: The root mean square error is obtained according to the regional control error measurement signal and the regional control error prediction signal, and the specific expression is: ， in, is the root mean square error, is the regional control error prediction signal, is the regional control error measurement signal, is the number of nodes in the power grid control area.

5. The frequency response service attack detection method according to claim 4, characterized in that: The training process of the adaptive neuro-fuzzy inference model includes: Step S301, obtaining multiple historical frequency measurement signals and multiple historical regional control error measurement signals of a power grid control area, and merging each historical frequency measurement signal and the corresponding historical regional control error measurement signal into one historical data; Step S302, dividing the plurality of historical data into a training set and a test set according to a preset ratio; Step S303, inputting the training set into the adaptive neural fuzzy inference model to adjust the parameters, and obtaining a preliminary adaptive neural fuzzy inference model; Step S304, inputting the verification set into the preliminary adaptive neural fuzzy inference model to obtain a regional control error prediction signal, and obtaining a training error according to the historical regional control error measurement signal and the regional control error prediction signal; Step S305, if the training error meets the output condition, outputting the preliminary adaptive neural-fuzzy inference model as the trained adaptive neural-fuzzy inference model; Step S306: If the training error does not meet the output condition, return to steps S303-S304 until the training error meets the output condition.

6. The frequency response service attack detection method according to claim 5, characterized in that: The expression of the training error is: ， ， in, is the training error, is the regional control error estimation signal, is the historical area control error measurement signal, is the sample size, are the parameters of the adjusted adaptive neuro-fuzzy inference model, are the parameters of the adaptive neuro-fuzzy inference model before adjustment, is the learning rate.

7. The frequency response service attack detection method according to claim 6, characterized in that: Attacks in the power grid control area include frequency measurement data attacks, regional tie line power measurement data attacks, and regional ACE signal attacks; The specific expression of the frequency measurement data attack is: ， in, For the The grid control area is Frequency measurement data of attack signal at the moment, For the The frequency deviation coefficient of each power grid control area, For the Frequency measurement signals of the power grid control area, is the first attack signal, For the The regional capacity factor of the grid control area, For the The grid control area and The power deviation of the tie lines between the control areas of the power grid is is the total number of power grid control areas; The specific expression of the regional tie line power measurement data attack is: ， in, For the The grid control area and The grid control area is The regional interconnection line power measurement data of the attack signal at the moment, is the second attack signal, For the The regional capacity factor of the grid control area, For the The grid control area and The power deviation of the tie lines between the control areas of the power grid; The specific expression of the regional ACE signal attack is: ， in, For the The grid control area is The ACE signal of the moment is the attack signal. For the The grid control area is Normal ACE signal at all times, This is the third attack signal.

8. A frequency response service attack detection device, the frequency response service attack detection device is applied to a frequency response control system, characterized in that: include: A signal acquisition module, used to acquire a frequency measurement signal and an area control error measurement signal of a power grid control area; A signal prediction module, used for inputting the frequency measurement signal into the trained adaptive neural fuzzy inference model to obtain a regional control error prediction signal of a power grid control area; A root mean square error calculation module, used for obtaining a root mean square error according to the regional control error measurement signal and the regional control error prediction signal; An attack detection module, configured to, if the root mean square error exceeds a preset threshold, indicate that an attack behavior exists in the power grid control area, and set the regional control error as a regional control error prediction signal; The running time setting module is used to repeat all the above modules until the running time of the frequency response control system reaches a preset time threshold.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the frequency response service attack detection method according to any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it performs the frequency response service attack detection method according to any one of claims 1 to 7.

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