Power grid safety monitoring system based on threat alarm semantic knowledge mining transfer learning

Abstract

The invention discloses a power grid safety monitoring system based on threat alarm semantic knowledge mining transfer learning, and particularly relates to the field of power grid safety. Comprising a threat alarm semantic migration learning module, a monitoring area division module, a power grid security data acquisition module, a power grid security data preprocessing module, a power grid security data analysis module, a power grid security evaluation index calculation module, a power grid security monitoring judgment module, a threat alarm data processing and analysis module and a threat alarm monitoring judgment module. And the man-machine interaction module is used for establishing a preprocessing model, an analysis model and a power grid safety evaluation index calculation model by collecting power stability data and environment safety data of the monitoring sub-region, calculating a power stability state proportionality coefficient, an environment safety state proportionality coefficient and a power grid safety evaluation index, judging the state of the monitoring sub-region, and outputting a power grid safety evaluation result. The monitoring sub-areas in the abnormal state are optimized in time, and an important guarantee is provided for taking measures in time to avoid safety accidents.

Description

Technical Field

[0001] The present invention relates to the field of power grid security technology, and more specifically, to a power grid security monitoring system based on threat warning semantic knowledge mining and transfer learning. Background Art

[0002] The modern power grid is a highly complex and interconnected system consisting of multiple links such as power generation, transmission, distribution and consumption. This complexity makes it easy for the power grid to trigger a chain reaction when it is subjected to various internal and external threats, such as natural disasters, equipment failures and human attacks, leading to large-scale power outages and even system collapse. Therefore, it is particularly important to conduct real-time and comprehensive security monitoring of the power grid.

[0003] The traditional power grid safety monitoring system includes a data acquisition module, a data analysis module and a human-computer interaction module. The data acquisition module collects power grid safety data through existing tools, including electric energy indicators such as current, voltage, and frequency; the data analysis module is used to analyze the collected data to obtain power-related parameters; the human-computer interaction module is used to display and store the analyzed power-related parameters, and the staff handles the unsafe points of power grid monitoring based on the displayed results.

[0004] However, there are still some shortcomings in its actual use. First, due to the complexity of the power grid and the diversity of threats, the traditional power grid security monitoring system can no longer meet the needs. Therefore, it is necessary to introduce advanced technical means to improve the intelligence level and response speed of power grid security monitoring. For example, threat alarm semantic transfer learning can analyze and learn a large amount of historical data, and then build a more comprehensive and accurate security monitoring model; secondly, the data collected by the traditional power grid security monitoring system is often incomplete, and a lot of computational analysis is required to obtain the analysis results, which takes a lot of time and effort. Therefore, it is necessary to establish a more efficient and accurate model to improve the efficiency of power grid security monitoring. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a power grid security monitoring system based on threat alarm semantic knowledge mining and transfer learning, and solves the problems raised in the above-mentioned background technology through the following scheme.

[0006] To achieve the above object, the present invention provides the following technical solution: a power grid security monitoring system based on threat warning semantic knowledge mining and transfer learning, comprising:

[0007] The threat warning semantic transfer learning module is used to migrate a security monitoring model trained in a power system to a target security monitoring system that needs to be applied, and to perform fine-tuning to adapt to the new data set;

[0008] The monitoring area division module is used to deploy monitoring equipment on key nodes and important equipment in the power grid security monitoring area after transfer learning, establish a complete power grid security monitoring network, and determine the key nodes and important equipment where the monitoring equipment is deployed as each monitoring sub-area, which are numbered 1, 2, ..., i, ..., n in sequence;

[0009] The power grid safety data acquisition module is used to collect various types of data in each sub-monitoring area, including a power stability data acquisition unit and an environmental safety data acquisition unit, and transmit the collected data to the power grid safety data preprocessing module;

[0010] The power grid safety data preprocessing module is used to preprocess the data collected in the power stability data collection module, including a power stability preprocessing unit and an environmental safety preprocessing unit, obtaining a voltage stability coefficient, a current stability coefficient and a frequency stability coefficient through the power stability preprocessing unit, obtaining an environmental temperature coefficient and an environmental humidity coefficient through the environmental safety preprocessing unit, and transmitting the preprocessed data to the power grid safety data analysis module;

[0011] The power grid security data analysis module is used to analyze the data preprocessed in the power stability data preprocessing module, including a power stability analysis unit and an environmental safety analysis unit, and transmit the analyzed data to the power grid security assessment index calculation module;

[0012] The power grid security assessment index calculation module is used to establish a power grid security assessment index calculation model, import the power stability state proportional coefficient and the environmental safety state proportional coefficient into the power grid security assessment index calculation model, calculate the power grid security assessment index, and transmit the analysis data to the power grid security monitoring and judgment module;

[0013] The power grid security detection judgment module is used to judge the data transmitted by the power grid security detection comprehensive analysis module, screen the monitoring sub-areas in the normal working state and the abnormal working state in the monitoring network, and count the numbers of the monitoring sub-areas in the normal working state and the abnormal working state in the monitoring network;

[0014] The threat warning data processing and analysis module is used to establish a threat warning data processing and analysis model, including a threat warning data processing model and a threat warning data analysis model, and to process and analyze the threat warning data;

[0015] The threat alarm monitoring and judgment module is used to establish a preset value of the threat alarm monitoring efficiency evaluation index, judge the threat alarm monitoring efficiency evaluation index of the specified monitoring area according to the preset value of the threat alarm monitoring efficiency evaluation index, and send a signal to the human-computer interaction module according to the judgment result;

[0016] The human-computer interaction module is used to receive the threat alarm warning instruction transmitted by the threat alarm monitoring and judgment module, and display the threat alarm warning instruction on the terminal display screen, and at the same time, perform corresponding warning reminders according to the received threat alarm warning instruction.

[0017] Preferably, the security monitoring model in the threat alarm semantic transfer learning module learns rich security knowledge and threat alarm semantic information based on training using a large amount of data; the target security monitoring system that needs to be applied faces challenges such as data scarcity or the emergence of new threats, and transfer learning is required; during the migration process, the security monitoring model and the data of the target security monitoring system that needs to be applied need to be cleaned, denoised, and standardized to ensure the quality and consistency of the data; based on the similarity between the security monitoring model and the target security monitoring system that needs to be applied and the availability of data, model-based migration is performed, and the data of the target security monitoring system is used for fine-tuning, and the parameters or structure of the model are adjusted during the fine-tuning process to adapt to the new data set of the target security monitoring system; the migrated security monitoring model is evaluated using the test set of the target security monitoring system, and the model is further optimized and adjusted according to the evaluation results to improve its accuracy; the optimized security monitoring model is deployed to the actual security monitoring system for real-time monitoring and identification of potential threats.

[0018] Preferably, the power stability data acquisition unit in the power grid safety data acquisition module is used to collect power stability data of each sub-monitoring area, including voltage stability data, current stability data, and power stability data. The voltage stability data includes voltage effective value, voltage harmonic content, fluctuating voltage and voltage imbalance, which are respectively recorded as Uc, Uh, Uf and Ue; the current stability data includes current effective value, current harmonic content, fluctuating current and current imbalance, which are respectively recorded as Ic, Ih, If and Ie; the power stability data includes post-accident steady-state power, fault impact power and post-accident static stable maximum power, which are respectively recorded as Ps, Pb and Pm; the environmental safety data acquisition unit is used to collect environmental safety data of each monitoring sub-area, including ambient temperature data and ambient humidity data. The ambient temperature data includes insulation temperature and temperature gradient change rate, which are respectively recorded as Tc and Tl; the internal environment data includes relative humidity and non-condensing humidity change rate, which are respectively recorded as Hc and Hl.

[0019] Preferably, the power stability preprocessing unit in the power grid security data preprocessing module is used to establish a power stability preprocessing model, import the data transmitted in the power grid security data acquisition module into the power stability preprocessing model, and obtain the voltage stability coefficient, current stability coefficient and frequency stability coefficient. The specific calculation formula of the voltage stability coefficient is:

[0020]

[0021] Among them Ur i represents the voltage stability coefficient of the ith monitoring sub-area, Uc i Indicates the effective value of the voltage in the ith monitoring sub-area, Uc 允 Indicates the maximum allowable value of the effective value of the power grid safety monitoring voltage, Ue i Indicates the voltage unbalance degree of the ith monitoring sub-area, Uh i represents the voltage harmonic content of the ith monitoring sub-area, Uf i represents the voltage fluctuation of the ith monitoring sub-area;

[0022] The specific calculation formula of the current stability coefficient is:

[0023]

[0024] Among them, Ir i represents the current stability coefficient of the ith monitoring sub-area, Ic i represents the effective value of the current in the ith monitoring sub-area, Ic 允 Indicates the maximum allowable value of the effective value of the power grid safety monitoring current, Ie i Indicates the current unbalance degree of the i-th monitoring sub-area, Ih i Indicates the current harmonic content of the i-th monitoring sub-area, If i represents the current fluctuation of the i-th monitoring sub-area;

[0025] The specific calculation formula of the frequency stability coefficient is:

[0026]

[0027] Among them, Pr i represents the frequency stability coefficient of the ith monitoring sub-area, Ps i represents the post-accident steady-state power of the ith monitoring sub-area, Ps 标 represents the standard post-accident steady-state power for power grid security monitoring, Pb i represents the fault impulse power of the ith monitoring sub-area, Pb max Indicates the maximum fault impact power, Pm i Pm represents the maximum static stable power after the accident in the ith monitoring sub-area. 标 It represents the standard post-accident static stability maximum power of power grid security monitoring.

[0028] Preferably, the environmental safety preprocessing unit in the power grid safety data preprocessing module is used to establish an environmental safety preprocessing model, import the data transmitted in the power grid safety data acquisition module into the environmental safety preprocessing model, and obtain the environmental temperature stability coefficient and the environmental humidity stability coefficient. The specific analysis formula of the environmental temperature stability coefficient is:

[0029]

[0030] where Tr i represents the ambient temperature stability coefficient of the ith monitoring sub-area, Tc i represents the adiabatic temperature of the ith monitoring sub-area, Tl i represents the temperature gradient change rate of the i-th monitoring sub-area;

[0031] The specific analysis formula of the environmental humidity stability coefficient is:

[0032]

[0033] Among them, Hr i represents the environmental humidity stability coefficient of the ith monitoring sub-area, Hc i represents the relative humidity of the ith monitoring sub-area, Hl i Represents the non-condensing humidity change rate of the i-th monitoring sub-area.

[0034] Preferably, the power stability analysis unit in the power grid security data analysis module is used to establish a power stability analysis model, import the data transmitted in the power stability preprocessing module into the power stability data analysis model, and obtain the power stability state proportional coefficient. The specific calculation formula is: i =Ur i ×Ir i ×Pr i , where φ i represents the power stability proportionality coefficient of the ith sub-region, Ur i represents the voltage stability coefficient of the ith monitoring sub-area, Ir i represents the current stability coefficient of the ith monitoring sub-area, Pr i represents the frequency stability coefficient of the ith monitoring sub-area; the environmental safety analysis unit is used to establish an environmental safety analysis model, import the data transmitted in the power stability preprocessing module into the environmental safety data analysis model, and obtain the environmental safety state proportional coefficient. The specific calculation formula is: ψ i =Tr i ×Hr i , where ψ i represents the environmental safety state proportional coefficient of the i-th sub-area, Tr i represents the ambient temperature stability coefficient of the ith monitoring sub-area, Hri Represents the environmental humidity stability coefficient of the i-th monitoring sub-area.

[0035] Preferably, the power grid security assessment index is obtained by establishing a power grid security assessment index calculation model, and the specific analysis formula of the power grid security assessment index is:

[0036] Υ i =log2(1+φ i )+log3(1+ψ i )

[0037] where Y i represents the power grid security assessment index of the ith monitoring sub-area, φ i represents the power stability proportionality coefficient of the ith sub-region, ψ i Represents the environmental safety status proportional coefficient of the i-th sub-area.

[0038] Preferably, the power grid security monitoring and judgment module compares the power grid security assessment index corresponding to each monitoring sub-area in the monitoring network with the preset standard power grid security assessment index corresponding to each monitoring sub-area. If the power grid security assessment index corresponding to a monitoring sub-area is greater than or equal to the preset standard power grid security assessment index of the normal working state, it indicates that the monitoring sub-area is in a normal working state. Otherwise, it indicates that the monitoring sub-area is in an abnormal working state. The system then detects the existence of an abnormal situation that affects the normal operation of the power grid, and screens and counts the monitoring sub-areas in the abnormal working state and the monitoring sub-areas in the normal working state according to the working state of the monitoring sub-area, maintains data collection and analysis of the monitoring sub-areas in the normal working state, and records the monitoring sub-areas in the abnormal working state as designated monitoring sub-areas, and numbers them in sequence as 1, 2, ..., j, ..., k.

[0039] Preferably, the threat alarm data processing model is based on each detection sub-area in an abnormal state, collects historical threat alarm data through the power grid security monitoring system, cleans the collected data, and uses a cluster analysis method to perform defense measures on the cluster analysis results, and obtains the threat alarm recognition rate, emergency response speed and threat alarm protection rate of each designated monitoring sub-area, which are respectively recorded as Er, Et and Ed; the threat alarm data analysis model is used to analyze the results after system identification and protection, and obtain the threat alarm monitoring efficiency evaluation index, and its specific analysis formula is:

[0040]

[0041] Where η represents the threat monitoring efficiency evaluation index of the specified monitoring area, Er j represents the threat alarm recognition rate of the jth specified monitoring sub-area, Ed jrepresents the threat alarm protection rate of the jth designated monitoring sub-area, Et j Represents the emergency response speed of the jth designated monitoring sub-area.

[0042] Preferably, the threat alarm monitoring judgment module compares the threat alarm monitoring efficiency evaluation index of the specified monitoring area with the preset value η of the threat alarm monitoring efficiency evaluation index. D For comparison, if η D ≤η, indicating that the designated monitoring area has good threat warning processing capabilities. D >η, indicating that the designated monitoring area has poor threat warning processing capability, and a threat warning instruction is issued to the human-computer interaction module.

[0043] Technical effects and advantages of the present invention:

[0044] 1. The present invention transfers the target power grid security monitoring model through the threat alarm semantic transfer learning module, and provides a more refined mathematical model for subsequent power grid security monitoring; the monitoring area is divided into multiple monitoring sub-areas through the monitoring area division module, which can realize the refined monitoring of power grid security. The data of each monitoring sub-area is carried out independently, so that the system can more accurately capture the subtle changes during monitoring; the power grid security data acquisition module collects the power stability data and environmental safety data of each monitoring sub-area, which are important bases for evaluating power grid security, and provide data support for subsequent data analysis and warnings; the power grid security data preprocessing module preprocesses the data in the power grid security data acquisition module, establishes a preprocessing model, simplifies the complex and changeable data, reduces the system operation load, and improves the system operation efficiency; the power grid security data analysis module further analyzes the data in the power grid security data preprocessing module, establishes an analysis model, and obtains the power stability state proportional coefficient and the environmental safety state proportional coefficient by calculation, which is of great significance for evaluating the safety and stability of power grid operation;

[0045] 2. A power grid security assessment index calculation model is established through the power grid security assessment index calculation module, which can comprehensively reflect the overall operating status of the power grid; the overall operating status of the power grid is judged through the power grid security monitoring and judgment module, and the monitoring sub-areas in abnormal status and normal status are judged, and the monitoring sub-areas in abnormal status are further analyzed; the threat alarm data processing and analysis module and the threat alarm monitoring and judgment module are used to further process, analyze and judge the designated monitoring sub-areas in abnormal status, and timely optimize the monitoring sub-areas in abnormal status, which provides important guarantees for taking timely measures to avoid the occurrence of safety accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0047] 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 of 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.

[0048] As attached Figure 1 The power grid security monitoring system shown is based on threat alarm semantic knowledge mining and transfer learning, including a threat alarm semantic transfer learning module, a monitoring area division module, a power grid security data acquisition module, a power grid security data preprocessing module, a power grid security data analysis module, a power grid security assessment index calculation module, a power grid security monitoring and judgment module, a threat alarm data processing and analysis module, a threat alarm monitoring and judgment module, and a human-computer interaction module.

[0049] The output end of the threat alarm semantic transfer learning module is telecom connected to the input end of the monitoring area division module, the output end of the monitoring area division module is telecom connected to the input end of the power grid security data acquisition module, the output end of the power grid security data acquisition module is telecom connected to the input end of the power grid security data preprocessing module, the output end of the power grid security data preprocessing module is telecom connected to the input end of the power grid security data analysis module, the output end of the power grid security data analysis module is telecom connected to the input end of the power grid security assessment index calculation module, the output end of the power grid security assessment index calculation module is telecom connected to the input end of the power grid security data acquisition module, the output end of the power grid security data acquisition module is telecom connected to the input end of the power grid security monitoring and judgment module, the output end of the power grid security monitoring and judgment module is telecom connected to the input end of the threat alarm data processing and analysis module, the output end of the threat alarm data processing and analysis module is telecom connected to the input end of the threat alarm monitoring and judgment module, and the output end of the threat alarm monitoring and judgment module is telecom connected to the input end of the human-computer interaction module.

[0050] The threat warning semantic transfer learning module is used to migrate a security monitoring model trained in a power system to a target security monitoring system that needs to be applied, and to perform fine-tuning to adapt to a new data set.

[0051] In this embodiment, it is necessary to specifically explain that the security monitoring model learns rich security knowledge and threat warning semantic information based on training using a large amount of data; the target security monitoring system to be applied faces challenges such as data scarcity or the continuous emergence of new threats, and transfer learning is required; during the migration process, the data of the security monitoring model and the target security monitoring system to be applied need to be cleaned, denoised, and standardized to ensure the quality and consistency of the data; based on the similarity between the security monitoring model and the target security monitoring system to be applied and the availability of data, a model-based migration is performed, and the data of the target security monitoring system is used for fine-tuning, and the parameters or structure of the model are adjusted during the fine-tuning process to adapt to the new data set of the target security monitoring system; the migrated security monitoring model is evaluated using the test set of the target security monitoring system, and the model is further optimized and adjusted based on the evaluation results to improve its accuracy; the optimized security monitoring model is deployed to the actual security monitoring system for real-time monitoring and identification of potential threats.

[0052] The monitoring area division module is used to deploy monitoring equipment on key nodes and important equipment in the power grid security monitoring area after transfer learning, establish a complete power grid security monitoring network, and determine the key nodes and important equipment where the monitoring equipment is deployed as monitoring sub-areas, numbered 1, 2, ..., i, ..., n in sequence.

[0053] In this embodiment, it should be specifically explained that the monitoring equipment can collect power grid operation data in real time and accurately. Common monitoring equipment includes sensors, data collectors, and monitoring instruments. The key nodes and important equipment in the power grid security monitoring area after transfer learning usually include high-voltage substations, intersections of transmission lines, and important load access points. These nodes and equipment undertake important transmission and distribution tasks in the power grid. Once a fault or abnormality occurs, it will have a major impact on the overall operation of the power grid.

[0054] The power grid safety data acquisition module is used to collect various types of data in each sub-monitoring area, including a power stability data acquisition unit and an environmental safety data acquisition unit, and transmits the collected data to the power grid safety data preprocessing module.

[0055] In this embodiment, it should be specifically explained that the power stability data acquisition unit is used to collect power stability data of each sub-monitoring area, including voltage stability data, current stability data, and power stability data. The voltage stability data includes voltage effective value, voltage harmonic content, fluctuating voltage and voltage imbalance, which are respectively denoted as Uc, Uh, Uf and Ue; the current stability data includes current effective value, current harmonic content, fluctuating current and current imbalance, which are respectively denoted as Ic, Ih, If and Ie; the power stability data includes steady-state power after the accident, fault impact power and static stable maximum power after the accident, which are respectively denoted as Ps, Pb and Pm.

[0056] Furthermore, the voltage harmonic content and the current harmonic content are obtained by sampling and digitizing the current signal through fast Fourier transform to obtain the frequency and amplitude of each harmonic, and the percentage of the fundamental amplitude is calculated to obtain the harmonic content; the fluctuating voltage and the fluctuating current are measured in each monitoring area by a voltage fluctuation recorder and a current fluctuation recorder respectively, and the fluctuating voltage and the fluctuating current are obtained by the square demodulation detection method; the voltage unbalance and the current unbalance are measured by a power quality analyzer; the steady-state power after the accident is the power value in the new steady state reached by the system after the fault or disturbance; the fault impact power is the power peak value determined by the transient energy accumulated during the fault; the static stable maximum power after the accident is the maximum power that the system can transmit while maintaining static stability after the fault.

[0057] In this embodiment, it should be specifically explained that the environmental safety data acquisition unit is used to collect environmental safety data of each monitoring sub-area, including environmental temperature data and environmental humidity data. The environmental temperature data includes the insulation temperature and the temperature gradient change rate, which are respectively denoted as Tc and Tl. The internal environment data includes the relative humidity and the non-condensing humidity change rate, which are respectively denoted as Hc and Hl.

[0058] The power grid safety data preprocessing module is used to preprocess the data collected in the power stability data acquisition module, and includes a power stability preprocessing unit and an environmental safety preprocessing unit. The voltage stability coefficient, current stability coefficient and frequency stability coefficient are obtained through the power stability preprocessing unit, and the ambient temperature coefficient and ambient humidity coefficient are obtained through the environmental safety preprocessing unit, and the preprocessed data is transmitted to the power grid safety data analysis module.

[0059] In this embodiment, it should be specifically explained that the power stability preprocessing unit is used to establish a power stability preprocessing model, import the data transmitted in the power grid safety data acquisition module into the power stability preprocessing model, and obtain the voltage stability coefficient, current stability coefficient and frequency stability coefficient. The specific calculation formula of the voltage stability coefficient is:

[0060]

[0061] Among them Ur i represents the voltage stability coefficient of the ith monitoring sub-area, Uc i Indicates the effective value of the voltage in the ith monitoring sub-area, Uc 允 Indicates the maximum allowable value of the effective value of the power grid safety monitoring voltage, Ue i Indicates the voltage unbalance degree of the ith monitoring sub-area, Uh i represents the voltage harmonic content of the ith monitoring sub-area, Uf i represents the voltage fluctuation of the ith monitoring sub-area;

[0062] The specific calculation formula of the current stability coefficient is:

[0063]

[0064] Among them, Ir i represents the current stability coefficient of the ith monitoring sub-area, Ic i represents the effective value of the current in the ith monitoring sub-area, Ic 允 Indicates the maximum allowable value of the effective value of the power grid safety monitoring current, Ie i Indicates the current unbalance degree of the i-th monitoring sub-area, Ih i Indicates the current harmonic content of the i-th monitoring sub-area, If i represents the current fluctuation of the i-th monitoring sub-area;

[0065] The specific calculation formula of the frequency stability coefficient is:

[0066]

[0067] Among them, Pr i represents the frequency stability coefficient of the ith monitoring sub-area, Ps i represents the post-accident steady-state power of the ith monitoring sub-area, Ps 标 represents the standard post-accident steady-state power for power grid security monitoring, Pb i represents the fault impulse power of the ith monitoring sub-area, Pb max Indicates the maximum fault impact power, Pm i Pm represents the maximum static stable power after the accident in the ith monitoring sub-area. 标 It represents the standard post-accident static stability maximum power of power grid security monitoring.

[0068] In this embodiment, it should be specifically explained that the environmental safety preprocessing unit is used to establish an environmental safety preprocessing model, import the data transmitted in the power grid safety data acquisition module into the environmental safety preprocessing model, and obtain the environmental temperature stability coefficient and the environmental humidity stability coefficient. The specific analysis formula of the environmental temperature stability coefficient is:

[0069]

[0070] where Tr i represents the ambient temperature stability coefficient of the ith monitoring sub-area, Tc i represents the adiabatic temperature of the ith monitoring sub-area, Tl i represents the temperature gradient change rate of the i-th monitoring sub-area;

[0071] The specific analysis formula of the environmental humidity stability coefficient is:

[0072]

[0073] Among them, Hr i represents the environmental humidity stability coefficient of the ith monitoring sub-area, Hc i represents the relative humidity of the ith monitoring sub-area, Hl i Represents the non-condensing humidity change rate of the i-th monitoring sub-area.

[0074] The power grid security data analysis module is used to analyze the data preprocessed in the power stability data preprocessing module, includes a power stability analysis unit and an environmental safety analysis unit, and transmits the analyzed data to the power grid security assessment index calculation module.

[0075] In this embodiment, it should be specifically explained that the power stability analysis unit is used to establish a power stability analysis model, import the data transmitted in the power stability preprocessing module into the power stability data analysis model, and obtain the power stability state proportional coefficient. The specific calculation formula is: i =Ur i ×Ir i ×Pr i , where φ i represents the power stability proportionality coefficient of the ith sub-region, Ur i represents the voltage stability coefficient of the ith monitoring sub-area, Ir i represents the current stability coefficient of the ith monitoring sub-area, Pr i represents the frequency stability coefficient of the ith monitoring sub-area.

[0076] In this embodiment, it should be specifically explained that the environmental safety analysis unit is used to establish an environmental safety analysis model, import the data transmitted in the power stability preprocessing module into the environmental safety data analysis model, and obtain the environmental safety state proportional coefficient. The specific calculation formula is: i =Tr i ×Hr i , where ψ i represents the environmental safety state proportional coefficient of the i-th sub-area, Tr i represents the ambient temperature stability coefficient of the ith monitoring sub-area, Hr i Represents the environmental humidity stability coefficient of the i-th monitoring sub-area.

[0077] The power grid security assessment index calculation module is used to establish a power grid security assessment index calculation model, import the power stability state proportional coefficient and the environmental safety state proportional coefficient into the power grid security assessment index calculation model, calculate the power grid security assessment index, and transmit the analysis data to the power grid security monitoring and judgment module.

[0078] In this embodiment, it should be specifically explained that the power grid security assessment index is obtained by establishing a power grid security assessment index calculation model, and the specific analysis formula of the power grid security assessment index is:

[0079] Υ i =log2(1+φ i )+log3(1+ψ i )

[0080] where Y i represents the power grid security assessment index of the ith monitoring sub-area, φ i represents the power stability proportionality coefficient of the ith sub-region, ψ i Represents the environmental safety status proportional coefficient of the i-th sub-area.

[0081] The power grid safety detection judgment module is used to judge the data transmitted by the power grid safety detection comprehensive analysis module, screen the monitoring sub-areas in the normal working state and the abnormal working state in the monitoring network, and count the numbers of the monitoring sub-areas in the normal working state and the abnormal working state in the monitoring network.

[0082] In this embodiment, it is necessary to specifically explain that the power grid security assessment index corresponding to each monitoring sub-area in the monitoring network is compared with the preset standard power grid security assessment index corresponding to each monitoring sub-area. If the power grid security assessment index corresponding to a monitoring sub-area is greater than or equal to the preset standard power grid security assessment index of the normal working state, it indicates that the monitoring sub-area is in a normal working state. Otherwise, it indicates that the monitoring sub-area is in an abnormal working state. The system detects the existence of abnormal conditions that affect the normal operation of the power grid, such as equipment failure, network attack, and power load abnormality, and selects and counts the monitoring sub-areas in abnormal working state and the monitoring sub-areas in normal working state according to the working state of the monitoring sub-area, maintains data collection and analysis of the monitoring sub-areas in normal working state, and records the monitoring sub-areas in abnormal working state as designated monitoring sub-areas, and numbers them in sequence as 1, 2, ..., j, ..., k.

[0083] The threat warning data processing and analysis module is used to establish a threat warning data processing and analysis model, including a threat warning data processing model and a threat warning data analysis model, to process and analyze the threat warning data.

[0084] In this embodiment, it should be specifically explained that the threat alarm data processing model is based on each detection sub-area in an abnormal state, collects historical threat alarm data through the power grid security monitoring system, including threat alarm type, threat alarm level, threatened equipment information and fault description, etc., cleans the collected data, removes duplicate data, invalid data and noise data, normalizes alarms with similar characteristics, and organizes a large amount of threat alarm data into groups through a cluster analysis method to identify common threat patterns, abnormal behaviors and potential risks. Defense measures are taken for the cluster analysis results through transfer learning, and the threat alarm recognition rate, emergency response speed and threat alarm protection rate of each designated monitoring sub-area are obtained, which are recorded as Er, Et and Ed respectively; the threat alarm data analysis model is used to analyze the results after system identification and protection, and obtain the threat alarm monitoring efficiency evaluation index, and its specific analysis formula is:

[0085]

[0086] Where η represents the threat monitoring efficiency evaluation index of the specified monitoring area, Er j represents the threat alarm recognition rate of the jth specified monitoring sub-area, Ed j represents the threat alarm protection rate of the jth designated monitoring sub-area, Et j Represents the emergency response speed of the jth designated monitoring sub-area.

[0087] The threat alarm monitoring judgment module is used to establish a preset value of the threat alarm monitoring efficiency evaluation index, judge the threat alarm monitoring efficiency evaluation index of the specified monitoring area according to the preset value of the threat alarm monitoring efficiency evaluation index, and send a signal to the human-computer interaction module according to the judgment result.

[0088] In this embodiment, it should be specifically explained that the threat alarm monitoring efficiency evaluation index of the specified monitoring area is equal to the preset value η of the threat alarm monitoring efficiency evaluation index. D For comparison, if η D ≤η, indicating that the threat alarm monitoring efficiency evaluation index of the specified monitoring area is greater than or equal to the preset value of the threat alarm monitoring efficiency evaluation index, indicating that the threat alarm prevention capability of the specified monitoring area is good. D >η, indicating that the threat alarm monitoring efficiency evaluation index of the designated monitoring area is less than the preset value of the threat alarm monitoring efficiency evaluation index, indicating that the threat alarm prevention capability of the designated monitoring area is poor, and a threat alarm warning instruction is issued to the human-computer interaction module.

[0089] The human-computer interaction module is used to receive the threat alarm warning instruction transmitted by the threat alarm monitoring and judgment module, and display the threat alarm warning instruction on the terminal display screen, and at the same time, perform corresponding warning reminders according to the received threat alarm warning instruction.

[0090] In this embodiment, it should be specifically explained that the model of the designated monitoring area with poor anti-threat alarm capability is optimized through threat alarm warning instructions, and a monitoring report is generated based on the power stability state proportional coefficient, the environmental safety state proportional coefficient, the power grid security assessment index and the threat alarm monitoring efficiency assessment index. The monitoring report is presented to the operation and maintenance personnel. The operation and maintenance personnel improve and optimize the analysis methods and results according to the monitoring report, thereby improving the safety and stability of the power grid security monitoring area and ensuring the safe operation of the power grid.

[0091] The present invention transfers the target power grid security monitoring model through the threat alarm semantic transfer learning module, and provides a more refined mathematical model for subsequent power grid security monitoring; the monitoring area is divided into multiple monitoring sub-areas through the monitoring area division module, which can realize the refined monitoring of power grid security, and the data of each monitoring sub-area is carried out independently, so that the system can more accurately capture the subtle changes during monitoring; the power grid security data acquisition module collects the power stability data and environmental safety data of each monitoring sub-area, which are important bases for evaluating power grid security, and provide data support for subsequent data analysis and warnings; the power grid security data preprocessing module preprocesses the data in the power grid security data acquisition module, establishes a preprocessing model, simplifies the complex and changeable data, reduces the system operation load, and improves the system operation efficiency; the power grid security data analysis module The data in the security data preprocessing module is further analyzed to establish an analysis model. The power stability state proportional coefficient and the environmental safety state proportional coefficient are calculated, which is of great significance for evaluating the safety and stability of power grid operation. The power grid security assessment index calculation model is established through the power grid security assessment index calculation module, which can comprehensively reflect the overall operation status of the power grid. The overall operation status of the power grid is judged through the power grid security monitoring and judgment module, and the monitoring sub-areas in abnormal state and normal state are judged, and the monitoring sub-areas in abnormal state are further analyzed. The threat alarm data processing and analysis module and the threat alarm monitoring and judgment module are used to further process, analyze and judge the designated monitoring sub-areas in abnormal state, and timely optimize the monitoring sub-areas in abnormal state, which provides an important guarantee for taking timely measures to avoid the occurrence of safety accidents.

[0092] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0093] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A power grid security monitoring system based on threat warning semantic knowledge mining and transfer learning, characterized in that: include: Threat alert semantic transfer learning module: used to migrate a security monitoring model trained in a power system to the target security monitoring system where it needs to be applied, and fine-tune it to adapt to the new data set; Monitoring area division module: used to deploy monitoring equipment on key nodes and important equipment in the power grid security monitoring area after transfer learning, establish a complete power grid security monitoring network, and determine the key nodes and important equipment where monitoring equipment are deployed as each monitoring sub-area, numbered 1, 2, ..., i, ..., n in sequence; Grid security data acquisition module: used to collect various types of data from each sub-monitoring area, including power stability data acquisition unit and environmental safety data acquisition unit, and transmit the collected data to the grid security data preprocessing module; The power grid safety data preprocessing module is used to preprocess the data collected in the power stability data acquisition module, including a power stability preprocessing unit and an environmental safety preprocessing unit. The voltage stability coefficient, current stability coefficient and frequency stability coefficient are obtained through the power stability preprocessing unit, and the environmental temperature coefficient and environmental humidity coefficient are obtained through the environmental safety preprocessing unit, and the preprocessed data is transmitted to the power grid safety data analysis module; Power grid security data analysis module: used to analyze the data preprocessed in the power stability data preprocessing module, including the power stability analysis unit and the environmental safety analysis unit, and transmit the analyzed data to the power grid security assessment index calculation module; Power grid security assessment index calculation module: used to establish a power grid security assessment index calculation model, import the power stability state proportional coefficient and the environmental safety state proportional coefficient into the power grid security assessment index calculation model, calculate the power grid security assessment index, and transmit the analysis data to the power grid security monitoring and judgment module; Power grid safety detection and judgment module: used to judge the data transmitted by the power grid safety detection comprehensive analysis module, screen the monitoring sub-areas in the normal working state and the abnormal working state in the monitoring network, and count the numbers of the monitoring sub-areas in the normal working state and the abnormal working state in the monitoring network; Threat alarm data processing and analysis module: used to establish threat alarm data processing and analysis models, including threat alarm data processing model and threat alarm data analysis model, and process and analyze threat alarm data; Threat alarm monitoring and judgment module: used to establish a preset value of the threat alarm monitoring efficiency evaluation index, judge the threat alarm monitoring efficiency evaluation index of the specified monitoring area according to the preset value of the threat alarm monitoring efficiency evaluation index, and send a signal to the human-computer interaction module according to the judgment result; Human-computer interaction module: used to receive the threat alarm warning instructions transmitted by the threat alarm monitoring and judgment module, and display the threat alarm warning instructions on the terminal display screen, and at the same time make corresponding warning reminders according to the received threat alarm warning instructions.

2. A power grid security monitoring system based on threat warning semantic knowledge mining and transfer learning according to claim 1, characterized in that: The power stability data acquisition unit in the power grid safety data acquisition module is used to collect power stability data of each sub-monitoring area, including voltage stability data, current stability data, and power stability data. The voltage stability data includes voltage effective value, voltage harmonic content, fluctuating voltage and voltage imbalance, which are respectively recorded as Uc, Uh, Uf and Ue. The current stability data includes current effective value, current harmonic content, fluctuating current and current imbalance, which are respectively recorded as Ic, Ih, If and Ie; the power stability data includes steady-state power after the accident, fault impact power and static stable maximum power after the accident, which are respectively recorded as Ps, Pb and Pm; the environmental safety data acquisition unit is used to collect environmental safety data of each monitoring sub-area, including ambient temperature data and ambient humidity data. The ambient temperature data includes insulation temperature and temperature gradient change rate, which are respectively recorded as Tc and Tl; the internal environment data includes relative humidity and non-condensing humidity change rate, which are respectively recorded as Hc and Hl.

3. A power grid security monitoring system based on threat warning semantic knowledge mining and transfer learning according to claim 1, characterized in that: The power stability preprocessing unit in the power grid security data preprocessing module is used to establish a power stability preprocessing model, import the data transmitted in the power grid security data acquisition module into the power stability preprocessing model, and obtain the voltage stability coefficient, current stability coefficient and frequency stability coefficient. The specific calculation formula of the voltage stability coefficient is: Among them Ur i represents the voltage stability coefficient of the ith monitoring sub-area, Uc i Indicates the effective value of the voltage in the ith monitoring sub-area, Uc 允 Indicates the maximum allowable value of the effective value of the power grid safety monitoring voltage, Ue i Indicates the voltage unbalance degree of the ith monitoring sub-area, Uh i represents the voltage harmonic content of the ith monitoring sub-area, Uf i represents the voltage fluctuation of the ith monitoring sub-area; The specific calculation formula of the current stability coefficient is: Among them, Ir i represents the current stability coefficient of the ith monitoring sub-area, Ic i represents the effective value of the current in the ith monitoring sub-area, Ic 允 Indicates the maximum allowable value of the effective value of the power grid safety monitoring current, Ie i Indicates the current unbalance degree of the i-th monitoring sub-area, Ih i Indicates the current harmonic content of the i-th monitoring sub-area, If i represents the current fluctuation of the i-th monitoring sub-area; The specific calculation formula of the frequency stability coefficient is: Among them, Pr i represents the frequency stability coefficient of the ith monitoring sub-area, Ps i represents the post-accident steady-state power of the ith monitoring sub-area, Ps 标 represents the standard post-accident steady-state power for power grid security monitoring, Pb i represents the fault impulse power of the ith monitoring sub-area, Pb max Indicates the maximum fault impact power, Pm i Pm represents the maximum static stable power after the accident in the ith monitoring sub-area. 标 It represents the standard post-accident static stability maximum power of power grid security monitoring.

4. A power grid security monitoring system based on threat warning semantic knowledge mining and transfer learning according to claim 1, characterized in that: The environmental safety preprocessing unit in the power grid safety data preprocessing module is used to establish an environmental safety preprocessing model, import the data transmitted in the power grid safety data acquisition module into the environmental safety preprocessing model, and obtain the environmental temperature stability coefficient and the environmental humidity stability coefficient. The specific analysis formula of the environmental temperature stability coefficient is: Medium Tr i represents the ambient temperature stability coefficient of the ith monitoring sub-area, Tc i represents the adiabatic temperature of the ith monitoring sub-area, Tl i represents the temperature gradient change rate of the i-th monitoring sub-area; The specific analysis formula of the environmental humidity stability coefficient is: Among them, Hr i represents the environmental humidity stability coefficient of the ith monitoring sub-area, Hc i represents the relative humidity of the ith monitoring sub-area, Hl i Represents the non-condensing humidity change rate of the i-th monitoring sub-area.

5. The power grid security monitoring system based on threat warning semantic knowledge mining and transfer learning according to claim 1 is characterized by: The power stability analysis unit in the power grid security data analysis module is used to establish a power stability analysis model, import the data transmitted in the power stability preprocessing module into the power stability data analysis model, and obtain the power stability state proportional coefficient. The specific calculation formula is: i =Ur i ×Ir i ×Pr i , where φ i represents the power stability proportionality coefficient of the ith sub-region, Ur i represents the voltage stability coefficient of the ith monitoring sub-area, Ir i represents the current stability coefficient of the ith monitoring sub-area, Pr i represents the frequency stability coefficient of the ith monitoring sub-area; the environmental safety analysis unit is used to establish an environmental safety analysis model, import the data transmitted in the power stability preprocessing module into the environmental safety data analysis model, and obtain the environmental safety state proportional coefficient. The specific calculation formula is: ψ i =Tr i ×Hr i , where ψ i represents the environmental safety state proportional coefficient of the i-th sub-area, Tr i represents the ambient temperature stability coefficient of the ith monitoring sub-area, Hr i Represents the environmental humidity stability coefficient of the i-th monitoring sub-area.

6. A power grid security monitoring system based on threat warning semantic knowledge mining and transfer learning according to claim 1, characterized in that: The power grid security assessment index calculation module obtains the power grid security assessment index by establishing a power grid security assessment index calculation model. The specific analysis formula of the power grid security assessment index is: Y i =log2(1+φ i )+log3(1+ψ i ) where Y i represents the power grid security assessment index of the ith monitoring sub-area, φ i represents the power stability proportionality coefficient of the ith sub-region, ψ i Represents the environmental safety status proportional coefficient of the i-th sub-area.

7. A power grid security monitoring system based on threat warning semantic knowledge mining and transfer learning according to claim 1, characterized in that: The power grid security monitoring and judgment module compares the power grid security assessment index corresponding to each monitoring sub-area in the monitoring network with the preset standard power grid security assessment index corresponding to each monitoring sub-area. If the power grid security assessment index corresponding to a monitoring sub-area is greater than or equal to the preset standard power grid security assessment index of normal working status, it indicates that the monitoring sub-area is in normal working status. Otherwise, it indicates that the monitoring sub-area is in abnormal working status. The monitoring sub-areas in abnormal working status and the monitoring sub-areas in normal working status are screened and counted according to the working status of the monitoring sub-areas. The data collection and analysis of the monitoring sub-areas in normal working status are maintained. The monitoring sub-areas in abnormal working status are recorded as designated monitoring sub-areas, and they are numbered 1, 2, ..., j, ..., k in sequence.

8. The power grid security monitoring system based on threat warning semantic knowledge mining and transfer learning according to claim 1 is characterized by: The threat alarm data processing model is based on each detection sub-area in an abnormal state, collects historical threat alarm data through the power grid security monitoring system, cleans the collected data, and uses the obtained data for defense measures through a cluster analysis method, and obtains the threat alarm recognition rate, emergency response speed and threat alarm protection rate of each designated monitoring sub-area, which are recorded as Er, Et and Ed respectively; the threat alarm data analysis model is used to analyze the results after system identification and protection, and obtain the threat alarm monitoring efficiency evaluation index, and its specific analysis formula is: Where η represents the threat monitoring efficiency evaluation index of the specified monitoring area, Er j represents the threat alarm recognition rate of the jth specified monitoring sub-area, Ed j represents the threat alarm protection rate of the jth designated monitoring sub-area, Et j Represents the emergency response speed of the jth designated monitoring sub-area.

9. The power grid security monitoring system based on threat warning semantic knowledge mining and transfer learning according to claim 1 is characterized by: The threat alarm monitoring judgment module compares the threat alarm monitoring efficiency evaluation index of the specified monitoring area with the preset value η of the threat alarm monitoring efficiency evaluation index. D For comparison, if η D ≤η, indicating that the designated monitoring area has good threat warning processing capabilities. D >η, indicating that the designated monitoring area has poor threat warning processing capability.

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