Electric power safety monitoring system and method based on cloud platform

Abstract

The invention relates to the technical field of electric power safety monitoring, and particularly discloses an electric power safety monitoring system and method based on a cloud platform. Comprising the steps of constructing a cloud platform module, a self-adaptive data acquisition module, a power balance analysis and judgment module, a power grid anomaly analysis and judgment module, a power data error adjustment module, a power safety analysis module, a power safety evaluation module and a man-machine interaction module; according to the invention, the cloud platform is constructed to carry out adaptive data acquisition and analysis to obtain the power balance evaluation coefficient and the power grid abnormity evaluation coefficient, the power safety evaluation index is obtained through analysis, and the power safety evaluation index is compared with the preset safety threshold to judge whether the power system has safety abnormity or not. The on-cloud and under-cloud adaptive joint protection technology is introduced, and through integration of cloud computing, big data analysis and artificial intelligence advanced technologies, many defects of a traditional system are overcome, and the safety and stability of a power system are significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power safety monitoring, and in particular to a power safety monitoring system and method based on a cloud platform. Background Art

[0002] Under the background of the rapid advancement of the construction of new power systems, cloud platforms, as key infrastructure, are carrying more and more business systems and playing an increasingly important role. However, with the continuous deepening of the company's cloud platform construction and the continuous migration of business systems to the cloud, the security protection requirements of the cloud platform and its cloud-based businesses are also increasing, which poses new challenges to security protection technology. Through the joint scheduling research of security protection resources on and off the cloud, an adaptive cloud security protection system that adapts to the coexistence of multi-cloud applications, flexible component deployment, and centralized control of protection capabilities is constructed. This system will be able to achieve effective integration and optimization of security protection resources on and off the cloud, improve the coordination and integrity of security protection, and thus ensure the safe and stable operation of the cloud platform and its cloud-based businesses. On the basis of this cloud platform, power safety monitoring has become an important link in ensuring the stable operation of the power grid and power supply safety.

[0003] In recent years, the rapid development of cloud computing technology has provided new solutions for power safety monitoring. The cloud platform has powerful data processing capabilities, high reliability and scalability, and can support the real-time collection, storage and analysis of large-scale data. By migrating the power safety monitoring system to the cloud platform, remote centralized management and efficient use of monitoring data can be achieved, reducing system maintenance costs and improving the efficiency of data sharing and collaborative work.

[0004] Traditional power safety monitoring systems mainly rely on local data acquisition and monitoring equipment. Although they play an important role in ensuring the safe operation of power systems, with the increasing complexity and demand of power systems, traditional systems have gradually exposed some obvious defects. On the one hand, various transmission lines will be disturbed by the power supply or magnetic field caused by the alternating electric field of the substation, which will cause interference to the monitoring system and affect the accuracy and stability of monitoring; on the other hand, due to technical limitations, traditional power safety monitoring systems have deficiencies in data security. For example, the lack of perfect anti-attack and anti-tampering technical means may lead to illegal access or tampering of data. At the same time, the system may also have loopholes that hackers can exploit to attack. 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 safety monitoring system and method based on a cloud platform to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: a power safety monitoring system based on a cloud platform, comprising a cloud platform construction module, an adaptive data acquisition module, a power balance analysis and judgment module, a power grid anomaly analysis and judgment module, a power data error adjustment module, a power safety analysis module, a power safety assessment module and a human-computer interaction module.

[0007] Building a cloud platform module: This module is used to build a cloud platform that includes a virtualized resource management layer, a security protection layer, and a service management layer for storing, processing, and analyzing data, and to conduct network security management and control through a centralized method of joint security protection capabilities on and off the cloud.

[0008] Adaptive data acquisition module: used to collect comprehensive power parameters, adaptively adjust the data acquisition frequency according to the real-time load and operation status of the power system, and transmit the comprehensive power parameters to the power balance analysis and judgment module and the power grid abnormality analysis and judgment module;

[0009] The power balance analysis and judgment module is used to import the data transmitted by the adaptive data acquisition module into the power balance evaluation coefficient model to calculate the power balance evaluation coefficient, perform judgment and analysis on the power balance evaluation coefficient and the preset threshold value, and transmit the judgment result to the power data error adjustment module;

[0010] The power grid anomaly analysis and judgment module is used to import the data transmitted by the adaptive data acquisition module into the power grid anomaly assessment coefficient model to calculate the power grid anomaly assessment coefficient, perform judgment and analysis on the power grid anomaly assessment coefficient and the preset threshold value, and transmit the judgment result to the power data error adjustment module;

[0011] Power data error adjustment module: used to perform error adjustment on the data of abnormal results transmitted by the power balance analysis and judgment module and the power grid abnormality analysis and judgment module, establish a power data error adjustment model, and transmit the error-adjusted data to the power safety analysis module;

[0012] Power safety analysis module: establishes a power safety assessment index model based on the error-adjusted data of the power data error adjustment module, calculates the power safety assessment index, and transmits it to the power safety assessment module;

[0013] Power safety assessment module: compares and judges the power safety assessment index with the preset safety threshold, and transmits the judgment and analysis results to the human-computer interaction module;

[0014] Human-computer interaction module: used to visualize power safety monitoring data and assessment results on the mobile devices of operation and maintenance personnel and users in the form of data tables, curves and graphics, so as to facilitate operation and maintenance personnel to make decisions on power safety.

[0015] Preferably, the comprehensive power parameters include power balancing parameters and power grid parameters, the power balancing parameters include power load value, system frequency, actual output power, maximum output power, voltage value and rated voltage value; the power grid parameters include the amplitude of the fundamental wave, the amplitude of each harmonic, the average load of the transformer, the rated capacity of the transformer, the short-circuit current and the rated current.

[0016] Preferably, the specific content of adaptively adjusting the data acquisition frequency in the adaptive data acquisition module is: deploying multiple distributed sensors at the power grid nodes, the sensors include temperature sensors, current sensors and vibration sensors, the sensors monitor the operation status of the power grid through a built-in adaptive adjustment algorithm, and automatically increase the data acquisition frequency when an abnormal signal is detected; the adaptive adjustment algorithm can be described by the following steps:

[0017] Step 1: Initialize: set the basic data acquisition frequency f0 and the standard parameter threshold for normal operation;

[0018] Step 2: Real-time monitoring: The sensor monitors the comprehensive power parameter X(t) in real time;

[0019] Step 3: Perform anomaly detection: Calculate the moving average X and standard deviation σ of the comprehensive power parameter X(t) X , set the judgment condition. If X(t) exceeds the normal range, it is judged as abnormal. The judgment condition can be expressed as: X(t)>X+k×σ X , where k is a constant used to adjust the sensitivity of detection;

[0020] Step 4: Adjust the acquisition frequency: When an abnormality is detected, increase the data acquisition frequency. The new acquisition frequency f(t) can be expressed as: Wherein, n is a constant greater than 1, indicating the multiple of the frequency increase.

[0021] Preferably, the calculation steps of the power balance evaluation coefficient are as follows:

[0022] S1. Calculate the load fluctuation value Δp. The calculation model is as follows:

[0023] Among them, P max Expressed as the maximum load value, P min Expressed as the minimum load value, P i It is represented as the load value at the i-th time point, and n is represented as the total number of time points;

[0024] S2. Calculate the frequency deviation value Δf. The calculation model is as follows:

[0025] Among them, f 实Expressed as the actual measured system frequency, f 标 Indicates the standard frequency value specified for the system;

[0026] S3. Calculate the power balance assessment coefficient. The calculation model is as follows:

[0027] Among them, EEC represents the power balance evaluation coefficient, OP represents the actual output power of the power system, and OP max It is expressed as the maximum output power of the power system, Δp is expressed as the load fluctuation value, V 实 Indicated as the actual measured voltage value, V 额 It is expressed as the rated voltage value, Δf is expressed as the frequency deviation value, and e is expressed as a natural constant.

[0028] Preferably, the specific content of judging and analyzing the power balance evaluation coefficient and the preset threshold value in the power balance analysis and judgment module is:

[0029] The power balance assessment coefficient is extracted and compared with the preset power balance threshold. If the power balance assessment coefficient is less than the preset power balance threshold, it is judged that there is an abnormality in the power balance of the power system, and safety control operations are performed on the abnormal data and an early warning signal is issued. Otherwise, it is judged that there is no abnormality in the power balance of the power system.

[0030] Preferably, the calculation steps of the power grid anomaly assessment coefficient are specifically as follows:

[0031] S1. Calculate the harmonic content THD. The calculation model is as follows:

[0032] Among them, H1 represents the amplitude of the fundamental wave, H2, H3, H z They are respectively represented as the amplitudes of the 2nd, 3rd, and zth harmonics, and z represents the total number of harmonics;

[0033] S2. Calculate the transformer load factor TLF. The calculation model is as follows:

[0034] Among them, Al represents the average load of the transformer, and Rc represents the rated capacity of the transformer;

[0035] S3. Calculate the power grid abnormality assessment coefficient. The calculation model is as follows:

[0036] Among them, AEC represents the power grid abnormality assessment coefficient, THD represents the harmonic content, TLF represents the transformer load factor, and TLF 预 It represents the preset transformer load rate, e represents the natural constant, Δf represents the frequency deviation value, Ic represents the short-circuit current, and Ir represents the rated current.

[0037] Preferably, the specific content of judging and analyzing the power grid anomaly assessment coefficient and a preset threshold value in the power grid anomaly analysis and judgment module is:

[0038] The grid anomaly assessment coefficient is extracted and compared with the preset grid anomaly threshold. If the grid anomaly assessment coefficient is greater than the preset grid anomaly threshold, it is judged that there is an abnormality in the grid operation in the power system, and safety control operations are performed on the abnormal data and an early warning signal is issued. Otherwise, it is judged that there is no abnormality in the grid operation in the power system.

[0039] Preferably, the calculation model of the power safety assessment index is specifically: Among them, SAI represents the power security assessment index, EEC represents the power balance assessment coefficient, AEC represents the power grid anomaly assessment coefficient, and α represents other influencing factors of power security.

[0040] Preferably, the specific method of comparing and judging the power safety assessment index with a preset safety threshold in the power safety assessment module is:

[0041] The power safety assessment index is extracted and compared with the preset safety threshold. If the power safety assessment index is less than the preset safety threshold, it is judged that there is an abnormality in the power safety of the power system, and the operation and maintenance personnel are notified to perform security control on the abnormal data and issue an early warning signal. Otherwise, it is judged that there is no abnormality in the power safety of the power system.

[0042] Preferably, a power safety monitoring method based on a cloud platform comprises the following steps:

[0043] Step S1: Building a cloud platform: Building a cloud platform including a virtualized resource management layer, a security protection layer, and a service management layer for storing, processing, and analyzing data, and performing network security management and control through a centralized method of joint security protection capabilities on and off the cloud;

[0044] Step S2: Adaptive data collection: Collect comprehensive power parameters and adaptively adjust the data collection frequency according to the real-time load and operating status of the power system;

[0045] Step S3: Analyze and judge the power balance: import the data transmitted in step S2 into the power balance evaluation coefficient model to calculate the power balance evaluation coefficient, and judge and analyze the power balance evaluation coefficient with the preset threshold;

[0046] Step S4: Analyze and judge the power grid anomaly: import the data transmitted in step S2 into the power grid anomaly assessment coefficient model to calculate the power grid anomaly assessment coefficient, and judge and analyze the power grid anomaly assessment coefficient and the preset threshold value;

[0047] Step S5: Performing power data error adjustment: performing error adjustment on the data of the abnormal results transmitted in step S3 and step S4, and establishing a power data error adjustment model;

[0048] Step S6: Analyze power safety: establish a power safety assessment index model based on the error-adjusted data in step S5, and calculate the power safety assessment index;

[0049] Step S7: Evaluate power safety: compare and judge the power safety evaluation index with the preset safety threshold, and transmit the judgment and analysis results to the human-computer interaction module;

[0050] Step S8: Perform human-computer interaction: Visually display the power monitoring data and evaluation results on the mobile devices of operation and maintenance personnel and users in the form of data tables, curves and graphics, so as to facilitate operation and maintenance personnel to make decisions on power safety.

[0051] Technical effects and advantages of the present invention:

[0052] 1. The present invention can realize real-time data collection and transmission of the power system through the cloud computing platform, ensuring the timeliness and accuracy of the data; it can immediately issue an alarm when an abnormal situation is detected and initiate the corresponding emergency plan, greatly shortening the fault handling time and improving the response speed of the system;

[0053] 2. The present invention constructs a cloud platform to conduct adaptive data collection and analysis to obtain the power balance assessment coefficient and the power grid anomaly assessment coefficient, and then analyzes to obtain the power safety assessment index. By comparing the power safety assessment index with a preset safety threshold, it is determined whether there is any safety anomaly in the power system. The cloud-based and off-cloud adaptive joint protection technology is introduced, and by integrating cloud computing, big data analysis, and artificial intelligence advanced technologies, many defects of the traditional system are overcome, and the safety and stability of the power system are significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0055] Figure 1 It is a schematic diagram of the overall structure of the system of the present invention.

[0056] Figure 2 It is a schematic diagram of the method flow structure of the present invention. DETAILED DESCRIPTION

[0057] 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.

[0058] Example 1

[0059] See also Figure 1 As shown, the present invention provides a power safety monitoring system based on a cloud platform, including a cloud platform construction module, an adaptive data acquisition module, a power balance analysis and judgment module, a power grid anomaly analysis and judgment module, a power data error adjustment module, a power safety analysis module, a power safety assessment module and a human-computer interaction module.

[0060] The cloud platform construction module is connected to the adaptive data acquisition module, the adaptive data acquisition module is respectively connected to the power balance analysis and judgment module and the power grid abnormality analysis and judgment module, the power balance analysis and judgment module is connected to the power data error adjustment module, the power grid abnormality analysis and judgment module is connected to the power data error adjustment module, the power data error adjustment module is connected to the power safety analysis module, the power safety analysis module is connected to the power safety assessment module, and the power safety assessment module is connected to the human-computer interaction module.

[0061] Building a cloud platform module: This module is used to build a cloud platform that includes a virtualized resource management layer, a security protection layer, and a service management layer for storing, processing, and analyzing data, and to conduct network security management and control through a centralized method of joint security protection capabilities on and off the cloud.

[0062] In this embodiment, it should be specifically explained that the virtualized resource management layer is used to manage and allocate computing, storage and network resources; the security protection layer is used to implement security protection of the cloud platform, including data encryption, access control and intrusion detection; the service management layer is used to deploy and manage application services related to power safety monitoring; by building a unified security technical defense measures capability integration architecture, the protection capabilities of multiple security technical defense measures on and off the cloud are uniformly deployed. At the same time, with the help of the elastic scalability of the cloud, it can respond to attack scenarios of different types and scales, realize the full process network security management and control capabilities of prediction, detection, response and tracing, and create a larger-scale and more systematic power grid cloud security adaptive ecosystem.

[0063] Adaptive data acquisition module: used to collect comprehensive power parameters, adaptively adjust the data acquisition frequency according to the real-time load and operation status of the power system, and transmit the comprehensive power parameters to the power balance analysis and judgment module and the power grid abnormality analysis and judgment module;

[0064] In this embodiment, it should be specifically explained that the comprehensive power parameters include power balancing parameters and power grid parameters. The power balancing parameters include power load value, system frequency, actual output power, maximum output power, voltage value and rated voltage value; the power grid parameters include the amplitude of the fundamental wave, the amplitude of each harmonic, the average load of the transformer, the rated capacity of the transformer, the short-circuit current and the rated current.

[0065] In this embodiment, it should be specifically explained that the specific content of adaptively adjusting the data acquisition frequency in the adaptive data acquisition module is: deploying multiple distributed sensors at the power grid nodes, the sensors include temperature sensors, current sensors and vibration sensors, the sensors monitor the operation status of the power grid through a built-in adaptive adjustment algorithm, and when an abnormal signal is detected, the data acquisition frequency is automatically increased to capture more detailed information; the adaptive adjustment algorithm can be described by the following steps:

[0066] Step 1: Initialize: set the basic data acquisition frequency f0 and the standard parameter threshold for normal operation;

[0067] Step 2: Real-time monitoring: The sensor monitors the comprehensive power parameter X(t) in real time;

[0068] Step 3: Perform anomaly detection: Calculate the moving average X and standard deviation σ of the comprehensive power parameter X(t) X , set the judgment condition. If X(t) exceeds the normal range, it is judged as abnormal. The judgment condition can be expressed as: X(t)>X+k×σ X , where k is a constant used to adjust the sensitivity of detection;

[0069] Step 4: Adjust the acquisition frequency: When an abnormality is detected, increase the data acquisition frequency. The new acquisition frequency f(t) can be expressed as: Wherein, n is a constant greater than 1, indicating the multiple of the frequency increase.

[0070] The power balance analysis and judgment module is used to import the data transmitted by the adaptive data acquisition module into the power balance evaluation coefficient model to calculate the power balance evaluation coefficient, perform judgment and analysis on the power balance evaluation coefficient and the preset threshold value, and transmit the judgment result to the power data error adjustment module;

[0071] In this embodiment, it should be specifically explained that the calculation steps of the power balance evaluation coefficient are as follows:

[0072] S1. Calculate the load fluctuation value Δp. The calculation model is as follows:

[0073] Among them, P max Expressed as the maximum load value, P min Expressed as the minimum load value, P i It is represented as the load value at the i-th time point, and n is represented as the total number of time points;

[0074] S2. Calculate the frequency deviation value Δf. The calculation model is as follows:

[0075] Among them, f 实 Expressed as the actual measured system frequency, f 标 Indicates the standard frequency value specified for the system;

[0076] S3. Calculate the power balance assessment coefficient. The calculation model is as follows:

[0077] Among them, EEC represents the power balance evaluation coefficient, OP represents the actual output power of the power system, and OP max It is expressed as the maximum output power of the power system, Δp is expressed as the load fluctuation value, V 实 Indicated as the actual measured voltage value, V 额 It is expressed as the rated voltage value, Δf is expressed as the frequency deviation value, and e is expressed as a natural constant.

[0078] In this embodiment, it should be specifically noted that the deviation values ​​of voltage and frequency should be controlled within a smaller range to ensure the stable operation of the power system and the normal operation of electrical equipment; for example, according to international standards, the voltage deviation should generally not exceed ±5%, and the frequency deviation should generally not exceed ±0.5 Hz or ±1% of the nominal frequency; the specific standards may vary depending on the region and power system.

[0079] In this embodiment, it should be specifically explained that the specific content of the judgment and analysis between the power balance evaluation coefficient and the preset threshold in the power balance analysis and judgment module is:

[0080] The power balance assessment coefficient is extracted and compared with the preset power balance threshold. If the power balance assessment coefficient is less than the preset power balance threshold, it is judged that there is an abnormality in the power balance of the power system, and safety control operations are performed on the abnormal data and an early warning signal is issued. Otherwise, it is judged that there is no abnormality in the power balance of the power system.

[0081] The power grid anomaly analysis and judgment module is used to import the data transmitted by the adaptive data acquisition module into the power grid anomaly assessment coefficient model to calculate the power grid anomaly assessment coefficient, perform judgment and analysis on the power grid anomaly assessment coefficient and the preset threshold value, and transmit the judgment result to the power data error adjustment module;

[0082] In this embodiment, it should be specifically explained that the calculation steps of the power grid abnormality assessment coefficient are as follows:

[0083] S1. Calculate the harmonic content THD. The calculation model is as follows:

[0084] Among them, H1 represents the amplitude of the fundamental wave, H2, H3, H m They are respectively represented as the amplitudes of the 2nd, 3rd, and zth harmonics, and z represents the total number of harmonics;

[0085] S2. Calculate the transformer load factor TLF. The calculation model is as follows:

[0086] Among them, Al represents the average load of the transformer, and Rc represents the rated capacity of the transformer;

[0087] S3. Calculate the power grid abnormality assessment coefficient. The calculation model is as follows:

[0088] Among them, AEC represents the power grid abnormality assessment coefficient, THD represents the harmonic content, TLF represents the transformer load factor, and TLF 预 It represents the preset transformer load rate, e represents the natural constant, Δf represents the frequency deviation value, Ic represents the short-circuit current, and Ir represents the rated current.

[0089] In this embodiment, it should be specifically explained that the specific content of the judgment and analysis of the power grid abnormality assessment coefficient and the preset threshold value in the power grid abnormality analysis and judgment module is:

[0090] The grid anomaly assessment coefficient is extracted and compared with the preset grid anomaly threshold. If the grid anomaly assessment coefficient is greater than the preset grid anomaly threshold, it is judged that there is an abnormality in the grid operation in the power system, and safety control operations are performed on the abnormal data and an early warning signal is issued. Otherwise, it is judged that there is no abnormality in the grid operation in the power system.

[0091] Power data error adjustment module: used to perform error adjustment on the data of abnormal results transmitted by the power balance analysis and judgment module and the power grid abnormality analysis and judgment module, establish a power data error adjustment model, and transmit the error-adjusted data to the power safety analysis module;

[0092] In this embodiment, it should be specifically explained that the establishment of the power data error adjustment model is specifically as follows:

[0093] Among them, yk represents the error-adjusted output value of the power data at the kth node, g represents the activation function, and v jkIt is represented by the weight of the power data output from the jth node to the kth node after error adjustment, h j represents the power data of the kth node before error adjustment, ck represents the bias of the kth node of the power data output value, m represents the total number of grid nodes, j and k represent the numbers of grid nodes respectively, where m=1, 2, 3, ...k, ...j.

[0094] Power safety analysis module: establishes a power safety assessment index model based on the error-adjusted data of the power data error adjustment module, calculates the power safety assessment index, and transmits it to the power safety assessment module;

[0095] In this embodiment, it should be specifically explained that the calculation model of the power safety assessment index is specifically: Among them, SAI represents the power security assessment index, EEC represents the power balance assessment coefficient, AEC represents the power grid anomaly assessment coefficient, and α represents other influencing factors of power security.

[0096] Power safety assessment module: compares and judges the power safety assessment index with the preset safety threshold, and transmits the judgment and analysis results to the human-computer interaction module;

[0097] In this embodiment, it should be specifically explained that the specific method of comparing and judging the power safety assessment index with the preset safety threshold in the power safety assessment module is:

[0098] The power safety assessment index is extracted and compared with the preset safety threshold. If the power safety assessment index is less than the preset safety threshold, it is judged that there is an abnormality in the power safety of the power system, and the operation and maintenance personnel are notified to perform security control on the abnormal data and issue an early warning signal. Otherwise, it is judged that there is no abnormality in the power safety of the power system.

[0099] Human-computer interaction module: used to visualize power safety monitoring data and assessment results on the mobile devices of operation and maintenance personnel and users in the form of data tables, curves and graphics, so as to facilitate operation and maintenance personnel to make decisions on power safety.

[0100] Example 2

[0101] See also Figure 2 As shown, the present invention provides a power safety monitoring method based on a cloud platform, comprising the following steps: step S1: building a cloud platform, step S2: adaptive data acquisition, step S3: analyzing and judging power balance, step S4: analyzing and judging power grid anomalies, step S5: adjusting power data errors, step S6: analyzing power safety, step S7: evaluating power safety, and step S8: performing human-computer interaction.

[0102] Step S1: Building a cloud platform: Building a cloud platform including a virtualized resource management layer, a security protection layer, and a service management layer for storing, processing, and analyzing data, and performing network security management and control through a centralized method of joint security protection capabilities on and off the cloud;

[0103] Step S2: Adaptive data collection: Collect comprehensive power parameters and adaptively adjust the data collection frequency according to the real-time load and operating status of the power system;

[0104] Step S3: Analyze and judge the power balance: import the data transmitted in step S2 into the power balance evaluation coefficient model to calculate the power balance evaluation coefficient, and judge and analyze the power balance evaluation coefficient with the preset threshold;

[0105] Step S4: Analyze and judge the power grid anomaly: import the data transmitted in step S2 into the power grid anomaly assessment coefficient model to calculate the power grid anomaly assessment coefficient, and judge and analyze the power grid anomaly assessment coefficient and the preset threshold value;

[0106] Step S5: Performing power data error adjustment: performing error adjustment on the data of the abnormal results transmitted in step S3 and step S4, and establishing a power data error adjustment model;

[0107] Step S6: Analyze power safety: establish a power safety assessment index model based on the error-adjusted data in step S5, and calculate the power safety assessment index;

[0108] Step S7: Evaluate power safety: compare and judge the power safety evaluation index with the preset safety threshold, and transmit the judgment and analysis results to the human-computer interaction module;

[0109] Step S8: Perform human-computer interaction: Visually display the power monitoring data and evaluation results on the mobile devices of operation and maintenance personnel and users in the form of data tables, curves and graphics, so as to facilitate operation and maintenance personnel to make decisions on power safety.

[0110] 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.

[0111] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A power safety monitoring system based on a cloud platform, characterized in that: include: Building a cloud platform module: This module is used to build a cloud platform that includes a virtualized resource management layer, a security protection layer, and a service management layer for storing, processing, and analyzing data, and to conduct network security management and control through a centralized method of joint security protection capabilities on and off the cloud. Adaptive data acquisition module: used to collect comprehensive power parameters, adaptively adjust the data acquisition frequency according to the real-time load and operation status of the power system, and transmit the comprehensive power parameters to the power balance analysis and judgment module and the power grid abnormality analysis and judgment module; The power balance analysis and judgment module is used to import the data transmitted by the adaptive data acquisition module into the power balance evaluation coefficient model to calculate the power balance evaluation coefficient, perform judgment and analysis on the power balance evaluation coefficient and the preset threshold value, and transmit the judgment result to the power data error adjustment module; The power grid anomaly analysis and judgment module is used to import the data transmitted by the adaptive data acquisition module into the power grid anomaly assessment coefficient model to calculate the power grid anomaly assessment coefficient, perform judgment and analysis on the power grid anomaly assessment coefficient and the preset threshold value, and transmit the judgment result to the power data error adjustment module; Power data error adjustment module: used to perform error adjustment on the data of abnormal results transmitted by the power balance analysis and judgment module and the power grid abnormality analysis and judgment module, establish a power data error adjustment model, and transmit the error-adjusted data to the power safety analysis module; Power safety analysis module: establishes a power safety assessment index model based on the error-adjusted data of the power data error adjustment module, calculates the power safety assessment index, and transmits it to the power safety assessment module; Power safety assessment module: compares and judges the power safety assessment index with the preset safety threshold, and transmits the judgment and analysis results to the human-computer interaction module; Human-computer interaction module: used to visualize power safety monitoring data and assessment results on the mobile devices of operation and maintenance personnel and users in the form of data tables, curves and graphics, so as to facilitate operation and maintenance personnel to make decisions on power safety.

2. The cloud platform-based power safety monitoring system according to claim 1, characterized in that: The comprehensive power parameters include power balancing parameters and power grid parameters. The power balancing parameters include power load value, system frequency, actual output power, maximum output power, voltage value and rated voltage value; the power grid parameters include the amplitude of the fundamental wave, the amplitude of each harmonic, the average load of the transformer, the rated capacity of the transformer, the short-circuit current and the rated current.

3. The cloud platform-based power safety monitoring system according to claim 1, characterized in that: The specific content of adaptively adjusting the data acquisition frequency in the adaptive data acquisition module is: deploying multiple distributed sensors at the power grid nodes, the sensors include temperature sensors, current sensors and vibration sensors, the sensors monitor the operation status of the power grid through a built-in adaptive adjustment algorithm, and automatically increase the data acquisition frequency when an abnormal signal is detected; the adaptive adjustment algorithm can be described by the following steps: Step 1: Initialize: set the basic data acquisition frequency f0 and the standard parameter threshold for normal operation; Step 2: Real-time monitoring: The sensor monitors the comprehensive power parameter X(t) in real time; Step 3: Perform anomaly detection: Calculate the moving average of the comprehensive power parameter X(t) and standard deviation σ X , set the judgment condition. If X(t) exceeds the normal range, it is judged as abnormal. The judgment condition can be expressed as: Among them, k is a constant used to adjust the sensitivity of detection; Step 4: Adjust the acquisition frequency: When an abnormality is detected, increase the data acquisition frequency. The new acquisition frequency f(t) can be expressed as: Wherein, n is a constant greater than 1, indicating the multiple of the frequency increase.

4. The power safety monitoring system based on a cloud platform according to claim 1, characterized in that: The calculation steps of the power balance evaluation coefficient are as follows: S1. Calculate the load fluctuation value Δp. The calculation model is as follows: Among them, P max Expressed as the maximum load value, P min Expressed as the minimum load value, P i It is represented as the load value at the i-th time point, and n is represented as the total number of time points; S2. Calculate the frequency deviation value Δf. The calculation model is as follows: Among them, f 实 Expressed as the actual measured system frequency, f 标 Indicates the standard frequency value specified for the system; S3. Calculate the power balance assessment coefficient. The calculation model is as follows: Among them, EEC represents the power balance evaluation coefficient, OP represents the actual output power of the power system, and OP max It is expressed as the maximum output power of the power system, Δp is expressed as the load fluctuation value, V 实 Indicated as the actual measured voltage value, V 额 It is expressed as the rated voltage value, Δf is expressed as the frequency deviation value, and e is expressed as a natural constant.

5. The cloud platform-based power safety monitoring system according to claim 1, characterized in that: The specific content of judging and analyzing the power balance evaluation coefficient and the preset threshold value in the power balance analysis and judgment module is as follows: The power balance assessment coefficient is extracted and compared with the preset power balance threshold. If the power balance assessment coefficient is less than the preset power balance threshold, it is judged that there is an abnormality in the power balance of the power system, and safety control operations are performed on the abnormal data and an early warning signal is issued. Otherwise, it is judged that there is no abnormality in the power balance of the power system.

6. The cloud platform-based power safety monitoring system according to claim 1, characterized in that: The calculation steps of the power grid abnormality assessment coefficient are specifically as follows: S1. Calculate the harmonic content THD. The calculation model is as follows: Among them, H1 represents the amplitude of the fundamental wave, H2, H3, H z They are respectively represented as the amplitudes of the 2nd, 3rd, and zth harmonics, and z represents the total number of harmonics; S2. Calculate the transformer load factor TLF. The calculation model is as follows: Among them, Al represents the average load of the transformer, and Rc represents the rated capacity of the transformer; S3. Calculate the power grid abnormality assessment coefficient. The calculation model is as follows: Among them, AEC represents the power grid abnormality assessment coefficient, THD represents the harmonic content, TLF represents the transformer load factor, and TLF 预 It represents the preset transformer load rate, e represents the natural constant, Δf represents the frequency deviation value, Ic represents the short-circuit current, and Ir represents the rated current.

7. The power safety monitoring system based on a cloud platform according to claim 1, characterized in that: The specific content of judging and analyzing the power grid abnormality assessment coefficient and the preset threshold value in the power grid abnormality analysis and judgment module is as follows: The grid anomaly assessment coefficient is extracted and compared with the preset grid anomaly threshold. If the grid anomaly assessment coefficient is greater than the preset grid anomaly threshold, it is judged that there is an abnormality in the grid operation in the power system, and safety control operations are performed on the abnormal data and an early warning signal is issued. Otherwise, it is judged that there is no abnormality in the grid operation in the power system.

8. The cloud platform-based power safety monitoring system according to claim 1, characterized in that: The calculation model of the power safety assessment index is specifically as follows: Among them, SAI represents the power security assessment index, EEC represents the power balance assessment coefficient, AEC represents the power grid anomaly assessment coefficient, and α represents other influencing factors of power security.

9. The cloud platform-based power safety monitoring system according to claim 1, characterized in that: The specific method of comparing and judging the power safety assessment index with the preset safety threshold in the power safety assessment module is: The power safety assessment index is extracted and compared with the preset safety threshold. If the power safety assessment index is less than the preset safety threshold, it is judged that there is an abnormality in the power safety of the power system, and the operation and maintenance personnel are notified to perform security control on the abnormal data and issue an early warning signal. Otherwise, it is judged that there is no abnormality in the power safety of the power system.

10. A cloud platform-based power safety monitoring method, used for using a cloud platform-based power safety monitoring system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step S1: Building a cloud platform: Building a cloud platform including a virtualized resource management layer, a security protection layer, and a service management layer for storing, processing, and analyzing data, and performing network security management and control through a centralized method of joint security protection capabilities on and off the cloud; Step S2: Adaptive data collection: Collect comprehensive power parameters and adaptively adjust the data collection frequency according to the real-time load and operating status of the power system; Step S3: Analyze and judge the power balance: import the data transmitted in step S2 into the power balance evaluation coefficient model to calculate the power balance evaluation coefficient, and judge and analyze the power balance evaluation coefficient with the preset threshold; Step S4: Analyze and judge the power grid anomaly: import the data transmitted in step S2 into the power grid anomaly assessment coefficient model to calculate the power grid anomaly assessment coefficient, and judge and analyze the power grid anomaly assessment coefficient and the preset threshold value; Step S5: Performing power data error adjustment: performing error adjustment on the data of the abnormal results transmitted in the steps S3 and S4, and establishing a power data error adjustment model; Step S6: Analyze power safety: establish a power safety assessment index model based on the error-adjusted data in step S5, and calculate the power safety assessment index; Step S7: Evaluate power safety: compare and judge the power safety evaluation index with the preset safety threshold, and transmit the judgment and analysis results to the human-computer interaction module; Step S8: Perform human-computer interaction: Visually display the power monitoring data and evaluation results on the mobile devices of operation and maintenance personnel and users in the form of data tables, curves and graphics, so as to facilitate operation and maintenance personnel to make decisions on power safety.

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