Power grid limit intelligent alarm method and system based on risk assessment model

Abstract

The invention relates to the technical field of power grid limit alarm, and particularly discloses a power grid limit intelligent alarm method and system based on a risk assessment model. The multi-dimensional information of material aging, component loosening and component corrosion in the power equipment can be known more accurately, the performance influence value corresponding to each power equipment of each power node is analyzed, the equipment influence strength in the power grid can be known accurately, and the operation risk assessment degree of each power node in the power grid can be obtained through the risk assessment model. Complexity of the power grid limit and uncertainty of the risk are quantified and compared with an operation risk assessment threshold value, an accurate data comparison basis is provided for a power grid limit management system to execute intelligent limit alarm on each power node of the power grid finally, accuracy and response efficiency of power grid limit alarm can be improved, and reliability of the power grid limit alarm is improved. And safe and stable operation of a power system in a power grid is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid limit alarm, and in particular to a power grid limit intelligent alarm method and system based on a risk assessment model. Background Art

[0002] Due to the large scale of modern power systems, involving numerous power equipment and complex control logic, the uncertainty of power systems in the power grid has increased. The risk assessment model helps to quantify this complexity and uncertainty and provide a reference for the power grid's limit alarm. However, when conducting risk assessment on power grid limits, although the risk assessment model is applied, there are many inaccurate analysis factors in the risk assessment process, such as not considering the multidimensionality of data and inaccurate data obtained, which leads to inaccurate subsequent risk assessment and ultimately cannot guarantee the stable operation of the power grid.

[0003] For example, the invention patent with announcement number CN112150020B announced a regional power grid equipment safety risk assessment method based on alarm big data. According to the actual operation of the power grid, the two major indicators of equipment reliability index and equipment tripping rate are determined. For the equipment reliability index, the number of regional substations is fully considered, and an evaluation process based on alarm big data-equipment-substation-region is formed to obtain the equipment reliability index; for the equipment tripping rate, the line length, the number of main transformers, the number of circuit breakers and the repeated tripping rate of the same equipment are fully considered, and an evaluation process based on alarm big data statistics of tripping times-tripping events-equipment ratio adjustment-repeated tripping weighting is formed to obtain the equipment tripping rate. Based on the equipment reliability coefficient and the equipment tripping rate, the total score is calculated according to the specified weight, that is, the safety risk assessment result of the power grid equipment in a certain region, and finally the highest score is converted to obtain the safety risk assessment results of multiple regional power grid equipment; the safety risk assessment results of multi-level modeling and empowerment realize the horizontal comparison of multiple regions.

[0004] For example, the invention patent with announcement number CN111967738B announces a risk warning method, system and medium for the power grid information energy fusion system, establishes a power grid operation simulation model; identifies the input information of the information layer, and if it is a potential attack threat, classifies the potential attack threat. When it is a first type of information attack, the simulated operation status information is checked for security constraints. If the check passes, a first alarm signal is generated. If the check fails, a second alarm signal is generated, and the first warning scheduling plan is stored in the dispatch center; when it is a second type of information attack, the simulated unit plan is checked for security constraints. If the check passes, a third alarm signal is generated, and the second warning scheduling plan is stored in the dispatch center. If the check fails, a fourth alarm signal is generated, and the third warning scheduling plan is stored in the dispatch center. By pre-classifying the risk assessment of potential information attacks, rapid risk analysis and warning are made according to the real-time status of the energy layer.

[0005] Combining the above technical solutions, it is found that most technical solutions for power grid risk warning use partial data of the power grid as the data basis for power grid warning, which not only makes the power grid risk assessment inaccurate, but also leads to false alarms of power grid warning, which is not conducive to the stable operation of the power grid. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a method and system for intelligent warning of power grid limits based on a risk assessment model, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides a power grid limit intelligent alarm method based on a risk assessment model, including: locating each power device at each power node in the power grid, collecting the performance parameters corresponding to each power device at each power node, and analyzing to obtain the performance impact value corresponding to each power device at each power node; monitoring the operating status of each power node in the power grid to collect the operating data of each power node in the power grid within the risk assessment period, and through data preprocessing operations, recording the preprocessed operating data as analyzable data, thereby evaluating the analyzable data, and at the same time comprehensively analyzing the performance impact values ​​corresponding to each power device at each power node to obtain the operating stability index of each power node in the power grid; using the operating stability index of each power node in the power grid, and using the risk assessment model to obtain the operating risk assessment degree of each power node in the power grid, and comparing it with the operating risk assessment threshold preset in the power grid limit database, thereby the power grid limit management system executes limit intelligent alarm for each power node in the power grid.

[0008] As a further method, the performance impact value corresponding to each power device of each power node is specifically expressed as follows:

[0009] In the formula, is the performance impact value corresponding to the mth power device of the hth power node, is the material aging degree corresponding to the mth power equipment at the hth power node, is the component looseness corresponding to the mth power device of the hth power node, is the component corrosion degree corresponding to the mth power device at the hth power node, is the correction factor corresponding to the performance impact value preset in the power grid limit database, h is the number of each power node, , u is the total number of power nodes, m is the number of each power device, , n is the total number of electrical equipment.

[0010] As a further method, the data preprocessing operation is specifically to perform data cleaning operations on each operating data point in the operating data. If the data cleaning value of a certain operating data point is higher than the data cleaning threshold preset in the power grid limit database, the operating data point is eliminated, and the operating data points that are not eliminated are counted and recorded as analyzable data.

[0011] As a further method, the operation risk assessment degree of each power node in the power grid is obtained through the risk assessment model, and the specific process is as follows: The operation stability index of each power node in the power grid is used as the input mapping set of the risk assessment model. Through the built-in risk assessment function of the risk assessment model, the output mapping set of the risk assessment model is obtained, which is recorded as the operation risk assessment degree of each power node in the power grid.

[0012] As a further method, the power grid limit management system performs limit intelligent alarm on each power node of the power grid, and the specific process is: The operation risk assessment degree of each power node in the power grid is compared with the operation risk assessment threshold preset in the power grid limit database. If the operation risk assessment degree of a power node is greater than the operation risk assessment threshold, the power grid limit management system will execute a limit intelligent alarm on the power node in the power grid. Thus, the power nodes corresponding to the operation risk assessment degrees greater than the operation risk assessment threshold are counted and recorded as each limit alarm node. Finally, the power grid limit management system will execute a limit intelligent alarm on each limit alarm node in the power grid.

[0013] The second aspect of the present invention provides a system for a power grid limit intelligent alarm method based on a risk assessment model, comprising: an equipment performance analysis module, which is used to locate each power device of each power node in the power grid, collect the performance parameters corresponding to each power device of each power node, and analyze and obtain the performance impact value corresponding to each power device of each power node; a power grid operation evaluation module, which is used to monitor the operating status of each power node in the power grid, thereby collecting the operating data of each power node in the power grid within the risk assessment period, and through data preprocessing operations, the preprocessed operating data is recorded as analyzable data, thereby evaluating the analyzable data, and at the same time, the performance impact value corresponding to each power device of each power node is integrated to obtain the operating stability index of each power node in the power grid; a power grid limit alarm module, which is used to obtain the operating risk assessment degree of each power node in the power grid through the risk assessment model, and compare it with the operating risk assessment threshold preset in the power grid limit database, so that the power grid limit management system executes limit intelligent alarm for each power node in the power grid.

[0014] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention provides a method and system for intelligent alarm of power grid limit based on risk assessment model, analyzes the performance impact value corresponding to each power equipment of each power node, and can accurately know the impact strength of the equipment in the power grid. At the same time, the operating status of each power node in the power grid is monitored and evaluated to obtain the operating stability index of each power node in the power grid. The operating risk assessment degree of each power node in the power grid is obtained through the risk assessment model, and compared with the operating risk assessment threshold. This provides accurate data comparison basis for the final power grid limit management system to execute limit intelligent alarm on each power node in the power grid, which is beneficial to the accuracy of power grid limit alarm, so as to improve the stable operation efficiency of the power grid.

[0015] (2) The present invention locates each power device at each power node in the power grid and collects the performance parameters corresponding to each power device at each power node, so as to more accurately understand the multi-dimensional information of material aging, component loosening, and component corrosion in the power equipment, and provide more detailed parameter conditions for the subsequent comprehensive derivation of the performance impact value corresponding to each power device at each power node.

[0016] (3) The present invention monitors the operating status of each power node in the power grid, thereby obtaining the operating data of each power node in the power grid within the risk assessment period in real time, and through data preprocessing operations, the preprocessed operating data is made more accurate. By evaluating the analyzable data and integrating the performance impact values ​​corresponding to each power equipment of each power node, a more comprehensive operating stability index of each power node in the power grid can be obtained. The operating risk assessment degree of each power node in the power grid is obtained through the risk assessment model, which helps to quantify the complexity of the power grid limit and the uncertainty of the risk. Through the power grid limit management system, the power nodes of the power grid are executed with limit intelligent alarm, which can not only improve the accuracy and response efficiency of the power grid limit alarm, but also reduce unnecessary operation and maintenance costs, and at the same time ensure the safe and stable operation of the power system in the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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.

[0018] Figure 1 The figure is a schematic flow chart of the method steps of the present invention.

[0019] Figure 2 It is a schematic diagram of system module connection of the present invention. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0021] Reference Figure 1 As shown, the first aspect of the present invention provides a power grid limit intelligent alarm method based on a risk assessment model, including: locating each power equipment at each power node in the power grid, collecting the performance parameters corresponding to each power equipment at each power node, and analyzing to obtain the performance impact value corresponding to each power equipment at each power node.

[0022] It should be explained that the above-mentioned positioning of each power equipment at each power node in the power grid is performed based on the data acquisition and monitoring system in the power system. The data acquisition and monitoring system has a positioning function and can display the location information of the power equipment. Therefore, this example locates the power equipment in the power grid through the data acquisition and monitoring system.

[0023] Specifically, the performance impact value corresponding to each power device of each power node is obtained by comprehensive analysis of the material aging degree, component looseness, and component corrosion degree corresponding to each power device of each power node in this example. The specific expression is:

[0024] In the formula, is the performance impact value corresponding to the mth power device at the hth power node. In this example, since the power grid is greatly affected by the power equipment during operation, among which the performance impact is more extensive, if this example wants to maintain the stable operation of the power grid, it is necessary to analyze the impact of the performance of the power equipment on its operation. The materials and components of the power equipment are the key to maintaining its stable performance. Therefore, by evaluating the aging of the materials of the power equipment, whether the components are loose, and the corrosion of the components, the performance of the power equipment can be accurately and comprehensively determined.

[0025] is the material aging degree corresponding to the mth power equipment of the hth power node, indicating that the strength and toughness of the materials used in the power equipment gradually decrease after a certain period of use, which is material aging.

[0026] is the looseness of the component corresponding to the mth power device of the hth power node, indicating the degree to which the originally tightened connection gradually loosens in the power device due to factors such as vibration.

[0027] is the component corrosion degree corresponding to the mth power device at the hth power node, indicating that the metal in the power device undergoes a chemical reaction, thereby destroying the structural integrity of the material, which is component corrosion.

[0028] The correction factor corresponding to the performance impact value preset in the power grid limit database can be obtained by fitting the corresponding relationship between the historical current excess times, the historical voltage excess times, the historical vibration amplitude values ​​and the performance impact values ​​in the power equipment, and the fitting curve corresponding to the performance impact value is obtained, and the real-time current excess times, voltage excess times, and vibration amplitude values ​​are brought into the fitting curve corresponding to the performance impact value to obtain the correction factor corresponding to the performance impact value in this example. At the same time, the value range in this example is (0, 1).

[0029] h is the number of each power node, , u is the total number of power nodes, m is the number of each power device, , n is the total number of electrical equipment.

[0030] In this example embodiment, the above-mentioned power nodes refer to the key points in the power grid where the loads are connected. These nodes constitute the basic structure of the power system, such as distribution stations, switch stations, distribution lines, etc.; power equipment includes but is not limited to transformers, circuit breakers, and relays.

[0031] Furthermore, the analysis obtains the performance impact value corresponding to each power device of each power node, and the specific analysis process is as follows: The current excess times and voltage excess times corresponding to each power equipment of each power node are accumulated to obtain the basic power excess index corresponding to each power equipment of each power node, which represents the total number of current excess and voltage excess times, and compared with the material aging degree corresponding to each basic power excess index interval preset in the power grid limit database, and the material aging degree corresponding to each power equipment of each power node is obtained by matching. The specific matching process is as follows: Each basic excess power index interval corresponds to the material aging degree. The basic excess power index interval corresponding to the basic excess power index of a certain power equipment at a certain power node is queried from the line fault information database, and the material aging degree corresponding to the basic excess power index is the material aging degree corresponding to the power equipment at the power node.

[0032] Extract the vibration allowable amplitude value corresponding to each power device from the power grid limit database, which represents the maximum allowable value within the vibration amplitude standard range, and compare it with the vibration amplitude value of each vibration corresponding to each power device of each power node, and count the number of vibrations corresponding to the vibration amplitude value greater than the vibration allowable amplitude value, which is recorded as the number of abnormal vibrations corresponding to each power device of each power node, and match it with the component looseness corresponding to each abnormal vibration number interval preset in the power grid limit database, and obtain the component looseness corresponding to each power device of each power node. The specific matching process is as follows: Each abnormal vibration frequency interval corresponds to a component looseness. The abnormal vibration frequency interval corresponding to the abnormal vibration frequency of a certain power equipment at a certain power node is queried from the line fault information database, and the component looseness corresponding to the abnormal vibration frequency interval is the component looseness corresponding to the power equipment at the power node.

[0033] According to the operating years corresponding to each power equipment of each power node, the number of extreme weather events within the corresponding operating years of each power equipment of each power node is extracted. For example, typhoons, floods, droughts and blizzards are all extreme weather events. The component corrosion degrees corresponding to each extreme weather frequency interval preset in the power grid limit database are matched to obtain the component corrosion degrees corresponding to each power equipment of each power node. The specific matching process is as follows: Each extreme weather frequency interval corresponds to a component corrosion degree. The extreme weather frequency interval corresponding to the extreme weather frequency of a certain power equipment at a certain power node is queried from the line fault information database. The component corrosion degree corresponding to the extreme weather frequency interval is the component corrosion degree corresponding to the power equipment at the power node.

[0034] Specifically, the performance parameters corresponding to each power equipment at each power node include the number of current excesses, the number of voltage excesses, the vibration amplitude values ​​of each vibration corresponding to each power equipment at each power node, and the operating years corresponding to each power equipment at each power node.

[0035] It needs to be explained that the above-mentioned current excess times and voltage excess times are obtained by searching, through the equipment control system in the power system, the times when the operating current of the power equipment exceeds the rated current and the times when the operating voltage exceeds the rated voltage during operation, which are the current excess times and voltage excess times; the vibration amplitude value is obtained by searching, through the equipment control system in the power system, the amplitude of each vibration of the power equipment, and the time is within the operating life of the power equipment; the operating life is obtained by directly finding out through the equipment control system in the power system, which indicates the length of time from the formal operation of the power equipment to the current monitoring.

[0036] In a specific embodiment, the present invention locates each power equipment at each power node in the power grid, and collects the performance parameters corresponding to each power equipment at each power node, so as to more accurately understand the multidimensional information of material aging, component loosening, and component corrosion in the power equipment, and provide more detailed parameter conditions for the subsequent comprehensive derivation of the performance impact values ​​corresponding to each power equipment at each power node.

[0037] The operating status of each power node in the power grid is monitored to collect the operating data of each power node in the power grid during the risk assessment period, and through data preprocessing operations, the preprocessed operating data is recorded as analyzable data, thereby evaluating the analyzable data. At the same time, the performance impact value corresponding to each power equipment of each power node is integrated to obtain the operating stability index of each power node in the power grid.

[0038] Specifically, the data preprocessing operation is to perform a data cleaning operation on each operating data point in the operating data, wherein the operating data point represents a series of data in the operating data, and the data cleaning operation refers to a series of processing on the operating data to remove erroneous, incomplete, redundant or irrelevant information, so as to make the operating data more accurate, consistent and useful. If the data cleaning value of an operating data point is higher than the data cleaning threshold preset in the power grid limit database, it means that the value displayed by the operating data point after the cleaning process exceeds the data cleaning operation range specified by the power grid, and the operating data point is removed, thereby counting the operating data points that have not been removed and recording them as analyzable data.

[0039] Furthermore, the analyzable data specifically includes the input active power of each power node at each operating time point, the output active power of each power node at each operating time point, and the frequency of each power node at each operating time point.

[0040] It needs to be explained that the above-mentioned input active power and output active power are obtained by power sensors, and the frequency can be obtained through the remote monitoring system in the power system, and the remote monitoring system obtains the frequency information of each power node in real time; the operation time points are obtained by dividing the operation cycle into equal time intervals. For example, 1 hour is divided into 20 minutes, and the operation time points are 0, 20 minutes, 40 minutes, and 60 minutes; the operation cycle refers to the length of time for monitoring the power nodes in this example. The specific time period changes according to changes in factors such as the number of power nodes and the impact of power equipment, and this example does not make any special limitations.

[0041] Specifically, the operation stability index of each power node in the power grid is analyzed in the following process: The reference frequency of the power grid is extracted from the power grid limit database, and the mean reactive power of each power node in the power grid operation cycle is obtained. The acquisition method is to obtain the reactive power at each operation time point through the built-in power sensor of the power node. The mean reactive power in the power grid operation cycle is obtained by averaging, and the performance impact value corresponding to each power device of each power node is integrated to obtain the operation stability index of each power node in the power grid. The specific expression is as follows:

[0042] In the formula, is the operation stability index of the hth power node in the power grid. In this example, if the active power input and active power output of the power node are not kept in a stable balance, the reactive power of the power node will fluctuate greatly, which will affect the frequency of the power grid and make the power equipment unstable, which will eventually lead to power instability and make it impossible to improve the operation efficiency of the power node. Therefore, in summary, if you want to ensure the stable operation of the power grid, you need to comprehensively evaluate multiple influencing factors, that is, the distribution balance of input power and output power, reactive power and frequency are always maintained within a reasonable range, and the performance of the power equipment related to it is good, so as to ultimately ensure the stability of the power system.

[0043] The influencing factor of the active power deviation unit value preset in the power grid limit database can be obtained by fitting the corresponding relationship between the historical input active power, the historical output active power and the active power deviation value of the power node, and the fitting curve corresponding to the active power deviation value is obtained, and the real-time input active power and output active power are brought into the fitting curve corresponding to the active power deviation value to obtain the influencing factor of the active power deviation unit value in this example. At the same time, the value range in this example is (0, 1).

[0044] is the input active power of the hth power node at the cth operating time point, indicating the energy received by the power node from the outside.

[0045] is the output active power of the hth power node at the cth operating time point, indicating the energy sent by the power node to the outside.

[0046] It is the reference frequency of the power grid, indicating that the frequency of the power grid is within a reasonable range.

[0047] is the frequency of the hth power node at the cth operating time point, indicating the rate of periodic fluctuation of the alternating current in the power node.

[0048] is the mean reactive power of the hth power node during the grid operation cycle, indicating the power used to establish and maintain the magnetic field in the power system.

[0049] The influencing factor of the unit value corresponding to the mean reactive power value preset in the power grid limit database can be obtained by fitting the fitting curve corresponding to the mean reactive power value through the correspondence between the historical temperature change values, historical reactive power change values ​​and the mean reactive power value of the power equipment, and the real-time temperature change values ​​and reactive power change values ​​are brought into the fitting curve corresponding to the mean reactive power value to obtain the influencing factor of the unit value corresponding to the mean reactive power value in this example. At the same time, the value range in this example is (0, 1).

[0050] The weight factor corresponding to the performance impact value preset in the power grid limit database can be obtained by fitting the fitting curve corresponding to the performance impact value through the correspondence between the historical temperature change value of the power equipment, the ambient temperature change value and the performance impact value, and the real-time equipment temperature and ambient temperature are brought into the fitting curve corresponding to the performance impact value to obtain the weight factor corresponding to the performance impact value in this example. At the same time, the value range in this example is (0, 1).

[0051] It is the performance impact value corresponding to the mth power equipment of the hth power node, which represents the value obtained by comprehensive analysis of the material aging degree, component looseness, and component corrosion degree corresponding to each power equipment of each power node.

[0052] h is the number of each power node, , u is the total number of power nodes, m is the number of each power device, , n is the total number of power equipment, c is the number of each operating time point, , y is the total number of running time points.

[0053] The operation stability index of each power node in the power grid is used to obtain the operation risk assessment degree of each power node in the power grid through the risk assessment model, and is compared with the operation risk assessment threshold preset in the power grid limit database. The power grid limit management system thus executes limit intelligent alarm for each power node in the power grid.

[0054] Specifically, the operation risk assessment degree of each power node in the power grid is obtained through the risk assessment model, and the specific process is as follows: Taking the operation stability index of each power node in the power grid as the input mapping set of the risk assessment model means that the risk assessment model can quantify the operation stability of each key power node in the power grid and build a comprehensive risk assessment framework. Therefore, through the built-in risk assessment function of the risk assessment model, the risk level of each power node is quantitatively assessed according to the weights and algorithms pre-set in the risk assessment model, and the output mapping set of the risk assessment model is obtained. Usually, the risk assessment model can convert qualitative risk perception into quantitative risk level, which is recorded as the operation risk assessment degree of each power node in the power grid.

[0055] It should be explained that the above risk assessment model is a dynamic system. With the continuous changes in grid operating conditions, equipment status and technological progress, the results of its risk assessment also change accordingly. Its input and output ends can be one data or multiple data. This example operates as multi-dimensional data.

[0056] Furthermore, the power grid limit management system executes limit intelligent alarm for each power node of the power grid, and the specific process is as follows: The operation risk assessment of each power node in the power grid is compared with the operation risk assessment threshold preset in the power grid limit database. The operation risk assessment threshold represents the maximum value of the operation risk assessment within a reasonable range. If the operation risk assessment of a power node is greater than the operation risk assessment threshold, it means that the current operation state of the power node has a high risk level, which may indicate potential unstable factors, equipment failures, power supply interruptions or other safety issues. In this case, the power grid limit management system will execute a limit intelligent alarm for the power node in the power grid. The specific alarm operations are as follows: The power grid limit management system first detects that the risk assessment of the power node exceeds the preset threshold and immediately activates the alarm mechanism. At the same time, the power grid limit management system generates detailed alarm information, which may include the name and location of the power node, the specific value of the risk assessment, the comparison result with the threshold, the risk type (such as voltage instability, frequency fluctuation, overload, etc.) and other key information; according to the severity of the risk, the alarm information is divided into different levels (such as minor, medium, and serious) to facilitate managers at different levels to respond according to priority; the alarm information is distributed through various channels, including but not limited to e-mail, SMS, mobile application push, in-site message notification, etc., to ensure that all relevant operations, maintenance and decision-makers can receive warnings in time, thereby completing the intelligent alarm of power node limits.

[0057] The power grid limit management system is a system for managing the operation of the power system. Its core purpose is to ensure the safe, stable and economical operation of the power network. Its contents include but are not limited to operation limit setting, early warning and alarm, and risk assessment management.

[0058] Thus, each power node corresponding to a statistical operation risk assessment degree greater than the operation risk assessment threshold is recorded as each limit alarm node. Finally, the power grid limit management system executes limit intelligent alarm on each limit alarm node of the power grid.

[0059] If the operation risk assessment degree of a power node is less than or equal to the operation risk assessment threshold, it means that the current operation status of the power node is considered to be safe and controllable, and the risk level is low, and there is no need to issue a limit alarm for the power node.

[0060] In a specific embodiment, the present invention monitors the operating status of each power node in the power grid, so as to obtain the operating data of each power node in the power grid within the risk assessment period in real time, and through data preprocessing operations, the preprocessed operating data is made more accurate. By evaluating the analyzable data and integrating the performance impact values ​​corresponding to each power equipment of each power node, a more comprehensive operating stability index of each power node in the power grid can be obtained. The operating risk assessment degree of each power node in the power grid is obtained through a risk assessment model, which helps to quantify the complexity of power grid limits and the uncertainty of risks. Through the power grid limit management system, the limit intelligent alarm is executed on each power node in the power grid, which can not only improve the accuracy and response efficiency of the power grid limit alarm, but also reduce unnecessary operation and maintenance costs, and ensure the safe and stable operation of the power system in the power grid.

[0061] Reference Figure 2 As shown, the second aspect of the present invention provides a system for a power grid limit intelligent alarm method based on a risk assessment model, including: an equipment performance analysis module, a power grid operation assessment module, and a power grid limit alarm module.

[0062] The second aspect of the present invention provides a system for an intelligent alarm method for power grid limit based on a risk assessment model, which also includes a power grid limit database for storing the material aging degree corresponding to each preset basic power excess index interval, the component looseness corresponding to each preset abnormal vibration frequency interval, the component corrosion degree corresponding to each preset extreme weather frequency interval, the correction factor corresponding to the preset performance impact value, the preset data cleaning threshold, the influence factor of the preset unit value of the active power deviation, the influence factor of the preset unit value corresponding to the reactive power mean, the weight factor corresponding to the preset performance impact value, the preset operation risk assessment threshold, the vibration allowable amplitude value corresponding to each power equipment, and the reference frequency of the power grid.

[0063] The equipment performance analysis module is connected to the power grid operation evaluation module, the power grid operation evaluation module is connected to the power grid limit alarm module, and the equipment performance analysis module, the power grid operation evaluation module and the power grid limit alarm module are all connected to the power grid limit database.

[0064] The equipment performance analysis module is used to locate each power device at each power node in the power grid, collect performance parameters corresponding to each power device at each power node, and analyze and obtain performance impact values ​​corresponding to each power device at each power node.

[0065] The power grid operation evaluation module is used to monitor the operating status of each power node in the power grid, so as to collect the operating data of each power node in the power grid during the risk assessment period, and through data preprocessing operations, record the preprocessed operating data as analyzable data, thereby evaluating the analyzable data, and at the same time comprehensively analyze the performance impact value corresponding to each power equipment of each power node to obtain the operating stability index of each power node in the power grid.

[0066] The power grid limit alarm module is used to obtain the operation risk assessment degree of each power node in the power grid through the risk assessment model based on the operation stability index of each power node in the power grid, and compare it with the operation risk assessment threshold preset in the power grid limit database, so that the power grid limit management system executes limit intelligent alarm for each power node in the power grid.

[0067] In a specific embodiment, the present invention provides a power grid limit intelligent alarm method and system based on a risk assessment model, analyzes the performance impact value corresponding to each power equipment of each power node, and can accurately know the equipment impact strength in the power grid. At the same time, the operating status of each power node in the power grid is monitored and evaluated to obtain the operating stability index of each power node in the power grid. The operating risk assessment degree of each power node in the power grid is obtained through the risk assessment model and compared with the operating risk assessment threshold. This provides accurate data comparison basis for the final power grid limit management system to execute limit intelligent alarms on each power node in the power grid, which is beneficial to the accuracy of power grid limit alarms and improves the stable operation efficiency of the power grid.

[0068] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A power grid limit intelligent alarm method based on risk assessment model, characterized in that: include: Locate each power device at each power node in the power grid, collect performance parameters corresponding to each power device at each power node, and analyze and obtain the performance impact value corresponding to each power device at each power node; Monitor the operating status of each power node in the power grid to collect the operating data of each power node in the power grid during the risk assessment period, and record the preprocessed operating data as analyzable data through data preprocessing operations, thereby evaluating the analyzable data, and at the same time, comprehensively consider the performance impact values ​​corresponding to each power device of each power node to obtain the operating stability index of each power node in the power grid; The operation stability index of each power node in the power grid is used to obtain the operation risk assessment degree of each power node in the power grid through the risk assessment model, and is compared with the operation risk assessment threshold preset in the power grid limit database. The power grid limit management system thus executes limit intelligent alarm for each power node in the power grid.

2. According to claim 1, a method for intelligent warning of power grid quota based on risk assessment model is characterized in that: The performance parameters corresponding to each power equipment at each power node specifically include the number of current excesses, the number of voltage excesses, the vibration amplitude values ​​of each vibration corresponding to each power equipment at each power node, and the operating years corresponding to each power equipment at each power node.

3. The method for intelligent warning of power grid quota based on risk assessment model according to claim 2 is characterized in that: The analysis obtains the performance impact value corresponding to each power device at each power node, and the specific analysis process is as follows: The current excess times and voltage excess times corresponding to each power device of each power node are accumulated to obtain the basic power excess index corresponding to each power device of each power node, and compared with the material aging degree corresponding to each basic power excess index interval preset in the power grid limit database, and the material aging degree corresponding to each power device of each power node is obtained by matching; Extract the vibration allowable amplitude value corresponding to each power device from the power grid limit database, compare it with the vibration amplitude value of each vibration corresponding to each power device of each power node, count the number of vibrations corresponding to the vibration amplitude value greater than the vibration allowable amplitude value, record it as the number of abnormal vibrations corresponding to each power device of each power node, and match it with the component looseness corresponding to each abnormal vibration number interval preset in the power grid limit database, and obtain the component looseness corresponding to each power device of each power node; According to the corresponding operating years of each power equipment at each power node, the number of extreme weather times of each power equipment at each power node within the corresponding operating years is extracted, and matched with the component corrosion degrees corresponding to each extreme weather number interval preset in the power grid limit database to obtain the component corrosion degrees corresponding to each power equipment at each power node.

4. The method for intelligent warning of power grid quota based on risk assessment model according to claim 3 is characterized in that: The performance impact value corresponding to each power device of each power node is specifically expressed as follows: In the formula, is the performance impact value corresponding to the mth power device of the hth power node, is the material aging degree corresponding to the mth power equipment at the hth power node, is the component looseness corresponding to the mth power device of the hth power node, is the component corrosion degree corresponding to the mth power device at the hth power node, is the correction factor corresponding to the performance impact value preset in the power grid limit database, h is the number of each power node, , u is the total number of power nodes, m is the number of each power device, , n is the total number of electrical equipment.

5. The method for intelligent warning of power grid quota based on risk assessment model according to claim 1 is characterized in that: The data preprocessing operation specifically performs a data cleaning operation on each operating data point in the operating data. If the data cleaning value of a certain operating data point is higher than the data cleaning threshold preset in the power grid limit database, the operating data point is eliminated, thereby counting the operating data points that have not been eliminated and recording them as analyzable data.

6. The method for intelligent warning of power grid quota based on risk assessment model according to claim 5 is characterized in that: The analyzable data specifically include the input active power of each power node at each operating time point, the output active power of each power node at each operating time point, and the frequency of each power node at each operating time point.

7. The method for intelligent warning of power grid quota based on risk assessment model according to claim 6 is characterized in that: The specific analysis process of the operation stability index of each power node in the power grid is as follows: The reference frequency of the power grid is extracted from the power grid limit database, and the mean reactive power of each power node in the power grid operation cycle is obtained. The performance impact value corresponding to each power device of each power node is integrated to obtain the operation stability index of each power node in the power grid. The specific expression is as follows: In the formula, is the operation stability index of the hth power node in the power grid, is the influencing factor of the active power deviation unit value preset in the power grid limit database, is the input active power of the hth power node at the cth operating time point, is the output active power of the hth power node at the cth operating time point, is the reference frequency of the power grid, is the frequency of the hth power node at the cth operating time point, is the mean reactive power of the hth power node during the grid operation cycle, is the impact factor of the unit value corresponding to the mean reactive power value preset in the power grid limit database, is the weight factor corresponding to the performance impact value preset in the power grid limit database, is the performance impact value corresponding to the mth power device of the hth power node, h is the number of each power node, , u is the total number of power nodes, m is the number of each power device, , n is the total number of power equipment, c is the number of each operating time point, , y is the total number of running time points.

8. The method for intelligent warning of power grid quota based on risk assessment model according to claim 1 is characterized in that: The operation risk assessment degree of each power node in the power grid is obtained by the risk assessment model, and the specific process is as follows: The operation stability index of each power node in the power grid is used as the input mapping set of the risk assessment model. Through the built-in risk assessment function of the risk assessment model, the output mapping set of the risk assessment model is obtained, which is recorded as the operation risk assessment degree of each power node in the power grid.

9. The method for intelligent warning of power grid quota based on risk assessment model according to claim 8 is characterized in that: The power grid limit management system executes limit intelligent alarm for each power node of the power grid, and the specific process is as follows: The operation risk assessment degree of each power node in the power grid is compared with the operation risk assessment threshold preset in the power grid limit database. If the operation risk assessment degree of a power node is greater than the operation risk assessment threshold, the power grid limit management system will execute a limit intelligent alarm on the power node in the power grid. Thus, the power nodes corresponding to the operation risk assessment degrees greater than the operation risk assessment threshold are counted and recorded as each limit alarm node. Finally, the power grid limit management system will execute a limit intelligent alarm on each limit alarm node in the power grid.

10. A system for a power grid quota intelligent alarm method based on a risk assessment model according to any one of claims 1 to 9, characterized in that: include: The equipment performance analysis module is used to locate each power device at each power node in the power grid, collect the performance parameters corresponding to each power device at each power node, and analyze and obtain the performance impact value corresponding to each power device at each power node; The power grid operation evaluation module is used to monitor the operation status of each power node in the power grid, so as to collect the operation data of each power node in the power grid during the risk evaluation period, and through data preprocessing operations, record the preprocessed operation data as analyzable data, thereby evaluating the analyzable data, and at the same time comprehensively integrate the performance impact values ​​corresponding to each power device of each power node to obtain the operation stability index of each power node in the power grid; The power grid limit alarm module is used to use the operation stability index of each power node in the power grid to derive the operation risk assessment degree of each power node in the power grid through the risk assessment model, and compare it with the operation risk assessment threshold preset in the power grid limit database. The power grid limit management system thus executes limit intelligent alarm for each power node in the power grid.

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