A monitoring method, system, device and medium for abnormal events in the power grid

Abstract

The present disclosure relates to the technical field of power grid monitoring, and provides a method, a system, a device and a medium for monitoring power grid abnormal events. The method includes collecting monitoring information of historical abnormal events of the power grid, including real-time control commands, alarm information, and measurement information, which are combined into different event data blocks after numerical processing, and each block corresponds to a power grid abnormal event; setting weights for the alarm information, calculating an event weight coefficient, and at the same time calculating the event occurrence frequency, and multiplying the two to obtain the support degree; collecting power grid monitoring information in real time, and when it matches the information of a certain event data block and the support degree of the corresponding event is higher than the threshold, combining the event data block into an event container and pushing it to the staff, so as to realize the monitoring of power grid abnormal events. This method can effectively monitor power grid abnormal events and improve the safety and stability of power grid operation.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of power grid monitoring, and particularly relates to a method, system, device, and medium for monitoring power grid abnormal events. Background Art

[0002] With the rapid development of the domestic smart grid, the digitization, informatization, and intelligence of the power system have been continuously improved, and data such as power energy flow, information control flow, and management service flow have grown explosively. However, traditional data processing technologies have encountered bottlenecks and cannot meet the analysis requirements of the power industry for quickly extracting knowledge and information from massive data.

[0003] In the unattended centralized monitoring and management mode of substations, when large-scale fault events occur in the power grid, disorderly and chaotic monitoring signals interfere with the monitoring personnel's identification of events, classification of the importance levels of events, and decision-making, resulting in low quality and efficiency of power grid monitoring work. Summary of the Invention

[0004] To solve the above problems, the present disclosure provides a method, system, device, and medium for monitoring power grid abnormal events. By using the method of synthesizing information into event data blocks, it can effectively monitor power grid abnormal events and improve the safety and stability of power grid operation.

[0005] The technical solution of the present invention is as follows:

[0006] Collect monitoring information when abnormal events occur in the power grid history. The monitoring information includes: real-time control commands, alarm information, and measurement information;

[0007] After numerically processing the monitoring information, combine the monitoring information. Different numerical combinations of monitoring information correspond to different event data blocks, and each event data block corresponds to a power grid abnormal event;

[0008] Set weights for the alarms in each alarm information in the monitoring information; after adding the weights of multiple alarms triggered by a power grid abnormal event and dividing by the number of alarms, obtain the event weight coefficient of the power grid abnormal event;

[0009] Calculate the occurrence frequency of the power grid abnormal event; multiply the occurrence frequency of the power grid abnormal event by the event weight coefficient of the power grid abnormal event to obtain the support degree of the power grid abnormal event;

[0010] Real-time collect the monitoring information of the power grid. When the monitoring information matches the information in a certain event data block A and the support degree of the power grid abnormal event corresponding to the event data block A is higher than the threshold, combine the event data block A into an event container and push it to the staff to achieve the monitoring of power grid abnormal events.

[0011] Furthermore,

[0012] The event weight coefficient is as follows:

[0013]

[0014] Among them, QZi represents the weight coefficient of the power grid abnormal event SJ in ; represents the sum of the alarm weights triggered by the power grid abnormal event SJ in , where the weight of each alarm needs to be set manually; represents the number of alarms that occur;

[0015] The occurrence frequency of the power grid abnormal event is as follows:

[0016]

[0017] Among them, SW(SJ in ) represents the number of times the SJ event appears in the monitoring information table, and SW(TO) represents the total number of power grid abnormal events that appear in the monitoring information point table. in

[0018] Furthermore,

[0019] collect the monitoring information when abnormal events occurred in the historical power grid, including:

[0020] Set the CK value and the SS value. The CK value is the observation time of the monitoring information; the SS value is the adjustment amount of the CK value, which is used to include the marked special monitoring information within the observation time of the monitoring information;

[0021] Aggregate all the monitoring information within the time period of CK+SS when the abnormal event occurs to achieve the collection of the monitoring information when the abnormal event occurs.

[0022] Furthermore,

[0023] Perform numerical processing on the monitoring information; including:

[0024] Set the first variable to represent the control command information, and set two values for the first variable, which respectively represent: control closing position, control opening position;

[0025] Obtain the device status quantity information from the alarm information, set the second variable to represent the device status quantity information, and set two values for the second variable, which respectively represent: action, reset;

[0026] Set the third variable to represent the measurement information, and set two values for the third variable, which respectively represent: the measurement data exceeds the standard value, the measurement data is normal.

[0027] Furthermore, ​

[0028] The measurement information includes:

[0029] The current, voltage, frequency, and angle of the same measurement point.

[0030] Furthermore,

[0031] The information of the event data block includes:

[0032] Event name, event level, associated monitoring and warning information, measurement data, basic device parameters, and historical information of similar events.

[0033] Furthermore,

[0034] The event name includes:

[0035] Accident, anomaly, operation, maintenance, defect, outage.

[0036] A monitoring system for power grid abnormal events, characterized by comprising:

[0037] An information acquisition module, configured to acquire monitoring information when an abnormal event occurs in the power grid history, and the monitoring information includes: real-time control commands, warning information, and measurement information;

[0038] An event construction module, configured to numerically process the monitoring information and then combine the monitoring information. Different numerical combinations of monitoring information correspond to different event data blocks, and each event data block corresponds to a power grid abnormal event;

[0039] A calculation module, configured to set weights for each warning information in the monitoring information; after adding the weights of multiple warnings triggered by a power grid abnormal event and dividing by the number of warnings, obtain the event weight coefficient of the power grid abnormal event;

[0040] Calculate the occurrence frequency of the power grid abnormal event; multiply the occurrence frequency of the power grid abnormal event by the event weight coefficient of the power grid abnormal event to obtain the support degree of the power grid abnormal event;

[0041] A monitoring module, configured to acquire the monitoring information of the power grid in real time. When the monitoring information matches the information in a certain event data block A and the support degree of the power grid abnormal event corresponding to the event data block A is higher than the threshold, combine the event data block A into an event container and push it to the staff to implement the monitoring of the power grid abnormal event.

[0042] Compared with the prior art, the present disclosure has the following advantages:

[0043] By collecting the monitoring information of historical abnormal events in the power grid (including real-time control commands, alarm information, and measurement information) and performing numerical processing to combine them into event data blocks, the problem of disorderly and chaotic monitoring signals in the background technology is solved; in the unattended centralized monitoring mode, the monitoring signals are made orderly and the corresponding relationships are clear, which helps the monitoring personnel to identify and distinguish events.

[0044] On the one hand, by setting weights for the alarm information to obtain the event weight coefficient and combining it with the occurrence frequency, the support degree can highlight important events, that is, events with high weight and high frequency have high support degree, which can highlight events with great impact on the power grid; on the other hand, only event data blocks with a support degree higher than the threshold will be pushed, realizing effective screening, avoiding interfering with the staff, and improving the monitoring quality and efficiency.

[0045] Other features and advantages of the present disclosure will be described in the subsequent specification, and, in part, will be obvious from the specification, or will be understood by implementing the present disclosure. The objectives and other advantages of the present disclosure can be achieved and obtained through the structures pointed out in the specification, claims, and drawings. Brief Description of the Drawings

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present disclosure. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0047] Figure 1 Shows the schematic diagram of the method of the present invention;

[0048] Figure 2 Shows the schematic diagram of the execution flow of the present invention;

[0049] Figure 3 Shows the schematic diagram of the software system architecture of the embodiment of the present invention;

[0050] Figure 4 Shows the schematic diagram of the interaction of system modules of the present invention. Detailed Description of the Embodiments

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present disclosure with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are some, but not all, of the embodiments of the present disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present disclosure.

[0052] Figure 1 , Figure 2 shows the schematic diagram and flowchart of the method according to the present invention. The specific implementation details of the present invention include:

[0053] Step 1: Data extraction:

[0054] Collect historical monitoring information of the power grid, such as Figure 1 shown, the historical monitoring information of the power grid generally includes real-time control commands, alarm information, and measurement information.

[0055] During sampling, in order to process complex events, it is necessary to aggregate alarm information, set CK value and SS value. The CK value is the observation time of the alarm. For example, if the CK value is 5s, then sampling needs to divide and aggregate all alarms within 5s; the SS value is to ensure that special alarms are not missed. Information can be collected within SS seconds to the left or right of the window value, or both left and right can be collected, that is, the CK value can be variably limited by SS seconds on both the left and right limits.

[0056] Step 2: Information processing:

[0057] First, perform regularization processing on the alarm information. Such as Figure 2 shown, the information included in the complex events of the power grid generally includes real-time control commands, alarm information, and measurement information.

[0058] Define the control commands of the same measuring point as KZ. KZ includes 2 states, where KZ = 1 represents "control on", and KZ = 0 represents "control off";

[0059] Define the status quantity information of the same measuring point as GJ. GJ includes 2 states, where GJ = 1 represents "action", and GJ = 0 represents "reset";

[0060] Through measurement information, values such as current, voltage, frequency, and angle (phase angle between voltage and current) of the same measuring point can be obtained, and their characteristics can be obtained by comparing with the set values, which is defined as LC. Use LCi to identify the current quantity, LCi1 represents the first current quantity, LCu to identify the voltage quantity, and LCu1 represents the first voltage quantity. LC includes 2 states, where LC = 1 represents over-limit, and LC = 0 represents normal.

[0061] Example of regularizing alarms. Parse the information, and the input is a certain alarm information of the monitoring system. For example, "SOE January 1, 2021 0:0:10:245 110kV test substation 115 switch tripped" is defined as alarm text T (that is, the text form of Gi above). T = (TYPE, ATTRI, STATION, EQU, VOL, SECEQU, PROTDEVICE, ACTION, TIME):

[0062] TYPE represents the type of information, including control, alarm, and measurement.

[0063] ATTRI represents the attributes of the alarm. The attribute types include maintenance, SOE, and COS.

[0064] STATION represents the location where the alarm information occurs, generally referring to the name of a certain substation; EQU represents the primary equipment represented by the alarm information, generally taking the dispatching number.

[0065] VOL represents the voltage level corresponding to the alarm information, generally including 1000 kV and above, 500 kV, 200 kV, 110 kV, 35 kV, and 10 kV and below.

[0066] SECEQU represents the secondary equipment in the alarm information, including measurement and control equipment, protection equipment, auxiliary equipment (such as astronomical clocks, DC power supplies, UPS, etc.).

[0067] PROTDEVICE is an attribute of the alarm information, including information such as action, alarm, abnormality, over upper limit, over lower limit, protection section I, II, III, and IV.

[0068] ACTION is the action behavior of the relevant equipment, including action, trip, etc.

[0069] TIME represents the time when the fault information occurs. By comparing the alarm information text with the dispatching monitoring information table, the alarm information text is determined in sequence. For example, T=(GJ, SOE, test substation, 315 switch, 110 kV, measurement and control, action, trip, January 1, 2021 0:0:10:245).

[0070] To enable the machine to better understand, it is necessary to convert the alarm text T into a vector. Gi=(TYPE, ATTRI, STATION, EQU, VOL, SECEQU, PROTDEVICE, ACTION, TIME)

[0071] TYPE represents the type of information, including control, alarm, and measurement. If control is 0, alarm is 1, and measurement is 2.

[0072] ATTRI represents the attributes of the alarm. The attribute types include maintenance, SOE, and COS. Maintenance is 0, SOE is 1, and COS is 2.

[0073] STATION represents the location where the alarm information occurs, generally referring to the name of a certain substation. The substation list needs to correspond according to the specific situation. For example, if there are 30 stations, 0 - 29 correspond to different substations, and the test station can be represented by 0.

[0074] EQU represents the primary equipment represented by the alarm information, usually taking the dispatching number;

[0075] VOL represents the voltage level corresponding to the alarm information, generally including 1000 kV and above, 500 kV, 200 kV, 110 kV, 35 kV, 10 kV and below. For 1000 kV it is 0, for 500 kV it is 1, for 200 kV it is 2, for 110 kV it is 3, for 35 kV it is 4, for 10 kV it is 5 and below;

[0076] SECEQU represents the secondary equipment in the alarm information, including measurement and control equipment, protection equipment, auxiliary equipment (such as astronomical clocks, DC power supplies, UPS, etc.). For measurement and control equipment it is 0, for protection equipment it is 1, for auxiliary equipment it is 1;

[0077] PROTDEVICE is the attribute of the alarm information, including information such as action, alarm, anomaly, over upper limit, over lower limit, protection section I, II, III, IV, etc. For action it is 0, for alarm it is 1, for anomaly it is 2, for over upper limit it is 3, for over lower limit it is 4, for protection section I it is 5, for section II it is 6, for section III it is 7, for section IV it is 8;

[0078] ACTION is the action behavior of the relevant equipment, including action, trip, etc. For action it is 0, for trip it is 1; TIME represents the time when the fault information occurred. 0:0:10:245 on January 1, 2021 is represented as 20210101000010245. The example alarm vector G=(1,1,0,315,3,0,0,1,20210101000010245)

[0079] And an alarm event SJin may contain at least one Gi.

[0080] Step 3: Construct the event data block:

[0081] According to the processed information, different information is combined into different event data blocks, and each event data block corresponds to a type of power grid abnormal event.

[0082] The information of the event data block mainly includes content such as event name, event level, associated monitoring alarm information, measurement data, basic equipment parameters, historical information of similar events, etc.

[0083] Steps 1 to 3 follow the projection relationship of data - information - business. First, extract effective information from the data, and then form event "data blocks" according to various "eventification" rules, so as to support analysis and decision-making and standardized business handling.

[0084] The "eventization" process is essentially a user-oriented information synthesis and push mechanism, which is divided into two parts: event synthesis and event push from the program structure. Event synthesis relies on the "eventization" rule algorithms of the platform layer and the application layer, and is the core of the "eventization" processing mechanism.

[0085] The "eventized" content can be divided into 7 categories: accidents, anomalies, operations, maintenance, defects, outages, and others. Each category of power grid event is coupled by data sorted with tags according to the "strong and weak association" relationship between the two.

[0086] Step 4: Set weights for event data blocks:

[0087] Through Figure 1 the data extraction of the monitoring information electricity meters in, the information table of the monitoring information points of the entire power grid can be obtained. Each information point is defined as triggering an alarm of G1…Gi according to its position in the database, and the corresponding alarm combination event is {SJ in}, and the weights corresponding to different alarms are Sc1…Scj, (j represents the weight level, which can be defined by the user, and the specific value of Scj can also be specifically set).

[0088] In power grid anomaly events, the weight coefficient of the power grid anomaly event SJ in is:

[0089]

[0090] Among them, QZi represents the weight coefficient of the power grid anomaly event SJ in , represents the sum of the weights of the alarms triggered corresponding to the power grid anomaly event SJ in , and the weight of each alarm needs to be set manually; represents the number of alarms that appear.

[0091] Step 5: Set the support degree for power grid anomaly events:

[0092] Define the support degree of power grid anomaly events:

[0093]

[0094] Among them, SW(SJ in ) represents the number of times the SJ in event appears in the monitoring information table, and SW(TO) represents the total number of power grid anomaly events that appear in the monitoring information point table.

[0095] JSP(SJ in ) = QZi * SP(SJ in ), where JSP(SJ in)Indicates the support degree of the weighted power grid abnormal event SJ in .

[0096] The closer the JSP value is to 1, the closer the fault information is associated, that is, the fault information can form an association to find the associated fault point device information.

[0097] Step six: Use the support degree to screen events, which includes:

[0098] As Figure 2 , Figure 3 shown, combine the power grid abnormal events with JSP values higher than the threshold into an event container. By setting the minimum support degree for the event container, scan the events in the event container. Remove duplicate alarms and noises in the event container. And normalize the same measurement points, and output the normalized events.

[0099] Step seven: Event push:

[0100] Real-time collect the monitoring information of the power grid. When the monitoring information matches the information in a certain event data block A and the support degree of the power grid abnormal event corresponding to the event data block A is higher than the threshold, combine the event data blocks into an event container and push it to the staff to realize the monitoring of the power grid abnormal event.

[0101] Based on the method of the present invention, a software system architecture is provided, which includes:

[0102] Platform layer: Responsible for data access, cleaning, extension, sorting and storage.

[0103] Application layer: Responsible for data extraction, information processing, "eventization" of information and event push.

[0104] To sum up:

[0105] 1. The present invention is based on the research of the "eventization" modeling method of monitoring signals. Relying on the internal logical relationship between monitoring signals and specific events, an information and event association rule library is defined, and intelligent reasoning and judgment are carried out on the real-time monitoring signal group to realize the accurate display of power grid abnormal information and support the monitoring personnel to judge and handle faults.

[0106] 2. The present invention collects various monitoring information of historical abnormal events of the power grid, including real-time control command information, device status quantity information and measurement information, providing a rich data basis for subsequent analysis. At the same time, three variables are set to represent different types of information respectively, and clear numerical values are assigned, simplifying the complex monitoring information into a clear numerical representation, making the data easier to process and analyze, so as to meet the need of quickly extracting knowledge and information from massive data;

[0107] Under the unattended centralized monitoring and management mode of the substation, in the face of disorderly and chaotic monitoring signals during large-scale power grid failures, three variables are combined into an event data block, and each block corresponds to a power grid abnormal event, realizing the structured representation of complex power grid events and making the monitoring signals orderly and having a clear corresponding relationship. By collecting monitoring information in real time and matching it with the event data blocks, the corresponding abnormal events are pushed to the staff, helping the monitoring personnel quickly identify the events, distinguish the importance levels of the events, reduce interference, and greatly improve the quality and efficiency of power grid monitoring work.

[0108] Based on the method of the present invention, the embodiments of the present disclosure also provide a system corresponding to the above method, which includes:

[0109] An information collection module, configured to collect monitoring information when abnormal events occur in the power grid history. The monitoring information includes: real-time control commands, alarm information, and measurement information;

[0110] An event construction module, configured to numerically process the monitoring information and then combine the monitoring information. Different numerical combinations of monitoring information correspond to different event data blocks, and each event data block corresponds to a power grid abnormal event;

[0111] A calculation module, configured to set weights for each alarm in the monitoring information; after adding the weights of multiple alarms triggered by a power grid abnormal event and dividing by the number of power grid abnormal events, obtain the event weight coefficient of the power grid abnormal event;

[0112] Calculate the occurrence frequency of the power grid abnormal event; multiply the occurrence frequency of the power grid abnormal event by the event weight coefficient of the power grid abnormal event to obtain the support degree of the power grid abnormal event;

[0113] A monitoring module, configured to collect the monitoring information of the power grid in real time. When the monitoring information matches the information in a certain event data block A and the support degree of the power grid abnormal event corresponding to the event data block A is higher than the threshold, combine the event data block A into an event container and push it to the staff to realize the monitoring of the power grid abnormal event.

[0114] The interaction schematic diagram of the system modules of the present invention is as Figure 4 shown.

[0115] Based on the same inventive concept as the above-disclosed content, the embodiments of the present disclosure also provide a device corresponding to the above method, which includes at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the above method.

[0116] It should be noted that the electrical connections between the above-mentioned units do not necessarily represent the connections between the circuits. The indirect connection method can be applied to the embodiments of the present disclosure as long as the purpose of the present disclosure is achieved.

[0117] Based on the same inventive concept, the present disclosure also provides a computer storage medium, on which executable instructions are stored, and when the instructions are executed by a processor, the processor implements the above method.

[0118] Although the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A method for monitoring abnormal power grid events, characterized in that: include: Collect monitoring information when abnormal events occur in the power grid history, including real-time control commands, alarm information, and measurement information; After the monitoring information is numerically processed, the monitoring information is combined, and different combinations of monitoring information values ​​correspond to different event data blocks, and each event data block corresponds to an abnormal power grid event; Setting weights for each alarm in the alarm information in the monitoring information; adding weights of multiple alarms triggered by an abnormal power grid event and dividing by the number of alarms to obtain an event weight coefficient of the abnormal power grid event; Calculate the frequency of occurrence of abnormal power grid events; multiply the frequency of occurrence of abnormal power grid events by the event weight coefficient of the abnormal power grid events to obtain the support degree of the abnormal power grid events; Collect monitoring information of the power grid in real time. When the monitoring information matches the information in a certain event data block A and the support degree of the abnormal power grid event corresponding to the event data block A is higher than the threshold, the event data block A is combined into an event container and pushed to the staff to realize the monitoring of abnormal power grid events. The digital processing of monitoring information includes: A first variable is set to represent the control command information, and two values ​​are set for the first variable, the two values ​​respectively representing: a control position and a control position; Acquire device state quantity information from the alarm information, set a second variable to represent the device state quantity information, and set two values ​​for the second variable, the two values ​​respectively representing: action and reset; The third variable is set to represent the measurement information, and two values ​​are set for the third variable. The two values ​​respectively represent: the measurement data exceeds the standard value and the measurement data is normal.

2. A method for monitoring abnormal power grid events according to claim 1, characterized in that: The event weight coefficient is: Among them, QZi represents the abnormal event SJ of the power grid in The weight coefficient of Indicates abnormal power grid event SJ in The corresponding alarm weights and the weight of each alarm need to be set manually; Indicates the number of alarms that occurred; The frequency of occurrence of abnormal power grid events is: Among them, SW(SJ in ) means in the monitoring information table, SJ in The number of times an event occurs, SW(TO) represents the total number of abnormal power grid events in the monitoring information point table.

3. A method for monitoring abnormal power grid events according to claim 1, characterized in that: The monitoring information of abnormal events in the history of the power grid is collected, which includes: Set the CK value and SS value. The CK value is the observation time of the monitoring information. The SS value is the adjustment amount of the CK value, which is used to include the marked special monitoring information within the observation time of the monitoring information. Aggregate all monitoring information within the CK+SS time period when an abnormal event occurs to collect monitoring information when an abnormal event occurs.

4. A method for monitoring abnormal power grid events according to claim 1, characterized in that: The measurement information includes: Current, voltage, frequency and angle at the same measuring point.

5. The method for monitoring abnormal power grid events according to claim 1, characterized in that: The information of the event data block includes: Event name, event level, related monitoring alarm information, measurement data, basic equipment parameters, and historical similar event information.

6. A method for monitoring abnormal power grid events according to claim 5, characterized in that: The event names include: Accident, abnormality, operation, maintenance, defect, outage.

7. A monitoring system for abnormal power grid events, characterized in that: include: The information collection module is used to collect monitoring information when abnormal events occur in the history of the power grid. The monitoring information includes: real-time control commands, alarm information, and measurement information; The event construction module is used to combine the monitoring information after numerical processing, and different numerical combinations of monitoring information correspond to different event data blocks, and each event data block corresponds to an abnormal power grid event; A calculation module is used to set a weight for each alarm in the alarm information in the monitoring information; after adding the weights of multiple alarms triggered by an abnormal power grid event, the weight coefficient of the abnormal power grid event is obtained by dividing the weights by the number of alarms; Calculate the frequency of occurrence of abnormal power grid events; multiply the frequency of occurrence of abnormal power grid events by the event weight coefficient of the abnormal power grid events to obtain the support degree of the abnormal power grid events; The monitoring module is used to collect monitoring information of the power grid in real time. When the monitoring information matches the information in a certain event data block A and the support degree of the abnormal power grid event corresponding to the event data block A is higher than the threshold, the event data block A is combined into an event container and pushed to the staff to realize the monitoring of abnormal power grid events; The digital processing of monitoring information includes: A first variable is set to represent the control command information, and two values ​​are set for the first variable, the two values ​​respectively representing: a control position and a control position; Acquire device state quantity information from the alarm information, set a second variable to represent the device state quantity information, and set two values ​​for the second variable, the two values ​​respectively representing: action and reset; The third variable is set to represent the measurement information, and two values ​​are set for the third variable. The two values ​​respectively represent: the measurement data exceeds the standard value and the measurement data is normal.

8. A monitoring device for abnormal power grid events, comprising: at least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for monitoring abnormal power grid events as described in any one of claims 1 to 6.

9. A computer storage medium having executable instructions stored thereon, wherein when the instructions are executed by a processor, the processor implements the method according to any one of claims 1 to 6.

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