Power grid abnormal event monitoring method, system, equipment and medium

Abstract

The invention relates to the technical field of power grid monitoring, and provides a power grid abnormal event monitoring method, system, device and medium, and the method comprises the steps: collecting monitoring information, including real-time control commands, alarm information and measurement information, of historical abnormal events of a power grid, carrying out the numerical processing, combining the monitoring information into different event data blocks, and storing the data blocks in a server; each block corresponds to a power grid abnormal event; setting a weight for the alarm information, calculating an event weight coefficient, calculating an event occurrence frequency, and multiplying the two to obtain a support degree; power grid monitoring information is collected in real time, and when the information is matched with information of a certain event data block and the corresponding event support degree is higher than a threshold value, the event data block is combined into an event container to be pushed to a worker, so that power grid abnormal event monitoring is achieved. According to the method, the power grid abnormal events can be effectively monitored, and the safety and stability of power grid operation are improved.

Description

Technical Field

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

[0002] With the rapid development of domestic smart grids, the digitization, informatization and intelligence of power systems are constantly improving, and data such as power energy flow, information control flow and management business flow are growing explosively. However, traditional data processing technology has encountered bottlenecks and cannot meet the power industry's analytical needs for quickly extracting knowledge and information from massive data.

[0003] Under the unmanned centralized monitoring and management mode of substations, when a large-scale fault event occurs in the power grid, the disordered and chaotic monitoring signals interfere with the monitoring personnel's identification of events, distinction of their importance 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 invention provides a monitoring method, system, device and medium for abnormal power grid events. By synthesizing information into event data blocks, effective monitoring of abnormal power grid events can be achieved, thereby improving the safety and stability of power grid operation.

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

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

[0007] 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;

[0008] 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;

[0009] 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;

[0010] The monitoring information of the power grid is collected 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.

[0011] Further,

[0012] The event weight coefficient is:

[0013]

[0014] 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;

[0015] The frequency of occurrence of abnormal power grid events is:

[0016]

[0017] 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.

[0018] Further,

[0019] The monitoring information of abnormal events in the history of the power grid is collected, which includes:

[0020] 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.

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

[0022] Further,

[0023] The digital processing of monitoring information includes:

[0024] 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;

[0025] 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;

[0026] 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.

[0027] Further,

[0028] The measurement information includes:

[0029] Current, voltage, frequency and angle at the same measuring point.

[0030] Further,

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

[0032] Event name, event level, related monitoring alarm information, measurement data, basic equipment parameters, and historical similar event information.

[0033] Further,

[0034] The event names include:

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

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

[0037] 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;

[0038] 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;

[0039] 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;

[0040] 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;

[0041] 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.

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

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

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

[0045] Other features and advantages of the present disclosure will be described in the following description, and partly become apparent from the description, or be understood by implementing the present disclosure. The purpose and other advantages of the present disclosure can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 A schematic diagram of the method of the present invention is shown;

[0048] Figure 2 A schematic diagram of the execution flow of the present invention is shown;

[0049] Figure 3 A schematic diagram of the software system architecture of an embodiment of the present invention is shown;

[0050] Figure 4 A schematic diagram of the interaction of system modules of the present invention is shown. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0052] Figure 1 , Figure 2 A schematic diagram and a flow chart of the method according to the present invention are shown, and 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 As 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 the alarm information and set the CK value and SS value. The CK value is the observation time of the alarm. For example, if the CK value is 5s, then the 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 it can be collected simultaneously on the left and right sides, that is, the CK value can be changed to a maximum of SS seconds.

[0056] Step 2: Processing information:

[0057] First, the alarm information is processed in a regular manner. Figure 2 As shown, the information included in complex power grid events generally includes real-time control commands, alarm information, and measurement information.

[0058] The control command of the same measuring point is defined as KZ, KZ includes 2 states, where KZ=1 means "control closed", and KZ=0 means "control closed";

[0059] The state quantity information of the same measuring point is defined as GJ, which includes two states, where GJ=1 means "action" and GJ=0 means "reset";

[0060] The current, voltage, frequency, angle (phase angle between voltage and current) and other values ​​of the same measuring point can be obtained through measurement information, and compared with the fixed value to obtain its characteristics, which are defined as LC. LCi is used to identify the current, LCi1 represents the first current, LCu identifies the voltage, and LCu1 represents the first voltage. LC includes 2 states, where LC=1 indicates over-limit and LC=0 indicates normal.

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

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

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

[0064] STATION indicates the location where the alarm occurs, usually the name of a substation; EQU indicates the primary equipment represented by the alarm, usually the dispatch number;

[0065] VOL indicates the voltage level corresponding to the alarm information, which is generally 1000kV and above, 500kV, 200kV, 110kV, 35kV, 10kV and below;

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

[0067] PROTDEVICE is the attribute of the alarm information, including action, alarm, abnormality, crossing the upper limit, crossing the lower limit, protection stage I, stage II, stage III, stage IV and other information;

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

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

[0070] In order to make the machine understand better, the alarm text T needs to be converted into a vector. Gi = (TYPE, ATTRI, STATION, EQU, VOL, SECEQU, PROTDEVICE, ACTION, TIME)

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

[0072] ATTRI indicates 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 indicates the location where the alarm occurs, which generally refers to the name of a substation. The substation list needs to correspond 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 indicates the primary equipment represented by the alarm information, which is usually the dispatch number;

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

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

[0077] PROTDEVICE is the attribute of the alarm information, including action, alarm, abnormality, over-upper limit, over-lower limit, protection section I, section II, section III, section IV and other information. Action is 0, alarm is 1, abnormality is 2, over-upper limit is 3, over-lower limit is 4, protection section I is 5, section II is 6, section III is 7, section IV is 8;

[0078] ACTION is the action behavior of the relevant equipment, including action and tripping, etc. Action is 0 and tripping is 1; TIME indicates the time when the fault information occurs, and January 1, 2021 0:0:10:245 is expressed as, 20210101000010245. 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 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 in the event data block mainly includes the event name, event level, related monitoring alarm information, measurement data, basic equipment parameters, historical similar event information, etc.

[0083] Steps one to three follow the projection relationship of data-information-business, first extracting valid information from the data, and then forming event "data blocks" according to various "eventization" rules, thereby supporting analytical decision-making and business standardization processing.

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

[0085] "Event-based" content can be divided into seven categories: accidents, anomalies, operations, maintenance, defects, outages, and others. Each type of power grid event is coupled by labeled and sorted data according to the "strong and weak correlation" relationship between the two.

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

[0087] pass Figure 1 By extracting the data of the monitoring information meter, the information table of the monitoring information points of the entire power grid can be obtained. Each information point is defined as the triggered alarm G1…Gi according to its position in the database, and the corresponding alarm combination event is {SJ in}, 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 the power grid abnormal event, the power grid abnormal event SJ in The weight coefficient is:

[0089]

[0090] 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.

[0091] Step 5: Set support for abnormal power grid events:

[0092] Define the support level of abnormal power grid events:

[0093]

[0094] 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.

[0095] JSP(SJ in )=QZi*SP(SJ in ), among which JSP(SJ in) represents the weighted abnormal power grid event SJ in support.

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

[0097] Step 6: Use support to filter events, including:

[0098] like Figure 2 , Figure 3 As shown in the figure, the abnormal power grid events with JSP values ​​higher than the threshold are combined into event containers. By setting the minimum support for the event container, the events in the event container are scanned. The repeated alarms and noise in the event container are removed. The same measurement points are normalized and the normalized events are output.

[0099] Step 7: Event push:

[0100] The monitoring information of the power grid is collected 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 blocks are combined into event containers and pushed to the staff to realize the monitoring of abnormal power grid events.

[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, expansion, sorting and storage.

[0103] Application layer: responsible for data extraction, information processing, "event-based" information processing and event push.

[0104] In summary:

[0105] 1. The present invention is based on the research of the "event-based" modeling method of monitoring signals. Relying on the inherent logical relationship between monitoring signals and specific events, the information and event association rule base is defined to perform intelligent reasoning and judgment on real-time monitoring signal groups, realize accurate display of abnormal power grid information, and support monitoring personnel in judging and handling faults.

[0106] 2. The present invention provides a rich data basis for subsequent analysis by collecting a variety of monitoring information of historical abnormal events in the power grid, including real-time control command information, equipment status information and measurement information. At the same time, three variables are set to represent different types of information and assigned clear numerical values, simplifying complex monitoring information into clear numerical representations, making data easier to process and analyze, thereby meeting the need to quickly extract knowledge and information from massive data;

[0107] In the unattended centralized monitoring and management mode of substations, in the face of disordered and chaotic monitoring signals when a large area of ​​power grid fails, three variables are combined into event data blocks, each block corresponds to an abnormal power grid event, and a structured representation of complex power grid events is achieved, making the monitoring signals orderly and with clear corresponding relationships. By collecting monitoring information in real time and matching it with event data blocks, the corresponding abnormal events are pushed to the staff, helping monitoring personnel to quickly identify events, distinguish the importance of events, reduce interference, and greatly improve the quality and efficiency of power grid monitoring.

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

[0109] 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;

[0110] 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;

[0111] 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 abnormal power grid events;

[0112] 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;

[0113] 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.

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

[0115] Based on the same inventive concept as the above disclosed content, an embodiment of the present disclosure also provides 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 that can be executed 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 connection between the above-mentioned units does not necessarily mean the connection between lines. An 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. When the instructions are executed by a processor, the processor performs the above method.

[0118] Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; 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 history of the power grid, 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; The monitoring information of the power grid is collected 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.

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 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.

5. The 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.

6. A 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.

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

8. 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.

9. 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 7.

10. A computer storage medium having executable instructions stored thereon, wherein when the instructions are executed by a processor, the processor is enabled to implement the method according to any one of claims 1 to 7.

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