Abnormality judgment method, device, system and readable storage medium based on hydropower centralized control monitoring event information

Abstract

The present invention discloses an abnormality judgment method, device, system and readable storage medium based on hydropower centralized control monitoring event information; the method comprises obtaining a working condition process event knowledge base; online identification of the working condition process of the unit, tracking and monitoring event information occurring during the working condition process; based on the event information occurring during the working condition process obtained, combined with the working condition process event knowledge base, abnormality judgment is realized. The present invention can greatly reduce the work pressure of the centralized control center monitoring panel, and plays an important role in ensuring the safe and stable operation of the power station.

Description

Technical Field

[0001] The present invention belongs to the technical field of hydropower centralized control, and specifically relates to an abnormality judgment method, device, system and readable storage medium based on hydropower centralized control monitoring event information. Background Art

[0002] The centralized control of hydropower realizes the centralized production control and management of the power stations in the entire river basin, improves the operation and management level of the entire river basin, and provides favorable support for the development of power companies. The monitoring system mainly adopts the method of dividing event signals into accident, fault, abnormality, operation, and state change signals to realize the screening and display of important signals. With the development of computer technology and automatic control theory and the construction of large-scale river basin centralized control, the scale of event information in the centralized control center has expanded dramatically. In addition, a single signal can only be considered abnormal when it appears in certain working conditions. Simple signal type classification cannot adapt to the rapid development of large-scale river basin centralized control. Operators have to face a large amount of event information every day. The work pressure and labor intensity are too high, which is easy to cause misoperation or relax the vigilance of fault information, bringing hidden dangers to safe production.

[0003] In order to reduce the work pressure of operation and maintenance personnel, many signal screening methods have emerged. Usually, neural network and other algorithms are used to learn various fault samples, label key fault signals, and realize the screening and display of key signals. However, in the actual operation of the centralized control center, in addition to paying attention to the fault signals generated by the system, the operators also need to pay attention to certain signals that do not appear in the process. The failure of these signals to appear normally will cause equipment failure or accidents in the hydropower station later. Timely discovery of these hidden dangers can avoid the loss of personal or property of the enterprise. Therefore, filtering signals by the above method can only serve as an auxiliary function of operation, and cannot avoid the monitoring of the full event information of the centralized control center by the operators. Summary of the invention

[0004] In response to the above problems, the present invention proposes an abnormality judgment method, device, system and readable storage medium based on hydropower centralized control monitoring event information, which can greatly reduce the monitoring workload of the centralized control center and play an important role in ensuring the safe and stable operation of the power station.

[0005] In order to achieve the above technical objectives and the above technical effects, the present invention is implemented through the following technical solutions:

[0006] In a first aspect, the present invention provides an abnormality judgment method based on hydropower centralized control monitoring event information, comprising:

[0007] Obtain the working process event knowledge base;

[0008] Online identification of the unit's operating process, tracking and monitoring of event information that occurs during the operating process;

[0009] Based on the obtained event information occurring during the working process and in combination with the working process event knowledge base, abnormality judgment is achieved.

[0010] Optionally, the operating condition includes a transient operating condition and a steady-state operating condition; and the method for acquiring the operating condition process event knowledge base includes:

[0011] Find the start and end events of the operating condition changes in the historical records, and then obtain all the events that occurred during the unit operating condition changes;

[0012] All events are analyzed to obtain a set of events that are bound to occur and a set of events that are likely to occur corresponding to different types of operating conditions.

[0013] Optionally, the expression of the set of events that are bound to occur is:

[0014] A′=A1∩A2∩A3…∩A n ;

[0015] The expression of the set of possible events is:

[0016] A″=A1∪A2∪A3…∪A n

[0017] Where n is the number of events of a certain type of unit operating condition change, A1, A2, A3…A n are the event sequence sets of each operating process, A′ is the process event that must occur, and A″ is the process event that may occur.

[0018] Optionally, the online identification of the operating condition of the unit and tracking and monitoring of event information occurring during the operating condition specifically include:

[0019] Obtain hydropower centralized control monitoring event information online;

[0020] When a working condition switching start event is identified based on the hydropower centralized control monitoring event information, the working condition change tracking is ended and the process event tracking is performed;

[0021] When an operating condition switching end event is identified based on the hydropower centralized control monitoring event information, the process event tracking is ended and the operating condition change tracking is executed.

[0022] Optionally, the operating mode switching start event is obtained by identifying the following formula:

[0023]

[0024] Among them, M k represents the kth event information, P i Indicates the i plant station, L j Indicates unit j, St represents the tth working condition, and B represents the start-up process string;

[0025] The operating mode switching end event is obtained by identifying the following formula:

[0026]

[0027] Among them, M k represents the kth event information, P i Indicates the i plant station, L j Indicates unit j, S t Indicates the tth working condition, and E indicates the end process string.

[0028] Optionally, the method for implementing the abnormality judgment includes:

[0029] Determine the working condition type corresponding to each event information, and find out the set of events that must occur and the set of events that may occur corresponding to the working condition type from the working condition process event knowledge base;

[0030] Determine the inclusion relationship between each event information and the corresponding set of events that must occur and the set of events that may occur;

[0031] According to the inclusion relationship results, abnormality judgment is made.

[0032] Optionally, performing abnormality judgment according to the inclusion relationship result specifically includes:

[0033] Get new event information M without plant unit information k ′,M k ′=Mk-{P i ,L j},M k It is the original event information and marks the working condition type;

[0034] Under steady-state conditions, if M k ′∈A′ and / or M k ′∈A″, then determine M k Normal event information, if Then determine M k Abnormal event information;

[0035] Under transient conditions, if M k ′∈A″ and Then determine M k Normal event information; if Determine M k is the abnormal event information; if M k ′∈A′, and M k ' is not arranged in the correct order in A', then it is determined to be Mk 'The skipped event information is missing;

[0036] Under transient conditions, if M k ′∈A′ in the corresponding T k If it does not appear within the time, it is determined that a timeout event message is generated.

[0037] In a second aspect, the present invention provides an abnormality judgment device based on hydropower centralized control monitoring event information, comprising:

[0038] A working condition process event knowledge base acquisition module is used to acquire a working condition process event knowledge base;

[0039] Online event information acquisition module, used to identify the unit's operating process online and track and monitor event information that occurs during the operating process;

[0040] The abnormality judgment module is used to realize abnormality judgment based on the event information occurring in the working process obtained in combination with the working process event knowledge base.

[0041] In a third aspect, the present invention provides an abnormality judgment system based on hydropower centralized control monitoring event information, including a storage medium and a processor;

[0042] The storage medium is used to store instructions;

[0043] The processor is configured to operate according to the instructions to execute the steps of the method according to any one of the first aspects.

[0044] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any one of the methods described in the first aspect.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention analyzes and compares the event occurrence process of various working conditions of all units in subordinate plants and stations from the historical operation information of the monitoring system of the centralized control center, establishes a knowledge base of working condition process events, automatically tracks and identifies the working condition changes of the units, tracks and monitors the event information that occurs during the working condition changes, marks and displays key events, and simultaneously realizes the judgment and supplementary alarm of timeout events and missing events. Since the operation and maintenance personnel focus on monitoring key events, timeout events and missing events, and only check the process classification alarm information when necessary, the work pressure of the centralized control center monitoring panel is greatly reduced, which plays an important role in ensuring the safe and stable operation of the power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments and in conjunction with the accompanying drawings, wherein:

[0048] Figure 1 A schematic diagram of the process flow of establishing a knowledge base of process events for different working conditions according to an embodiment of the present invention;

[0049] Figure 2 A schematic diagram of a process flow of online identification of unit operating conditions according to an embodiment of the present invention;

[0050] Figure 3 The diagram is a key information tracking, comparison and alarm process diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the scope of protection of the present invention.

[0052] The application principle of the present invention is described in detail below in conjunction with the accompanying drawings.

[0053] Example 1

[0054] The present invention provides an abnormality judgment method based on hydropower centralized control monitoring event information, which specifically includes the following steps:

[0055] Step (1), obtaining a working condition process event knowledge base;

[0056] Step (2), online identification of the unit's operating process, tracking and monitoring event information occurring during the operating process;

[0057] Step (3) implements abnormality judgment based on the event information occurring during the working process obtained and in combination with the working process event knowledge base.

[0058] In a specific implementation of the embodiment of the present invention, the operating condition includes a transient operating condition and a steady-state operating condition; and the method for acquiring the operating condition process event knowledge base includes:

[0059] Find the start and end events of the operating condition changes in the historical records, and then obtain all the events that occurred during the unit operating condition changes;

[0060] All events are analyzed to obtain a set of events that are bound to occur and a set of events that are likely to occur corresponding to different types of operating conditions.

[0061] The expression of the set of events that are bound to occur is:

[0062] A′=A1∩A2∩A3…∩A n ;

[0063] The expression of the set of possible events is:

[0064] A″=A1∪A2∪A3…∪A n

[0065] Where n is the number of events of a certain type of unit operating condition change, A1, A2, A3…A n are the event sequence sets of each operating process, A′ is the process event that must occur, and A″ is the process event that may occur.

[0066] Combine the following Figure 1 , the process of establishing the working condition process event knowledge base in the embodiment of the present invention is described in detail.

[0067] The operating conditions of conventional hydropower station equipment are divided into different types of conditions, such as the startup process, shutdown process, knife switch operation process, shutdown steady state, and operation steady state. For each type of condition, the event information that occurs in the same operating condition changes of all units in the power plants under the centralized control center is compared based on historical records to determine the event signals that must appear and the event signals that may appear in all hydropower units during the operation process, and the sequence and maximum time interval of the event signals that must appear in transient conditions (startup process, shutdown process, knife switch operation process) are recorded to realize the event process knowledge base for each condition.

[0068] First, extract the process records of different working conditions from the historical records of the centralized control center monitoring system. Event information is written in combination with plant name, unit name, equipment name and other auxiliary information. Transient condition changes start with the start operation event and end with the end operation event. Steady-state conditions start with the end of transient conditions and end with the beginning of the next transient condition. For example, the shutdown process ends and enters the shutdown steady-state condition, and the startup process ends and enters the operating steady-state condition. The meaning of the event can be determined by the inclusion of keywords.

[0069] The establishment process specifically includes:

[0070] 1) Find the start event of transient operating condition change in historical records;

[0071] The start event is identified by the following formula:

[0072]

[0073] Among them, M k represents the kth event information, P i Indicates the i plant station, L j Indicates unit j, S trepresents the tth working condition, and B represents the start-up process string;

[0074] In the historical records, search for records that meet the above conditions in order of events, obtain the start event of the unit operating condition change, and record the event occurrence time T m .

[0075] 2) Find the end event of transient operating condition change in the historical records;

[0076] The end event is identified by the following formula:

[0077]

[0078] Among them, M k represents the kth event information, P i Indicates the i plant station, L j Indicates unit j, S t Indicates the tth working condition, and E indicates the end process string.

[0079] In history, from T m Starting from the time point, search for the closest T in order of events. m For the records that meet the above conditions, obtain the end event corresponding to the unit operating condition change and record the event occurrence time T m+1 .

[0080] 3) According to T m With T m+1 , find the event information that occurs when the operating condition of unit j in plant i changes;

[0081] The event information satisfies the following formula:

[0082]

[0083] Among them, P i Indicates the i plant station, L j Indicates unit j, M k Represents the kth event information.

[0084] In history, from T n Start at time T n+1 At the end of the time point, search for records that meet the above conditions in order of events to obtain M k ′=M k -{P i ,L j} and its occurrence time T k , that is, event information M k Remove the plant unit information to obtain new event information M k ′;T m Time starts at Tm+1 All M at the end of time k 'Forming the event sequence A corresponding to the change of unit operating conditions n , while storing M k The time interval T between the occurrence time of the event and the start time k .

[0085] 4) Working process analysis

[0086] According to the above method, the set of events that must occur and the set of events that may occur corresponding to different types of working conditions are obtained:

[0087] The expression of the set of events that are bound to occur is:

[0088] A′=A1∩A2∩A3…∩A n ;

[0089] The expression of the set of possible events is:

[0090] A″=A1∪A2∪A3…∪A n

[0091] Where n is the number of events of a certain type of unit operating condition change, A1, A2, A3…A n are the event sequence sets of each operating process, A′ is the process event that must occur, and A″ is the process event that may occur.

[0092] For a steady-state process, the set of events that must occur and the set of events that may occur corresponding to different types of operating conditions are acquired and stored according to the above steps 1) to 4).

[0093] For transient processes, when M k ′∈A′, compare A1, A2, A3…A n Medium k ′ corresponds to the k sequence number, the event information M that is inconsistent in all sequences k ' should be removed from A'. Finally, compare A1, A2, A3...A n Medium k The time T between the time when the event corresponding to ′ occurs and the time when it starts k , determine M in A′ k The time T between the time when the event corresponding to ′ occurs and the time when it starts k ′=max(T k1 ,T k2 ,T k3 …T kn ).

[0094] 5) Establish a knowledge base for event records during the working condition switching process

[0095] Repeat steps 1)-4) to analyze the event sequences of different types of transient conditions, and then repeat steps 3)-4) based on the end of the transient condition and the start of the next transient condition to complete the event sequence analysis of different types of steady-state conditions.

[0096] In a specific implementation of the embodiment of the present invention, the online identification of the operating condition of the unit and tracking and monitoring of event information occurring during the operating condition specifically include:

[0097] Obtain hydropower centralized control monitoring event information online;

[0098] When a working condition switching start event is identified based on the hydropower centralized control monitoring event information, the working condition change tracking is ended and the process event tracking is performed;

[0099] When an operating condition switching end event is identified based on the hydropower centralized control monitoring event information, the process event tracking is ended and the operating condition change tracking is executed.

[0100] Combine the following Figure 2 The online identification process of the operating condition of the unit and the tracking and monitoring of event information occurring during the operating condition in the embodiment of the present invention are described in detail.

[0101] Connect with the real-time data bus of the hydropower centralized control monitoring system to track and monitor event information in real time. When the operating condition switching operation event information is found, the judgment process is started.

[0102] 1) Connect to the real-time data bus of the hydropower centralized control and monitoring system to track and monitor event information in real time;

[0103] 2) Check the event information and identify the working condition switching operation online;

[0104] Among them, the operating mode switching start event satisfies the following formula:

[0105]

[0106] Among them, M k represents the kth event information, P i Indicates the i plant station, L j Indicates unit j, S t Indicates the tth operating condition, and B indicates the startup process string.

[0107] When it is found that the jth unit of the i plant has a working condition switching operation, the plant unit enters the working condition switching operation process. t The corresponding event sets are A′ and A″, A′ is the process information that is bound to occur, and A″ is the process information that may occur.

[0108] Complete the steady-state operating condition change tracking of unit j at plant i and execute process event tracking.

[0109] 3) Check the event information and identify the operation end event of the working mode switching operation;

[0110] The operating mode switching operation end event satisfies the following formula:

[0111]

[0112] Among them, M k represents the kth event information, P i Indicates the i plant station, L j Indicates unit j, S t Indicates the tth working condition, and E indicates the end process string.

[0113] When the switching operation is completed, the unit j of plant i is in a steady-state state, and the operating condition S is obtained from the knowledge base. t The corresponding event sets are A′ and A″, A′ is the process information that is bound to occur, and A″ is the process information that may occur.

[0114] Complete the process event tracking of unit j at the current plant i and execute the steady-state operating condition change tracking.

[0115] In a specific implementation of the embodiment of the present invention, the method for implementing the abnormality judgment includes:

[0116] Determine the working condition type corresponding to each event information, and find out the set of events that must occur and the set of events that may occur corresponding to the working condition type from the working condition process event knowledge base;

[0117] Determine the inclusion relationship between each event information and the corresponding set of events that must occur and the set of events that may occur;

[0118] According to the inclusion relationship results, abnormality judgment is made.

[0119] The abnormality judgment is performed according to the inclusion relationship result, specifically including:

[0120] Get new event information M without plant unit information k ′,M k ′=Mk-{P i ,L j},M k It is the original event information and marks the working condition type;

[0121] Under steady-state conditions, if M k ′∈A′ and / or M k ′∈A″, then determine M k Normal event information, if Then determine M k Abnormal event information;

[0122] Under transient conditions, if M k ′∈A″ and Then determine M k Normal event information; if Determine M k is the abnormal event information; if M k ′∈A′, and M k ' is not arranged in the correct order in A', then it is determined to be M k 'The skipped event information is missing;

[0123] Under transient conditions, if M k ′∈A′ in the corresponding T k If it does not appear within the time, it is determined that a timeout event message is generated.

[0124] Combine the following Figure 3 The specific process of abnormality judgment is described in detail.

[0125] Track the monitoring event information of the centralized control center and compare the operating condition S of unit j in plant i t The corresponding event sets A′ and A″ and the subsequent event information generated by the jth unit of the i plant. Determine whether the alarm information belongs to the normal situation through the inclusion relationship between the sets A′ and A″ and the event, and mark the operating condition type S t , identify missing events, execution timeouts, etc. The event display interface only displays abnormal situations, the start and end of operating mode switching, and the process information that must appear in the steady state through important event reminders. When necessary, the operation and maintenance personnel can follow the S t View by category to reduce the pressure of daily work. The specific process is as follows:

[0126] 1) M k Based on the event information M of the plant unit information is obtained k ′=M k -{P i ,L j}, for M k Event marker condition type.

[0127] 2) Under steady-state conditions. If M k ′∈A′, determine normal event information, push important events, and return to step 1; if M k ′∈A″, determine the normal event information and return to step 1; if Determine abnormal event information, push important events, and return to step 1).

[0128] 3) Under transient conditions, if Mk ′∈A″ and Determine normal event information and return to step 1; if Determine abnormal event information, push important events, and return to step 1; if M k '∈A', determine whether k matches the sequence number in A'. If not, determine that the skipped event information is missing, push important events, and return to step 1);

[0129] 4) Under transient conditions, if the event information M k ∈A′ in the corresponding T k If it does not appear within the time limit, a timeout event message is generated, an important event is pushed, and the process returns to step 1).

[0130] 5) Repeat steps 1-4 until the current working condition tracking is completed.

[0131] Example 2

[0132] Based on the same inventive concept as in Example 1, the present invention provides an abnormality judgment device based on hydropower centralized control monitoring event information, comprising:

[0133] A working condition process event knowledge base acquisition module is used to acquire a working condition process event knowledge base;

[0134] Online event information acquisition module, used to identify the unit's operating process online and track and monitor event information that occurs during the operating process;

[0135] The abnormality judgment module is used to realize abnormality judgment based on the event information occurring in the working process obtained in combination with the working process event knowledge base.

[0136] The rest are the same as in Example 1.

[0137] Example 3

[0138] An embodiment of the present invention provides an abnormality judgment system based on hydropower centralized control monitoring event information, including a storage medium and a processor;

[0139] The storage medium is used to store instructions;

[0140] The processor is used to operate according to the instructions to execute the steps of the method according to any one of Embodiment 1.

[0141] Example 4

[0142] In an embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps of the method described in any one of Embodiment 1 are implemented.

[0143] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for abnormality judgment based on hydropower centralized control monitoring event information, characterized in that: include: Obtain the working process event knowledge base; Online identification of the unit's operating process, tracking and monitoring of event information that occurs during the operating process; Based on the event information that occurs during the working process, the abnormality judgment is realized in combination with the event knowledge base of the working process; The online identification of the unit's operating condition process and tracking of event information occurring during the monitoring of the operating condition process specifically include: Obtain hydropower centralized control monitoring event information online; When a working condition switching start event is identified based on the hydropower centralized control monitoring event information, the working condition change tracking is ended and the process event tracking is performed; When a working condition switching end event is identified based on the hydropower centralized control monitoring event information, the process event tracking is ended and the working condition change tracking is performed; The operating mode switching start event is obtained by identifying the following formula: Among them, M k represents the kth event information, P i Indicates the i plant station, L j Indicates unit j, S t represents the tth working condition, and B represents the start-up process string; The operating mode switching end event is obtained by identifying the following formula: Among them, M k represents the kth event information, P i Indicates the i plant station, L j Indicates unit j, S t Indicates the tth working condition, and E indicates the end process string; The implementation method of the abnormality judgment includes: Determine the working condition type corresponding to each event information, and find out the set of events that must occur and the set of events that may occur corresponding to the working condition type from the working condition process event knowledge base; Determine the inclusion relationship between each event information and the corresponding set of events that must occur and the set of events that may occur; According to the inclusion relationship results, make anomaly judgments; The abnormality judgment is performed according to the inclusion relationship result, specifically including: Get new event information M without plant unit information k ′,M k ′=Mk-{P i ,L j },M k Represents the kth original event information and marks the working condition type, P i Indicates the i plant station, L j Indicates unit j; Under steady-state conditions, if M k ′∈A′ and / or M k ′∈A″, then determine M k Normal event information, if Then determine M k Abnormal event information; Under transient conditions, if M k ′∈A″ and Then determine M k Normal event information; if Determine M k is the abnormal event information; if M k ′∈A′, and M k ' is not arranged in the correct order in A', then it is determined to be M k 'The skipped event information is missing; Under transient conditions, if M k ′∈A′ in the corresponding T k If it does not appear within the time, it is determined that a timeout event message is generated.

2. The abnormality judgment method based on hydropower centralized control monitoring event information according to claim 1 is characterized in that: The operating conditions include transient operating conditions and steady-state operating conditions; The method for acquiring the working process event knowledge base includes: Find the start and end events of the operating condition changes in the historical records, and then obtain all the events that occurred during the unit operating condition changes; All events are analyzed to obtain a set of events that are bound to occur and a set of events that are likely to occur corresponding to different types of operating conditions.

3. The abnormality judgment method based on hydropower centralized control monitoring event information according to claim 2 is characterized in that: The expression of the set of events that must occur is: A′=A1∩A2∩A3...∩A n ; The expression of the set of possible events is: A″=A1∪A2∪A3...∪A n Where n is the number of events of a certain type of unit operating condition change, A1, A2, A3...A n are the event sequence sets of each operating process, A′ is the process event that must occur, and A″ is the process event that may occur.

4. An abnormality judgment device based on hydropower centralized control monitoring event information, characterized in that: include: A working condition process event knowledge base acquisition module is used to acquire a working condition process event knowledge base; Online event information acquisition module, used to identify the unit's operating process online and track and monitor event information that occurs during the operating process; An abnormality judgment module, used to realize abnormality judgment based on the event information occurring in the working process obtained and in combination with the working process event knowledge base; The online identification of the unit's operating condition process and tracking of event information occurring during the monitoring of the operating condition process specifically include: Obtain hydropower centralized control monitoring event information online; When a working condition switching start event is identified based on the hydropower centralized control monitoring event information, the working condition change tracking is ended and the process event tracking is performed; When a working condition switching end event is identified based on the hydropower centralized control monitoring event information, the process event tracking is ended and the working condition change tracking is performed; The operating mode switching start event is obtained by identifying the following formula: Among them, M k represents the kth event information, P i Indicates the i plant station, L j Indicates unit j, S t represents the tth working condition, and B represents the start-up process string; The operating mode switching end event is obtained by identifying the following formula: Among them, M k represents the kth event information, P i Indicates the i plant station, L j Indicates unit j, S t Indicates the tth working condition, and E indicates the end process string; The implementation method of the abnormality judgment includes: Determine the working condition type corresponding to each event information, and find out the set of events that must occur and the set of events that may occur corresponding to the working condition type from the working condition process event knowledge base; Determine the inclusion relationship between each event information and the corresponding set of events that must occur and the set of events that may occur; According to the inclusion relationship results, make anomaly judgments; The abnormality judgment is performed according to the inclusion relationship result, specifically including: Get new event information M without plant unit information k ′,M k ′=Mk-{P i ,L j },M k Represents the kth original event information and marks the working condition type, P i Indicates the i plant station, L j Indicates unit j; Under steady-state conditions, if M k ′∈A′ and / or M k ′∈A″, then determine M k Normal event information, if Then determine M k Abnormal event information; Under transient conditions, if M k ′∈A″ and Then determine M k Normal event information; if Determine M k is the abnormal event information; if M k ′∈A′, and M k ' is not arranged in the correct order in A', then it is determined to be M k 'The skipped event information is missing; Under transient conditions, if M k ′∈A′ in the corresponding T k If it does not appear within the time, it is determined that a timeout event message is generated.

5. An abnormality judgment system based on hydropower centralized control monitoring event information, characterized in that: including storage media and processors; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

Images