Power grid equipment state abnormity monitoring method based on big data

Abstract

The invention relates to the field of power system dispatching automation, in particular to a big data-based power grid equipment state abnormity monitoring method, which comprises the following steps of: acquiring associated operation state data of each associated power grid equipment of each piece of fault information in each monitoring period; and adjusting the current transmission time interval of the associated operation state data of the associated power grid equipment according to the operation state data associated with other fault information. According to the invention, the operation state data influencing the fault information can be transmitted at a high transmission frequency, so that the reason of the fault information can be known more accurately.

Description

Technical Field

[0001] The present invention relates to the field of power system dispatching automation, and in particular to a method for monitoring abnormal power grid equipment status based on big data. Background Art

[0002] With the rapid development of big data analysis technology, it has been applied to the field of abnormal monitoring of power grid equipment status. For example, the patent document (CN110032557A) discloses a method and system for monitoring the abnormal status of power grid equipment based on big data. The scheme performs structured data cleaning and analysis based on the partial mathematical modeling method of high-dimensional random matrices. It is not necessary to know the topology and parameter information of the power system during analysis, which can reduce the professional threshold of data analysis and mining and improve the efficiency of abnormal identification. However, this scheme is only used to determine whether the power grid equipment exists, and it is impossible to accurately know the abnormal cause of the power grid equipment. The accurate abnormal cause can help reduce the number of abnormalities of the power grid equipment. Therefore, accurate analysis of the abnormal cause of the power grid equipment is a topic worth exploring. Summary of the invention

[0003] In view of the above technical problems, the technical solution adopted by the present invention is:

[0004] The present invention provides a method for monitoring abnormal power grid equipment status based on big data, the method comprising the following steps:

[0005] S100, obtaining a fault information set F of the power grid equipment monitored in the current monitoring period = {F i} i＝1……n If n≥n0, execute S200; n0 is the set quantity threshold, F i is the i-th fault information in F, F i =(U i , D i ), n is the number of fault information in F, U i is the fault ID corresponding to the i-th fault information, D i is the operating status data set of the associated power grid equipment associated with the i-th fault information, D i ={d ij} j＝1……f(i) , d ij F i With D i The associated target operating status data set of the jth associated power grid device in, f(i) is the number of associated power grid devices of the i-th fault information, d ij ={s ij r} r＝1……g(i，j) ,s ij r is d ijThe rth target running status data in d ij The number of target operating status data in;

[0006] S200, set i=1; execute S300.

[0007] S300, if i≤n, set j=1, execute S400, if i>n, exit the current control program.

[0008] S400, if j≤f(i), for d ij , based on the division of F in F i The target operating status data of the jth associated power grid equipment associated with the remaining fault information except i The candidate operating status data associated with the jth associated power grid device is obtained as F i For the associated operating status data associated with the j-th associated power grid device, execute S500; if j>f(i), set i=i+1 and execute S300.

[0009] S500, get F i The target weight of the associated operating status data associated with the jth associated power grid device, and the i The target weight corresponding to the associated operating status data associated with the j-th associated power grid device and the current transmission time interval are used to obtain the reference transmission time interval of the j-th associated power grid device in the next monitoring cycle; and S600 is executed.

[0010] S600, based on the reference transmission time interval of the jth associated power grid device in the next monitoring cycle, i The current transmission time interval of the associated operating status data associated with the j-th associated power grid device is adjusted.

[0011] The present invention has at least the following beneficial effects:

[0012] An embodiment of the present invention provides a method for monitoring abnormal status of power grid equipment based on big data. In each monitoring cycle, for the associated operating status data of each associated power grid equipment of each fault information, the current transmission time interval of the associated operating status data of the associated power grid equipment is adjusted according to the operating status data associated with other fault information. This allows the operating status data that affects the fault information to be transmitted at a higher transmission frequency, thereby enabling the cause of the fault information to be known more accurately.

[0013] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 A flowchart of a method for monitoring abnormal power grid equipment status based on big data is provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0018] It should be noted that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the steps as sequential processes, many of the steps can be implemented in parallel, concurrently or simultaneously. In addition, the order of the steps can be rearranged. The process can be terminated when its operation is completed, but it can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0019] The inventor of the present invention realizes that in some practical application scenarios, the reporting period of the operation status data upload of different power grid devices is different, which may result in that when fault information is monitored, it may be impossible to determine whether some operation status data is related to the fault information because it is not uploaded in time. In this way, it may be impossible to accurately know the cause of the fault information. In view of this, the present invention provides a method for monitoring the abnormal state of power grid equipment based on big data, aiming to obtain the cause of the fault information of power grid equipment as accurately as possible.

[0020] An embodiment of the present invention provides a method for monitoring abnormal power grid equipment status based on big data, which may include: Figure 1The following steps are shown:

[0021] S100, obtaining a fault information set F of the power grid equipment monitored in the current monitoring period = {F i} i＝1……n If n≥n0, execute S200; n0 is the set quantity threshold, F i is the i-th fault information in F, F i =(U i , D i ), n is the number of fault information in F, U i is the fault ID corresponding to the i-th fault information, D i is the operating status data set of the associated power grid equipment associated with the i-th fault information, D i ={d ij} j＝1……f(i) , d ij F i With D i The associated target operating status data set of the jth associated power grid device in, f(i) is the number of associated power grid devices of the i-th fault information, d ij ={s ij r} r＝1……g(i，j) ,s ij r is d ij The rth target running status data in d ij The number of target operating status data in .

[0022] S200, set i=1; execute S300.

[0023] S300, if i≤n, set j=1, execute S400, if i>n, exit the current control program.

[0024] S400, if j≤f(i), for d ij , based on the division of F in F i The target operating status data of the jth associated power grid equipment associated with the remaining fault information except i The candidate operating status data associated with the jth associated power grid device is obtained as F i For the associated operating status data associated with the j-th associated power grid device, execute S500; if j>f(i), set i=i+1 and execute S300.

[0025] S500, get F i The target weight of the associated operating status data associated with the jth associated power grid device, and the iThe target weight corresponding to the associated operating status data associated with the j-th associated power grid device and the current transmission time interval are used to obtain the reference transmission time interval of the j-th associated power grid device in the next monitoring cycle; and S600 is executed.

[0026] S600, based on the reference transmission time interval of the jth associated power grid device in the next monitoring cycle, i The current transmission time interval of the associated operating status data associated with the j-th associated power grid device is adjusted.

[0027] An embodiment of the present invention provides a method for monitoring abnormal status of power grid equipment based on big data. In each monitoring cycle, for the associated operating status data of each associated power grid equipment of each fault information, the current transmission time interval of the associated operating status data of the associated power grid equipment is adjusted according to the operating status data associated with other fault information. This allows the operating status data that affects the fault information to be transmitted at a higher transmission frequency, thereby obtaining the operating status data related to the fault information, and further more accurately knowing the cause of the fault information.

[0028] Further, in the embodiment of the present invention, the power grid equipment mainly includes primary equipment and secondary equipment. The primary equipment directly participates in the generation, transmission, distribution and use of electric energy, including generators, transformers, circuit breakers, disconnectors, mutual inductors, transmission lines, power cables, etc. The secondary equipment is an auxiliary equipment for monitoring, measuring, controlling and protecting the primary equipment and system, including relay protection devices, signal devices, measurement and control devices, metering devices, etc. The operating status data of the power grid equipment includes steady-state data, dynamic data and transient data, etc. Among them, the steady-state data may include switch displacement, total accident signal, protection action signal, etc., the dynamic data may include synchronized phasor data collected in real time by the PMU device, etc., and the transient data includes fault recording, etc. The operating status data of the power grid equipment can be accessed to the data processing center through access methods such as ETL tools and stream computing. The ETL tool is mainly used for extracting, converting and loading data from commercial databases and time series databases, and loading data into the data warehouse. The stream computing access is mainly used for accessing data collected in real time in the real-time database to the stream computing system.

[0029] In an embodiment of the present invention, the fault information in each monitoring cycle can be obtained by existing methods, such as the technical solution disclosed in patent document CN110032557A. The fault ID can be the name of the fault. The length of the monitoring cycle can be set based on actual needs, for example, it can be one week, one month, or half a year.

[0030] In the embodiment of the present invention, the target operating state data associated with the fault information is the operating state data that has been determined to affect the fault information, and the target operating state data associated with the fault information can be obtained by existing methods.

[0031] In the embodiment of the present invention, n0 may be set based on actual needs. In an exemplary embodiment, n0=2.

[0032] Those skilled in the art know that if n<n0, it means that the monitored fault information is very small and there is little fault information to refer to. It is impossible to adjust the data transmission time interval within the current monitoring cycle based on the monitored fault information, that is, the data transmission time interval within the current monitoring cycle will not be adjusted, and wait for the fault information of the next monitoring cycle.

[0033] Further, in S400, the method based on F is performed by removing F from F. i The target operating status data of the jth associated power grid equipment associated with the remaining fault information except i The candidate operating status data associated with the j-th associated power grid device can be obtained by the following steps:

[0034] S401, traverse F, obtain d ij and D h If d ij and D h The intersection of ij , D h , add d ij The corresponding current associated data set; d ij The initial value of the corresponding current associated data set is empty, the value of h ranges from 1 to n-1, and the initial value of h is 1, and h≠i.

[0035] In the embodiment of the present invention, if d ij and D h The intersection of ij , indicating that the jth associated power grid device is related to F i The associated running status data is also associated with the fault corresponding to the hth fault information, indicating that d ij It also affects the fault corresponding to the hth fault information. ij Other related fault information.

[0036] S402, obtain d ij The intersection of all data in the corresponding current associated data set. If the intersection of all data is not empty, the running status data corresponding to the intersection is used as F i The candidate operating status data associated with the j-th associated power grid device.

[0037] In the embodiment of the present invention, due to d ij The corresponding faults of the current associated data set include i Therefore, the common target operating status data corresponding to the faults corresponding to these associated data sets may also affect F i The corresponding fault.

[0038] It is known to those skilled in the art that if d ij The intersection of all data in the corresponding current associated data set is empty, then F i The candidate operating status data associated with the j-th associated power grid device is empty, that is, there is no candidate operating status data.

[0039] Furthermore, in S500, F i The target weight Wt of the rth target operating status data associated with the jth associated power grid device ij The following conditions must be met:

[0040] Wt ij r =W1 ij r / ((∑ g(i，j) r＝1 W1 ij r )+(∑ z(i，j) k＝1 W2 ij k ).

[0041] Among them, W1 ij r F i The initial weight of the rth target operating status data associated with the jth associated power grid device, W2 ij k F i The initial weight of the kth candidate operating status data associated with the jth associated power grid device, k ranges from 1 to z(i, j), z(i, j) is F i The number of candidate operating status data associated with the jth associated power grid device, W2 ij k Based on d ij The number of power grid devices corresponding to the corresponding current associated data set is determined.

[0042] Furthermore, in S500, F i The target weight Wc of the kth candidate operating status data associated with the jth associated power grid device ij k The following conditions must be met:

[0043] W ij k =W2 ij k / ((∑ g(i，j) r＝1 W1 ij r )+(∑ z(i，j) k＝1 W2 ij k ).

[0044] In an exemplary embodiment of the present invention, W1 ij 1 =……=W1 ij r =……=W1 ij g(i，j) ;W2 ij 1 =……=W2 ij k =……=W2 ij z(i，j) , that is, the initial weights of all target running state data are the same, and the initial weights of all candidate running state data are the same. In a specific embodiment, W1 ij r =1,W2 ij k =m / f(i), m is d ij The number of faults corresponding to the current associated data set. For example, if f(i) = 10, d ij The number of faults corresponding to the current associated data set is 5, and the initial weight of the candidate operating status data is 5 / 10=0.5.

[0045] Further, in S500, the jth associated power grid device has a reference transmission time interval △t in the next monitoring cycle. j next The following conditions must be met:

[0046] △t j next =∑ p(i，j) x＝1 W ij x ×△t ij x ;

[0047] Among them, W ij x F i The target weight of the xth associated operating status data associated with the jth associated power grid device, where x ranges from 1 to p(i, j), and p(i, j) is Fi The number of associated operating status data associated with the jth associated power grid device, △t ij x F i The current transmission time interval corresponding to the x-th associated operating status data associated with the j-th associated power grid device is, that is, the current reporting period of the operating status data.

[0048] Furthermore, S600 specifically includes:

[0049] If △t ij x ≤△t j next , indicating that the reporting cycle of the running status data is reasonable and does not need to be adjusted, that is, △t is not adjusted. ij x Otherwise, it means that the reporting cycle of the running status data is unreasonable, the data is not reported in time, and needs to be adjusted, that is, set △t ij x =△t j next .

[0050] Furthermore, the method for monitoring abnormal power grid equipment status based on big data provided by an embodiment of the present invention may also include the following steps:

[0051] If at least one candidate operating status data of a certain associated power grid device corresponding to a certain fault information monitored in the current monitoring cycle does not exist in the target operating status data of the certain associated power grid device associated with the fault information monitored in the current monitoring cycle, the current transmission time interval of the at least one candidate operating status data is adjusted to the transmission time interval corresponding to the previous monitoring cycle.

[0052] If at least one candidate operating status data of a certain associated power grid device corresponding to a certain fault information monitored in the current monitoring cycle does not exist in the target operating status data of the certain associated power grid device associated with the fault information monitored in the current monitoring cycle, it means that the fault information may not be associated with the at least one candidate operating status data, and its current transmission time interval can be adjusted to the transmission time interval of the previous monitoring cycle.

[0053] Those skilled in the art are aware that if at least one of the candidate operating status data of a certain associated power grid device corresponding to a certain fault information monitored in the current monitoring cycle in the previous monitoring cycle does not exist in the target operating status data of the certain associated power grid device associated with the fault information monitored in the current monitoring cycle, its current transmission time interval may not be adjusted.

[0054] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document does not limit this.

[0055] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for monitoring abnormal power grid equipment status based on big data, characterized in that: The method comprises the following steps: S100, obtaining a fault information set F of the power grid equipment monitored in the current monitoring period = {F i } i＝1……n If n≥n0, execute S200; n0 is the set quantity threshold, F i is the i-th fault information in F, F i =(U i , D i ), n is the number of fault information in F, U i is the fault ID corresponding to the i-th fault information, D i is the operating status data set of the associated power grid equipment associated with the i-th fault information, D i ={d ij } j＝1……f(i) , d ij F i With D i The associated target operating status data set of the jth associated power grid device in, f(i) is the number of associated power grid devices of the i-th fault information, d ij ={s ij r } r＝1……g(i，j) ,s ij r is d ij The rth target running status data in d ij The number of target operating status data in; S200, set i=1; execute S300; S300, if i≤n, set j=1, execute S400, if i>n, exit the current control program; S400, if j≤f(i), for d ij , based on the division of F in F i The target operating status data of the jth associated power grid equipment associated with the remaining fault information except i The candidate operating status data associated with the jth associated power grid device is obtained as F i The associated operating status data associated with the j-th associated power grid device, execute S500; if j>f(i), set i=i+1, and execute S300; S500, get F i The target weight of the associated operating status data associated with the jth associated power grid device, and the i The target weight and the current transmission time interval corresponding to the associated operation status data associated with the j-th associated power grid device are used to obtain the reference transmission time interval of the j-th associated power grid device in the next monitoring cycle; executing S600; S600, based on the reference transmission time interval of the jth associated power grid device in the next monitoring cycle, i The current transmission time interval of the associated operating status data associated with the j-th associated power grid device is adjusted.

2. The method according to claim 1, characterized in that In S400, the method based on F is to divide F i The target operating status data of the jth associated power grid equipment associated with the remaining fault information except i The candidate operating status data associated with the j-th associated power grid device is obtained by the following steps: S401, traverse F, obtain d ij and D h If d ij and D h The intersection of ij , D h Add ij The corresponding current associated data set; d ij The initial value of the corresponding current associated data set is empty, the value of h ranges from 1 to n-1, and the initial value of h is 1, and h≠i; S402, obtain d ij The intersection of all data in the corresponding current associated data set. If the intersection of all data is not empty, the running status data corresponding to the intersection is used as F i The candidate operating status data associated with the j-th associated power grid device.

3. The method according to claim 2, characterized in that In S500, F i The target weight Wt of the rth target operating status data associated with the jth associated power grid device ij The following conditions must be met: W1 ij r F i The initial weight of the rth target operating status data associated with the jth associated power grid device, W2 ij k F i The initial weight of the kth candidate operating status data associated with the jth associated power grid device, k ranges from 1 to z(i, j), z(i, j) is F i The number of candidate operating status data associated with the jth associated power grid device, W2 ij k Based on d ij The number of power grid devices corresponding to the corresponding current associated data set is determined.

4. The method according to claim 3, characterized in that In S500, F i The target weight Wc of the kth candidate operating status data associated with the jth associated power grid device ij k The following conditions must be met: Toilet ij k =W2 ij k / ((∑ g(i，j) r＝1 W1 ij r )+(∑ z(i，j) k＝1 W2 ij k )。 5. The method according to claim 3, characterized in that: In S500, the jth associated grid device has a reference transmission time interval △t in the next monitoring cycle. j next The following conditions must be met: △t j next =∑ p(i，j) x＝1 IN ij x ×△t ij x ; Among them, W ij x F i The target weight of the xth associated operating status data associated with the jth associated power grid device, where x ranges from 1 to p(i, j), and p(i, j) is F i The number of associated operating status data associated with the jth associated power grid device, △t ij x F i The current transmission time interval corresponding to the xth associated operating status data associated with the jth associated power grid device.

6. The method according to claim 5, characterized in that W1 ij 1 =……=W1 ij r =……=W1 ij g(i，j) ; W2 ij 1 =……=W2 ij k =……=W2 ij z(i，j) 。 7. The method according to claim 6, characterized in that W1 ij r =1,W2 ij k =m / f(i), m is d ij The number of faults corresponding to the current associated data set.

8. The method according to claim 5, characterized in that S600 specifically includes: If △t ij x ≤△t j next , without adjusting △t ij x , otherwise, set △t ij x =△t j next .

9. The method according to claim 1, characterized in that: The following steps are also included: If at least one candidate operating status data of a certain associated power grid device corresponding to a certain fault information monitored in the current monitoring cycle does not exist in the target operating status data of the certain associated power grid device associated with the fault information monitored in the current monitoring cycle, the current transmission time interval of the at least one candidate operating status data is adjusted to the transmission time interval corresponding to the previous monitoring cycle.

10. The method according to claim 1, characterized in that The operating status data includes steady-state data, dynamic data and transient data.

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