CN116596518B - Digital twinning-based power grid fault hidden danger management system and method - Google Patents

Abstract

The invention discloses a digital twinning-based power grid fault hidden danger management system and method, and belongs to the technical field of digital twinning. Carrying out digital twin mapping on power grid equipment, and establishing a digital twin fault database to avoid the grid system breakout caused by imperfect processing operation; constructing a historical fault event library, and analyzing the flow change condition of fault processing operation; capturing core fault processing operation and analyzing the association relation of power grid equipment; constructing an troubleshooting randomized iteration model, analyzing hidden danger evasion degree of a fault troubleshooting sequence table, and selecting a corresponding fault troubleshooting sequence table when the hidden danger evasion degree is maximum; the fault hidden danger range can be further solved, the condition that the definition is unclear is avoided, repeated work is avoided, more hidden danger fault events can be covered as much as possible when each fault is detected, the limited working time can be maximally utilized, the working efficiency is improved, and the overall hidden danger of a power grid system under each detection is maximally guaranteed to be minimized.

Description

Digital twinning-based power grid fault hidden danger management system and method

Technical Field

The invention relates to the technical field of digital twinning, in particular to a system and a method for managing hidden trouble of a power grid based on digital twinning.

Background

The digital twin is a technology for carrying out capacity expansion or enhancement on physical entities by utilizing technologies such as digital modeling, data analysis and the like, the digital twin technology is applied to the field of power grids, a power grid twin in a digital space is constructed, real-time bidirectional interaction can be carried out between the digital twin technology and the physical power grid, the data knowledge hybrid drive modeling is utilized, multi-scale, comprehensive and real-time power grid digital mapping is constructed, and mutual fusion and symbiosis among virtual entities are carried out in power grid application scenes with twin requirements such as data prediction, anomaly diagnosis, decision making and the like;

in the prior art, when a fault happens to the processing of a power grid fault, a worker performs on-site operation to solve the fault, but in reality, because of insufficient manpower and limited time, the power grid fault generally has the condition that the fault cannot be solved at one time, the troubleshooting task is often required to be arranged, the personnel are carried out according to time periods, and because of the complexity of a power grid system and the linkage influence effect of a power grid event, the condition that the fault hidden danger evasion range of each troubleshooting task is not clearly defined exists, so that the intelligence and rationality of the allocation of each troubleshooting task cannot be ensured.

Disclosure of Invention

The invention aims to provide a digital twinning-based power grid fault hidden danger management system and method, which are used for solving the problems in the background technology.

In order to solve the technical problems, the invention provides the following technical scheme:

digital twinning-based power grid fault hidden danger management system comprises: the system comprises a digital twin fault database module, a fault processing operation flow change analysis module, an associated hidden trouble analysis module and a fault intelligent response module;

the digital twin fault database module is used for carrying out digital twin mapping on the power grid equipment according to a digital twin technology, establishing a digital twin fault database and storing all fault processing operations when the analog power grid equipment breaks down; uniformly numbering power grid equipment and fault processing operation to generate a fault processing operation flow sample set;

the fault processing operation flow change analysis module is used for constructing a historical fault event library, storing the fault processing operation when the power grid equipment in the power grid system breaks down each time, and generating a historical fault processing operation flow set; generating a flow change set of fault processing operation according to the fault processing operation flow sample set and the historical fault processing operation flow set;

The associated hidden danger analysis module calculates a label value of the fault processing operation according to the fault processing operation flow sample set, the historical fault processing operation flow set and the flow change set, captures the core fault processing operation and generates a core fault processing operation flow set; searching the association relation of any two power grid devices according to the core fault processing operation flow set, calculating the association degree of any two power grid devices, and generating an association hidden trouble fault device set;

the intelligent fault coping module is used for acquiring power grid equipment with faults in a power grid system in real time, generating a fault checking equipment list, generating a fault checking time table according to working time, acquiring working time of each checking in the fault checking time table, constructing a checking randomization iteration model, generating a fault checking sequence table and calculating hidden trouble checking degree of the faults; after iteration is stopped, analyzing the hidden danger avoidance degree of the fault investigation sequence table, selecting the corresponding fault investigation sequence table when the hidden danger avoidance degree is maximum, and outputting the fault investigation sequence table.

Further, the digital twin fault database module further comprises a storage unit and an encoding unit;

the storage unit is used for carrying out digital twin mapping on all power grid equipment in a power grid system, and establishing a digital twin fault database according to a fault processing operation flow when each power grid equipment breaks down, wherein all fault processing operations when analog power grid equipment breaks down are stored in the digital twin fault database;

The coding unit is used for respectively carrying out unified coding on the power grid equipment and the fault processing operation, integrally planning all fault processing operations corresponding to different power grid equipment, generating a fault processing operation flow sample set, and recording as I _i ={O ₁ ，O ₂ ，...，O _n Wherein i represents grid device code, O ₁ ，O ₂ ，...，O _n Respectively representing the 1 st, 2 nd, n-th fault handling operations corresponding to the grid device i.

Further, the fault processing operation flow change analysis module further comprises a historical fault event library construction unit and a flow change analysis unit;

the historical fault event library construction unit is used for constructing a historical fault event library, storing the fault processing operation of each time of the faults of the power grid equipment in the power grid system in the history fault event library, generating a historical fault processing operation flow set, and recording the historical fault processing operation flow set as i _k ={O ₁ ，O ₂ ，...，O _m -wherein i _k Representing a historical fault handling operation flow set, O, of the power grid equipment i when a historical kth fault occurs ₁ ，O ₂ ，...，O _m Respectively representing the 1 st, 2 nd, m th fault handling operations of the grid device i when a historic kth fault occurs;

the flow change analysis unit obtains a flow change set of fault processing operation according to the fault processing operation flow sample set and the historical fault processing operation flow set, and marks the flow change set as FC _i (k→k+1)=i _k ∩i _k+1 Wherein FCi (k→k+1) represents a flow change set of fault handling operations generated by the power grid device i at the time of the kth to the kth+1 fault, i _k+1 And the historical fault processing operation flow set of the power grid equipment i when the k+1st time of the historical faults occurs is represented.

Further, the associated hidden danger analysis module further comprises a core fault processing operation analysis unit and an associated hidden danger analysis unit;

the core fault processing operation analysis unit calculates a label value of any fault processing operation according to a fault processing operation flow sample set, a historical fault processing operation flow set and a flow change set, and a specific calculation formula is as follows:

LV(O _v )=∑ _k=1 ^Nk-1 h[O _v ∈FC _i (k→k+1)]/∑ _k=1 ^Nk H(O _v ∈i _k )

wherein LV (O) _v ) Representing any one of the fault handling operations O _v And O _v ∈I _i Nk represents the total number of times the grid device i fails in history;

if O _v ∈FC _i (k.fwdarw.k+1), let h [ O ] _v ∈FC _i (k→k+1)]=1, otherwise let h [ O ] _v ∈FC _i (k→k+1)]=0；

If O _v ∈i _k Let H (O) _v ∈i _k ) =1, otherwise, let H (O _v ∈i _k )=0；

Presetting a label value threshold value if any one of fault handling operations O _v Is a label of (2)If the value is greater than or equal to the label value threshold value, any fault processing operation O _v The core fault processing operation is marked as any one of the power grid equipment i; counting all core fault handling operations of any one power grid equipment i, generating a core fault handling operation flow set, and recording the core fault handling operation flow set as CI (customer care) equipment i _i ；

The association hidden danger analysis unit searches the association relation of any two power grid devices according to the core fault processing operation flow set, calculates the association degree of any two power grid devices, and the specific calculation formula is as follows:

CD _ij =1-NUM ^-1 (CI _i ∩CI _j )×[NUM(CI _i -CI _i ∩CI _j )/NUM(CI _i )+NUM(CI _j -CI _i ∩CI _j )/NUM(CI _j )]

wherein, CD _ij Representing the degree of association between grid device i and grid device j, j representing the number of the grid device and i not equal j, CI _j Representing a set of core fault handling operation flows generated for a corresponding grid device j, NUM (CI _i ∩CI _j )、NUM(CI _i -CI _i ∩CI _j )、NUM(CI _i )、NUM(CI _j -CI _i ∩CI _j ) And NUM (CI) _j ) Respectively represent the collection CI _i ∩CI _j 、CI _i -CI _i ∩CI _j 、CI _i 、CI _j -CI _i ∩CI _j And CI (CI) _j The number of core fault handling operations involved;

presetting a relevance threshold, and if the relevance is greater than or equal to the relevance threshold, indicating that a relevance exists between the power grid equipment i and the power grid equipment j; in a power grid system, all power grid equipment with association relation with power grid equipment i is counted, and an association hidden trouble fault equipment set is generated and recorded as RHD _i 。

Further, the intelligent fault handling module further comprises a real-time fault perception processing unit and a hidden danger avoiding analysis unit;

the real-time fault perception processing unit is used for acquiring faulty power grid equipment in a power grid system in real time and counting the faultsThe total number of the power grid devices is Q, and a fault checking device list is generated; generating a fault checking time table according to the working time, acquiring the working time of each checking in the fault checking time table, and recording the working time of the x-th checking as T _x ；

Constructing a troubleshooting randomization iteration model according to the troubleshooting equipment list and the troubleshooting time table, randomizing a group of troubleshooting sequence tables, and enabling the troubleshooting sequence table corresponding to the e-th randomization to be SL _e The randomization needs to satisfy the condition:

∑ _u∈R t _u ≤T _x

∑ _x=1 ^F NUM(R _x )=Q

wherein R is _x An inspection set representing the composition of power grid equipment contained in the x-th inspection, wherein R=R _x ，t _u Representing an investigation set R _x Average troubleshooting duration of any one of the power grid equipment u in the history fault troubleshooting process, NUM (R) _x ) Representing an investigation set R _x The number of the power grid equipment contained in the system, wherein F represents the total number of times of troubleshooting in a troubleshooting schedule;

the hidden danger investigation degree of the nth investigation in the fault investigation sequence table corresponding to the e-th randomization is calculated, and the specific calculation formula is as follows:

y：HD _e =NUM[(⋃ _x=y+1 ^F ⋃ _b∈R RHD _b )⋂(⋃ _x=1 ^y ⋃ _a∈R RHD _a )]/NUM(⋃ _x=1 ^y ⋃ _a∈R RHD _a )

wherein y: HD (HD) _e Represents hidden trouble shooting degree of the (y) th shooting in a fault shooting sequence table corresponding to the (e) th randomization _b And RHD _a Respectively representing a set of associated hidden trouble fault devices generated by the power grid device b and the power grid device a correspondingly, NUM [ (⋃) _x=y+1 ^F ⋃ _b∈R RHD _b )⋂(⋃ _x=1 ^y ⋃ _a∈R RHD _a )]And NUM (⋃) _x=1 ^y ⋃ _a∈R RHD _a ) Respectively represent a collection (⋃) _x=y+1 ^F ⋃ _b∈R RHD _b )⋂(⋃ _x=1 ^y ⋃ _a∈R RHD _a ) Sum set ⋃ _x=1 ^y ⋃ _a∈R RHD _a The number of medium power grid equipment, y is less than or equal to F-1;

let e=e+1, return to check the randomized iteration model; when e=e, the iteration stops;

the hidden danger avoiding analysis unit is used for checking a corresponding fault checking sequence table SL when the hidden danger checking degree is maximum in the y-th checking according to the fault checking time table _e Marking and counting the fault-finding sequence list SL _e The total number of markers in the F rounds of investigation was noted as F (SL _e ) Calculate the troubleshooting sequence table SL _e Is (1) the degree of avoidance S (SL _e )=f(SL _e ) And F, selecting a corresponding fault checking sequence table when the hidden danger evasion degree is maximum, and outputting the fault checking sequence table.

A digital twinning-based power grid fault hidden danger management method comprises the following steps:

step S100: according to a digital twin technology, digital twin mapping is carried out on power grid equipment, a digital twin fault database is established, and all fault processing operations when the analog power grid equipment breaks down are stored; uniformly numbering power grid equipment and fault processing operation to generate a fault processing operation flow sample set;

step S200: constructing a historical fault event library, storing the fault processing operation of each time of the fault of the power grid equipment in the power grid system, and generating a historical fault processing operation flow set; generating a flow change set of fault processing operation according to the fault processing operation flow sample set and the historical fault processing operation flow set;

step S300: according to the fault processing operation flow sample set, the historical fault processing operation flow set and the flow change set, calculating a label value of the fault processing operation, capturing the core fault processing operation, and generating a core fault processing operation flow set; searching the association relation of any two power grid devices according to the core fault processing operation flow set, calculating the association degree of any two power grid devices, and generating an association hidden trouble fault device set;

Step S400: acquiring power grid equipment with faults in a power grid system in real time, generating a fault checking equipment list, generating a fault checking time table according to working time, acquiring working time of each checking in the fault checking time table, constructing a checking randomization iteration model, generating a fault checking sequence table, and calculating hidden trouble checking degree of the faults; after iteration is stopped, analyzing hidden danger avoidance degrees of the fault investigation sequence table, selecting a corresponding fault investigation sequence table when the hidden danger avoidance degrees are maximum, and outputting the fault investigation sequence table;

further, the specific implementation process of the step S100 includes:

step S101: carrying out digital twin mapping on all power grid equipment in a power grid system, and establishing a digital twin fault database according to a fault processing operation flow when each power grid equipment fails, wherein all fault processing operations when analog power grid equipment fails are stored in the digital twin fault database;

step S102: respectively carrying out unified coding on power grid equipment and fault processing operation, comprehensively planning all fault processing operations corresponding to different power grid equipment, generating a fault processing operation flow sample set, and recording as I _i ={O ₁ ，O ₂ ，...，O _n Wherein i represents grid device code, O ₁ ，O ₂ ，...，O _n Respectively representing the 1 st, 2 nd, n-th fault handling operations corresponding to the grid device i.

Further, the specific implementation process of the step S200 includes:

step S201: constructing a historical fault event library, wherein the historical fault event library stores fault processing operation when power grid equipment in a power grid system breaks down each time, generates a historical fault processing operation flow set and records as i _k ={O ₁ ，O ₂ ，...，O _m -wherein i _k Representing a historical fault handling operation flow set, O, of the power grid equipment i when a historical kth fault occurs ₁ ，O ₂ ，...，O _m Respectively representing the 1 st, 2 nd, m th fault handling operations of the grid device i when a historic kth fault occurs;

step S202: obtaining a flow change set of fault processing operation according to the fault processing operation flow sample set and the historical fault processing operation flow set, and recording the flow change set as FC (fiber channel) _i (k→k+1)=i _k ∩i _k+1 Wherein FCi (k→k+1) represents a flow change set of fault handling operations generated by the power grid device i at the time of the kth to the kth+1 fault, i _k+1 And the historical fault processing operation flow set of the power grid equipment i when the k+1st time of the historical faults occurs is represented.

Further, the implementation process of the step S300 includes:

step S301: according to the sample set of fault processing operation flow, the historical fault processing operation flow set and the flow change set, calculating the label value of any fault processing operation, wherein the specific calculation formula is as follows:

LV(O _v )=∑ _k=1 ^Nk-1 h[O _v ∈FC _i (k→k+1)]/∑ _k=1 ^Nk H(O _v ∈i _k )

Wherein LV (O) _v ) Representing any one of the fault handling operations O _v And O _v ∈I _i Nk represents the total number of times the grid device i fails in history;

if O _v ∈FC _i (k.fwdarw.k+1), let h [ O ] _v ∈FC _i (k→k+1)]=1, otherwise let h [ O ] _v ∈FC _i (k→k+1)]=0；

If O _v ∈i _k Let H (O) _v ∈i _k ) =1, otherwise, let H (O _v ∈i _k )=0；

Presetting a label value threshold value if any one of fault handling operations O _v If the tag value of (2) is greater than or equal to the tag value threshold, any fault processing operation O _v The core fault processing operation is marked as any one of the power grid equipment i; counting all core fault handling operations of any one power grid equipment i, generating a core fault handling operation flow set, and recording the core fault handling operation flow set as CI (customer care) equipment i _i ；

Step S302: according to the core fault processing operation flow set, searching the association relation of any two power grid devices, and calculating the association relation of any two power grid devices, wherein the specific calculation formula is as follows:

CD _ij =1-NUM ^-1 (CI _i ∩CI _j )×[NUM(CI _i -CI _i ∩CI _j )/NUM(CI _i )+NUM(CI _j -CI _i ∩CI _j )/NUM(CI _j )]

wherein, CD _ij Representing the degree of association between grid device i and grid device j, j representing the number of the grid device and i not equal j, CI _j Representing a set of core fault handling operation flows generated for a corresponding grid device j, NUM (CI _i ∩CI _j )、NUM(CI _i -CI _i ∩CI _j )、NUM(CI _i )、NUM(CI _j -CI _i ∩CI _j ) And NUM (CI) _j ) Respectively represent the collection CI _i ∩CI _j 、CI _i -CI _i ∩CI _j 、CI _i 、CI _j -CI _i ∩CI _j And CI (CI) _j The number of core fault handling operations involved;

presetting a relevance threshold, and if the relevance is greater than or equal to the relevance threshold, indicating that a relevance exists between the power grid equipment i and the power grid equipment j; in a power grid system, all power grid equipment with association relation with power grid equipment i is counted, and an association hidden trouble fault equipment set is generated and recorded as RHD _i 。

Further, the specific implementation process of the step S400 includes:

step S401: acquiring faulty power grid equipment in a power grid system in real time, counting the total number of the faulty power grid equipment as Q, and generating a fault checking equipment list; generating a fault checking time table according to the working time, acquiring the working time of each checking in the fault checking time table, and recording the working time of the x-th checking as T _x ；

Constructing a troubleshooting randomization iteration model according to the troubleshooting equipment list and the troubleshooting time table, randomizing a group of troubleshooting sequence tables, and enabling the troubleshooting sequence table corresponding to the e-th randomization to be SL _e The randomization needs to satisfy the condition:

∑ _u∈R t _u ≤T _x

∑ _x=1 ^F NUM(R _x )=Q

wherein R is _x An inspection set representing the composition of power grid equipment contained in the x-th inspection, wherein R=R _x ，t _u Representing an investigation set R _x Average troubleshooting duration of any one of the power grid equipment u in the history fault troubleshooting process, NUM (R) _x ) Representing an investigation set R _x The number of the power grid equipment contained in the system, wherein F represents the total number of times of troubleshooting in a troubleshooting schedule;

the hidden danger investigation degree of the nth investigation in the fault investigation sequence table corresponding to the e-th randomization is calculated, and the specific calculation formula is as follows:

y：HD _e =NUM[(⋃ _x=y+1 ^F ⋃ _b∈R RHD _b )⋂(⋃ _x=1 ^y ⋃ _a∈R RHD _a )]/NUM(⋃ _x=1 ^y ⋃ _a∈R RHD _a )

wherein y: HD (HD) _e Represents hidden trouble shooting degree of the (y) th shooting in a fault shooting sequence table corresponding to the (e) th randomization _b And RHD _a Respectively representing a set of associated hidden trouble fault devices generated by the power grid device b and the power grid device a correspondingly, NUM [ (⋃) _x=y+1 ^F ⋃ _b∈R RHD _b )⋂(⋃ _x=1 ^y ⋃ _a∈R RHD _a )]And NUM (⋃) _x=1 ^y ⋃ _a∈R RHD _a ) Respectively represent a collection (⋃) _x=y+1 ^F ⋃ _b∈R RHD _b )⋂(⋃ _x=1 ^y ⋃ _a∈R RHD _a ) Sum set ⋃ _x=1 ^y ⋃ _a∈R RHD _a The number of medium power grid equipment, y is less than or equal to F-1;

let e=e+1, return to check the randomized iteration model; when e=e, the iteration stops;

step S402: according to the fault checking schedule, in the y-th checking, the fault checking sequence table SL corresponding to the highest hidden trouble checking degree _e Marking and counting the fault-finding sequence list SL _e The total number of markers in the F rounds of investigation was noted as F (SL _e ) Calculate the troubleshooting sequence table SL _e Is (1) the degree of avoidance S (SL _e )=f(SL _e ) F, selecting a corresponding fault checking sequence table when the hidden danger evasion degree is maximum, and outputting the fault checking sequence table;

according to the method, due to the complex power grid system and the linkage influence effect of the power grid events, when one power grid device in the power grid system fails, linkage reaction conditions often exist, so that other power grid devices also have interlocking faults, and further the hidden trouble of the interlocking faults often has a large scope, if all the faults are subjected to troubleshooting, a lot of manpower and material resources are required, and the linked power grid fault events are difficult to find; meanwhile, when faults occur, due to interlocking association of power grid equipment, the same fault processing operation is often existed for different fault equipment, and after a fault event is processed, the similar fault processing operation also solves the fault problems of other fault equipment together with high probability;

Furthermore, in the application, the specific implementation of the step S100 aims at comprehensively planning all fault processing operations in the power grid system, and the application of the digital twin technology in the application has the advantages that the power grid system can be subjected to one-to-one digital mapping to simulate a fault processing operation flow sample set, so that the situation that the power grid system is broken up due to imperfect processing operation can be avoided in reality, and meanwhile, the processing operation is generally carried out according to actual conditions when the fault event is solved each time, and further, for the same fault event, operation differences often exist in different processing processes;

therefore, in step S200, the operation data of the historical fault event is combined to further analyze the flow change condition of the fault handling operation;

through the flow change condition, calculating a label value of each fault processing operation in step S300, and for the same fault event, although the same fault processing operation may exist, feeding back that in the grid system, the fault processing operation of the grid equipment has different emphasis points, wherein the larger the label value is indicative of the greater probability that the fault processing operation is taken as a key point of the grid equipment, further generating a core fault processing operation flow set, analyzing the association degree between the grid equipment according to the core fault processing operation, and generating an association hidden danger fault equipment set;

When one device fails, other power grid devices with hidden trouble can be quickly found out; in reality, because of insufficient manpower and limited time, the power grid faults generally have the condition that the faults cannot be solved at one time, the faults are often required to be arranged and checked by personnel according to time periods, then a fault checking time table is generated according to working time, the personnel perform the working tasks according to the fault time table, and the fault checking time is discontinuous, the condition that the definition of the hidden trouble range is unclear after each checking can be solved, in order to improve the working efficiency, more hidden trouble events can be covered as much as possible during each fault checking, meanwhile, the limited working time can be utilized to the maximum extent, further, the step S400 is used for constructing a checking randomization iteration model, and the condition that the working time is utilized to the maximum extent is ensured for all the checking tasks, and the condition that the missing checking range is not existed is ensured; because of the difference of core fault processing operation, the next investigation range is as small as possible after each working time investigation, so that each working time can be maximized, and meanwhile, the next investigation range is as small as possible, so that the integral hidden danger of the power grid system after each working under the intermittent working time can be minimized, and the hidden danger investigation degree is calculated, and the hidden danger range before and after the current investigation is combined with the current investigation as a node (⋃) _x=y+1 ^F ⋃ _b∈R RHD _b )⋂(⋃ _x=1 ^y ⋃ _a∈R RHD _a ) Coincident power grid equipment representing hidden danger ranges before and after investigation and formula ⋃ _x=1 ^y ⋃ _a∈R RHD _a The hidden danger range before the current investigation is shown, and the hidden danger range after the current investigation is shown inAfter the current investigation, the investigation is not needed, so that repeated work can be avoided, the working efficiency is improved, the fault investigation sequence table corresponding to the fault investigation time table with the largest hidden danger investigation degree in each investigation is marked according to the fault investigation time table, the hidden danger avoidance degree is calculated, and the greater the hidden danger avoidance degree is, the fault investigation sequence table can ensure that the overall hidden danger of the power grid system under each investigation is minimized to the greatest extent.

Compared with the prior art, the invention has the following beneficial effects: in the digital twinning-based power grid fault hidden danger management system and method provided by the invention, digital twinning mapping is carried out on power grid equipment, a digital twinning fault database is established, and the situation that a power grid system is broken up due to imperfect processing operation is avoided; constructing a historical fault event library, and analyzing the flow change condition of fault processing operation; capturing core fault processing operation and analyzing the association relation of power grid equipment; constructing an troubleshooting randomized iteration model, analyzing hidden danger evasion degree of a fault troubleshooting sequence table, and selecting a corresponding fault troubleshooting sequence table when the hidden danger evasion degree is maximum; the fault hidden danger range can be further solved, the condition that definition is unclear is avoided, repeated work is avoided, more hidden danger fault events can be covered as much as possible when each fault is detected, limited working time can be utilized to the greatest extent, working efficiency is improved, and the overall hidden danger of a power grid system under each detection is guaranteed to the greatest extent.

Drawings

The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:

FIG. 1 is a schematic diagram of a digital twinning-based power grid fault hidden danger management system;

fig. 2 is a schematic step diagram of a digital twin-based power grid fault hidden danger management method according to the present invention.

Description of the embodiments

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Referring to fig. 1-2, the present invention provides the following technical solutions:

referring to fig. 1, in a first embodiment: provided is a digital twinning-based power grid fault hidden danger management system, which comprises: the system comprises a digital twin fault database module, a fault processing operation flow change analysis module, an associated hidden trouble analysis module and a fault intelligent response module;

The digital twin fault database module is used for carrying out digital twin mapping on the power grid equipment according to a digital twin technology, establishing a digital twin fault database and storing all fault processing operations when the analog power grid equipment breaks down; uniformly numbering power grid equipment and fault processing operation to generate a fault processing operation flow sample set;

the digital twin fault database module further comprises a storage unit and an encoding unit;

the storage unit is used for carrying out digital twin mapping on all power grid equipment in the power grid system, establishing a digital twin fault database according to the fault processing operation flow when each power grid equipment fails, and storing all fault processing operations when the analog power grid equipment fails in the digital twin fault database;

the coding unit is used for respectively carrying out unified coding on the power grid equipment and the fault processing operation, integrally planning all fault processing operations corresponding to different power grid equipment, generating a fault processing operation flow sample set and recording as I _i ={O ₁ ，O ₂ ，...，O _n Wherein i represents grid device code, O ₁ ，O ₂ ，...，O _n Respectively representing the 1 st, 2 nd, the n fault handling operations corresponding to the power grid equipment i;

the fault processing operation flow change analysis module is used for constructing a historical fault event library, storing the fault processing operation when the power grid equipment in the power grid system breaks down each time, and generating a historical fault processing operation flow set; generating a flow change set of fault processing operation according to the fault processing operation flow sample set and the historical fault processing operation flow set;

The fault processing operation flow change analysis module further comprises a historical fault event library construction unit and a flow change analysis unit;

the historical fault event library construction unit is used for constructing a historical fault event library, storing the fault processing operation of each time of the power grid equipment in the power grid system in the history, generating a historical fault processing operation flow set, and recording the historical fault processing operation flow set as i _k ={O ₁ ，O ₂ ，...，O _m -wherein i _k Representing a historical fault handling operation flow set, O, of the power grid equipment i when a historical kth fault occurs ₁ ，O ₂ ，...，O _m Respectively representing the 1 st, 2 nd, m th fault handling operations of the grid device i when a historic kth fault occurs;

a flow change analysis unit for acquiring a flow change set of the fault handling operation, which is recorded as FC, based on the sample set of the fault handling operation flow and the historical fault handling operation flow set _i (k→k+1)=i _k ∩i _k+1 Wherein FCi (k→k+1) represents a flow change set of fault handling operations generated by the power grid device i at the time of the kth to the kth+1 fault, i _k+1 Representing a historical fault processing operation flow set of the power grid equipment i when the k+1st time of the historical fault occurs;

the associated hidden danger analysis module is used for calculating a label value of the fault processing operation according to the fault processing operation flow sample set, the historical fault processing operation flow set and the flow change set, capturing the core fault processing operation and generating the core fault processing operation flow set; searching the association relation of any two power grid devices according to the core fault processing operation flow set, calculating the association degree of any two power grid devices, and generating an association hidden trouble fault device set;

The associated hidden danger analysis module further comprises a core fault processing operation analysis unit and an associated hidden danger analysis unit;

the core fault processing operation analysis unit calculates a label value of any fault processing operation according to a fault processing operation flow sample set, a historical fault processing operation flow set and a flow change set, and a specific calculation formula is as follows:

LV(O _v )=∑ _k=1 ^Nk-1 h[O _v ∈FC _i (k→k+1)]/∑ _k=1 ^Nk H(O _v ∈i _k )

wherein LV (O) _v ) Representing any one of the fault handling operations O _v And O _v ∈I _i Nk represents the total number of times the grid device i fails in history;

if O _v ∈FC _i (k.fwdarw.k+1), let h [ O ] _v ∈FC _i (k→k+1)]=1, otherwise let h [ O ] _v ∈FC _i (k→k+1)]=0；

If O _v ∈i _k Let H (O) _v ∈i _k ) =1, otherwise, let H (O _v ∈i _k )=0；

Presetting a label value threshold value if any one of fault handling operations O _v If the tag value of (2) is greater than or equal to the tag value threshold, any fault processing operation O _v The core fault processing operation is marked as any one of the power grid equipment i; counting all core fault handling operations of any one power grid equipment i, generating a core fault handling operation flow set, and recording the core fault handling operation flow set as CI (customer care) equipment i _i ；

The association hidden danger analysis unit is used for searching association relation of any two power grid devices according to the core fault processing operation flow set, and calculating association degree of any two power grid devices, wherein the specific calculation formula is as follows:

CD _ij =1-NUM ^-1 (CI _i ∩CI _j )×[NUM(CI _i -CI _i ∩CI _j )/NUM(CI _i )+NUM(CI _j -CI _i ∩CI _j )/NUM(CI _j )]

Wherein, CD _ij Representing an association between grid device i and grid device jThe degree, j represents the number of the power grid device and i not equal to j, CI _j Representing a set of core fault handling operation flows generated for a corresponding grid device j, NUM (CI _i ∩CI _j )、NUM(CI _i -CI _i ∩CI _j )、NUM(CI _i )、NUM(CI _j -CI _i ∩CI _j ) And NUM (CI) _j ) Respectively represent the collection CI _i ∩CI _j 、CI _i -CI _i ∩CI _j 、CI _i 、CI _j -CI _i ∩CI _j And CI (CI) _j The number of core fault handling operations involved;

presetting a relevance threshold, and if the relevance is greater than or equal to the relevance threshold, indicating that a relevance exists between the power grid equipment i and the power grid equipment j; in a power grid system, all power grid equipment with association relation with power grid equipment i is counted, and an association hidden trouble fault equipment set is generated and recorded as RHD _i ；

The intelligent fault coping module is used for acquiring power grid equipment with faults in a power grid system in real time, generating a fault checking equipment list, generating a fault checking time table according to the working time, acquiring the working time of each checking in the fault checking time table, constructing a checking randomization iteration model, generating a fault checking sequence table and calculating hidden trouble checking degree of the faults; after iteration is stopped, analyzing hidden danger avoidance degrees of the fault investigation sequence table, selecting a corresponding fault investigation sequence table when the hidden danger avoidance degrees are maximum, and outputting the fault investigation sequence table;

The intelligent fault handling module further comprises a real-time fault perception processing unit and a hidden danger avoiding analysis unit;

the real-time fault perception processing unit is used for acquiring faulty power grid equipment in a power grid system in real time, counting the total number of the faulty power grid equipment as Q, and generating a fault troubleshooting equipment list; generating a fault checking time table according to the working time, acquiring the working time of each checking in the fault checking time table, and recording the working time of the x-th checking as T _x ；

Constructing an troubleshooting randomization iteration model according to the troubleshooting equipment list and the troubleshooting time table,randomizing a group of fault checking sequence table to make the fault checking sequence table corresponding to the e-th randomization be SL _e Randomization needs to satisfy the conditions:

∑ _u∈R t _u ≤T _x

∑ _x=1 ^F NUM(R _x )=Q

wherein R is _x An inspection set representing the composition of power grid equipment contained in the x-th inspection, wherein R=R _x ，t _u Representing an investigation set R _x Average troubleshooting duration of any one of the power grid equipment u in the history fault troubleshooting process, NUM (R) _x ) Representing an investigation set R _x The number of the power grid equipment contained in the system, wherein F represents the total number of times of troubleshooting in a troubleshooting schedule;

the hidden danger investigation degree of the nth investigation in the fault investigation sequence table corresponding to the e-th randomization is calculated, and the specific calculation formula is as follows:

y：HD _e =NUM[(⋃ _x=y+1 ^F ⋃ _b∈R RHD _b )⋂(⋃ _x=1 ^y ⋃ _a∈R RHD _a )]/NUM(⋃ _x=1 ^y ⋃ _a∈R RHD _a )

Wherein y: HD (HD) _e Represents hidden trouble shooting degree of the (y) th shooting in a fault shooting sequence table corresponding to the (e) th randomization _b And RHD _a Respectively representing a set of associated hidden trouble fault devices generated by the power grid device b and the power grid device a correspondingly, NUM [ (⋃) _x=y+1 ^F ⋃ _b∈R RHD _b )⋂(⋃ _x=1 ^y ⋃ _a∈R RHD _a )]And NUM (⋃) _x=1 ^y ⋃ _a∈R RHD _a ) Respectively represent a collection (⋃) _x=y+1 ^F ⋃ _b∈R RHD _b )⋂(⋃ _x=1 ^y ⋃ _a∈R RHD _a ) Sum set ⋃ _x=1 ^y ⋃ _a∈R RHD _a The number of medium power grid equipment, y is less than or equal to F-1;

let e=e+1, return to check the randomized iteration model; when e=e, the iteration stops;

hidden danger avoiding analysis unit, rootAccording to the fault checking schedule, in the y-th checking, the fault checking sequence table SL corresponding to the highest hidden trouble checking degree _e Marking and counting the fault-finding sequence list SL _e The total number of markers in the F rounds of investigation was noted as F (SL _e ) Calculate the troubleshooting sequence table SL _e Is (1) the degree of avoidance S (SL _e )=f(SL _e ) And F, selecting a corresponding fault checking sequence table when the hidden danger evasion degree is maximum, and outputting the fault checking sequence table.

Referring to fig. 2, in the second embodiment: the utility model provides a power grid fault hidden danger management method based on digital twin, which comprises the following steps:

according to a digital twin technology, digital twin mapping is carried out on power grid equipment, a digital twin fault database is established, and all fault processing operations when the analog power grid equipment breaks down are stored; uniformly numbering power grid equipment and fault processing operation to generate a fault processing operation flow sample set;

Carrying out digital twin mapping on all power grid equipment in a power grid system, and establishing a digital twin fault database according to a fault processing operation flow when each power grid equipment fails, wherein all fault processing operations when analog power grid equipment fails are stored in the digital twin fault database;

respectively carrying out unified coding on power grid equipment and fault processing operation, comprehensively planning all fault processing operations corresponding to different power grid equipment, generating a fault processing operation flow sample set, and recording as I _i ={O ₁ ，O ₂ ，...，O _n Wherein i represents grid device code, O ₁ ，O ₂ ，...，O _n Respectively representing the 1 st, 2 nd, the n fault handling operations corresponding to the power grid equipment i;

constructing a historical fault event library, storing the fault processing operation of each time of the fault of the power grid equipment in the power grid system, and generating a historical fault processing operation flow set; generating a flow change set of fault processing operation according to the fault processing operation flow sample set and the historical fault processing operation flow set;

construction of historical failure eventsThe library stores the fault processing operation of each time of the fault of the power grid equipment in the power grid system in the history fault event library, generates a history fault processing operation flow set and records as i _k ={O ₁ ，O ₂ ，...，O _m -wherein i _k Representing a historical fault handling operation flow set, O, of the power grid equipment i when a historical kth fault occurs ₁ ，O ₂ ，...，O _m Respectively representing the 1 st, 2 nd, m th fault handling operations of the grid device i when a historic kth fault occurs;

obtaining a flow change set of fault processing operation according to the fault processing operation flow sample set and the historical fault processing operation flow set, and recording the flow change set as FC (fiber channel) _i (k→k+1)=i _k ∩i _k+1 Wherein FCi (k→k+1) represents a flow change set of fault handling operations generated by the power grid device i at the time of the kth to the kth+1 fault, i _k+1 Representing a historical fault processing operation flow set of the power grid equipment i when the k+1st time of the historical fault occurs;

according to the fault processing operation flow sample set, the historical fault processing operation flow set and the flow change set, calculating a label value of the fault processing operation, capturing the core fault processing operation, and generating a core fault processing operation flow set; searching the association relation of any two power grid devices according to the core fault processing operation flow set, calculating the association degree of any two power grid devices, and generating an association hidden trouble fault device set;

according to the sample set of fault processing operation flow, the historical fault processing operation flow set and the flow change set, calculating the label value of any fault processing operation, wherein the specific calculation formula is as follows:

LV(O _v )=∑ _k=1 ^Nk-1 h[O _v ∈FC _i (k→k+1)]/∑ _k=1 ^Nk H(O _v ∈i _k )

Wherein LV (O) _v ) Representing any one of the fault handling operations O _v And O _v ∈I _i Nk represents the total number of times the grid device i fails in history;

if O _v ∈FC _i (k.fwdarw.k+1), let h [ O ] _v ∈FC _i (k→k+1)]=1, otherwise let h [ O ] _v ∈FC _i (k→k+1)]=0；

If O _v ∈i _k Let H (O) _v ∈i _k ) =1, otherwise, let H (O _v ∈i _k )=0；

Presetting a label value threshold value if any one of fault handling operations O _v If the tag value of (2) is greater than or equal to the tag value threshold, any fault processing operation O _v The core fault processing operation is marked as any one of the power grid equipment i; counting all core fault handling operations of any one power grid equipment i, generating a core fault handling operation flow set, and recording the core fault handling operation flow set as CI (customer care) equipment i _i ；

According to the core fault processing operation flow set, searching the association relation of any two power grid devices, and calculating the association relation of any two power grid devices, wherein the specific calculation formula is as follows:

CD _ij =1-NUM ^-1 (CI _i ∩CI _j )×[NUM(CI _i -CI _i ∩CI _j )/NUM(CI _i )+NUM(CI _j -CI _i ∩CI _j )/NUM(CI _j )]

wherein, CD _ij Representing the degree of association between grid device i and grid device j, j representing the number of the grid device and i not equal j, CI _j Representing a set of core fault handling operation flows generated for a corresponding grid device j, NUM (CI _i ∩CI _j )、NUM(CI _i -CI _i ∩CI _j )、NUM(CI _i )、NUM(CI _j -CI _i ∩CI _j ) And NUM (CI) _j ) Respectively represent the collection CI _i ∩CI _j 、CI _i -CI _i ∩CI _j 、CI _i 、CI _j -CI _i ∩CI _j And CI (CI) _j The number of core fault handling operations involved;

presetting a relevance threshold, and if the relevance is greater than or equal to the relevance threshold, indicating that a relevance exists between the power grid equipment i and the power grid equipment j; in the grid system, statistics are related to the presence of grid equipment i All power grid equipment in the connection relationship and generating a connection hidden trouble fault equipment set which is recorded as RHD _i ；

Acquiring power grid equipment with faults in a power grid system in real time, generating a fault checking equipment list, generating a fault checking time table according to working time, acquiring working time of each checking in the fault checking time table, constructing a checking randomization iteration model, generating a fault checking sequence table, and calculating hidden trouble checking degree of the faults; after iteration is stopped, analyzing hidden danger avoidance degrees of the fault investigation sequence table, selecting a corresponding fault investigation sequence table when the hidden danger avoidance degrees are maximum, and outputting the fault investigation sequence table;

acquiring faulty power grid equipment in a power grid system in real time, counting the total number of the faulty power grid equipment as Q, and generating a fault checking equipment list; generating a fault checking time table according to the working time, acquiring the working time of each checking in the fault checking time table, and recording the working time of the x-th checking as T _x ；

Constructing a troubleshooting randomization iteration model according to the troubleshooting equipment list and the troubleshooting time table, randomizing a group of troubleshooting sequence tables, and enabling the troubleshooting sequence table corresponding to the e-th randomization to be SL _e Randomization needs to satisfy the conditions:

∑ _u∈R t _u ≤T _x

∑ _x=1 ^F NUM(R _x )=Q

wherein R is _x An inspection set representing the composition of power grid equipment contained in the x-th inspection, wherein R=R _x ，t _u Representing an investigation set R _x Average troubleshooting duration of any one of the power grid equipment u in the history fault troubleshooting process, NUM (R) _x ) Representing an investigation set R _x The number of the power grid equipment contained in the system, wherein F represents the total number of times of troubleshooting in a troubleshooting schedule;

the hidden danger investigation degree of the nth investigation in the fault investigation sequence table corresponding to the e-th randomization is calculated, and the specific calculation formula is as follows:

y：HD _e =NUM[(⋃ _x=y+1 ^F ⋃ _b∈R RHD _b )⋂(⋃ _x=1 ^y ⋃ _a∈R RHD _a )]/NUM(⋃ _x=1 ^y ⋃ _a∈R RHD _a )

wherein y: HD (HD) _e Represents hidden trouble shooting degree of the (y) th shooting in a fault shooting sequence table corresponding to the (e) th randomization _b And RHD _a Respectively representing a set of associated hidden trouble fault devices generated by the power grid device b and the power grid device a correspondingly, NUM [ (⋃) _x=y+1 ^F ⋃ _b∈R RHD _b )⋂(⋃ _x=1 ^y ⋃ _a∈R RHD _a )]And NUM (⋃) _x=1 ^y ⋃ _a∈R RHD _a ) Respectively represent a collection (⋃) _x=y+1 ^F ⋃ _b∈R RHD _b )⋂(⋃ _x=1 ^y ⋃ _a∈R RHD _a ) Sum set ⋃ _x=1 ^y ⋃ _a∈R RHD _a The number of medium power grid equipment, y is less than or equal to F-1;

let e=e+1, return to check the randomized iteration model; when e=e, the iteration stops;

according to the fault checking schedule, in the y-th checking, the fault checking sequence table SL corresponding to the highest hidden trouble checking degree _e Marking and counting the fault-finding sequence list SL _e The total number of markers in the F rounds of investigation was noted as F (SL _e ) Calculate the troubleshooting sequence table SL _e Is (1) the degree of avoidance S (SL _e )=f(SL _e ) F, selecting a corresponding fault checking sequence table when the hidden danger evasion degree is maximum, and outputting the fault checking sequence table;

for example, according to the working time of the staff, generating a fault checking schedule of 5 months, 6 days, 5 months, 8 days and 5 months, 9 days; the method comprises the steps of acquiring power grid equipment which has faults in a power grid system in real time as equipment 1, equipment 3 and equipment 5;

in a power grid system, all power grid equipment with association relation with power grid equipment 1, 3 and 5 are counted respectively to obtain an association hidden trouble fault equipment set: RHD (RHD) ₁ = { device 3, device 6, device 8}, RHD ₃ = { device 1, device 5}, RHD ₅ = { device 1, device 6, device 8};

the working time of each investigation in the fault investigation schedule is 5 months and 6 days: 4 hours, 5 months and 8 days: 4 hours, 5 months and 9 days: 4 hours; the average troubleshooting duration of the power grid equipment 1, 3 and 5 in the historical fault troubleshooting process is respectively as follows: 4 hours, 3 hours;

two randomized fault investigation sequence tables are respectively: SL (SL) device ₁ = {5 months 6 days: device 1, 5 months 8 days: device 3, 5 months 9 days: device 5 and SL ₂ = {5 months 6 days: device 1, 5 months 8 days: device 5, 5 month 9 day: device 3};

For SL ₁ ：

5 month 6 days, 1 st investigation, 1: HD (HD) ₁ NUM [ (devices 3, 6, 8) ⋂ (devices 1, 5, 6, 8)]NUM (devices 3, 6, 8) =2/3≡0.67;

5 month 8 days, 2 nd investigation, 2: HD (HD) ₁ NUM [ (devices 1, 3, 5, 6, 8) ⋂ (devices 1, 6, 8)]NUM (devices 1, 3, 5, 6, 8) =3/5=0.6;

5 month 9 days, 3 rd investigation, 3: HD (HD) ₁ =0；

For SL ₂ ：

5 month 6 days, 1 st investigation, 1: HD (HD) ₂ NUM [ (devices 3, 6, 8) ⋂ (devices 1, 5, 6, 8)]NUM (devices 3, 6, 8) =2/3≡0.67;

5 month 9 days, 2 nd investigation, 2: HD (HD) ₂ NUM [ (devices 1, 3, 6, 8) ⋂ (devices 1, 5)]NUM (devices 1, 3, 6, 8) =1/5=0.2;

5 month 9 days, 3 rd investigation, 3: HD (HD) ₂ =0；

Then: in the 1 st investigation, SL ₁ And SL (SL) ₂ Marked 1 time respectively; in the 2 nd investigation, SL ₁ Marked 1 time; in the 3 rd investigation, SL ₁ And SL (SL) ₂ Marked 1 time respectively;

degree of avoidance S (SL) ₁ )=f(SL ₁ ) /f=3/3=1, the potential avoidance degree S (SL ₂ )=f(SL ₂ ) If/f=2/3, the fault diagnosis sequence table SL is output ₁ = {5 months 6 days: device 1, 5 months 8 days: device 3, 5 months 9 days: device 5}.

It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention and is not intended to limit the present invention, but although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the technical solutions described in the foregoing embodiments, or equivalents may be substituted for some of the technical features thereof. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (7)

1. The digital twinning-based power grid fault hidden danger management method is characterized by comprising the following steps of:

step S100: according to a digital twin technology, digital twin mapping is carried out on power grid equipment, a digital twin fault database is established, and all fault processing operations when the analog power grid equipment breaks down are stored; uniformly numbering power grid equipment and fault processing operation to generate a fault processing operation flow sample set;

step S200: constructing a historical fault event library, storing the fault processing operation of each time of the fault of the power grid equipment in the power grid system, and generating a historical fault processing operation flow set; generating a flow change set of fault processing operation according to the fault processing operation flow sample set and the historical fault processing operation flow set;

Step S300: according to the fault processing operation flow sample set, the historical fault processing operation flow set and the flow change set, calculating a label value of the fault processing operation, capturing the core fault processing operation, and generating a core fault processing operation flow set; searching the association relation of any two power grid devices according to the core fault processing operation flow set, calculating the association degree of any two power grid devices, and generating an association hidden trouble fault device set;

step S400: acquiring power grid equipment with faults in a power grid system in real time, generating a fault checking equipment list, generating a fault checking time table according to working time, acquiring working time of each checking in the fault checking time table, constructing a checking randomization iteration model, generating a fault checking sequence table, and calculating hidden trouble checking degree of the faults; after iteration is stopped, analyzing hidden danger avoidance degrees of the fault investigation sequence table, selecting a corresponding fault investigation sequence table when the hidden danger avoidance degrees are maximum, and outputting the fault investigation sequence table;

the specific implementation process of the step S100 includes:

step S101: carrying out digital twin mapping on all power grid equipment in a power grid system, and establishing a digital twin fault database according to a fault processing operation flow when each power grid equipment fails, wherein all fault processing operations when analog power grid equipment fails are stored in the digital twin fault database;

Step S102: respectively carrying out unified coding on power grid equipment and fault processing operation, comprehensively planning all fault processing operations corresponding to different power grid equipment, generating a fault processing operation flow sample set, and recording as I _i ={O ₁ ，O ₂ ，...，O _n Wherein i represents grid device code, O ₁ ，O ₂ ，...，O _n Respectively representing the 1 st, 2 nd, the n fault handling operations corresponding to the power grid equipment i;

the specific implementation process of the step S200 includes:

step S201: constructing a historical fault event library, wherein the historical fault event library stores fault processing operation when power grid equipment in a power grid system breaks down each time, generates a historical fault processing operation flow set and records as i _k ={O ₁ ，O ₂ ，...，O _m -wherein i _k Representing a historical fault handling operation flow set, O, of the power grid equipment i when a historical kth fault occurs ₁ ，O ₂ ，...，O _m Respectively representing the 1 st, 2 nd, m th fault handling operations of the grid device i when a historic kth fault occurs;

step S202: obtaining a flow change set of fault processing operation according to the fault processing operation flow sample set and the historical fault processing operation flow set, and recording the flow change set as FC (fiber channel) _i (k→k+1)=i _k ∩i _k+1 Wherein FCi (k→k+1) represents a flow change set of fault handling operations generated by the power grid device i at the time of the kth to the kth+1 fault, i _k+1 Representing a historical fault processing operation flow set of the power grid equipment i when the k+1st time of the historical fault occurs;

the specific implementation process of the step S300 includes:

step S301: according to the sample set of fault processing operation flow, the historical fault processing operation flow set and the flow change set, calculating the label value of any fault processing operation, wherein the specific calculation formula is as follows:

LV(O _v )=∑ _k=1 ^Nk-1 h[O _v ∈FC _i (k→k+1)]/∑ _k=1 ^Nk H(O _v ∈i _k )

wherein LV (O) _v ) Representing any one of the fault handling operations O _v And O _v ∈I _i Nk represents the total number of times the grid device i fails in history;

if O _v ∈FC _i (k.fwdarw.k+1), let h [ O ] _v ∈FC _i (k→k+1)]=1, otherwise let h [ O ] _v ∈FC _i (k→k+1)]=0；

If O _v ∈i _k Let H (O) _v ∈i _k ) =1, otherwise, let H (O _v ∈i _k )=0；

Presetting a label value threshold value if any one of fault handling operations O _v If the tag value of (2) is greater than or equal to the tag value threshold, any fault processing operation O _v Marked as any one of the electric gridsCore fault handling operation of device i; counting all core fault handling operations of any one power grid equipment i, generating a core fault handling operation flow set, and recording the core fault handling operation flow set as CI (customer care) equipment i _i ；

Step S302: according to the core fault processing operation flow set, searching the association relation of any two power grid devices, and calculating the association relation of any two power grid devices, wherein the specific calculation formula is as follows:

CD _ij =1-NUM ^-1 (CI _i ∩CI _j )×[NUM(CI _i -CI _i ∩CI _j )/NUM(CI _i )+NUM(CI _j -CI _i ∩CI _j )/NUM(CI _j )]

Wherein, CD _ij Representing the degree of association between grid device i and grid device j, j representing the number of the grid device and i not equal j, CI _j Representing a set of core fault handling operation flows generated for a corresponding grid device j, NUM (CI _i ∩CI _j )、NUM(CI _i -CI _i ∩CI _j )、NUM(CI _i )、NUM(CI _j -CI _i ∩CI _j ) And NUM (CI) _j ) Respectively represent the collection CI _i ∩CI _j 、CI _i -CI _i ∩CI _j 、CI _i 、CI _j -CI _i ∩CI _j And CI (CI) _j The number of core fault handling operations involved;

presetting a relevance threshold, and if the relevance is greater than or equal to the relevance threshold, indicating that a relevance exists between the power grid equipment i and the power grid equipment j; in a power grid system, all power grid equipment with association relation with power grid equipment i is counted, and an association hidden trouble fault equipment set is generated and recorded as RHD _i 。

2. The method for managing potential grid faults based on digital twinning according to claim 1, wherein the specific implementation process of the step S400 includes:

step S401: acquiring faulty power grid equipment in a power grid system in real time, counting the total number of the faulty power grid equipment as Q, and generating a fault checking equipment list; according to the working timeGenerating a fault checking time table, acquiring the working time of each checking in the fault checking time table, and recording the working time of the x-th checking as T _x ；

Constructing a troubleshooting randomization iteration model according to the troubleshooting equipment list and the troubleshooting time table, randomizing a group of troubleshooting sequence tables, and enabling the troubleshooting sequence table corresponding to the e-th randomization to be SL _e The randomization needs to satisfy the condition:

∑ _u∈R t _u ≤T _x

∑ _x=1 ^F NUM(R _x )=Q

wherein R is _x An inspection set representing the composition of power grid equipment contained in the x-th inspection, wherein R=R _x ，t _u Representing an investigation set R _x Average troubleshooting duration of any one of the power grid equipment u in the history fault troubleshooting process, NUM (R) _x ) Representing an investigation set R _x The number of the power grid equipment contained in the system, wherein F represents the total number of times of troubleshooting in a troubleshooting schedule;

the hidden danger investigation degree of the nth investigation in the fault investigation sequence table corresponding to the e-th randomization is calculated, and the specific calculation formula is as follows:

y：HD _e =NUM[(⋃ _x=y+1 ^F ⋃ _b∈R RHD _b )⋂(⋃ _x=1 ^y ⋃ _a∈R RHD _a )]/NUM(⋃ _x=1 ^y ⋃ _a∈R RHD _a )

wherein y: HD (HD) _e Represents hidden trouble shooting degree of the (y) th shooting in a fault shooting sequence table corresponding to the (e) th randomization _b And RHD _a Respectively representing a set of associated hidden trouble fault devices generated by the power grid device b and the power grid device a correspondingly, NUM [ (⋃) _x=y+1 ^F ⋃ _b∈R RHD _b )⋂(⋃ _x=1 ^y ⋃ _a∈R RHD _a )]And NUM (⋃) _x=1 ^y ⋃ _a∈R RHD _a ) Respectively represent a collection (⋃) _x=y+1 ^F ⋃ _b∈R RHD _b )⋂(⋃ _x=1 ^y ⋃ _a∈R RHD _a ) Sum set ⋃ _x=1 ^y ⋃ _a∈R RHD _a The number of medium power grid equipment, y is less than or equal to F-1;

let e=e+1, return to check the randomized iteration model; when e=e, the iteration stops;

step S402: according to the fault checking schedule, in the y-th checking, the fault checking sequence table SL corresponding to the highest hidden trouble checking degree _e Marking and counting the fault-finding sequence list SL _e The total number of markers in the F rounds of investigation was noted as F (SL _e ) Calculate the troubleshooting sequence table SL _e Is (1) the degree of avoidance S (SL _e )=f(SL _e ) And F, selecting a corresponding fault checking sequence table when the hidden danger evasion degree is maximum, and outputting the fault checking sequence table.

3. A digital twin-based grid fault hidden danger management system applying the digital twin-based grid fault hidden danger management method of any one of claims 1-2, characterized in that the system comprises: the system comprises a digital twin fault database module, a fault processing operation flow change analysis module, an associated hidden trouble analysis module and a fault intelligent response module;

the digital twin fault database module is used for carrying out digital twin mapping on the power grid equipment according to a digital twin technology, establishing a digital twin fault database and storing all fault processing operations when the analog power grid equipment breaks down; uniformly numbering power grid equipment and fault processing operation to generate a fault processing operation flow sample set;

the fault processing operation flow change analysis module is used for constructing a historical fault event library, storing the fault processing operation when the power grid equipment in the power grid system breaks down each time, and generating a historical fault processing operation flow set; generating a flow change set of fault processing operation according to the fault processing operation flow sample set and the historical fault processing operation flow set;

The associated hidden danger analysis module calculates a label value of the fault processing operation according to the fault processing operation flow sample set, the historical fault processing operation flow set and the flow change set, captures the core fault processing operation and generates a core fault processing operation flow set; searching the association relation of any two power grid devices according to the core fault processing operation flow set, calculating the association degree of any two power grid devices, and generating an association hidden trouble fault device set;

the intelligent fault coping module is used for acquiring power grid equipment with faults in a power grid system in real time, generating a fault checking equipment list, generating a fault checking time table according to working time, acquiring working time of each checking in the fault checking time table, constructing a checking randomization iteration model, generating a fault checking sequence table and calculating hidden trouble checking degree of the faults; after iteration is stopped, analyzing the hidden danger avoidance degree of the fault investigation sequence table, selecting the corresponding fault investigation sequence table when the hidden danger avoidance degree is maximum, and outputting the fault investigation sequence table.

4. A digital twinning-based grid fault hidden danger management system according to claim 3, wherein: the digital twin fault database module further comprises a storage unit and an encoding unit;

The storage unit is used for carrying out digital twin mapping on all power grid equipment in a power grid system, and establishing a digital twin fault database according to a fault processing operation flow when each power grid equipment breaks down, wherein all fault processing operations when analog power grid equipment breaks down are stored in the digital twin fault database;

the coding unit is used for respectively carrying out unified coding on the power grid equipment and the fault processing operation, integrally planning all fault processing operations corresponding to different power grid equipment, generating a fault processing operation flow sample set, and recording as I _i ={O ₁ ，O ₂ ，...，O _n Wherein i represents grid device code, O ₁ ，O ₂ ，...，O _n Respectively representing the 1 st, 2 nd, n-th fault handling operations corresponding to the grid device i.

5. The digital twinning-based grid fault hidden danger management system of claim 4, wherein: the fault processing operation flow change analysis module further comprises a historical fault event library construction unit and a flow change analysis unit;

the historical fault event library construction unit is used for constructing a historical fault event library, storing the fault processing operation of each time of the faults of the power grid equipment in the power grid system in the history fault event library, generating a historical fault processing operation flow set, and recording the historical fault processing operation flow set as i _k ={O ₁ ，O ₂ ，...，O _m -wherein i _k Representing a historical fault handling operation flow set, O, of the power grid equipment i when a historical kth fault occurs ₁ ，O ₂ ，...，O _m Respectively representing the 1 st, 2 nd, m th fault handling operations of the grid device i when a historic kth fault occurs;

the flow change analysis unit obtains a flow change set of fault processing operation according to the fault processing operation flow sample set and the historical fault processing operation flow set, and marks the flow change set as FC _i (k→k+1)=i _k ∩i _k+1 Wherein FCi (k→k+1) represents a flow change set of fault handling operations generated by the power grid device i at the time of the kth to the kth+1 fault, i _k+1 And the historical fault processing operation flow set of the power grid equipment i when the k+1st time of the historical faults occurs is represented.

6. The digital twinning-based grid fault hidden danger management system of claim 5, wherein: the associated hidden danger analysis module further comprises a core fault processing operation analysis unit and an associated hidden danger analysis unit;

the core fault processing operation analysis unit calculates a label value of any fault processing operation according to a fault processing operation flow sample set, a historical fault processing operation flow set and a flow change set, and a specific calculation formula is as follows:

LV(O _v )=∑ _k=1 ^Nk-1 h[O _v ∈FC _i (k→k+1)]/∑ _k=1 ^Nk H(O _v ∈i _k )

Wherein LV (O) _v ) Representing any one of the faultsProcessing operation O _v And O _v ∈I _i Nk represents the total number of times the grid device i fails in history;

if O _v ∈FC _i (k.fwdarw.k+1), let h [ O ] _v ∈FC _i (k→k+1)]=1, otherwise let h [ O ] _v ∈FC _i (k→k+1)]=0；

If O _v ∈i _k Let H (O) _v ∈i _k ) =1, otherwise, let H (O _v ∈i _k )=0；

Presetting a label value threshold value if any one of fault handling operations O _v If the tag value of (2) is greater than or equal to the tag value threshold, any fault processing operation O _v The core fault processing operation is marked as any one of the power grid equipment i; counting all core fault handling operations of any one power grid equipment i, generating a core fault handling operation flow set, and recording the core fault handling operation flow set as CI (customer care) equipment i _i ；

The association hidden danger analysis unit searches the association relation of any two power grid devices according to the core fault processing operation flow set, calculates the association degree of any two power grid devices, and the specific calculation formula is as follows:

CD _ij =1-NUM ^-1 (CI _i ∩CI _j )×[NUM(CI _i -CI _i ∩CI _j )/NUM(CI _i )+NUM(CI _j -CI _i ∩CI _j )/NUM(CI _j )]

wherein, CD _ij Representing the degree of association between grid device i and grid device j, j representing the number of the grid device and i not equal j, CI _j Representing a set of core fault handling operation flows generated for a corresponding grid device j, NUM (CI _i ∩CI _j )、NUM(CI _i -CI _i ∩CI _j )、NUM(CI _i )、NUM(CI _j -CI _i ∩CI _j ) And NUM (CI) _j ) Respectively represent the collection CI _i ∩CI _j 、CI _i -CI _i ∩CI _j 、CI _i 、CI _j -CI _i ∩CI _j And CI (CI) _j The number of core fault handling operations involved;

Presetting a relevance threshold, and if the relevance is greater than or equal to the relevance threshold, indicating that a relevance exists between the power grid equipment i and the power grid equipment j; in a power grid system, all power grid equipment with association relation with power grid equipment i is counted, and an association hidden trouble fault equipment set is generated and recorded as RHD _i 。

7. The digital twinning-based grid fault hidden danger management system of claim 6, wherein: the intelligent fault coping module further comprises a real-time fault perception processing unit and a hidden danger avoiding analysis unit;

the real-time fault perception processing unit is used for acquiring faulty power grid equipment in a power grid system in real time, counting the total number of the faulty power grid equipment as Q, and generating a fault troubleshooting equipment list; generating a fault checking time table according to the working time, acquiring the working time of each checking in the fault checking time table, and recording the working time of the x-th checking as T _x ；

Constructing a troubleshooting randomization iteration model according to the troubleshooting equipment list and the troubleshooting time table, randomizing a group of troubleshooting sequence tables, and enabling the troubleshooting sequence table corresponding to the e-th randomization to be SL _e The randomization needs to satisfy the condition:

∑ _u∈R t _u ≤T _x

∑ _x=1 ^F NUM(R _x )=Q

Wherein R is _x An inspection set representing the composition of power grid equipment contained in the x-th inspection, wherein R=R _x ，t _u Representing an investigation set R _x Average troubleshooting duration of any one of the power grid equipment u in the history fault troubleshooting process, NUM (R) _x ) Representing an investigation set R _x The number of the power grid equipment contained in the system, wherein F represents the total number of times of troubleshooting in a troubleshooting schedule;

the hidden danger investigation degree of the nth investigation in the fault investigation sequence table corresponding to the e-th randomization is calculated, and the specific calculation formula is as follows:

y：HD _e =NUM[(⋃ _x=y+1 ^F ⋃ _b∈R RHD _b )⋂(⋃ _x=1 ^y ⋃ _a∈R RHD _a )]/NUM(⋃ _x=1 ^y ⋃ _a∈R RHD _a )

wherein y: HD (HD) _e Represents hidden trouble shooting degree of the (y) th shooting in a fault shooting sequence table corresponding to the (e) th randomization _b And RHD _a Respectively representing a set of associated hidden trouble fault devices generated by the power grid device b and the power grid device a correspondingly, NUM [ (⋃) _x=y+1 ^F ⋃ _b∈R RHD _b )⋂(⋃ _x=1 ^y ⋃ _a∈R RHD _a )]And NUM (⋃) _x=1 ^y ⋃ _a∈R RHD _a ) Respectively represent a collection (⋃) _x=y+1 ^F ⋃ _b∈R RHD _b )⋂(⋃ _x=1 ^y ⋃ _a∈R RHD _a ) Sum set ⋃ _x=1 ^y ⋃ _a∈R RHD _a The number of medium power grid equipment, y is less than or equal to F-1;

let e=e+1, return to check the randomized iteration model; when e=e, the iteration stops;

the hidden danger avoiding analysis unit is used for checking a corresponding fault checking sequence table SL when the hidden danger checking degree is maximum in the y-th checking according to the fault checking time table _e Marking and counting the fault-finding sequence list SL _e The total number of markers in the F rounds of investigation was noted as F (SL _e ) Calculate the troubleshooting sequence table SL _e Is (1) the degree of avoidance S (SL _e )=f(SL _e ) And F, selecting a corresponding fault checking sequence table when the hidden danger evasion degree is maximum, and outputting the fault checking sequence table.