CN116205520A - Early warning method, system and application of power grid digital management index system - Google Patents

Early warning method, system and application of power grid digital management index system Download PDF

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CN116205520A
CN116205520A CN202310040012.6A CN202310040012A CN116205520A CN 116205520 A CN116205520 A CN 116205520A CN 202310040012 A CN202310040012 A CN 202310040012A CN 116205520 A CN116205520 A CN 116205520A
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李熙
刘林
卜轶锋
徐磊
张肖杰
刘钰
王天博
侯建
白兰
刘婷婷
侯剑
安博
赵军愉
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Baoding Power Supply Co of State Grid Hebei Electric Power Co Ltd
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Abstract

The invention belongs to the technical field of power grids, and discloses an early warning method, an early warning system and application of a digital management index system of a power grid. Collecting various data of a power grid, preprocessing, fusing different data, and extracting valuable data for application; constructing a three-dimensional power grid model, fusing data with space information, and displaying the overall condition of the power grid; and comprehensively carrying out fault and abnormality analysis of the power grid from the aspects of fault prediction, equipment management, operation analysis and inspection data, judging whether the power grid is abnormal or not, and early warning in time. According to the method, the three-dimensional power grid model is constructed to effectively fuse the data and the space information, the overall situation of the power grid is displayed more intuitively, the fault and abnormality analysis of the power grid is comprehensively carried out from multiple aspects of fault prediction, equipment management, operation analysis and inspection data, whether the power grid is abnormal or not can be comprehensively, timely and effectively judged, and early warning is timely carried out.

Description

Early warning method, system and application of power grid digital management index system
Technical Field
The invention belongs to the technical field of power grids, and particularly relates to an early warning method, an early warning system and application of a digital management index system of a power grid.
Background
The power system is characterized in that the whole body formed by power transformation stations with various voltages and power transmission and distribution lines is called a power grid, and the power grid comprises three units of power transformation, power transmission and power distribution, and is used for transmitting and distributing electric energy and changing the voltage. The traditional power grid digital management technology mainly acquires, stores and manages data, but with the development of the Internet, the traditional power grid digital management technology cannot fully exert the value of the data and has low processing efficiency of the data; meanwhile, different data cannot be subjected to fusion association analysis, the data and the map information are administrative, the total space analysis of the power grid cannot be performed, and the power grid service coverage links are multiple, the influence range is wide, the influence of environmental factors is easy, so that the faults of the power grid cannot be effectively analyzed in time.
Moreover, with the development of economy, the domestic and foreign power supply situation is relatively tense, and a plurality of serious power failure accidents occur in the power system, so that not only is huge economic loss caused, but also the living order of people is influenced, and great influence is brought to society; the power distribution network is an important component in the power system, and the safe and stable operation of the power distribution network is an important link for the safe operation of the whole power grid and is a key link for improving the operation level of the power supply system at present. However, the current distribution network is a fragile system, and once a large-area fault or a power failure accident occurs, the consequences are quite serious and even catastrophic; meanwhile, the operation of the power distribution network is also affected by the condition of the power distribution network and meteorological condition factors, so that in order to improve the safety stability and reliability of a power system, safety precaution is necessary to be carried out on risks faced in the operation of the power distribution network, and potential fault risks of the power distribution network are found out.
At present, the early warning of the power distribution network is to acquire real-time data of the power distribution network on line and calculate various indexes, so as to judge whether one index is out of limit or not and send out early warning; however, it cannot be predicted whether other indexes are out of limit in a future period of time, and an early warning is sent.
In order to solve the above problems, the prior art CN201510244527.3 is a power distribution network early warning method based on an improved Apriori algorithm, which includes the following steps:
step 1, collecting multiple groups of historical fault data and historical meteorological data of a power distribution network, and establishing an original database;
step 2, collecting power distribution network data and meteorological data of different areas in a power distribution network in real time, wherein the power distribution network data comprise bus voltage, branch power flow, cable tap temperature, load capacity and whether elements have faults or not; the meteorological data comprise abnormal temperature values, strong rainfall, strong snowfall, lightning grade and ice disaster grade;
step 3, acquiring various early warning indexes of the power distribution network according to the power distribution network data acquired in real time, wherein the early warning indexes comprise a bus voltage critical index, a branch power flow out-of-limit index, a cable tap temperature critical index and a load critical index;
step 4, judging whether the real-time acquired data has out-of-limit distribution network data or abnormal meteorological data according to all early warning indexes and meteorological data of the acquired distribution network, if yes, executing step 5, otherwise, returning to execute step 3;
Step 5, acquiring other indexes associated with out-of-limit power distribution network data or abnormal meteorological data from an original database by adopting an improved Apriori data mining algorithm;
and 6, displaying other indexes related to the obtained out-of-limit power distribution network data and the abnormal meteorological data, and prompting a dispatcher to take measures.
In addition, major construction accidents, public emergency events, bad-property illegal operations, earthquakes, floods and the like are extremely easy to cause large-scale power failure accidents and even power grid collapse, and the large-scale power failure accidents seriously affect social stability and life and property safety of people. When power abnormality occurs, the formulation of an emergency plan often depends on whether the collected data of emergency abnormality information is accurate, comprehensive and timely; however, during the abnormal power period, the actual condition of power failure can be changed in a second, the collection of abnormal power information is difficult to be performed in time, the emergency demand classification and the emergency plan formulation mainly depend on experience and subjective judgment of emergency management staff and emergency specialists, certain randomness and blindness exist in the dispatching, and a scientific classification method is lacked for guiding.
To solve the above problems, the prior art CN201810762392.3 provides a power grid early warning scheduling system, method, device and server, the system includes: the system comprises an information acquisition device, an early warning center, a provincial and local dispatching integrated platform and a dispatching center; firstly, an information acquisition device is arranged in a station to be monitored to acquire power abnormality information; secondly, enabling the early warning center to generate emergency demand grading information according to the electric power abnormal information and a preset fuzzy evaluation model; then, emergency demand grading information is sent to a dispatching center through a provincial and local dispatching integrated platform according to a preset interface and a preset protocol; finally, the dispatching center generates an emergency plan according to the emergency demand grading information, and dispatches emergency materials according to the emergency plan, so that the technical problems that the electric power abnormal information is difficult to collect, and scientific methods for guiding the emergency demand grading and the emergency plan are lacking are solved.
Through the above analysis, the problems and defects existing in the prior art are as follows: the data processing efficiency is low, the data utilization rate is low, the fusion analysis and the space analysis of different data and maps cannot be performed, and the fault analysis and the early warning cannot be effectively performed.
Disclosure of Invention
In order to overcome the problems in the related art, the embodiment of the invention provides an early warning method, an early warning system and application of a power grid digital management index system.
The technical scheme is as follows: the early warning method of the power grid digital management index system comprises the following steps:
step one, collecting various data of a power grid, preprocessing, fusing different data, and extracting valuable data for application;
step two, constructing a three-dimensional power grid model to fuse data with space information and display the overall condition of the power grid;
and thirdly, comprehensively carrying out fault and abnormality analysis of the power grid from the aspects of fault prediction, equipment management, operation analysis and inspection data, judging whether the power grid is abnormal or not, and early warning in time.
In step S2, the building a three-dimensional power grid model fuses data and spatial information, which specifically includes:
(1) Building a regional three-dimensional scene model based on the preprocessed regional environment data;
(2) Will beThe data and the space information are fused to construct a three-dimensional power grid model; first, a fault node F of space information is extracted D Unifying the coordinate system of the fault nodes, calculating the fault change nodes of the fault graph, converting the fault change nodes into a change set through calculation, and extracting the underlying fault characteristics F B For the bottom layer fault characteristics F B Transforming to obtain fault characteristics F S Failure node F D And fault feature F S And carrying out weight fusion to obtain a power grid identification fault classification model.
In the step (1), based on the preprocessed regional environment data, a regional three-dimensional scene model is built, and the method specifically comprises the following steps:
firstly, constructing three-dimensional components of all power grids contained in a region based on preprocessed regional power grid information and preprocessed environment images;
secondly, determining the position connection relation of the three-dimensional components of each power grid based on the geographic position coordinates and the environment images of the components of each power grid;
then, building a regional three-dimensional model based on the three-dimensional members of all the power grids and the position connection relation of the three-dimensional members of each power grid;
and finally, adding the obtained geological data into the regional three-dimensional model, and rendering the regional three-dimensional model to obtain a regional three-dimensional scene model.
In step (2), the fault node F extracting the spatial information D Projecting a fault map with time information onto a Cartesian product plane; if one fault diagram sequence has N frames, the fault diagram feature calculation formula is as follows:
Figure BDA0004050577140000041
wherein ,
Figure BDA0004050577140000042
representing a projection view of an ith frame fault map in a v direction; GLAC processing is carried out on the projection graph, STACOG processing is carried out on the fault graph sequence Calculating the autocorrelation of the gradient space and the direction to obtain the fault node characteristic F D ＝{F D1 ，F D2 Sparse representation with gradient magnitude n and gradient rate of change θ; each point r is encoded as b=8 influencing factors, including: total current, total voltage, user real-time current, user real-time voltage, external temperature, humidity, seasons and total operation duration, the gradient change rate theta and the weights of adjacent influence factors form a gradient direction vector f, and the characteristics of the gradient in the neighborhood are calculated:
0 th order features:
Figure BDA0004050577140000043
1 st order features:
Figure BDA0004050577140000044
wherein a1 Is the displacement vector from the adjacent point to r, and a is taken 1 ∈{±Δr，0}，f d Is the d-th element of vector f; performing ROI operation to make the same view angle images have the same size to obtain fault node characteristics F D ＝{F D1 ，F D2 }；
The coordinate system of the unified fault node is a new coordinate system which takes the world coordinate system as an origin point;
the fault change node of the fault graph is calculated, the variance from the fault node to the origin is used for defining the activity degree of each partial power transmission area, the larger the variance is, the more severe the node changes, and when the variance is larger than the average value, the point is considered as the fault change node;
the fault change node is converted into a change set through calculation, the node fault change with the maximum variance is converted into the change set, and the fault characteristic of the behavior is expressed by the change set characteristic;
Said extracting underlying fault signature F B The fault change node and the change set are used for extracting the inter-frame distance D, the external cube contour ratio O and the inter-frame change rate difference A from the change set as the underlying fault characteristic F B ；
The pair of bottom layer fault characteristics F B Transforming to obtain fault characteristics F S Overcoming the difference between different power transmission areas by normalization, and then performing fault feature F on the bottom layer B Fisher Vector treatment of the Gaussian mixture model was performed to a size of 2pK×1, K was taken 128 in the Gaussian mixture model, and p was F B Obtaining fault characteristics F S The method comprises the steps of carrying out a first treatment on the surface of the The fault node characteristics and the fault characteristics are subjected to weight fusion to obtain an identification classification model, and the fault characteristics F are subjected to S And fault node characteristics F D1 ，F D2 Input into classifier, and different weights mu are allocated 1 ，μ 2 ，(1-μ 1 -μ 2 ) Estimating global membership by a logarithmic function, and calculating a formula:
logP(l c |F)＝
μ 1 p 1 (l c |F S )+μ 2 p 2 (l c |F D1 )+(1-μ 1 -μ 2 )p 3 (l c |F D2 )
obtaining the final label l when the membership degree is maximum * The formula is calculated:
Figure BDA0004050577140000051
wherein ,p1 (l c |F S )、p 2 (l c |F D1 ) And p is as follows 3 (l c |F D2 ) Is F S ，F D1 ，F D2 Posterior probability obtained through Sigmoid function calculation;
extracting underlying fault feature F B The method specifically comprises the following steps: in the behavior fault change, the distance change of the fault change node is most obvious, the obtained change set is taken as a standard, and the size of each node on the change set is called as the inter-frame distance; p epsilon R 3 Representing the coordinates of the fault node under the space coordinate system, and calculating the formula:
Figure BDA0004050577140000052
for the bottom layer fault feature F B Transforming to obtain fault characteristics F S The method specifically comprises the following steps:
for F B Performing Fisher Vector processing of the Gaussian mixture model, wherein the size is changed to 2 pKx1, K is 128 in the Gaussian mixture model, and p is the number of rows of FB;
the construction process pseudo code is as follows: input: underlying fault signature for M images
Figure BDA0004050577140000061
And (3) outputting: normalized fisher vector:
Figure BDA0004050577140000062
initializing: average mu k As random numbers, variance sigma k Is the identity matrix E, probability w k ＝1/K
Calculating GMM parameter lambda= { w of Gaussian mixture model k ，μ k ，σ k ，k＝1，…，K}
for|p(F B |Φ)-p(F B |Φ)’|＜ε
Figure BDA0004050577140000063
Probability:
Figure BDA0004050577140000064
average value:
Figure BDA0004050577140000065
variance:
Figure BDA0004050577140000066
weight value:
Figure BDA0004050577140000067
another object of the present invention is to provide an early warning system for a power grid digital management index system, which implements the early warning method for the power grid digital management index system, where the early warning system for the power grid digital management index system includes:
the three-dimensional scene model construction module is connected with the central control module and is used for constructing a regional three-dimensional scene model based on the preprocessed regional environment data;
the three-dimensional power grid model construction module is connected with the central control module and is used for constructing a three-dimensional power grid model based on the constructed regional three-dimensional scene model in combination with the preprocessed power grid communication data and the power grid equipment positioning information;
The management system construction module is connected with the central control module and used for constructing a power grid digital management index system based on operation management, operation analysis, inspection real-time monitoring and fault prediction;
the management analysis module is connected with the central control module and is used for carrying out power grid management analysis based on the built power grid digital management index system and combining various real-time index data of the power grid;
the early warning module is connected with the central control module and is used for early warning when the power grid management analysis result shows that the power grid is abnormal or fails;
the sharing module is connected with the central control module and used for constructing a data sharing interface to share basic data of the power grid;
the visual display module is connected with the central control module and used for visually displaying the constructed three-dimensional power grid model and the early warning, monitoring and analysis information.
In one embodiment, the early warning system of the power grid digital management index system further comprises:
the historical parameter acquisition module is connected with the central control module and used for acquiring historical environment data of the power grid area and historical operation and monitoring data of the power grid;
the environment parameter acquisition module is connected with the central control module and used for acquiring environment image information, power grid information, geological data, geographic position coordinate data and other information of the area where the power grid is located;
The environment parameter acquisition module is connected with the central control module and used for acquiring weather information, thunder and lightning forecast information, climate information and environment temperature and humidity information of an area where the power grid is located;
the data acquisition module is connected with the central control module and is used for acquiring real-time operation data of the power grid, monitoring data of power grid equipment, positioning information and communication data of the power grid;
the central control module is connected with the historical parameter acquisition module, the environment parameter acquisition module, the data preprocessing module, the data encryption storage module, the three-dimensional scene model building module, the three-dimensional power grid model building module, the management system building module, the management analysis module, the early warning module, the sharing module and the visual display module and is used for controlling each module to work normally by utilizing the singlechip or the controller;
the data preprocessing module is connected with the central control module and is used for preprocessing the collected regional environment data, the environment parameters, the operation data of the power grid, the monitoring data of equipment and the power grid communication data;
the data encryption storage module is connected with the central control module and used for carrying out distributed encryption storage on the collected related data; and simultaneously, the related data stored in an encrypted mode is backed up.
In one embodiment, the management system construction module constructs a power grid digital management index system based on operation management, operation analysis, inspection real-time monitoring and fault prediction, and the method comprises the following steps:
firstly, taking power grid operation management data, operation analysis data, patrol real-time monitoring data and fault prediction data as power grid risk factors;
secondly, analyzing index elements and characteristic values of the power grid risk factors; meanwhile, determining the influence degree of each power grid risk factor on the power grid;
finally, determining the weight value of each power grid risk factor based on the influence degree of the power grid risk factor on the power grid; and constructing a digital management index system of the power grid based on the power grid risk factors and the weight values thereof.
In one embodiment, the system of digital management indexes of the power grid is as follows:
S＝R*B；
A1L1...A1Lm
R＝... ... ...；
AnL1...AnLm
B＝[W1，W2，......，Wn]；
s represents a power grid digital management index system; r represents a power grid risk factor matrix; b represents a power grid risk factor weight matrix, wi represents weight values of all power grid risk factors; n represents the number of grid risk factors; m represents the number of grid risk factor index elements;
the management analysis module performs power grid management analysis based on the constructed power grid digital management index system and various real-time index data of the power grid, and comprises the following steps:
Firstly, performing management analysis on power grid equipment based on a constructed three-dimensional power grid model and monitored equipment operation data; and carrying out operation analysis of the power grid based on the constructed three-dimensional power grid model and the preprocessed data;
secondly, acquiring real-time coordinate data of power grid inspection and power grid state data; predicting the fault probability of the power grid based on the collected historical data, the real-time operation data of the power grid and the environmental parameters;
finally, determining a power grid management analysis value and a power grid management analysis result based on a power grid equipment management analysis result, a power grid operation analysis result, power grid inspection data and a power grid digital management index system constructed by combining the predicted fault probability; outputting abnormal node coordinates and other information of the power grid;
the step of acquiring real-time coordinate data of the power grid inspection and power grid state data is performed before: and planning a patrol route and an overhaul shutdown route based on the constructed three-dimensional power grid model.
Another object of the present invention is to provide a computer device, where the computer device includes a memory and a processor, where the memory stores a computer program, and where the computer program when executed by the processor causes the processor to implement an early warning method of the power grid digital management index system.
Another object of the present invention is to provide a computer readable storage medium storing a computer program, where the computer program when executed by a processor causes the processor to implement an early warning method of the power grid digital management index system.
By combining all the technical schemes, the invention has the advantages and positive effects that:
first, aiming at the technical problems existing in the prior art and the difficulty of solving the problems, the technical problems solved by the technical scheme of the invention to be protected, results and data in the research and development process and the like are closely combined, the technical problems solved by the technical scheme of the invention are analyzed in detail and deeply, and some technical effects with creativity brought after the problems are solved are specifically described as follows:
according to the invention, various data are collected and preprocessed, so that different data can be effectively fused, and valuable data can be extracted for application; meanwhile, a three-dimensional power grid model is constructed to effectively fuse data and space information, the overall situation of the power grid is displayed more intuitively, faults and anomalies of the power grid are comprehensively analyzed from multiple aspects of fault prediction, equipment management, operation analysis and inspection data, whether the power grid is abnormal can be comprehensively, timely and effectively judged, and early warning is timely carried out.
Secondly, the technical scheme is regarded as a whole or the change rate of the product, and the technical scheme to be protected has the technical effects and advantages as follows:
the invention provides an early warning method of a power grid digital management index system, which comprises the following steps: collecting various data, preprocessing the various data, fusing different data, and extracting valuable data for application; constructing a three-dimensional power grid model, fusing data with space information, and displaying the overall condition of the power grid; and comprehensively carrying out fault and abnormality analysis on the power grid from multiple aspects of fault prediction, equipment management, operation analysis and inspection data, judging whether the power grid is abnormal or not, and early warning in time.
The invention can reduce the management time, improve the management efficiency, avoid the error generated by manual management, meet the existing digital management requirement of the power grid and have great application value.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure;
FIG. 1 is a schematic diagram of an early warning system of a power grid digital management index system provided by an embodiment of the invention;
FIG. 2 is a flowchart of a method for constructing a regional three-dimensional scene model by the three-dimensional scene model construction module according to the embodiment of the invention based on preprocessed regional environment data;
FIG. 3 is a flowchart of a method for constructing a digital management index system of a power grid by a management system construction module based on operation management, operation analysis, inspection real-time monitoring and fault prediction according to an embodiment of the present invention;
FIG. 4 is a flowchart of a method for performing power grid management analysis by the management analysis module according to the embodiment of the invention based on a constructed power grid digital management index system and various real-time index data of a power grid;
in the figure: 1. a history parameter acquisition module; 2. an environmental parameter acquisition module; 3. an environmental parameter acquisition module; 4. a data acquisition module; 5. a central control module; 6. a data preprocessing module; 7. a data encryption storage module; 8. a three-dimensional scene model construction module; 9. the three-dimensional power grid model building module; 10. a management system construction module; 11. a management analysis module; 12. an early warning module; 13. a sharing module; 14. and a visual display module.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to the appended drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. The invention may be embodied in many other forms than described herein and similarly modified by those skilled in the art without departing from the spirit or scope of the invention, which is therefore not limited to the specific embodiments disclosed below.
1. Explanation of the examples:
the embodiment of the invention provides an early warning method of a power grid digital management index system, which comprises the following steps:
step one, collecting various data of a power grid, preprocessing, fusing different data, and extracting valuable data for application;
step two, constructing a three-dimensional power grid model to fuse data with space information and display the overall condition of the power grid;
and thirdly, comprehensively carrying out fault and abnormality analysis of the power grid from the aspects of fault prediction, equipment management, operation analysis and inspection data, judging whether the power grid is abnormal or not, and early warning in time.
In a preferred embodiment of the present invention, constructing a three-dimensional power grid model in step S2 to fuse data with spatial information includes:
(1) Building a regional three-dimensional scene model based on the preprocessed regional environment data comprises the following steps:
(2) The constructed three-dimensional power grid model fuses data and space information, and specifically comprises the following steps:
(2.1) Fault node F extracting spatial information D ；
(2.2) unifying the coordinate system of the fault node;
(2.3) calculating a fault change node of the fault graph;
(2.4) converting the fault change node into a change set through calculation;
(2.5) extracting underlying failure feature F B ；
(2.6) for underlying failure feature F B Transforming to obtain fault characteristics F S ；
(2.7) failure node F D And fault feature F S Weight fusion is carried out to obtain power grid identification fault componentsClass models.
In a preferred embodiment of the present invention, in step (2), the fault node F extracting spatial information D Projecting a fault map with time information onto a Cartesian product plane; if one fault diagram sequence has N frames, the fault diagram feature calculation formula is as follows:
Figure BDA0004050577140000111
wherein
Figure BDA0004050577140000112
Representing a projection view of an ith frame fault map in a v direction; performing GLAC processing on the projection graph, performing STACOG processing on the fault graph sequence, and calculating the autocorrelation of gradient space and direction to obtain fault node characteristics F D ＝{F D1 ，F D2 Sparse representation with gradient magnitude n and gradient rate of change θ; each point r is encoded as b=8 influencing factors, including: total current, total voltage, user real-time current, user real-time voltage, external temperature, humidity, seasons and total operation duration, the gradient change rate theta and the weights of adjacent influence factors form a gradient direction vector f, and the characteristics of the gradient in the neighborhood are calculated:
0 th order features:
Figure BDA0004050577140000122
/>
1 st order features:
Figure BDA0004050577140000123
wherein a1 Is the displacement vector from the adjacent point to r, and a is taken 1 ∈{±Δr，0}，f d Is the d-th element of vector f; performing ROI operation to make the same view angle images have the same size to obtain fault node characteristics F D ＝{F D1 ，F D2 }；
The coordinate system of the unified fault node is a new coordinate system which takes the world coordinate system as an origin point;
calculating a fault change node of the fault graph, namely defining the activity degree of each partial power transmission area by using the variance from the fault node to the origin, wherein the larger the variance is, the more severe the node changes, and when the variance is larger than the average value, the point is considered as the fault change node;
the fault change nodes are converted into a change set through calculation, namely, the node fault change with the maximum variance is converted into the change set, and the fault characteristics of the behaviors are expressed by using the change set characteristics;
extracting underlying fault feature F B Namely, the fault change node and the change set extract the inter-frame distance D, the external cube contour ratio O and the inter-frame change rate difference A as the underlying fault characteristic F B ；
For the bottom layer fault feature F B Transforming to obtain fault characteristics F S The difference between different power transmission areas is overcome by normalization, and then the underlying fault characteristic F is calculated B Fisher Vector treatment of the Gaussian mixture model was performed to a size of 2pK×1, K was taken 128 in the Gaussian mixture model, and p was F B Obtaining fault characteristics F S The method comprises the steps of carrying out a first treatment on the surface of the The fault node characteristics and the fault characteristics are subjected to weight fusion to obtain an identification classification model, namely the fault characteristics F S And fault node characteristics F D1 ，F D2 Input into classifier, and different weights mu are allocated 1 ，μ 2 ，(1-μ 1 -μ 2 ) Estimating global membership by a logarithmic function, and calculating a formula:
logP(l c |F)＝
μ 1 p 1 (l c |F S )+μ 2 p 2 (l c |F D1 )+(1-μ 1 -μ 2 )p 3 (l c |F D2 )
obtaining the final label l when the membership degree is maximum * The formula is calculated:
Figure BDA0004050577140000121
wherein ,p1 (l c |F S )、p 2 (l c |F D1 ) And p is as follows 3 (l c |F D2 ) Is F S ，F D1 ，F D2 Posterior probability obtained through Sigmoid function calculation;
the bottom layer fault characteristics are extracted, specifically:
interframe spacing
In the behavior fault change, the distance change of the fault change node is most obvious, the obtained change set is taken as a standard, and the size of each node on the change set is called as the inter-frame distance;
P∈R 3 representing the coordinates of the fault node under the space coordinate system, and calculating the formula:
Figure BDA0004050577140000131
for the bottom layer fault feature F B Transforming to obtain fault characteristics F S The method specifically comprises the following steps:
for F B Fisher Vector treatment of the Gaussian mixture model was performed to a size of 2pK×1, K was taken 128 in the Gaussian mixture model, and p was F B The number of rows of (3);
the construction process pseudo code is as follows:
input: underlying fault signature for M images
Figure BDA0004050577140000132
And (3) outputting: normalized fisher vector:
Figure BDA0004050577140000133
initializing: average mu k As random numbers, variance sigma k Is the identity matrix E, probability w k ＝1/K
Calculating GMM parameter lambda= { w of Gaussian mixture model k ，μ k ，σ k ，k＝1，…，K}
for|p(F B |Φ)-p(F B |Φ)′|＜ε
Figure BDA0004050577140000134
Probability of
Figure BDA0004050577140000135
Average value:
Figure BDA0004050577140000136
variance:
Figure BDA0004050577140000137
weight value:
Figure BDA0004050577140000138
and (5) ending.
Example 1
As shown in fig. 1, the early warning system of the power grid digital management index system provided by the embodiment of the invention includes:
the historical parameter acquisition module 1 is connected with the central control module 5 and is used for acquiring historical environment data of a power grid area and historical operation and monitoring data of the power grid;
the environment parameter acquisition module 2 is connected with the central control module 5 and is used for acquiring environment image information, power grid information, geological data, geographic position coordinate data and other information of the area where the power grid is located;
the environment parameter acquisition module 3 is connected with the central control module 5 and is used for acquiring weather information, thunder and lightning forecast information, climate information and environment temperature and humidity information of an area where the power grid is located;
the data acquisition module 4 is connected with the central control module 5 and is used for acquiring real-time operation data of the power grid, monitoring data of power grid equipment, positioning information and communication data of the power grid;
the central control module 5 is connected with the historical parameter acquisition module 1, the environmental parameter acquisition module 2, the environmental parameter acquisition module 3, the data acquisition module 4, the data preprocessing module 6, the data encryption storage module 7, the three-dimensional scene model construction module 8, the three-dimensional power grid model construction module 9, the management system construction module 10, the management analysis module 11, the early warning module 12, the sharing module 13 and the visual display module 14 and is used for controlling each module to work normally by utilizing a singlechip or a controller;
The data preprocessing module 6 is connected with the central control module 5 and is used for preprocessing the collected regional environment data, the environment parameters, the operation data of the power grid, the monitoring data of equipment and the power grid communication data;
the data encryption storage module 7 is connected with the central control module 5 and used for carrying out distributed encryption storage on the collected related data; meanwhile, the related data stored in an encrypted mode is backed up;
the three-dimensional scene model construction module 8 is connected with the central control module 5 and is used for constructing a regional three-dimensional scene model based on the preprocessed regional environment data;
the three-dimensional power grid model construction module 9 is connected with the central control module 5 and is used for constructing a three-dimensional power grid model based on the constructed regional three-dimensional scene model and the preprocessed power grid communication data and the power grid equipment positioning information;
the management system construction module 10 is connected with the central control module 5 and is used for constructing a power grid digital management index system based on operation management, operation analysis, inspection real-time monitoring and fault prediction;
the management analysis module 11 is connected with the central control module 5 and is used for carrying out power grid management analysis based on the constructed power grid digital management index system and combining various real-time index data of the power grid;
The early warning module 12 is connected with the central control module 5 and is used for early warning when the power grid management analysis result shows that the power grid is abnormal or fails;
the sharing module 13 is connected with the central control module 5 and is used for constructing a data sharing interface to share basic data of the power grid;
the visual display module 14 is connected with the central control module 5 and is used for visually displaying the constructed three-dimensional power grid model and the early warning, monitoring and analysis information.
Example 2
As shown in fig. 2, according to the early warning method of the power grid digital management index system provided by the embodiment of the present invention, further, the three-dimensional scene model building module 8 builds a regional three-dimensional scene model based on the preprocessed regional environment data, where the method includes:
s101, constructing three-dimensional components of all power grids contained in the area based on the preprocessed regional power grid information and the preprocessed environmental images;
s102, determining the position connection relation of three-dimensional components of each power grid based on geographic position coordinates and environment images of the components of each power grid;
s103, building a regional three-dimensional model based on the three-dimensional members of all the power grids and the position connection relation of the three-dimensional members of each power grid;
And S104, adding the obtained geological data into the regional three-dimensional model, and rendering the regional three-dimensional model to obtain a regional three-dimensional scene model.
Example 3
Based on the early warning method of the power grid digital management index system provided by the embodiment of the present invention, further, as shown in fig. 3, the management system construction module 10 provided by the embodiment of the present invention constructs the power grid digital management index system based on operation management, operation analysis, routing inspection real-time monitoring and fault prediction, including:
s201, taking power grid operation management data, operation analysis data, patrol real-time monitoring data and fault prediction data as power grid risk factors;
s202, analyzing index elements and characteristic values of the power grid risk factors; meanwhile, determining the influence degree of each power grid risk factor on the power grid;
s203, determining weight values of all power grid risk factors based on the influence degree of the power grid risk factors on the power grid; and constructing a digital management index system of the power grid based on the power grid risk factors and the weight values thereof.
The power grid digital management index system provided by the embodiment of the invention is as follows:
S＝R*B；
A1L1...A1Lm
... ... ...
R＝AnL1...AnLm；
B＝[W1，W2，......，Wn]；
s represents a power grid digital management index system; r represents a power grid risk factor matrix; b represents a power grid risk factor weight matrix, wi represents weight values of all power grid risk factors; n represents the number of grid risk factors; m represents the number of the power grid risk factor index elements.
Example 3
Further, as shown in fig. 4, according to the early warning method of the power grid digital management index system provided by the embodiment of the present invention, the management analysis module provided by the embodiment of the present invention performs power grid management analysis based on the constructed power grid digital management index system in combination with each real-time index data of the power grid, where the power grid management analysis includes:
s301, performing management analysis on power grid equipment based on the constructed three-dimensional power grid model and monitored equipment operation data; and carrying out operation analysis of the power grid based on the constructed three-dimensional power grid model and the preprocessed data;
s302, planning a patrol route and a overhaul shutdown route based on the constructed three-dimensional power grid model; acquiring real-time coordinate data and power grid state data of power grid inspection; predicting the fault probability of the power grid based on the collected historical data, the real-time operation data of the power grid and the environmental parameters;
s303, determining a power grid management analysis value and a power grid management analysis result based on a power grid equipment management analysis result, a power grid operation analysis result, power grid inspection data and a power grid digital management index system constructed by combining the predicted fault probability; and outputting the coordinates of abnormal nodes of the power grid and other information.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
The content of the information interaction and the execution process between the devices/units and the like is based on the same conception as the method embodiment of the present invention, and specific functions and technical effects brought by the content can be referred to in the method embodiment section, and will not be described herein.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present invention. For specific working processes of the units and modules in the system, reference may be made to corresponding processes in the foregoing method embodiments.
2. Application examples:
the embodiment of the invention also provides a computer device, which comprises: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, which when executed by the processor performs the steps of any of the various method embodiments described above.
Embodiments of the present invention also provide a computer readable storage medium storing a computer program which, when executed by a processor, performs the steps of the respective method embodiments described above.
The embodiment of the invention also provides an information data processing terminal, which is used for providing a user input interface to implement the steps in the method embodiments when being implemented on an electronic device, and the information data processing terminal is not limited to a mobile phone, a computer and a switch.
The embodiment of the invention also provides a server, which is used for realizing the steps in the method embodiments when being executed on the electronic device and providing a user input interface.
Embodiments of the present invention provide a computer program product which, when run on an electronic device, causes the electronic device to perform the steps of the method embodiments described above.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing device/terminal apparatus, recording medium, computer Memory, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), electrical carrier signals, telecommunications signals, and software distribution media. Such as a U-disk, removable hard disk, magnetic or optical disk, etc.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
While the invention has been described with respect to what is presently considered to be the most practical and preferred embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications, equivalents, and alternatives falling within the spirit and scope of the invention.

Claims (10)

1. The early warning method of the power grid digital management index system is characterized by comprising the following steps of:
step one, collecting various data of a power grid, preprocessing, fusing different data, and extracting valuable data for application;
step two, constructing a three-dimensional power grid model to fuse data with space information and display the overall condition of the power grid;
and thirdly, comprehensively carrying out fault and abnormality analysis of the power grid from the aspects of fault prediction, equipment management, operation analysis and inspection data, judging whether the power grid is abnormal or not, and early warning in time.
2. The early warning method of the power grid digital management index system according to claim 1, wherein in step S2, the building of the three-dimensional power grid model fuses data and spatial information, specifically includes:
(1) Building a regional three-dimensional scene model based on the preprocessed regional environment data;
(2) Fusing the data with the space information to construct a three-dimensional power grid model; first, a fault node F of space information is extracted D Unifying the coordinate system of the fault nodes, calculating the fault change nodes of the fault graph, converting the fault change nodes into a change set through calculation, and extracting the underlying fault characteristics F B For the bottom layer fault characteristics F B Transforming to obtain fault characteristics F S Failure node F D And fault feature F S And carrying out weight fusion to obtain a power grid identification fault classification model.
3. The early warning method of a power grid digital management index system according to claim 2, wherein in the step (1), based on the preprocessed regional environment data, a regional three-dimensional scene model is built, and the method specifically comprises the following steps:
firstly, constructing three-dimensional components of all power grids contained in a region based on preprocessed regional power grid information and preprocessed environment images;
secondly, determining the position connection relation of the three-dimensional components of each power grid based on the geographic position coordinates and the environment images of the components of each power grid;
then, building a regional three-dimensional model based on the three-dimensional members of all the power grids and the position connection relation of the three-dimensional members of each power grid;
And finally, adding the obtained geological data into the regional three-dimensional model, and rendering the regional three-dimensional model to obtain a regional three-dimensional scene model.
4. The method for early warning of a power grid digital management index system according to claim 2, wherein in step (2), the fault node F that extracts spatial information D Projecting a fault map with time information onto a Cartesian product plane; if one fault diagram sequence has N frames, the fault diagram feature calculation formula is as follows:
Figure FDA0004050577130000021
wherein ,
Figure FDA0004050577130000022
representing a projection view of an ith frame fault map in a v direction; performing GLAC processing on the projection graph, performing STACOG processing on the fault graph sequence, and calculating the autocorrelation of gradient space and direction to obtain fault node characteristics F D ＝{F D1 ，F D2 Sparse representation with gradient magnitude n and gradient rate of change θ; each point r is encoded as b=8 influencing factors, including: total current, total voltage, user real-time current, user real-time voltage, external temperature, humidity, season, total duration of operation, gradient change rate theta and weights of adjacent influencing factors form gradient direction vector f,calculating characteristics of gradients in the neighborhood:
0 th order features:
Figure FDA0004050577130000023
1 st order features:
Figure FDA0004050577130000024
wherein a1 Is the displacement vector from the adjacent point to r, and a is taken 1 ∈{±Δr，0}，f d Is the d-th element of vector f; performing ROI operation to make the same view angle images have the same size to obtain fault node characteristics F D ＝{F D1 ，F D2 }；
The coordinate system of the unified fault node is a new coordinate system which takes the world coordinate system as an origin point;
the fault change node of the fault graph is calculated, the variance from the fault node to the origin is used for defining the activity degree of each partial power transmission area, the larger the variance is, the more severe the node changes, and when the variance is larger than the average value, the point is considered as the fault change node;
the fault change node is converted into a change set through calculation, the node fault change with the maximum variance is converted into the change set, and the fault characteristic of the behavior is expressed by the change set characteristic;
said extracting underlying fault signature F B The fault change node and the change set are used for extracting the inter-frame distance D, the external cube contour ratio O and the inter-frame change rate difference A from the change set as the underlying fault characteristic F B ；
The pair of bottom layer fault characteristics F B Transforming to obtain fault characteristics F S Overcoming the difference between different power transmission areas by normalization, and then performing fault feature F on the bottom layer B Fisher Vector treatment of the Gaussian mixture model was performed to a size of 2pK×1, K was taken 128 in the Gaussian mixture model, and p was F B Obtaining fault characteristics F S The method comprises the steps of carrying out a first treatment on the surface of the The fault node characteristics and the fault characteristics are subjected to weight fusion to obtain an identification classification model, and the fault characteristics F are subjected to S And fault node characteristics F D1 ，F D2 Input into classifier, and different weights mu are allocated 1 ，μ 2 ，(1-μ 1 -μ 2 ) Estimating global membership by a logarithmic function, and calculating a formula:
logP(l c |F)＝
μ 1 p 1 (l c |F S )+μ 2 p 2 (l c |F D1 )+(1-μ 1 -μ 2 )p 3 (l c |F D2 )
obtaining the final label l when the membership degree is maximum * The formula is calculated:
Figure FDA0004050577130000031
wherein ,p1 (l c |F S )、p 2 (l c |F D1 ) And p is as follows 3 (l c |F D2 ) Is F S ，F D1 ，F D2 Posterior probability obtained through Sigmoid function calculation;
extracting underlying fault feature F B The method specifically comprises the following steps: in the behavior fault change, the distance change of the fault change node is most obvious, the obtained change set is taken as a standard, and the size of each node on the change set is called as the inter-frame distance; p epsilon R 3 Representing the coordinates of the fault node under the space coordinate system, and calculating the formula:
Figure FDA0004050577130000032
for the bottom layer fault feature F B Transforming to obtain fault characteristics F S The method specifically comprises the following steps:
for F B Fisher Vector treatment of the Gaussian mixture model was performed to a size of 2pK×1, K was taken 128 in the Gaussian mixture model, and p was F B The number of rows of (3);
the construction process pseudo code is as follows: input: bottom layer fault feature of M imagesSign of sign
Figure FDA0004050577130000033
And (3) outputting: normalized fisher vector:
Figure FDA0004050577130000034
initializing: average mu k As random numbers, variance sigma k Is the identity matrix E, probability w k ＝1/K
Calculating GMM parameter lambda= { w of Gaussian mixture model k ，μ k ，σ k ，k＝1，…，K}
for|p(F B |Φ)-p(F B |Φ)′|＜ε
Figure FDA0004050577130000035
Probability:
Figure FDA0004050577130000041
average value:
Figure FDA0004050577130000042
/>
variance:
Figure FDA0004050577130000043
weight value:
Figure FDA0004050577130000044
5. an early warning system of a power grid digital management index system for implementing the early warning method of the power grid digital management index system according to any one of claims 1 to 4, characterized in that the early warning system of the power grid digital management index system comprises:
the three-dimensional scene model construction module (8) is connected with the central control module (5) and is used for constructing a regional three-dimensional scene model based on the preprocessed regional environment data;
the three-dimensional power grid model construction module (9) is connected with the central control module (5) and is used for constructing a three-dimensional power grid model based on the constructed regional three-dimensional scene model and the preprocessed power grid communication data and the power grid equipment positioning information;
the management system construction module (10) is connected with the central control module (5) and is used for constructing a power grid digital management index system based on operation management, operation analysis, inspection real-time monitoring and fault prediction;
the management analysis module (11) is connected with the central control module (5) and is used for carrying out power grid management analysis by combining various real-time index data of the power grid based on the constructed power grid digital management index system;
The early warning module (12) is connected with the central control module (5) and is used for early warning when the power grid management analysis result shows that the power grid is abnormal or fails;
the sharing module (13) is connected with the central control module (5) and is used for constructing a data sharing interface to share basic data of the power grid;
and the visual display module (14) is connected with the central control module (5) and is used for visually displaying the constructed three-dimensional power grid model and the early warning, monitoring and analysis information.
6. The system of claim 5, further comprising:
the historical parameter acquisition module (1) is connected with the central control module (5) and is used for acquiring historical environment data of a power grid area and historical operation and monitoring data of the power grid;
the environment parameter acquisition module (2) is connected with the central control module (5) and is used for acquiring environment image information, power grid information, geological data, geographic position coordinate data and other information of an area where the power grid is located;
the environment parameter acquisition module (3) is connected with the central control module (5) and is used for acquiring weather information, lightning forecast information, climate information and environment temperature and humidity information of an area where the power grid is located;
The data acquisition module (4) is connected with the central control module (5) and is used for acquiring real-time operation data of the power grid, monitoring data of power grid equipment, positioning information and communication data of the power grid;
the central control module (5) is connected with the historical parameter acquisition module (1), the environment parameter acquisition module (2), the environment parameter acquisition module (3), the data acquisition module (4), the data preprocessing module (6), the data encryption storage module (7), the three-dimensional scene model building module (8), the three-dimensional power grid model building module (9), the management system building module (10), the management analysis module (11), the early warning module (12), the sharing module (13) and the visual display module (14) and is used for controlling each module to work normally by utilizing a singlechip or a controller;
the data preprocessing module (6) is connected with the central control module (5) and is used for preprocessing the collected regional environment data, the environment parameters, the operation data of the power grid, the monitoring data of equipment and the power grid communication data;
the data encryption storage module (7) is connected with the central control module (5) and used for carrying out distributed encryption storage on the collected related data; and simultaneously, the related data stored in an encrypted mode is backed up.
7. The early warning system of a power grid digital management index system according to claim 5, wherein the management system construction module (10) constructs the power grid digital management index system based on operation management, operation analysis, inspection real-time monitoring and fault prediction, and comprises:
Firstly, taking power grid operation management data, operation analysis data, patrol real-time monitoring data and fault prediction data as power grid risk factors;
secondly, analyzing index elements and characteristic values of the power grid risk factors; meanwhile, determining the influence degree of each power grid risk factor on the power grid;
finally, determining the weight value of each power grid risk factor based on the influence degree of the power grid risk factor on the power grid; and constructing a digital management index system of the power grid based on the power grid risk factors and the weight values thereof.
8. The early warning system of a power grid digital management index system according to claim 5, wherein the power grid digital management index system is as follows:
S＝R*B；
Figure FDA0004050577130000061
B＝[W1，W2，......，Wn]；
s represents a power grid digital management index system; r represents a power grid risk factor matrix; b represents a power grid risk factor weight matrix, wi represents weight values of all power grid risk factors; n represents the number of grid risk factors; m represents the number of grid risk factor index elements;
the management analysis module performs power grid management analysis based on the constructed power grid digital management index system and various real-time index data of the power grid, and comprises the following steps:
firstly, performing management analysis on power grid equipment based on a constructed three-dimensional power grid model and monitored equipment operation data; and carrying out operation analysis of the power grid based on the constructed three-dimensional power grid model and the preprocessed data;
Secondly, acquiring real-time coordinate data of power grid inspection and power grid state data; predicting the fault probability of the power grid based on the collected historical data, the real-time operation data of the power grid and the environmental parameters;
finally, determining a power grid management analysis value and a power grid management analysis result based on a power grid equipment management analysis result, a power grid operation analysis result, power grid inspection data and a power grid digital management index system constructed by combining the predicted fault probability; outputting abnormal node coordinates and other information of the power grid;
the step of acquiring real-time coordinate data of the power grid inspection and power grid state data is performed before: and planning a patrol route and an overhaul shutdown route based on the constructed three-dimensional power grid model.
9. A computer device, characterized in that the computer device comprises a memory and a processor, the memory storing a computer program, which when executed by the processor causes the processor to implement the early warning method of the grid digital management index system according to any one of claims 1-4.
10. A computer readable storage medium, storing a computer program which, when executed by a processor, causes the processor to implement the method for early warning of a power grid digital management index system according to any one of claims 1 to 4.
CN202310040012.6A 2023-01-13 2023-01-13 Early warning method, system and application of power grid digital management index system Pending CN116205520A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116561707A (en) * 2023-07-11 2023-08-08 南京大全电气研究院有限公司 Transformer fault checking and early warning method and system

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116561707A (en) * 2023-07-11 2023-08-08 南京大全电气研究院有限公司 Transformer fault checking and early warning method and system
CN116561707B (en) * 2023-07-11 2023-09-05 南京大全电气研究院有限公司 Transformer fault checking and early warning method and system

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