CN116706904A - Power grid abnormal fault emergency processing system based on artificial intelligence - Google Patents
Power grid abnormal fault emergency processing system based on artificial intelligence Download PDFInfo
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- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 16
- 238000004458 analytical method Methods 0.000 claims abstract description 213
- 238000013439 planning Methods 0.000 claims abstract description 111
- 238000012544 monitoring process Methods 0.000 claims abstract description 49
- 230000005540 biological transmission Effects 0.000 claims abstract description 11
- 238000004891 communication Methods 0.000 claims abstract description 8
- 238000012423 maintenance Methods 0.000 claims description 37
- 238000000034 method Methods 0.000 claims description 36
- 238000005457 optimization Methods 0.000 claims description 31
- 238000007373 indentation Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000005259 measurement Methods 0.000 claims description 3
- 238000007726 management method Methods 0.000 claims description 2
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- 238000013433 optimization analysis Methods 0.000 description 2
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/088—Aspects of digital computing
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/001—Methods to deal with contingencies, e.g. abnormalities, faults or failures
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/001—Methods to deal with contingencies, e.g. abnormalities, faults or failures
- H02J3/0012—Contingency detection
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
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Abstract
The invention belongs to the field of power grid fault processing, relates to a data analysis technology, and is used for solving the problem that the existing power grid abnormal fault emergency processing system cannot rapidly and accurately analyze the fault position of a power grid, in particular to an artificial intelligence-based power grid abnormal fault emergency processing system, which comprises a fault monitoring module, a range analysis module, a processing planning module and an efficiency analysis module, wherein the fault monitoring module, the range analysis module, the processing planning module and the efficiency analysis module are sequentially in communication connection, and the efficiency analysis module is also in communication connection with the range analysis module; the fault monitoring module is used for monitoring and analyzing abnormal faults of the power grid; the invention can monitor and analyze the abnormal faults of the power grid, and marks the analysis object as a normal object or an abnormal object by carrying out abnormal monitoring and analysis on each power transmission line of the power grid, thereby feeding back the power transmission line with faults according to the marking result.
Description
Technical Field
The invention belongs to the field of power grid fault processing, relates to a data analysis technology, and particularly relates to an artificial intelligence-based power grid abnormal fault emergency processing system.
Background
The power system fault refers to a state that the equipment cannot work according to the expected index, that is, the equipment does not reach the function that the equipment should reach, and the faults are as follows: generator set faults, transmission line faults, substation faults, bus faults and the like.
The existing power grid abnormal fault emergency treatment system cannot rapidly and accurately analyze the fault position of the power grid, so that the power grid abnormal fault treatment efficiency is low, meanwhile, the existing power grid abnormal fault emergency treatment system lacks a function of monitoring the treatment efficiency of each link of fault treatment after maintenance is finished, so that the treatment efficiency cannot be effectively monitored, and the optimization analysis of each link of fault treatment cannot be performed.
In view of the above technical problems, a solution is proposed.
Disclosure of Invention
The invention aims to provide an artificial intelligence-based power grid abnormal fault emergency processing system which is used for solving the problem that the existing power grid abnormal fault emergency processing system cannot analyze the fault position of a power grid rapidly and accurately;
the technical problems to be solved by the invention are as follows: how to provide an artificial intelligence based power grid abnormal fault emergency processing system which can solve the problem that the existing power grid abnormal fault emergency processing system cannot analyze the fault position of a power grid rapidly and accurately.
The aim of the invention can be achieved by the following technical scheme:
the system comprises a fault monitoring module, a range analysis module, a processing planning module and an efficiency analysis module, wherein the fault monitoring module, the range analysis module, the processing planning module and the efficiency analysis module are sequentially in communication connection, and the efficiency analysis module is also in communication connection with the range analysis module;
the fault monitoring module is used for monitoring and analyzing abnormal faults of the power grid: marking a power transmission line of a power grid as an analysis object, acquiring a current value and a voltage value at the connection positions of two ends of the analysis object and a converter station when the power grid fails abnormally, marking the absolute value of a current value difference value at the connection positions of the two ends of the analysis object and the converter station as a current difference value LC of the analysis object, marking the absolute value of a voltage value difference value at the connection positions of the two ends of the analysis object and the converter station as a differential pressure value YC, marking the analysis object as a normal object or an abnormal object through the differential pressure value YC, and sending the abnormal object to a range analysis module;
the range analysis module is used for carrying out indentation analysis on the abnormal fault range of the power grid: setting a plurality of monitoring points on an abnormal object, analyzing a fault range by adopting a retraction mode or an expansion mode, obtaining range parameters, and sending the range parameters to a processing planning module;
the processing planning module is used for carrying out planning analysis on the abnormal faults of the power grid after receiving the range parameters, obtaining recommended objects of maintenance points, and sending the position information of the maintenance points to mobile phone terminals of recommended object management staff;
the efficiency analysis module is used for carrying out efficiency analysis on the emergency treatment process of the abnormal faults of the power grid.
As a preferred embodiment of the present invention, the specific process of marking an analysis object as a normal object or an abnormal object includes: comparing the fault coefficient GZ of the analysis object with a preset fault threshold value GZmax: if the fault coefficient GZ is smaller than the fault threshold GZmax, judging that the analysis object has no abnormal fault, and marking the analysis object as a normal object; if the failure coefficient GZ is equal to or greater than the failure threshold GZmax, it is determined that the analysis object has an abnormal failure, and the analysis object is marked as an abnormal object.
As a preferred embodiment of the present invention, the specific process of performing fault range analysis using the spread spectrum mode includes: marking monitoring points positioned at the center of an analysis object as marking points, marking adjacent monitoring points at two sides of the marking points as analysis points, obtaining fault coefficients GZ between the marking points and the two analysis points, and marking the fault characteristics of the corresponding monitoring points as 1 if the fault coefficients GZ are smaller than a fault threshold GZmax; if the fault coefficient GZ is greater than or equal to the fault threshold GZmax, marking the fault characteristic of the corresponding monitoring point as 0; if the fault characteristics of the two analysis points are 0 or 1, marking the adjacent monitoring points on the far side of the two analysis points as analysis points, and acquiring the fault coefficient GZ between the marking points and the two analysis points again until the fault characteristics of the two analysis points are 0 and 1; if the fault characteristics of the two analysis points are 0 and 1 respectively, an undetermined range is formed by the outermost analysis point and the adjacent internal measurement analysis point, a fault coefficient GZ between the two analysis points in the undetermined range is obtained, and the undetermined range with the fault coefficient GZ not smaller than a fault threshold GZmax is marked as the fault range.
As a preferred embodiment of the present invention, the specific process of performing fault range analysis using the retract mode includes: marking two monitoring points at the outermost side as analysis points, acquiring a fault coefficient GZ between the two analysis points, and if the fault coefficient GZ is smaller than a fault threshold GZmax, forming a pending range by the analysis points and connecting points of adjacent converter stations; if the fault coefficient GZ is greater than or equal to the fault threshold GZmax, marking adjacent monitoring points measured in the analysis points as analysis points and reacquiring the fault coefficient GZ between the analysis points until the fault coefficient GZ is smaller than the fault threshold GZmax; and acquiring fault coefficients GZ at two ends of the undetermined range, and marking the undetermined range of which the fault coefficient GZ is not smaller than a fault threshold value GZmax as a fault range.
As a preferred embodiment of the present invention, the range parameter obtaining process includes: drawing a circle by taking the central point of the fault range as the circle center and r1 as the radius, and marking the obtained circular area as a range to be maintained; the range parameters are composed of a fault range, a to-be-maintained range and converter stations on two sides of an abnormal object.
As a preferred embodiment of the invention, the specific process of planning and analyzing the abnormal power grid faults by the processing and planning module comprises the following steps: marking a central point of a fault range and converter stations on two sides of an abnormal object as maintenance points, drawing a circle by taking the maintenance points as circle centers and r2 as a radius to obtain a planning range, marking an electric rescue team in the planning range as a planning object, and obtaining on-duty data ZG, distance data JL and working age data GL of the planning object, wherein the on-duty data ZG is the current on-duty numerical value of the planning object, the distance data JL is the linear distance value between a standing point of the planning object and the maintenance points, and the working age data GL is the average working age value of the current on-duty personnel of the planning object; obtaining a recommendation coefficient TJ of the planning object by carrying out numerical calculation on the on-duty data ZG, the distance data JL and the working age data GL; and marking the planning object with the maximum recommendation coefficient TJ value as a recommendation object of the maintenance point, and sending the position information of the maintenance point to a mobile phone terminal of a recommendation object manager.
As a preferred embodiment of the invention, the specific process of the efficiency analysis module for carrying out the efficiency analysis on the emergency treatment process of the abnormal faults of the power grid comprises the following steps: marking the time when the range analysis module receives the abnormal object as the starting time, marking the time when the processing planning module receives the range parameter as the dividing time, marking the time when the recommended object manager receives the position of the maintenance point as the regular time, marking the difference value between the dividing time and the starting time as the analysis duration, and marking the difference value between the regular time and the dividing time as the planning duration; and respectively comparing the analysis time length and the planning time length with a preset analysis threshold value and a preset planning threshold value, and generating a planning optimization signal or a reduction optimization signal according to the comparison result.
As a preferred embodiment of the present invention, the specific process of comparing the analysis duration and the planning duration with the analysis threshold and the planning threshold respectively includes: if the analysis duration is smaller than the analysis threshold value and the planning duration is larger than or equal to the planning threshold value, generating a planning optimization signal and sending the planning optimization signal to a processing planning module; if the analysis time length is greater than or equal to the analysis threshold value and the planning time length is less than the planning threshold value, generating a reduction optimization signal and sending the reduction optimization signal to a range analysis module; if the analysis time length is greater than or equal to the analysis threshold value and the planning time length is greater than or equal to the planning threshold value, generating a planning optimization signal and a division optimization signal, and respectively transmitting the planning optimization signal and the division optimization signal to the processing planning module and the range analysis module.
The invention has the following beneficial effects:
the abnormal faults of the power grid can be monitored and analyzed through the fault monitoring module, and the analysis objects are marked as normal objects or abnormal objects through the abnormal monitoring and analysis of each power transmission line of the power grid, so that the power transmission line with the fault is fed back according to the marking result;
the power grid abnormal fault range can be subjected to indentation analysis through the range analysis module, the fault range is analyzed through the indentation mode and the outward expansion mode, the fault range is obtained, the range of a fault area is further reduced on the basis of abnormality monitoring analysis, the troubleshooting time of a fault position is shortened, and the abnormality processing efficiency is improved;
the power grid abnormal faults can be planned and analyzed after the range parameters are received through the processing planning module, and the power rescue teams are screened in the planning range in a range division mode, so that the execution priority of the power rescue teams is fed back according to the recommended coefficients obtained through comprehensive analysis and calculation of the parameters of the power rescue teams, and then the corresponding power rescue teams are screened out to carry out fault maintenance on maintenance points;
4. the efficiency analysis module can be used for carrying out efficiency analysis on the emergency treatment process of the abnormal power grid faults, and the emergency treatment efficiency is monitored by analyzing the consumed time length of each link in the fault treatment, so that the targeted link optimization treatment is carried out when the emergency treatment efficiency does not meet the requirement.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of the overall invention;
FIG. 2 is a system block diagram of a first embodiment of the present invention;
fig. 3 is a system block diagram of a second embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, 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.
As shown in FIG. 1, the power grid abnormal fault emergency processing system based on the artificial intelligence comprises a fault monitoring module, a range analysis module, a processing planning module and an efficiency analysis module, wherein the fault monitoring module, the range analysis module, the processing planning module and the efficiency analysis module are sequentially in communication connection, and the efficiency analysis module is also in communication connection with the range analysis module.
Embodiment one: as shown in fig. 2, the fault monitoring module is configured to monitor and analyze an abnormal fault of the power grid: marking a power transmission line of a power grid as an analysis object, acquiring a current value and a voltage value at the connection position of two ends of the analysis object and a converter station when the power grid fails abnormally, marking the absolute value of a current value difference value at the connection position of the two ends of the analysis object and the converter station as a current difference value LC of the analysis object, marking the absolute value of a voltage value difference value at the connection position of the two ends of the analysis object and the converter station as a differential pressure value YC, and obtaining a fault coefficient GZ of the analysis object through a formula GZ=α1xLC+α2xYC, wherein the fault coefficient is a numerical value reflecting the possibility of the power failure of the analysis object, and the larger the numerical value of the fault coefficient is, the greater the possibility of the power failure of the analysis object is indicated; wherein, alpha 1 and alpha 2 are both proportional coefficients, and alpha 1 is more than alpha 2 is more than 1; comparing the fault coefficient GZ of the analysis object with a preset fault threshold value GZmax: if the fault coefficient GZ is smaller than the fault threshold GZmax, judging that the analysis object has no abnormal fault, and marking the analysis object as a normal object; if the fault coefficient GZ is greater than or equal to the fault threshold GZmax, judging that the analysis object has abnormal faults, and marking the analysis object as an abnormal object; sending the abnormal object to a range analysis module; monitoring and analyzing abnormal faults of the power grid, marking an analysis object as a normal object or an abnormal object by carrying out abnormal monitoring and analyzing on each power transmission line of the power grid, and feeding back the power transmission line with faults according to the marking result.
The range analysis module is used for carrying out indentation analysis on the abnormal fault range of the power grid: setting a plurality of monitoring points on an abnormal object, adopting a retraction mode or an expansion mode to analyze the fault range, and adopting the expansion mode to analyze the fault range, wherein the specific process comprises the following steps: marking monitoring points positioned at the center of an analysis object as marking points, marking adjacent monitoring points at two sides of the marking points as analysis points, obtaining fault coefficients GZ between the marking points and the two analysis points, and marking the fault characteristics of the corresponding monitoring points as 1 if the fault coefficients GZ are smaller than a fault threshold GZmax; if the fault coefficient GZ is greater than or equal to the fault threshold GZmax, marking the fault characteristic of the corresponding monitoring point as 0; if the fault characteristics of the two analysis points are 0 or 1, marking the adjacent monitoring points on the far side of the two analysis points as analysis points, and acquiring the fault coefficient GZ between the marking points and the two analysis points again until the fault characteristics of the two analysis points are 0 and 1; if the fault characteristics of the two analysis points are 0 and 1 respectively, forming a pending range by the outermost analysis point and the adjacent internal measurement analysis point, acquiring a fault coefficient GZ between the two analysis points of the pending range, and marking the pending range with the fault coefficient GZ not smaller than a fault threshold GZmax as a fault range; the specific process of fault range analysis by adopting the retraction mode comprises the following steps: marking two monitoring points at the outermost side as analysis points, acquiring a fault coefficient GZ between the two analysis points, and if the fault coefficient GZ is smaller than a fault threshold GZmax, forming a pending range by the analysis points and connecting points of adjacent converter stations; if the fault coefficient GZ is greater than or equal to the fault threshold GZmax, marking adjacent monitoring points measured in the analysis points as analysis points and reacquiring the fault coefficient GZ between the analysis points until the fault coefficient GZ is smaller than the fault threshold GZmax; obtaining fault coefficients GZ at two ends of a to-be-determined range, and marking the to-be-determined range with the fault coefficient GZ not smaller than a fault threshold GZmax as a fault range; drawing a circle by taking the central point of the fault range as the circle center, r1 as the radius, and setting a specific numerical value of r1 by a manager; marking the obtained circular area as a range to be maintained; the method comprises the steps that a range parameter is formed by a fault range, a to-be-maintained range and converter stations on two sides of an abnormal object, and the range parameter is sent to a processing planning module; and carrying out retraction analysis on the abnormal fault range of the power grid, carrying out fault range analysis on the power grid through a retraction mode and an external expansion mode, obtaining the fault range, further reducing the range of a fault area on the basis of abnormality monitoring analysis, shortening the troubleshooting time of the fault position, and improving the abnormality processing efficiency.
It should be noted that, the to-be-maintained range is an area with higher influence degree from the power failure, the to-be-maintained range can be divided into a plurality of sub-areas, then maintenance necessity analysis is performed on the marking condition of the to-be-maintained range of the sub-areas in the historical power failure time, and power equipment maintenance is performed on the area with the maintenance necessity, and the power equipment maintenance process and the power failure overhaul process can be performed simultaneously, or the power equipment maintenance can be performed after the power failure overhaul is completed.
The processing planning module is used for carrying out planning analysis on the abnormal faults of the power grid after receiving the range parameters: marking a central point of a fault range and converter stations on two sides of an abnormal object as maintenance points, drawing a circle by taking the maintenance points as circle centers and r2 as radius to obtain a planning range, wherein r2 is a numerical constant, and the numerical value of r2 is set by a manager; marking an electric rescue team in a planning range as a planning object, and acquiring on-duty data ZG, distance data JL and working age data GL of the planning object, wherein the on-duty data ZG is the current on-duty numerical value of the planning object, the distance data JL is the linear distance value between a standing point of the planning object and a maintenance point, and the working age data GL is the average working age value of the current on-duty personnel of the planning object; obtaining a recommendation coefficient TJ of the planning object according to a formula TJ= (beta 1 x ZG+beta 2 x GL)/(beta 3 x JL), wherein the recommendation coefficient is a matching degree reflecting that the planning object executes the maintenance task, and the larger the numerical value of the recommendation coefficient is, the higher the matching degree indicating that the planning object executes the maintenance task is; wherein β1, β2 and β3 are proportionality coefficients, and β1 > β2 > β3 > 1; marking a planning object with the maximum recommendation coefficient TJ value as a recommended object of a maintenance point, and sending the position information of the maintenance point to a mobile phone terminal of a recommended object manager; the power grid abnormal faults can be planned and analyzed after the range parameters are received, and the power rescue teams are screened in the planning range in a range division mode, so that the execution priority of the power rescue teams is fed back according to the recommended coefficients obtained by comprehensive analysis and calculation of the parameters of the power rescue teams, and then the corresponding power rescue teams are screened out to carry out fault maintenance on maintenance points.
Embodiment two: the difference between the first embodiment and the second embodiment is that the processing efficiency of each link in the processing process is monitored after the power failure processing is completed, and the data support is provided for the optimization of the subsequent links while the processing efficiency of the current failure is monitored.
As shown in fig. 3, the efficiency analysis module is configured to perform efficiency analysis on the emergency processing procedure of the abnormal power grid faults: marking the time when the range analysis module receives the abnormal object as the starting time, marking the time when the processing planning module receives the range parameter as the dividing time, marking the time when the recommended object manager receives the position of the maintenance point as the regular time, marking the difference value between the dividing time and the starting time as the analysis duration, and marking the difference value between the regular time and the dividing time as the planning duration; comparing the analysis duration and the planning duration with a preset analysis threshold and a preset planning threshold respectively: if the analysis duration is smaller than the analysis threshold value and the planning duration is larger than or equal to the planning threshold value, generating a planning optimization signal and sending the planning optimization signal to a processing planning module; if the analysis time length is greater than or equal to the analysis threshold value and the planning time length is less than the planning threshold value, generating a reduction optimization signal and sending the reduction optimization signal to a range analysis module; if the analysis time length is greater than or equal to the analysis threshold value and the planning time length is greater than or equal to the planning threshold value, generating a planning optimization signal and a division optimization signal, and respectively transmitting the planning optimization signal and the division optimization signal to a processing planning module and a range analysis module; and (3) carrying out efficiency analysis on the emergency treatment process of the abnormal faults of the power grid, and monitoring the emergency treatment efficiency by analyzing the consumption time length of each link in the fault treatment, so that the targeted link optimization treatment is carried out when the emergency treatment efficiency does not meet the requirement.
Embodiment III: the difference between the present embodiment and the first and second embodiments is that after the efficiency analysis is completed, the efficiency of performing the fault range analysis on the expansion mode and the retraction mode in the history data is analyzed, and the selection priorities of the expansion mode and the retraction mode are marked.
After receiving the division optimizing signal, the range analyzing module optimizes and analyzes the selection priorities of the retraction mode and the expansion mode: acquiring the latest L1 times of processes for carrying out retraction analysis on the abnormal fault range of the power grid, marking the process as a retraction process, wherein L1 is a numerical constant, and the specific numerical value of L1 is set by a manager; the method comprises the steps of marking the distance value between a fault range central point of a retracting process and the central point of an abnormal object as a centering value, marking the distance value between the fault range central point of the retracting process and converter stations at two sides as a side link value, marking the minimum value of the side link values in the same retracting range as a side margin value, summing the centering values of all retracting processes to obtain a centering coefficient, summing the side margin values of all retracting processes to obtain a side margin coefficient, and comparing the centering coefficient with the side margin coefficient: if the centering coefficient is smaller than the margin coefficient, marking the outward expansion mode as a priority mode; if the centering coefficient is greater than or equal to the margin coefficient, marking the retracting mode as a priority mode; when the range analysis mode receives the abnormal object again, the priority mode is selected to carry out range indentation analysis; and when the range retraction analysis efficiency is low, performing optimization analysis on the fault range analysis mode, and analyzing the selection priorities of the retraction mode and the expansion mode through the centering coefficient and the margin coefficient in the historical data, thereby improving the analysis efficiency in the subsequent fault range analysis process.
An abnormal power grid fault emergency treatment system based on artificial intelligence monitors and analyzes abnormal power grid faults when in operation: marking a power transmission line of a power grid as an analysis object, obtaining a fault coefficient of the analysis object, marking the analysis object as a normal object or an abnormal object through the fault coefficient, setting a plurality of monitoring points on the abnormal object, carrying out fault range analysis by adopting a retraction mode or an expansion mode to obtain a fault range, and carrying out planning analysis on abnormal faults of the power grid by using the fault range, a to-be-maintained range and converter stations on two sides of the abnormal object to form range parameters: marking a central point of the fault range and converter stations on two sides of the abnormal object as maintenance points, drawing a circle by taking the maintenance points as circle centers and r2 as radius to obtain a planning range, marking the planning object with the largest numerical value of a recommendation coefficient in the planning range as a recommendation object, and sending the position information of the maintenance points to a mobile phone terminal of a manager of the recommendation object.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
The formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to a true value, and coefficients in the formulas are set by a person skilled in the art according to actual conditions; such as: formula tj= (β1×zg+β2×gl)/(β3×jl); collecting a plurality of groups of sample data by a person skilled in the art and setting a corresponding recommendation coefficient for each group of sample data; substituting the set recommended coefficient and the acquired sample data into a formula, forming a ternary one-time equation set by any three formulas, screening the calculated coefficient, and taking an average value to obtain values of beta 1, beta 2 and beta 3 of 3.48, 2.65 and 2.13 respectively;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and the corresponding recommended coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relationship of the parameter and the quantized value is not affected, for example, the recommended coefficient is proportional to the value of the on-duty data.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (8)
1. The power grid abnormal fault emergency processing system based on the artificial intelligence is characterized by comprising a fault monitoring module, a range analysis module, a processing planning module and an efficiency analysis module, wherein the fault monitoring module, the range analysis module, the processing planning module and the efficiency analysis module are sequentially in communication connection, and the efficiency analysis module is also in communication connection with the range analysis module;
the fault monitoring module is used for monitoring and analyzing abnormal faults of the power grid: marking a power transmission line of a power grid as an analysis object, acquiring a current value and a voltage value at the connection positions of two ends of the analysis object and a converter station when the power grid fails abnormally, marking the absolute value of a current value difference value at the connection positions of the two ends of the analysis object and the converter station as a current difference value LC of the analysis object, marking the absolute value of a voltage value difference value at the connection positions of the two ends of the analysis object and the converter station as a differential pressure value YC, marking the analysis object as a normal object or an abnormal object through the differential pressure value YC, and sending the abnormal object to a range analysis module;
the range analysis module is used for carrying out indentation analysis on the abnormal fault range of the power grid: setting a plurality of monitoring points on an abnormal object, analyzing a fault range by adopting a retraction mode or an expansion mode, obtaining range parameters, and sending the range parameters to a processing planning module;
the processing planning module is used for carrying out planning analysis on the abnormal faults of the power grid after receiving the range parameters, obtaining recommended objects of maintenance points, and sending the position information of the maintenance points to mobile phone terminals of recommended object management staff;
the efficiency analysis module is used for carrying out efficiency analysis on the emergency treatment process of the abnormal faults of the power grid.
2. The artificial intelligence based power grid abnormal fault emergency processing system according to claim 1, wherein the specific process of marking the analysis object as a normal object or an abnormal object comprises: comparing the fault coefficient GZ of the analysis object with a preset fault threshold value GZmax: if the fault coefficient GZ is smaller than the fault threshold GZmax, judging that the analysis object has no abnormal fault, and marking the analysis object as a normal object; if the failure coefficient GZ is equal to or greater than the failure threshold GZmax, it is determined that the analysis object has an abnormal failure, and the analysis object is marked as an abnormal object.
3. The system for emergency treatment of abnormal power grid faults based on artificial intelligence according to claim 1, wherein the specific process of performing fault range analysis by adopting an external expansion mode comprises the following steps: marking monitoring points positioned at the center of an analysis object as marking points, marking adjacent monitoring points at two sides of the marking points as analysis points, obtaining fault coefficients GZ between the marking points and the two analysis points, and marking the fault characteristics of the corresponding monitoring points as 1 if the fault coefficients GZ are smaller than a fault threshold GZmax; if the fault coefficient GZ is greater than or equal to the fault threshold GZmax, marking the fault characteristic of the corresponding monitoring point as 0; if the fault characteristics of the two analysis points are 0 or 1, marking the adjacent monitoring points on the far side of the two analysis points as analysis points, and acquiring the fault coefficient GZ between the marking points and the two analysis points again until the fault characteristics of the two analysis points are 0 and 1; if the fault characteristics of the two analysis points are 0 and 1 respectively, an undetermined range is formed by the outermost analysis point and the adjacent internal measurement analysis point, a fault coefficient GZ between the two analysis points in the undetermined range is obtained, and the undetermined range with the fault coefficient GZ not smaller than a fault threshold GZmax is marked as the fault range.
4. The system for emergency treatment of abnormal power grid faults based on artificial intelligence according to claim 1, wherein the specific process of adopting a retract mode for fault range analysis comprises the following steps: marking two monitoring points at the outermost side as analysis points, acquiring a fault coefficient GZ between the two analysis points, and if the fault coefficient GZ is smaller than a fault threshold GZmax, forming a pending range by the analysis points and connecting points of adjacent converter stations; if the fault coefficient GZ is greater than or equal to the fault threshold GZmax, marking adjacent monitoring points measured in the analysis points as analysis points and reacquiring the fault coefficient GZ between the analysis points until the fault coefficient GZ is smaller than the fault threshold GZmax; and acquiring fault coefficients GZ at two ends of the undetermined range, and marking the undetermined range of which the fault coefficient GZ is not smaller than a fault threshold value GZmax as a fault range.
5. An artificial intelligence based power grid abnormal fault emergency processing system according to claim 3 or 4, wherein the process of obtaining the range parameter comprises: drawing a circle by taking the central point of the fault range as the circle center and r1 as the radius, and marking the obtained circular area as a range to be maintained; the range parameters are composed of a fault range, a to-be-maintained range and converter stations on two sides of an abnormal object.
6. The system for emergency treatment of abnormal power grid faults based on artificial intelligence according to claim 5, wherein the specific process of planning and analyzing the abnormal power grid faults by the processing and planning module comprises the following steps: marking a central point of a fault range and converter stations on two sides of an abnormal object as maintenance points, drawing a circle by taking the maintenance points as circle centers and r2 as a radius to obtain a planning range, marking an electric rescue team in the planning range as a planning object, and obtaining on-duty data ZG, distance data JL and working age data GL of the planning object, wherein the on-duty data ZG is the current on-duty numerical value of the planning object, the distance data JL is the linear distance value between a standing point of the planning object and the maintenance points, and the working age data GL is the average working age value of the current on-duty personnel of the planning object; obtaining a recommendation coefficient TJ of the planning object by carrying out numerical calculation on the on-duty data ZG, the distance data JL and the working age data GL; and marking the planning object with the maximum recommendation coefficient TJ value as a recommendation object of the maintenance point, and sending the position information of the maintenance point to a mobile phone terminal of a recommendation object manager.
7. The system for emergency treatment of abnormal power grid faults based on artificial intelligence of claim 6, wherein the specific process of the efficiency analysis module for carrying out efficiency analysis on the emergency treatment process of abnormal power grid faults comprises the following steps: marking the time when the range analysis module receives the abnormal object as the starting time, marking the time when the processing planning module receives the range parameter as the dividing time, marking the time when the recommended object manager receives the position of the maintenance point as the regular time, marking the difference value between the dividing time and the starting time as the analysis duration, and marking the difference value between the regular time and the dividing time as the planning duration; and respectively comparing the analysis time length and the planning time length with a preset analysis threshold value and a preset planning threshold value, and generating a planning optimization signal or a reduction optimization signal according to the comparison result.
8. The system for emergency treatment of abnormal power grid faults based on artificial intelligence according to claim 7, wherein the specific process of comparing the analysis duration and the planning duration with the analysis threshold and the planning threshold respectively comprises the following steps: if the analysis duration is smaller than the analysis threshold value and the planning duration is larger than or equal to the planning threshold value, generating a planning optimization signal and sending the planning optimization signal to a processing planning module; if the analysis time length is greater than or equal to the analysis threshold value and the planning time length is less than the planning threshold value, generating a reduction optimization signal and sending the reduction optimization signal to a range analysis module; if the analysis time length is greater than or equal to the analysis threshold value and the planning time length is greater than or equal to the planning threshold value, generating a planning optimization signal and a division optimization signal, and respectively transmitting the planning optimization signal and the division optimization signal to the processing planning module and the range analysis module.
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