CN119599467B - Large-model-based intelligent management decision system for power grid faults - Google Patents
Large-model-based intelligent management decision system for power grid faults Download PDFInfo
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Abstract
本发明公开了一种基于大模型的电网故障智能管理决策系统，涉及电网故障决策技术领域，包括：合闸数据检测模块，获取电气值，获取设备值，通过电气值与设备值得到硬件安全系数，获取人员风险值，获取执行值，通过人员风险值与执行值得到人员安全系数；通过结合电气设备状态与维修人员情况得到保障系数，再通过保障系数与保障系数预设阈值判断是否进行合闸，从而便于综合判断合闸后的安全风险，进而便于对管理人员合闸执行前给予风险评估，进而便于对管理人员的合闸操作进行决策辅助，通过建立风险合闸数据库从而便于对每次合闸决策的数据进行记录，进而便于决策系统根据历史数据进行决策的优化调整。
The invention discloses a large-model-based intelligent management decision system for power grid faults, which relates to the technical field of power grid fault decision-making, and comprises: a closing data detection module, which obtains electrical values, obtains equipment values, obtains hardware safety factors through electrical values and equipment values, obtains personnel risk values, obtains execution values, and obtains personnel safety factors through personnel risk values and execution values; obtains a protection factor by combining the electrical equipment status with the maintenance personnel situation, and then determines whether to close the circuit breaker through the protection factor and a preset threshold value of the protection factor, thereby facilitating a comprehensive judgment of the safety risk after closing the circuit breaker, and further facilitating risk assessment for management personnel before closing the circuit breaker, and further facilitating decision-making assistance for the management personnel's closing operation, and establishing a risk closing database to facilitate recording of data for each closing decision, and further facilitating the decision-making system to optimize and adjust the decision according to historical data.
Description
Technical Field
The invention relates to the technical field of power grid fault decision making, in particular to a power grid fault intelligent management decision making system based on a large model.
Background
The power grid fault decision system is an advanced intelligent solution and aims at guaranteeing the stable operation of the power grid. When the power grid fails, the system can respond quickly, and the position, type and severity of the failure can be accurately judged through accurate analysis of a large amount of real-time data. By using advanced algorithm and model, the power grid fault decision system can generate optimal fault processing scheme in short time, guide maintenance personnel to rapidly and efficiently carry out rush repair work, and furthest reduce the influence of faults on power supply. The method not only improves the reliability and the safety of the power grid, but also improves the efficiency of fault treatment, and provides continuous and stable power guarantee for the production and the life of people.
Most of the prior grid fault decision-making systems are integrated with a large model in a linkage way through a grid fault diagnosis association system, data interaction is carried out through a universal interface, fault diagnosis association and data bidirectional circulation of the large model system are achieved, real-time monitoring data and a fault diagnosis primary result are transmitted to the large model through the association system, the large model feeds back optimized treatment decisions and supplementary fault analysis information to the association system, then a cooperative working mechanism is established, when the association system detects that fault signs trigger the large model, the large model synchronously starts a working flow, real-time data details provided by the association system, such as fault instant current and voltage waveform data, are combined on the basis of knowledge understanding and reasoning, fault diagnosis and treatment schemes are further refined, finally the association system executes corresponding control operations, such as remote tripping, standby line switching and the like, and feeds back operation results to the large model in real time, so that the model continuously optimizes subsequent decisions, closed-loop linkage is achieved, and stable operation of the grid is guaranteed.
The existing power grid fault decision system still has some defects, when judging whether the power grid can be switched on and restarted after the power grid is in short circuit fault, because the power grid coverage area is wide, the related maintenance personnel are numerous, and the related electrical equipment are numerous, if the power grid is switched on and restarted, the damage caused by secondary impact to the electrical equipment which is not completely overhauled is easy, the dangerous situation of electric shock of the maintenance personnel is easy to occur, and the decision system is not fed back in time after the power grid fault decision processing, so that the autonomous learning optimization capacity of the decision system is influenced, and the accurate judgment of the subsequent intelligent decision of the decision system is further influenced.
Disclosure of Invention
(One) solving the technical problems
Aiming at the defects of the prior art, the invention provides a large-model-based intelligent power grid fault management decision system, which aims to solve the problems in the prior art.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme that the intelligent power grid fault management decision system based on the large model comprises the following components:
the switching-on data detection module is used for obtaining an electrical value, obtaining a device value, obtaining a hardware safety coefficient through the electrical value and the device value, obtaining a personnel risk value, obtaining an execution value, and obtaining a personnel safety coefficient through the personnel risk value and the execution value;
The switching-on decision judging module obtains a guarantee coefficient through a hardware safety coefficient and a personnel safety coefficient, sets a guarantee coefficient preset threshold value, judges whether switching-on is carried out or not through the guarantee coefficient and the guarantee coefficient preset threshold value, establishes a risk switching-on database, and brings the guarantee coefficient and the switching-on judging result into the risk switching-on database for storage;
the switching-on feedback optimization module is used for acquiring the equipment state after switching-on, acquiring the personnel safety state after switching-on, acquiring the power grid running state after switching-on, obtaining switching-on scores according to the equipment state after switching-on, the personnel safety state after switching-on and the power grid running state after switching-on, and adjusting the value mode of the guarantee coefficient according to the switching-on scores.
Preferably, the electrical value is obtained in the closing data detection module, specifically:
step one, acquiring a voltage before a fault of a power grid, acquiring a voltage after the fault of the power grid, taking an absolute value to obtain a voltage value by the difference between the voltage after the fault and the voltage before the fault, setting a voltage preset threshold value, obtaining a voltage difference value by the difference between the voltage preset threshold value and the voltage after the fault, and obtaining a voltage reference value by summing the voltage value and the voltage difference value;
step two, obtaining the insulation resistance of the power grid, setting an insulation resistance preset threshold value, and obtaining an insulation resistance reference value by making a difference between the insulation resistance preset threshold value and the insulation resistance;
Step three, obtaining fault current of a power grid, setting a fault current preset threshold value, and obtaining a fault current reference value through difference between the fault current preset threshold value and the fault current;
Step four, passing through a fault current reference value, a voltage reference value and an insulation resistance reference value of the power grid;
the electrical value is calculated by the following specific steps:
;
wherein Dq is expressed as an electrical value, gz is expressed as a fault current reference value, dy is expressed as a voltage reference value, dz is expressed as an insulation resistance reference value, 、AndAre all the weights of the materials,,,。
Preferably, the device value is obtained in the closing data detection module, specifically:
photographing the appearance of each electrical device in a power grid to obtain a plurality of reference pictures, respectively carrying out burning, cracking and deformation identification on the electrical devices in each reference picture through an AOI system, and marking the defect number of each electrical device;
Acquiring the temperature of each electrical device, and obtaining a device value through the temperature of each electrical device and the defect number of the corresponding electrical device;
The calculation mode of the device value specifically comprises the following steps:
;
Wherein Bs is represented as a device value, wd is represented as a temperature of the electrical device, qx is represented as the number of defects of the electrical device, n is represented as the number of electrical devices, 。
Preferably, the calculation mode of the hardware safety coefficient in the closing data detection module specifically comprises the following steps:
;
Where Yj is represented as a hardware security factor, dq is represented as an electrical value, bs is represented as a device value, AndAre all the weights of the materials,,。
Preferably, the personnel risk value is obtained in the closing data detection module, specifically:
Step one, acquiring the working time of each maintainer, acquiring the number of historical operation accidents of each maintainer, summing the number of the historical operation accidents of each maintainer with 1 to obtain the number of accident references, obtaining the experience value of each maintainer by the quotient of the working time of each maintainer and the number of the accident references, and summing the experience value of each maintainer to obtain the average value to obtain the value of the maintainer;
Step two, acquiring a grounding resistor before an accident, acquiring a grounding resistor after the accident, and obtaining a grounding resistor change value by taking an absolute value through difference between the grounding resistor before the accident and the grounding resistor after the accident;
And thirdly, summing the grounding resistance change value with 1 to obtain a grounding resistance value, and weighting the maintenance personnel value and the grounding resistance value by a manufacturer to obtain a personnel risk value.
Preferably, the execution value is obtained in the closing data detection module, specifically:
step one, a maintenance person starts timing by pressing a maintenance button before the maintenance person leaves an office, and the maintenance person returns to the office after maintenance is finished and finishes timing by pressing the maintenance button, so that maintenance time is acquired through timing;
acquiring second maintenance button pressing time, acquiring current time, and obtaining interval time through difference between the current time and the second maintenance button pressing time;
step three, acquiring a second maintenance button pressing execution state, executing step four if the second maintenance button pressing execution state is pressed, and executing step two if the second maintenance button pressing execution state is not pressed;
Step four, obtaining maintenance time, setting a maintenance time preset threshold, obtaining maintenance reference time by making a difference between the maintenance time and the maintenance time preset threshold, obtaining interval time, obtaining interval reference time by summing the interval time and 1, and obtaining an execution value by a manufacturer of the maintenance reference time and the interval reference time.
Preferably, in the switching-on data detection module, the calculation mode of the personnel safety coefficient is specifically as follows:
;
wherein Ry is represented as a personal safety factor, rf is represented as a personal risk value, zx is represented as an execution value, AndAre all the weights of the materials,,。
Preferably, in the closing decision judging module, a guarantee coefficient is obtained through a hardware safety coefficient and a personnel safety coefficient, a guarantee coefficient preset threshold value is set, and whether closing is performed or not is judged through the guarantee coefficient and the guarantee coefficient preset threshold value, specifically:
The method comprises the steps of firstly, obtaining a hardware safety coefficient, setting a hardware safety coefficient weight, obtaining a hardware safety reference coefficient through the product of the hardware safety coefficient weight and the hardware safety coefficient, obtaining a personnel safety coefficient, setting a personnel safety coefficient weight, obtaining a personnel safety reference coefficient through the product of the personnel safety coefficient and the personnel safety coefficient weight, and obtaining a guarantee coefficient through summation of the hardware safety reference coefficient and the personnel safety reference coefficient;
Setting a guarantee coefficient preset threshold value, judging the magnitude relation between the guarantee coefficient and the guarantee coefficient preset threshold value, if the guarantee coefficient is larger than or equal to the guarantee coefficient preset threshold value, suggesting to execute the power grid closing, if the guarantee coefficient is smaller than the guarantee coefficient preset threshold value, suggesting to confirm the safety of the power grid closing, and repeatedly measuring the guarantee coefficient.
Preferably, the feedback excitation optimization module obtains a closing score according to the equipment state after closing, the personnel safety state after closing and the power grid running state after closing, and specifically comprises the following steps:
the method comprises the steps of firstly, obtaining the number of burn-out of electrical equipment after closing, obtaining personnel safety feedback conditions after closing, and obtaining dangerous accident occurrence times through the personnel safety feedback conditions before and after closing;
Setting a maintenance time preset threshold value, acquiring the continuous operation time of the power grid after closing, recording the operation state of the power grid after closing as 1 if the continuous operation time of the power grid after closing is greater than or equal to the maintenance time preset threshold value, and recording the operation state of the power grid after closing as 0 if the continuous operation time of the power grid after closing is less than the maintenance time preset threshold value;
and thirdly, obtaining a post-gate coefficient through weighting summation of the number of burning losses of the electrical equipment and the number of dangerous accidents, setting a pre-set closing score threshold value, obtaining a post-gate score through the product of the post-gate coefficient and the pre-set closing score threshold value, and obtaining a closing score through the product of the post-gate score and the running state of the power grid after closing.
Preferably, in the feedback excitation optimization module, the value mode of the guarantee coefficient is adjusted according to the closing score, specifically:
Setting a hardware safety coefficient weight preset threshold value, setting a personnel safety coefficient weight preset threshold value, and obtaining a closing score;
Judging whether the closing score is 0, if the closing score is 0, differencing the hardware safety coefficient weight and a preset threshold value of the hardware safety coefficient weight to obtain a hardware safety coefficient secondary weight when the closing score is 0, obtaining a hardware safety reference coefficient by multiplying the hardware safety coefficient by the hardware safety coefficient secondary weight, obtaining a personnel safety coefficient secondary weight by multiplying the personnel safety coefficient weight by the preset threshold value of the personnel safety coefficient weight, obtaining a personnel safety reference coefficient by multiplying the personnel safety coefficient by the personnel safety coefficient secondary weight, obtaining a guarantee coefficient by summing the hardware safety reference coefficient and the personnel safety reference coefficient, and if the closing score is not 0, executing the third step;
Setting a preset threshold value of the burning loss number of the electrical equipment, judging whether the burning loss number of the electrical equipment is larger than the preset threshold value of the burning loss number of the electrical equipment, if the burning loss number of the electrical equipment is larger than or equal to the preset threshold value of the burning loss number of the electrical equipment, obtaining a secondary weight of the personnel safety coefficient by making a difference between the weight of the personnel safety coefficient and the preset threshold value of the personnel safety coefficient when the guarantee coefficient is measured next time, obtaining a secondary weight of the personnel safety coefficient by multiplying the secondary weight of the personnel safety coefficient and the personnel safety coefficient, obtaining the guarantee coefficient by summing the hardware safety reference coefficient and the personnel safety reference coefficient, and executing the fourth step if the burning loss number of the electrical equipment is smaller than the preset threshold value of the burning loss number of the electrical equipment:
Setting a preset threshold value of the dangerous accident occurrence frequency, judging whether the dangerous accident occurrence frequency is larger than or equal to the preset threshold value of the dangerous accident occurrence frequency, if the dangerous accident occurrence frequency is larger than or equal to the preset threshold value of the dangerous accident occurrence frequency, then, subtracting the preset threshold value of the hardware safety coefficient weight from the hardware safety coefficient weight when the guarantee coefficient is measured next time to obtain a second weight of the hardware safety coefficient, obtaining a hardware safety reference coefficient through the product of the second weight of the hardware safety coefficient and the hardware safety coefficient, summing the hardware safety reference coefficient and the personnel safety reference coefficient to obtain the guarantee coefficient, and if the dangerous accident occurrence frequency is smaller than the preset threshold value of the dangerous accident occurrence frequency, keeping the value of the guarantee coefficient unchanged and feeding back to management personnel.
(III) beneficial effects
The invention provides a large-model-based intelligent power grid fault management decision system, which has the following beneficial effects:
According to the scheme, the guarantee coefficient is obtained by combining the state of the electrical equipment with the situation of maintenance personnel in the closing decision judging module, whether closing is carried out or not is judged through the preset threshold value of the guarantee coefficient and the guarantee coefficient, so that the safety risk after closing is judged comprehensively, risk assessment is conveniently given before closing execution of management personnel, decision assistance is conveniently carried out on closing operation of the management personnel, recording of data of each closing decision is conveniently carried out by establishing a risk closing database, and decision optimization and adjustment of decision making are conveniently carried out by a decision making system according to historical data.
According to the scheme, the equipment state, the maintenance personnel state and the power grid running state after closing are collected in the closing feedback optimization module to score the closing decision at this time, the estimated accuracy of the closing decision at this time is convenient to comprehensively judge through closing scoring, and the value and the calculation mode of the guarantee coefficient are adjusted according to the closing scoring result, so that the risk of closing failure is further reduced when the next closing decision is convenient to carry out, the safety of the electrical equipment and the maintenance personnel is further guaranteed, the closing decision is also convenient to adjust according to the historical closing situation pertinence, and therefore more accurate closing decision suggestions are provided for the management personnel when the next closing operation is convenient.
Drawings
FIG. 1 is a schematic diagram of a large model-based intelligent management decision system for power grid faults;
FIG. 2 is a block flow diagram of a large model-based intelligent management decision system for power grid faults.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the invention provides a large-model-based intelligent power grid fault management decision system, which comprises the following modules:
the switching-on data detection module is used for obtaining an electrical value, obtaining a device value, obtaining a hardware safety coefficient through the electrical value and the device value, obtaining a personnel risk value, obtaining an execution value, and obtaining a personnel safety coefficient through the personnel risk value and the execution value;
The switching-on decision judging module obtains a guarantee coefficient through a hardware safety coefficient and a personnel safety coefficient, sets a guarantee coefficient preset threshold value, judges whether switching-on is carried out or not through the guarantee coefficient and the guarantee coefficient preset threshold value, establishes a risk switching-on database, and brings the guarantee coefficient and the switching-on judging result into the risk switching-on database for storage;
The switching-on feedback optimization module is used for acquiring a device state after switching-on, acquiring a personnel safety state after switching-on, acquiring a power grid running state after switching-on, acquiring switching-on scores according to the device state after switching-on, the personnel safety state after switching-on and the power grid running state after switching-on, and adjusting the value mode of the guarantee coefficient according to the switching-on scores;
The calculation mode of the hardware safety coefficient in the closing data detection module specifically comprises the following steps:
;
Where Yj is represented as a hardware security factor, dq is represented as an electrical value, bs is represented as a device value, AndAre all the weights of the materials,,;
The calculation mode of the personnel safety coefficient in the closing data detection module specifically comprises the following steps:
;
wherein Ry is represented as a personal safety factor, rf is represented as a personal risk value, zx is represented as an execution value, AndAre all the weights of the materials,,。
In the embodiment, the method judges whether the continuous working exists after the state of the electric equipment is switched on by combining the current state and the damage condition of the electric equipment in the switching-on data detection module, and judges whether the maintenance progress of the maintenance personnel and the risk after the maintenance condition are switched on by combining the experience state of the power grid maintenance personnel and the operation state in the maintenance process, so that the follow-up comprehensive decision on whether to recommend switching on according to the state of the electric equipment and the maintenance state before the power grid switching on is facilitated, the situation that the electric equipment is damaged by secondary impact after the trade and the damage is avoided, and the situation that the maintenance personnel does not withdraw in time to generate electric shock and other risk accidents is avoided;
According to the scheme, the guarantee coefficient is obtained in the closing decision judging module through combining the state of the electrical equipment and the condition of maintenance personnel, and whether closing is carried out or not is judged through the preset threshold value of the guarantee coefficient and the guarantee coefficient, so that the safety risk after closing is conveniently judged comprehensively, risk assessment is conveniently given to management personnel before closing is carried out, decision assistance is conveniently carried out on closing operation of the management personnel, recording of data of each closing decision is conveniently carried out through establishing a risk closing database, and decision optimization and adjustment of a decision making system according to historical data are conveniently carried out;
The scheme scores the closing decision through collecting the equipment state, the maintenance personnel state and the power grid running state after closing in a closing feedback optimization module, the estimated accuracy of the closing decision is convenient to comprehensively judge through closing scoring, and the value and the calculation mode of a guarantee coefficient are adjusted according to the closing scoring result, so that the risk of closing failure is further reduced when the next closing decision is convenient, the safety of electrical equipment and maintenance personnel is further guaranteed, the closing decision is also convenient to be adjusted according to the historical closing situation, and more accurate closing decision suggestion is provided for the management personnel when the next closing operation is convenient;
it should be noted that, the weight value in the scheme can be obtained through an analytic hierarchy process, and the value of the preset threshold can be obtained through the weight analysis process, which is not described in detail herein.
The electrical value is acquired in a closing data detection module, specifically:
step one, acquiring a voltage before a fault of a power grid, acquiring a voltage after the fault of the power grid, taking an absolute value to obtain a voltage value by the difference between the voltage after the fault and the voltage before the fault, setting a voltage preset threshold value, obtaining a voltage difference value by the difference between the voltage preset threshold value and the voltage after the fault, and obtaining a voltage reference value by summing the voltage value and the voltage difference value;
step two, obtaining the insulation resistance of the power grid, setting an insulation resistance preset threshold value, and obtaining an insulation resistance reference value by making a difference between the insulation resistance preset threshold value and the insulation resistance;
Step three, obtaining fault current of a power grid, setting a fault current preset threshold value, and obtaining a fault current reference value through difference between the fault current preset threshold value and the fault current;
Step four, passing through a fault current reference value, a voltage reference value and an insulation resistance reference value of the power grid;
the electrical value is calculated by the following specific steps:
;
wherein Dq is expressed as an electrical value, gz is expressed as a fault current reference value, dy is expressed as a voltage reference value, dz is expressed as an insulation resistance reference value, 、AndAre all the weights of the materials,,,。
In this embodiment, the greater the fault current in the power grid, the greater the voltage abnormal fluctuation or the lower the voltage after the fault, the smaller the insulation resistance, which indicates that the greater the possibility that the fault in the power grid is not eliminated, the greater the possibility that the power grid is closed and shorted, the greater the possibility that the closing electrical equipment is damaged by secondary impact at the moment, so the more unsuitable closing at the moment, the state of the power grid at the moment is comprehensively judged by combining the fault current, the voltage variation and the insulation resistance in the power grid, and the accurate closing auxiliary decision is further conveniently made to the manager.
The device value is acquired in a closing data detection module, specifically:
photographing the appearance of each electrical device in a power grid to obtain a plurality of reference pictures, respectively carrying out burning, cracking and deformation identification on the electrical devices in each reference picture through an AOI system, and marking the defect number of each electrical device;
Acquiring the temperature of each electrical device, and obtaining a device value through the temperature of each electrical device and the defect number of the corresponding electrical device;
The calculation mode of the device value specifically comprises the following steps:
;
Wherein Bs is represented as a device value, wd is represented as a temperature of the electrical device, qx is represented as the number of defects of the electrical device, n is represented as the number of electrical devices, 。
In the embodiment, the appearance condition of the electrical equipment is identified through photographing the electrical equipment and then through the AOI system, so that the risk of secondary failure of the power grid after switching on at the moment is conveniently judged according to the burning condition of the surface of the electrical equipment, and the temperature of the electrical equipment is combined, so that the comprehensive judgment of whether the electrical equipment is damaged or not is facilitated, the requirement of switching on again can be met, and further the auxiliary decision on the switching on operation of a manager is facilitated;
It should be noted that, the defect identification by optical inspection through AOI is the prior art, and specific defect identification steps are not described herein in detail.
The personnel risk value is obtained in a closing data detection module, and specifically comprises the following steps:
Step one, acquiring the working time of each maintainer, acquiring the number of historical operation accidents of each maintainer, summing the number of the historical operation accidents of each maintainer with 1 to obtain the number of accident references, obtaining the experience value of each maintainer by the quotient of the working time of each maintainer and the number of the accident references, and summing the experience value of each maintainer to obtain the average value to obtain the value of the maintainer;
Step two, acquiring a grounding resistor before an accident, acquiring a grounding resistor after the accident, and obtaining a grounding resistor change value by taking an absolute value through difference between the grounding resistor before the accident and the grounding resistor after the accident;
And thirdly, summing the grounding resistance change value with 1 to obtain a grounding resistance value, and weighting the maintenance personnel value and the grounding resistance value by a manufacturer to obtain a personnel risk value.
In this embodiment, the longer the working time of the maintainer, the fewer the number of historical operation accidents, the lower the error rate of the maintainer in the maintenance, the stronger the self-protection capability, the lower the probability of occurrence of safety risk, the larger the fluctuation of the grounding resistance, the larger the connection risk of the surface grounding equipment, and the larger the possibility of electric shock of the maintainer after closing, so that the safety risk of closing at this time is convenient to judge by combining the experience of the maintainer and the fluctuation state of the grounding resistance, and further the auxiliary decision is convenient to be made for the closing operation of the manager.
The execution value is acquired in a closing data detection module, specifically:
step one, a maintenance person starts timing by pressing a maintenance button before the maintenance person leaves an office, and the maintenance person returns to the office after maintenance is finished and finishes timing by pressing the maintenance button, so that maintenance time is acquired through timing;
acquiring second maintenance button pressing time, acquiring current time, and obtaining interval time through difference between the current time and the second maintenance button pressing time;
step three, acquiring a second maintenance button pressing execution state, executing step four if the second maintenance button pressing execution state is pressed, and executing step two if the second maintenance button pressing execution state is not pressed;
Step four, obtaining maintenance time, setting a maintenance time preset threshold, obtaining maintenance reference time by making a difference between the maintenance time and the maintenance time preset threshold, obtaining interval time, obtaining interval reference time by summing the interval time and 1, and obtaining an execution value by a manufacturer of the maintenance reference time and the interval reference time.
In this embodiment, go to maintenance electrical equipment through maintenance personnel and press the button after the maintenance finishes to be convenient for master maintenance personnel current state, and then be favorable to avoiding the maintenance personnel to be in the maintenance state when the combined floodgate emergence dangerous, also be convenient for record maintenance personnel's maintenance time, thereby be convenient for judge whether maintenance personnel is simply looked and examined according to maintenance time, and then be convenient for master the accuracy of maintenance result, the maintenance personnel is the longer after finishing the maintenance to current time the surface maintenance personnel get back to near electrical equipment or near the electrical equipment the probability that someone passed through is bigger, consequently the accident risk of combined floodgate electric shock is stronger, and then be convenient for carry out auxiliary decision to management personnel's combined floodgate operation.
The switching-on decision judging module obtains a guarantee coefficient through a hardware safety coefficient and a personnel safety coefficient, sets a guarantee coefficient preset threshold value, judges whether switching-on is carried out or not through the guarantee coefficient and the guarantee coefficient preset threshold value, and specifically comprises the following steps:
The method comprises the steps of firstly, obtaining a hardware safety coefficient, setting a hardware safety coefficient weight, obtaining a hardware safety reference coefficient through the product of the hardware safety coefficient weight and the hardware safety coefficient, obtaining a personnel safety coefficient, setting a personnel safety coefficient weight, obtaining a personnel safety reference coefficient through the product of the personnel safety coefficient and the personnel safety coefficient weight, and obtaining a guarantee coefficient through summation of the hardware safety reference coefficient and the personnel safety reference coefficient;
Setting a guarantee coefficient preset threshold value, judging the magnitude relation between the guarantee coefficient and the guarantee coefficient preset threshold value, if the guarantee coefficient is larger than or equal to the guarantee coefficient preset threshold value, suggesting to execute the power grid closing, if the guarantee coefficient is smaller than the guarantee coefficient preset threshold value, suggesting to confirm the safety of the power grid closing, and repeatedly measuring the guarantee coefficient.
In the embodiment, the security coefficient is obtained through calculation of the hardware security coefficient, the hardware security coefficient weight, the personnel security coefficient and the personnel security coefficient weight, so that the state of the integrated electrical equipment and the maintenance state security state of the maintenance personnel are convenient to comprehensively analyze the risk security level of the closing at the time, the closing security is judged according to the security coefficient and the security coefficient preset threshold value, and further the closing security auxiliary feedback is conveniently carried out on a plurality of data to the management personnel through combination processing, and the improvement of the closing security of the power grid is facilitated.
The feedback excitation optimization module obtains a closing score according to the equipment state after closing, the personnel safety state after closing and the power grid running state after closing, and specifically comprises the following steps:
the method comprises the steps of firstly, obtaining the number of burn-out of electrical equipment after closing, obtaining personnel safety feedback conditions after closing, and obtaining dangerous accident occurrence times through the personnel safety feedback conditions before and after closing;
Setting a maintenance time preset threshold value, acquiring the continuous operation time of the power grid after closing, recording the operation state of the power grid after closing as 1 if the continuous operation time of the power grid after closing is greater than or equal to the maintenance time preset threshold value, and recording the operation state of the power grid after closing as 0 if the continuous operation time of the power grid after closing is less than the maintenance time preset threshold value;
and thirdly, obtaining a post-gate coefficient through weighting summation of the number of burning losses of the electrical equipment and the number of dangerous accidents, setting a pre-set closing score threshold value, obtaining a post-gate score through the product of the post-gate coefficient and the pre-set closing score threshold value, and obtaining a closing score through the product of the post-gate score and the running state of the power grid after closing.
In this embodiment, the state of the equipment after closing is mastered by the number of burn-out of the electrical equipment after closing, the safety state of personnel after closing is mastered by the occurrence of safety accidents such as electric shock of the power grid after closing, the running state of the power grid is mastered by the smooth running time of the power grid after closing, and finally the closing operation is scored according to various feedback states after closing, so that the estimated accuracy of the closing decision is conveniently comprehensively judged through closing scoring, and the decision calculation mode of a decision system is conveniently and optimally adjusted.
The feedback excitation optimization module adjusts the value mode of the guarantee coefficient according to the closing score, specifically:
Setting a hardware safety coefficient weight preset threshold value, setting a personnel safety coefficient weight preset threshold value, and obtaining a closing score;
Judging whether the closing score is 0, if the closing score is 0, differencing the hardware safety coefficient weight and a preset threshold value of the hardware safety coefficient weight to obtain a hardware safety coefficient secondary weight when the closing score is 0, obtaining a hardware safety reference coefficient by multiplying the hardware safety coefficient by the hardware safety coefficient secondary weight, obtaining a personnel safety coefficient secondary weight by multiplying the personnel safety coefficient weight by the preset threshold value of the personnel safety coefficient weight, obtaining a personnel safety reference coefficient by multiplying the personnel safety coefficient by the personnel safety coefficient secondary weight, obtaining a guarantee coefficient by summing the hardware safety reference coefficient and the personnel safety reference coefficient, and if the closing score is not 0, executing the third step;
Setting a preset threshold value of the burning loss number of the electrical equipment, judging whether the burning loss number of the electrical equipment is larger than the preset threshold value of the burning loss number of the electrical equipment, if the burning loss number of the electrical equipment is larger than or equal to the preset threshold value of the burning loss number of the electrical equipment, obtaining a secondary weight of the personnel safety coefficient by making a difference between the weight of the personnel safety coefficient and the preset threshold value of the personnel safety coefficient when the guarantee coefficient is measured next time, obtaining a secondary weight of the personnel safety coefficient by multiplying the secondary weight of the personnel safety coefficient and the personnel safety coefficient, obtaining the guarantee coefficient by summing the hardware safety reference coefficient and the personnel safety reference coefficient, and executing the fourth step if the burning loss number of the electrical equipment is smaller than the preset threshold value of the burning loss number of the electrical equipment:
Setting a preset threshold value of the dangerous accident occurrence frequency, judging whether the dangerous accident occurrence frequency is larger than or equal to the preset threshold value of the dangerous accident occurrence frequency, if the dangerous accident occurrence frequency is larger than or equal to the preset threshold value of the dangerous accident occurrence frequency, then, subtracting the preset threshold value of the hardware safety coefficient weight from the hardware safety coefficient weight when the guarantee coefficient is measured next time to obtain a second weight of the hardware safety coefficient, obtaining a hardware safety reference coefficient through the product of the second weight of the hardware safety coefficient and the hardware safety coefficient, summing the hardware safety reference coefficient and the personnel safety reference coefficient to obtain the guarantee coefficient, and if the dangerous accident occurrence frequency is smaller than the preset threshold value of the dangerous accident occurrence frequency, keeping the value of the guarantee coefficient unchanged and feeding back to management personnel.
In this embodiment, the reason of the switching-on failure is judged by the number of burning losses and the occurrence times of dangerous accidents of the electrical equipment, and the reason is related to maintenance of the electrical equipment or the safety of maintenance personnel, so that the hardware safety coefficient weight or personnel safety coefficient weight in the process of calculating the guarantee coefficient is adjusted in a targeted manner, the emphasis of the historical accidents is conveniently grasped in the process of calculating the guarantee coefficient in the next switching-on process, and further, decision-making auxiliary suggestions to management personnel are more accurately carried out next time.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application.
Claims (8)
1. The utility model provides a power grid fault intelligent management decision-making system based on big model which characterized in that includes:
the switching-on data detection module is used for obtaining an electrical value, obtaining a device value, obtaining a hardware safety coefficient through the electrical value and the device value, obtaining a personnel risk value, obtaining an execution value, and obtaining a personnel safety coefficient through the personnel risk value and the execution value;
The switching-on decision judging module obtains a guarantee coefficient through a hardware safety coefficient and a personnel safety coefficient, sets a guarantee coefficient preset threshold value, judges whether switching-on is carried out or not through the guarantee coefficient and the guarantee coefficient preset threshold value, establishes a risk switching-on database, and brings the guarantee coefficient and the switching-on judging result into the risk switching-on database for storage;
The switching-on feedback optimization module is used for acquiring a device state after switching-on, acquiring a personnel safety state after switching-on, acquiring a power grid running state after switching-on, acquiring switching-on scores according to the device state after switching-on, the personnel safety state after switching-on and the power grid running state after switching-on, and adjusting the value mode of the guarantee coefficient according to the switching-on scores;
the electrical value is calculated by the following specific steps:
;
wherein Dq is expressed as an electrical value, gz is expressed as a fault current reference value, dy is expressed as a voltage reference value, dz is expressed as an insulation resistance reference value, 、AndAre all the weights of the materials,,,;
The calculation mode of the device value specifically comprises the following steps:
;
Wherein Bs is represented as a device value, wd is represented as a temperature of the electrical device, qx is represented as the number of defects of the electrical device, n is represented as the number of electrical devices, ;
The calculation mode of the hardware safety coefficient in the closing data detection module specifically comprises the following steps:
;
Where Yj is represented as a hardware security factor, dq is represented as an electrical value, bs is represented as a device value, AndAre all the weights of the materials,,;
The calculation mode of the personnel safety coefficient in the closing data detection module specifically comprises the following steps:
;
wherein Ry is represented as a personal safety factor, rf is represented as a personal risk value, zx is represented as an execution value, AndAre all the weights of the materials,,。
2. The intelligent management decision system for power grid faults based on a large model of claim 1 is characterized in that the electrical value is obtained in a closing data detection module, and specifically comprises the following steps:
step one, acquiring a voltage before a fault of a power grid, acquiring a voltage after the fault of the power grid, taking an absolute value to obtain a voltage value by the difference between the voltage after the fault and the voltage before the fault, setting a voltage preset threshold value, obtaining a voltage difference value by the difference between the voltage preset threshold value and the voltage after the fault, and obtaining a voltage reference value by summing the voltage value and the voltage difference value;
step two, obtaining the insulation resistance of the power grid, setting an insulation resistance preset threshold value, and obtaining an insulation resistance reference value by making a difference between the insulation resistance preset threshold value and the insulation resistance;
Step three, obtaining fault current of a power grid, setting a fault current preset threshold value, and obtaining a fault current reference value through difference between the fault current preset threshold value and the fault current;
and step four, passing through a fault current reference value, a voltage reference value and an insulation resistance reference value of the power grid.
3. The intelligent management decision system for power grid faults based on a large model of claim 1 is characterized in that equipment values are obtained in a closing data detection module, and specifically:
photographing the appearance of each electrical device in a power grid to obtain a plurality of reference pictures, respectively carrying out burning, cracking and deformation identification on the electrical devices in each reference picture through an AOI system, and marking the defect number of each electrical device;
And step two, acquiring the temperature of each electrical device, and obtaining a device value through the temperature of each electrical device and the defect number of the corresponding electrical device.
4. The intelligent management decision system for power grid faults based on a large model of claim 1 is characterized in that personnel risk values are obtained in a closing data detection module, and specifically:
Step one, acquiring the working time of each maintainer, acquiring the number of historical operation accidents of each maintainer, summing the number of the historical operation accidents of each maintainer with 1 to obtain the number of accident references, obtaining the experience value of each maintainer by the quotient of the working time of each maintainer and the number of the accident references, and summing the experience value of each maintainer to obtain the average value to obtain the value of the maintainer;
Step two, acquiring a grounding resistor before an accident, acquiring a grounding resistor after the accident, and obtaining a grounding resistor change value by taking an absolute value through difference between the grounding resistor before the accident and the grounding resistor after the accident;
And thirdly, summing the grounding resistance change value with 1 to obtain a grounding resistance value, and weighting the maintenance personnel value and the grounding resistance value by a manufacturer to obtain a personnel risk value.
5. The intelligent management decision system for power grid faults based on a large model of claim 1 is characterized in that an execution value is obtained in a closing data detection module, and specifically comprises the following steps:
step one, a maintenance person starts timing by pressing a maintenance button before the maintenance person leaves an office, and the maintenance person returns to the office after maintenance is finished and finishes timing by pressing the maintenance button, so that maintenance time is acquired through timing;
acquiring second maintenance button pressing time, acquiring current time, and obtaining interval time through difference between the current time and the second maintenance button pressing time;
step three, acquiring a second maintenance button pressing execution state, executing step four if the second maintenance button pressing execution state is pressed, and executing step two if the second maintenance button pressing execution state is not pressed;
Step four, obtaining maintenance time, setting a maintenance time preset threshold, obtaining maintenance reference time by making a difference between the maintenance time and the maintenance time preset threshold, obtaining interval time, obtaining interval reference time by summing the interval time and 1, and obtaining an execution value by a manufacturer of the maintenance reference time and the interval reference time.
6. The intelligent management decision system for power grid faults based on a large model of claim 1 is characterized in that a guarantee coefficient is obtained through a hardware safety coefficient and a personnel safety coefficient in a closing decision judgment module, a guarantee coefficient preset threshold value is set, and whether closing is performed or not is judged through the guarantee coefficient and the guarantee coefficient preset threshold value, specifically:
The method comprises the steps of firstly, obtaining a hardware safety coefficient, setting a hardware safety coefficient weight, obtaining a hardware safety reference coefficient through the product of the hardware safety coefficient weight and the hardware safety coefficient, obtaining a personnel safety coefficient, setting a personnel safety coefficient weight, obtaining a personnel safety reference coefficient through the product of the personnel safety coefficient and the personnel safety coefficient weight, and obtaining a guarantee coefficient through summation of the hardware safety reference coefficient and the personnel safety reference coefficient;
Setting a guarantee coefficient preset threshold value, judging the magnitude relation between the guarantee coefficient and the guarantee coefficient preset threshold value, if the guarantee coefficient is larger than or equal to the guarantee coefficient preset threshold value, suggesting to execute the power grid closing, if the guarantee coefficient is smaller than the guarantee coefficient preset threshold value, suggesting to confirm the safety of the power grid closing, and repeatedly measuring the guarantee coefficient.
7. The intelligent management decision system for power grid faults based on the large model of claim 6 is characterized in that a closing score is obtained in a feedback excitation optimization module according to a device state after closing, a personnel safety state after closing and a power grid running state after closing, and specifically comprises the following steps:
the method comprises the steps of firstly, obtaining the number of burn-out of electrical equipment after closing, obtaining personnel safety feedback conditions after closing, and obtaining dangerous accident occurrence times through the personnel safety feedback conditions before and after closing;
Setting a maintenance time preset threshold value, acquiring the continuous operation time of the power grid after closing, recording the operation state of the power grid after closing as 1 if the continuous operation time of the power grid after closing is greater than or equal to the maintenance time preset threshold value, and recording the operation state of the power grid after closing as 0 if the continuous operation time of the power grid after closing is less than the maintenance time preset threshold value;
and thirdly, obtaining a post-gate coefficient through weighting summation of the number of burning losses of the electrical equipment and the number of dangerous accidents, setting a pre-set closing score threshold value, obtaining a post-gate score through the product of the post-gate coefficient and the pre-set closing score threshold value, and obtaining a closing score through the product of the post-gate score and the running state of the power grid after closing.
8. The intelligent power grid fault management decision system based on the large model as set forth in claim 7, wherein the feedback excitation optimization module adjusts the value mode of the guarantee coefficient according to the closing score, specifically:
Setting a hardware safety coefficient weight preset threshold value, setting a personnel safety coefficient weight preset threshold value, and obtaining a closing score;
Judging whether the closing score is 0, if the closing score is 0, differencing the hardware safety coefficient weight and a preset threshold value of the hardware safety coefficient weight to obtain a hardware safety coefficient secondary weight when the closing score is 0, obtaining a hardware safety reference coefficient by multiplying the hardware safety coefficient by the hardware safety coefficient secondary weight, obtaining a personnel safety coefficient secondary weight by multiplying the personnel safety coefficient weight by the preset threshold value of the personnel safety coefficient weight, obtaining a personnel safety reference coefficient by multiplying the personnel safety coefficient by the personnel safety coefficient secondary weight, obtaining a guarantee coefficient by summing the hardware safety reference coefficient and the personnel safety reference coefficient, and if the closing score is not 0, executing the third step;
Setting a preset threshold value of the burning loss number of the electrical equipment, judging whether the burning loss number of the electrical equipment is larger than the preset threshold value of the burning loss number of the electrical equipment, if the burning loss number of the electrical equipment is larger than or equal to the preset threshold value of the burning loss number of the electrical equipment, obtaining a secondary weight of the personnel safety coefficient by making a difference between the weight of the personnel safety coefficient and the preset threshold value of the personnel safety coefficient when the guarantee coefficient is measured next time, obtaining a secondary weight of the personnel safety coefficient by multiplying the secondary weight of the personnel safety coefficient and the personnel safety coefficient, obtaining the guarantee coefficient by summing the hardware safety reference coefficient and the personnel safety reference coefficient, and executing the fourth step if the burning loss number of the electrical equipment is smaller than the preset threshold value of the burning loss number of the electrical equipment:
Setting a preset threshold value of the dangerous accident occurrence frequency, judging whether the dangerous accident occurrence frequency is larger than or equal to the preset threshold value of the dangerous accident occurrence frequency, if the dangerous accident occurrence frequency is larger than or equal to the preset threshold value of the dangerous accident occurrence frequency, then, subtracting the preset threshold value of the hardware safety coefficient weight from the hardware safety coefficient weight when the guarantee coefficient is measured next time to obtain a second weight of the hardware safety coefficient, obtaining a hardware safety reference coefficient through the product of the second weight of the hardware safety coefficient and the hardware safety coefficient, summing the hardware safety reference coefficient and the personnel safety reference coefficient to obtain the guarantee coefficient, and if the dangerous accident occurrence frequency is smaller than the preset threshold value of the dangerous accident occurrence frequency, keeping the value of the guarantee coefficient unchanged and feeding back to management personnel.
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