CN116756966A

CN116756966A - Power grid fault early warning method, system, terminal equipment and storage medium

Info

Publication number: CN116756966A
Application number: CN202310715620.2A
Authority: CN
Inventors: 周贞卿; 李晓博
Original assignee: Guqiao Information Technology Zhengzhou Co ltd
Current assignee: Guqiao Information Technology Zhengzhou Co ltd
Priority date: 2023-06-15
Filing date: 2023-06-15
Publication date: 2023-09-15

Abstract

The present application relates to the field of power systems, and in particular, to a power grid fault early warning method, a system, a terminal device, and a storage medium. If the data types of the early warning data are multiple, judging whether correlation exists among the early warning data; if correlation exists between the early warning data, acquiring a corresponding associated fault item, and generating a target early warning processing instruction by combining a history processing instruction corresponding to the associated fault item; if no correlation exists between the early warning data, acquiring fault calibration grades corresponding to the early warning data; and generating a corresponding fault processing sequencing scheme as a target early warning processing instruction according to the fault calibration level. The power grid fault early warning method, the system, the terminal equipment and the storage medium can improve the accuracy of early warning indication during power grid faults.

Description

Power grid fault early warning method, system, terminal equipment and storage medium

Technical Field

The present application relates to the field of power systems, and in particular, to a power grid fault early warning method, a system, a terminal device, and a storage medium.

Background

The power grid fault early warning refers to a technical method for predicting the occurrence time, the position and the influence range of power grid faults through real-time monitoring, data analysis and model deduction of a power grid in the running process of a power grid, so that preparation and treatment measures are prepared in advance, and the power utilization safety and the stable running of the power grid are ensured.

The power grid fault early warning system is an intelligent system based on data analysis and algorithm technology, and aims to discover and predict possible faults and abnormal conditions in a power grid in advance, so that measures are taken in time to process and repair, and the normal operation of a power system is ensured.

In practical application, the grid fault early warning system is based on historical data analysis and model prediction, and experience and knowledge of an operator can determine which indexes and trends need to be concerned and monitored, if the experience of the operator is insufficient or the capability is insufficient, important information or less important information can be ignored, so that the accuracy of the early warning system is reduced and the early warning indication is wrong.

Disclosure of Invention

In order to improve the accuracy of early warning indication in power grid fault, the application provides a power grid fault early warning method, a system, terminal equipment and a storage medium.

In a first aspect, the present application provides a power grid fault early warning method, including the following steps:

acquiring power grid node data, and carrying out fault model prediction on the power grid node data to generate a corresponding prediction result;

if the prediction result meets a preset fault early warning standard, outputting a corresponding target early warning item;

if the early warning processing instruction corresponding to the target early warning item does not accord with the early warning processing specification, acquiring corresponding early warning data in the target early warning item;

if the data types of the early warning data are multiple, judging whether correlation exists among the early warning data;

if correlation exists among the early warning data, acquiring a corresponding associated fault item, and generating a target early warning processing instruction by combining a historical processing instruction corresponding to the associated fault item;

if no correlation exists between the early warning data, acquiring fault calibration grades corresponding to the early warning data;

and generating a corresponding fault processing ordering scheme as the target early warning processing indication according to the fault calibration level.

By adopting the technical scheme, the power grid node data are obtained and are subjected to fault model prediction, the fault condition of the power grid node, namely, a prediction result, can be rapidly and accurately predicted, then the prediction result is subjected to compliance judgment according to the preset fault early warning standard, the target early warning item with the fault of the current power system is output, through system evaluation analysis, if the corresponding early warning processing indication of the target early warning item does not accord with the standard, the suitability between the currently issued early warning operation indication and the target early warning item is poor, then the target early warning item is subjected to adaptability analysis again, namely, the correlation analysis between specific early warning data in the target early warning item is performed, and the target early warning processing indication corresponding to the target early warning processing indication is generated by combining the historical correlation processing condition of the early warning data. Due to the suitability evaluation of the early warning data and the corresponding early warning processing instructions of the power system, the power grid fault can be processed more quickly and scientifically by related staff, the situation of missed judgment or misjudgment is reduced, and the accuracy of the early warning instructions in the power grid fault is improved.

Optionally, if there is a correlation between the early warning data, acquiring a corresponding associated fault item, and generating a target early warning processing instruction by combining a history processing instruction corresponding to the associated fault item includes the following steps:

if correlation exists among the early warning data, judging whether the same associated fault item corresponds to a plurality of history processing instructions or not;

if the same associated fault item corresponds to a plurality of history processing instructions, acquiring the integrity of early warning information corresponding to each history processing instruction;

if the integrity of the early warning information accords with the early warning information indication specification, acquiring a corresponding pre-selected early warning processing indication;

if the number of the pre-selection early-warning processing instructions is multiple, obtaining the fault repair rate corresponding to each pre-selection early-warning processing instruction;

and associating the fault restoration rate with the pre-selected early warning processing indication corresponding to the fault restoration rate, and generating the corresponding target early warning processing indication.

By adopting the technical scheme, the early warning information integrity of the historical processing indication of the associated fault item is obtained, the fault condition can be more comprehensively known, the early warning processing indication is generated more scientifically, if the same associated fault item corresponds to a plurality of historical processing indications, the early warning information integrity corresponding to each processing indication is immediately obtained, the preselected early warning processing indication with the integrity meeting the specification is screened out, and if a plurality of preselected early warning processing indications exist, the more suitable target early warning processing indication is generated according to the fault repair rate corresponding to each indication, so that the feasibility of the early warning processing scheme is improved, and the early warning accuracy and the overall efficiency are further improved.

Optionally, after the obtaining the integrity of the early warning information corresponding to each history processing instruction if the same associated fault item corresponds to a plurality of history processing instructions, the method further includes the following steps:

if the integrity of the early warning information does not accord with the early warning information indication specification, acquiring an information deletion item corresponding to the history processing indication;

identifying the information missing item and obtaining a corresponding missing detection type;

if the missing detection type is instruction missing, generating an instruction missing feedback report corresponding to the information missing item according to the information to be grabbed of the instruction missing;

if the missing detection type is that the instruction is not generated, determining that the non-generated early warning instruction corresponding to the information missing item is used as an instruction non-generated feedback report according to an early warning instruction retrieval standard.

By adopting the technical scheme, under the condition that the integrity of the early warning information does not accord with the specification, the information missing item indicated by the historical processing is further obtained, the missing detection type is identified, the reasons and specific conditions of the incomplete early warning information can be better known, basic data are provided for subsequent processing, if the missing detection type is the instruction missing, an instruction missing feedback report is generated according to the information to be grabbed, the staff is helped to better know whether the instruction is not normally triggered, the fault processing flow is further improved, if the detection type is the instruction is not generated, the un-generated early warning instruction corresponding to the missing item is determined according to the early warning instruction retrieval standard, the instruction un-generated feedback report is provided, the staff is helped to further improve the generation process of the early warning instruction, and therefore the accuracy and timeliness of early warning are improved.

Optionally, after generating the instruction loss feedback report corresponding to the information loss item according to the information to be grabbed of the instruction loss if the loss detection type is the instruction loss, the method further includes the following steps:

acquiring the abnormal frequency of the instruction corresponding to the information to be grabbed;

if the instruction abnormal frequency exceeds a preset abnormal frequency threshold, determining a corresponding information acquisition module according to the information to be grabbed;

if the attribute type of the information acquisition module is single-function, associating the information to be grabbed and the information acquisition module corresponding to the information to be grabbed, and generating a corresponding instruction loss tracing result;

if the attribute type of the information acquisition module is multifunctional, acquiring the corresponding abnormal function subunit in the information acquisition module, correlating the information to be grabbed corresponding to the abnormal function subunit, and generating the corresponding instruction loss tracing result.

By adopting the technical scheme, aiming at the missing detection of the instruction missing type, whether the instruction is lost or not can be accurately judged by acquiring the abnormal frequency of the instruction corresponding to the information to be grabbed and comparing the abnormal frequency with the preset abnormal frequency threshold. Meanwhile, the corresponding information acquisition module is determined according to the information to be grabbed, so that the problem can be positioned more quickly, and corresponding tracing results and feedback reports can be generated. In addition, if the attribute type of the information acquisition module is multifunctional, the specific position of the abnormal instruction in the multifunctional module can be displayed more clearly by acquiring the abnormal function subunit and associating the corresponding information to be grabbed, so that the early warning accuracy is improved.

Optionally, if there is no correlation between the early warning data, acquiring the fault calibration level corresponding to each early warning data includes the following steps:

if no correlation exists between the early warning data, acquiring early warning frequency and early warning indication range corresponding to each early warning data;

setting an initial fault calibration level corresponding to the early warning data according to the early warning frequency and the early warning indication range;

and combining the initial fault calibration level and fault attributes corresponding to the early warning data to generate the fault calibration level corresponding to the early warning data.

By adopting the technical scheme, the early warning frequency and the early warning indication range corresponding to each early warning data are obtained, the situation of each early warning data can be more comprehensively known, the initial fault calibration level is set by combining factors such as fault attributes, and on the basis, the cause and the severity of fault occurrence can be more accurately judged by combining the initial fault calibration level and the fault attributes corresponding to the early warning data, and the corresponding fault calibration level is generated, so that the early warning accuracy is improved.

Optionally, the generating the corresponding fault handling ordering scheme as the target early warning processing indication according to the fault calibration level includes the following steps:

According to the fault calibration level, matching an early warning processing scheme corresponding to the early warning data;

if the number of the early warning processing schemes is multiple, acquiring a historical fault elimination record and a historical fault elimination rate corresponding to each early warning processing scheme;

combining the historical fault elimination record and the historical fault elimination rate to generate sequencing priorities corresponding to the early warning processing schemes;

and associating the fault calibration level with the sequencing priority, and generating the fault processing sequencing scheme corresponding to the early warning data as the target early warning processing indication.

By adopting the technical scheme, if a plurality of early warning treatment schemes exist according to the early warning treatment schemes corresponding to the early warning data matched by the fault calibration level, the effect of different early warning treatment schemes can be better known by acquiring the history fault elimination record and the history fault elimination rate corresponding to each early warning treatment scheme, and the sequencing priority corresponding to each early warning treatment scheme is generated, so that the fault treatment work can be guided more accurately through the target early warning treatment indication, the fault treatment efficiency and the success rate are improved, better auxiliary information can be provided for the treatment work, the problem solving capability of operators is improved, and the accuracy and the practicability of early warning are further improved.

Optionally, if the early warning processing instruction corresponding to the target early warning item does not meet the early warning processing specification, the method further includes the following steps after acquiring the early warning data corresponding to the target early warning item:

acquiring a history implementation record corresponding to the early warning processing instruction;

judging whether additional fault loss corresponding to the early warning processing instruction exists in the history implementation record;

and if the additional fault loss corresponding to the early warning processing instruction exists in the history implementation record, associating an estimated loss rate corresponding to the additional fault loss with an instruction issuing person corresponding to the early warning processing instruction to generate a corresponding early warning exception report.

By adopting the technical scheme, after the historical implementation record corresponding to the early warning processing instruction is obtained, whether additional fault loss corresponding to the early warning processing instruction exists is judged, if the additional fault loss occurs, the loss rate corresponding to the additional fault loss and the early warning processing instruction issuing personnel are associated immediately, a corresponding early warning abnormal report is generated, and further the early warning processing condition including the possible additional fault loss can be more comprehensively known through the early warning abnormal report, so that the root cause and the cause of the fault problem of the power system are better analyzed.

In a second aspect, the present application provides a power grid fault early warning system, including:

the fault prediction module is used for acquiring power grid node data and performing fault model prediction on the power grid node data to generate a corresponding prediction result;

the early warning output module is used for outputting corresponding target early warning items if the prediction result accords with a preset fault early warning standard;

the early warning data acquisition module is used for acquiring corresponding early warning data in the target early warning item if the early warning processing instruction corresponding to the target early warning item does not accord with the early warning processing specification;

the correlation analysis module is used for judging whether correlation exists among the early warning data or not if the data types of the early warning data are multiple;

the first indication module is used for acquiring corresponding associated fault items and generating target early warning processing instructions by combining historical processing instructions corresponding to the associated fault items if correlation exists among the early warning data;

the fault grade calibration module is used for acquiring fault calibration grades corresponding to the early warning data if no correlation exists between the early warning data;

And the second indication module is used for generating a corresponding fault processing ordering scheme as the target early warning processing indication according to the fault calibration level.

By adopting the technical scheme, the power grid node data are obtained according to the fault prediction module and are subjected to fault model prediction, the fault condition of the power grid node, namely the prediction result, can be rapidly and accurately predicted, then the prediction result is subjected to compliance judgment according to the preset fault early warning standard through the early warning output module, the target early warning item with the fault of the current power system is output, through system evaluation analysis, if the corresponding early warning processing indication of the target early warning item does not accord with the specification, the suitability between the currently issued early warning operation indication and the target early warning item is poor, then the target early warning item is subjected to adaptability analysis again, namely the correlation between specific early warning data in the target early warning item is analyzed through the early warning data acquisition module, and the corresponding target early warning processing indication is generated through the first indication module or the second indication module in combination with the historical related processing condition of the early warning data. Due to the suitability evaluation of the early warning data and the corresponding early warning processing instructions of the power system, the power grid fault can be processed more quickly and scientifically by related staff, the situation of missed judgment or misjudgment is reduced, and the accuracy of the early warning instructions in the power grid fault is improved.

In a third aspect, the present application provides a terminal device, which adopts the following technical scheme:

the terminal equipment comprises a memory and a processor, wherein the memory stores computer instructions capable of running on the processor, and the power grid fault early warning method is adopted when the processor loads and executes the computer instructions.

By adopting the technical scheme, the computer instruction is generated by the power grid fault early warning method and is stored in the memory to be loaded and executed by the processor, so that the terminal equipment is manufactured according to the memory and the processor, and the power grid fault early warning method is convenient to use.

In a fourth aspect, the present application provides a computer readable storage medium, which adopts the following technical scheme:

a computer readable storage medium having stored therein computer instructions which, when loaded and executed by a processor, employ a grid fault warning method as described above.

By adopting the technical scheme, the computer instruction is generated by the power grid fault early warning method and is stored in the computer readable storage medium to be loaded and executed by the processor, and the computer instruction is convenient to read and store by the computer readable storage medium.

In summary, the present application includes at least one of the following beneficial technical effects: the method comprises the steps of obtaining power grid node data and predicting a fault model, rapidly and accurately predicting a fault condition of the power grid node, namely a prediction result, carrying out compliance judgment on the prediction result according to a preset fault early warning standard, outputting a target early warning item with a fault of a current power system, carrying out system evaluation analysis, if a corresponding early warning processing instruction of the target early warning item does not meet the standard, indicating poor suitability between a currently issued early warning operation instruction and the target early warning item, carrying out adaptive analysis on the target early warning item again, namely carrying out correlation analysis on specific early warning data in the target early warning item, and generating a corresponding target early warning processing instruction by combining historical correlation processing conditions of the early warning data. Due to the suitability evaluation of the early warning data and the corresponding early warning processing instructions of the power system, the power grid fault can be processed more quickly and scientifically by related staff, the situation of missed judgment or misjudgment is reduced, and the early warning accuracy in the power grid fault is improved.

Drawings

Fig. 1 is a schematic flow chart of steps S101 to S107 in the grid fault early warning method of the present application.

Fig. 2 is a schematic flow chart of steps S201 to S205 in the grid fault early warning method of the present application.

Fig. 3 is a schematic flow chart of steps S301 to S304 in the grid fault early warning method of the present application.

Fig. 4 is a schematic flow chart of steps S401 to S404 in the grid fault early warning method of the present application.

Fig. 5 is a schematic flow chart of steps S501 to S503 in the grid fault early warning method according to the present application.

Fig. 6 is a schematic flow chart of steps S601 to S604 in the grid fault early warning method according to the present application.

Fig. 7 is a schematic flow chart of steps S701 to S703 in the grid fault early warning method of the present application.

Fig. 8 is a schematic block diagram of a power grid fault early warning system according to the present application.

Reference numerals illustrate:

1. a fault prediction module; 2. an early warning output module; 3. the early warning data acquisition module; 4. a correlation analysis module; 5. a first indication module; 6. a fault level calibration module; 7. and a second indication module.

Detailed Description

The application is described in further detail below with reference to fig. 1-8.

The embodiment of the application discloses a power grid fault early warning method, which is shown in fig. 1 and comprises the following steps:

s101, acquiring power grid node data, carrying out fault model prediction on the power grid node data, and generating a corresponding prediction result;

S102, if the prediction result meets a preset fault early warning standard, outputting a corresponding target early warning item;

s103, if the early warning processing instruction corresponding to the target early warning item does not accord with the early warning processing specification, acquiring corresponding early warning data in the target early warning item;

s104, if the data types of the early warning data are multiple, judging whether correlation exists among the early warning data;

s105, if correlation exists among the early warning data, acquiring corresponding associated fault items, and generating target early warning processing instructions by combining historical processing instructions corresponding to the associated fault items;

s106, if no correlation exists among the early warning data, acquiring fault calibration grades corresponding to the early warning data;

s107, generating a corresponding fault processing ordering scheme as a target early warning processing instruction according to the fault calibration level.

In step S101, the grid node data includes power system operation data such as voltage, current, power, frequency, etc. in the current grid operation. The method for predicting the fault model of the power grid node data is to establish a fault model by analyzing the change rule and trend of the power grid node data and utilizing technologies such as machine learning, data mining and the like, and predict the time, place, type and other information of possible occurrence of node faults in the power system.

Wherein generating the corresponding prediction result includes aspects such as predicting the failed node, the failure type, the time of failure occurrence, and the like. The prediction results can help power system operation staff to timely perform fault processing and maintenance, and reliability, safety and stability of the power system are improved.

For example, a node of the power system has a large voltage change in a period of time, and the data acquisition can be performed in the real-time data monitoring system, and the change rule and trend can be analyzed. By analyzing the historical data and building a fault model, it is predicted that the node may fail with insufficient load bearing capacity within the next 12 hours. The prediction result can help operation staff of the power system to perform fault processing in real time, so that problems of line overload, equipment damage and the like are avoided, and normal operation of the power system is maintained.

In step S102, the preset fault early warning standard refers to a fault early warning standard or threshold set according to factors such as electrical characteristics and operation experience in the operation of the power system. Typically including fault type, fault level, pre-warning threshold, etc.

The fault type refers to a specific form or kind of a fault, such as an open circuit fault, a short circuit fault, an undervoltage fault, an overvoltage fault, and the like. The fault level refers to the severity of the fault and can be generally classified into primary, secondary, tertiary, etc. The early warning threshold value refers to parameters or conditions used for judging whether the fault needs to send out early warning in the fault early warning system. For example, a current early warning threshold value of 100A is set in the fault early warning system, and when the node current exceeds or falls below the threshold value, the system automatically triggers early warning.

Specifically, the preset fault early warning standard is set by comprehensively considering factors such as characteristics and safety levels of different parts of the power system, so that the possibility of fault occurrence can be predicted according to actual conditions, and risks and potential safety hazards in the operation of the power system are reduced. For example, for a high-voltage line in a power system, the preset fault early warning standard can be set such that when the line current exceeds a certain threshold value or the voltage change exceeds a certain range, the system can automatically trigger early warning to remind operation and maintenance personnel to take corresponding measures in time for processing.

And secondly, after the node data in the power system is predicted by the fault model and related prediction results are obtained, if the prediction results accord with preset early warning standards, a corresponding target early warning item can be output. The target early warning items typically include content of relevant nodes, predicted time periods, predicted fault types, and the like.

In practical application, the power grid fault early warning system is a system for predicting and early warning power grid faults by utilizing technologies such as artificial intelligence, machine learning and the like based on real-time monitoring and analysis of power grid running state data. Specifically, the power grid fault early warning system monitors all nodes of the power grid in real time, including the running states of equipment such as power grid load, transformer stations, lines, switches and the like, measures parameters such as voltage, current, power and the like of all the nodes of the power grid in real time, and transmits the data to the central server for processing. The central server analyzes the data through machine learning, data mining and other technologies, and predicts possible fault conditions of the power grid through a model, such as current overload, short circuit, unstable voltage and the like. If the possible occurrence of the fault is predicted, early warning information is sent to the power grid management department, and corresponding suggestions and solutions are provided.

The target early warning item is usually that an early warning system judges whether a fault occurs in the power system according to a preset fault early warning standard, and timely outputs information meeting the early warning standard. The preset fault early warning standard can be a number, a parameter or a condition, for example, the voltage change of a certain power system exceeds +/-5%, the predicted time period is 8 hours, an inductance fault occurs, and the system can automatically push the node predicted fault information to operation and maintenance personnel or send corresponding short message early warning information. The target early warning items can help operation staff to know actual power system conditions and make decisions quickly.

For example, when a current mutation occurs in a high-voltage line of a certain power system at a certain time point, the node involved predicts a fault that may occur in a short circuit of the line through analysis of an intelligent prediction system. According to a preset fault early warning standard, the prediction time period is 4 hours, and under the condition that the prediction fault type is a line short circuit, the system automatically outputs related information such as nodes, fault types, the prediction time period and the like as target early warning items. Then, the power system operation staff can timely conduct line outage operation through the early warning information and conduct overhaul treatment, and therefore unnecessary loss and potential safety hazards caused by faults of the high-voltage line are reduced.

And if the predicted result does not accord with the preset fault early warning standard, the power grid data monitoring system continuously acquires and analyzes the monitoring data in real time.

In step S103, when a fault occurs in the power system, the relevant operation and maintenance manager will attach a specific early warning processing instruction according to the specific fault condition output by the system, where the early warning processing instruction refers to a specific target early warning item output by the system and is issued according to the relevant operation and maintenance manager.

The early warning information output by the power grid fault early warning system is required to be processed by related staff. These staff members may be maintenance personnel, operators, management personnel, etc. They analyze, evaluate, and react and process the pre-warning information appropriately based on various factors. These processes may include dispatching tasks, adjusting equipment, upgrading software, etc. to maximize the guarantee of stable operation of the grid. Therefore, the output of the early warning system is only one step, and manual processing is a very necessary step.

Secondly, when the early warning system detects that a power grid fault occurs in a certain area, the system can automatically send fault early warning to related personnel. The related staff can receive the early warning first, then carry out preliminary judgment and analysis on the faults according to the degree and the content of the early warning and own expertise and experience, and acquire corresponding early warning processing instructions.

In addition, because the early warning processing indication is influenced by artificial subjective and objective factors and self experience, in order to reduce the occurrence of untimely fault processing or failure to meet the fault processing standard condition caused by temporary lack of sentry or insufficient experience of related operation and maintenance management personnel, the system can judge whether the early warning processing indication corresponding to the current target early warning item meets the early warning processing standard. The early warning processing specification refers to a standard for standardizing specific processing steps and measures when an early warning event occurs according to the operation management specification of the power grid.

Further, if the pre-warning processing instruction corresponding to the target pre-warning item does not accord with the pre-warning processing specification, the pre-warning data corresponding to the target pre-warning item needs to be acquired for further analysis and processing. The corresponding early warning data in the target early warning item generally comprises information such as fault type, occurrence time, early warning level, related nodes or equipment and the like. By analyzing and comparing the early warning data, operation and maintenance personnel can be helped to find the cause of the problem indicated by the early warning processing, and corresponding measures are taken for correction and processing.

And if the early warning processing instruction corresponding to the target early warning item accords with the early warning processing specification, issuing a corresponding operation instruction command according to the current early warning processing instruction, and sending the operation instruction command to related operation and maintenance personnel for corresponding processing.

In step S104, the correlation between the early warning data refers to the degree of correlation between these data, i.e. whether there is a certain interdependence or influence between them. If correlation exists between the early warning data, it is indicated that some causal relationship may exist between the early warning data, and a change of one data may have some influence on other data.

For example, in grid operation, the early warning data types may include a plurality of indicators of grid load, voltage, current, frequency, and the like. There may be a correlation between these indicators, such as a positive correlation between grid load and current, and as the load increases, the current increases accordingly. And there may be an inverse relationship between voltage and frequency, and as the frequency decreases, the voltage increases accordingly. Through analysis and judgment of the correlations, potential risks can be predicted and identified more accurately, and safety and stability of the power grid are improved.

And if the data type of the early warning data is single, matching corresponding processing specifications according to the current single early warning data, and then issuing corresponding early warning operation instructions through comprehensive analysis of current operation and maintenance managers, wherein the system simultaneously records the specific contents of the current early warning operation instructions, including the issuing time of the early warning operation instructions, the execution information of related operation and maintenance personnel, the early warning fault elimination rate and the like.

In step S105, if there is correlation between the early warning data, it means that the early warning data may cause multiple associated faults, so that associated fault items related to the early warning data are further acquired. The history processing instruction refers to a related instruction scheme for performing solution processing on the related fault item in history. It should be noted that, all relevant indication schemes included in the history processing indication are schemes for successfully solving the associated fault item.

For example, the early warning data in grid operation is a switching operation and adjacent cable status, and a mismatch of the switching operation and the adjacent cable status may lead to an associated fault term of overload of the equipment and imbalance of voltage between the equipment.

Further, according to the determined associated fault item, obtaining the corresponding historical processing instruction includes: ensuring the normal state of adjacent cables before switching, and checking and testing; when the switching operation is carried out, the switching operation is carried out according to a specified program, so that the safety, stability and reliability of the switching operation are ensured; after switching, the cable state should be checked in time to ensure the effectiveness of the switching operation and prevent the potential risk possibly brought by the switching operation; and checking the influence generated by the switching operation, such as current load, voltage balance, equipment heat and the like, timely adjusting measures, and then generating a target early warning processing instruction according to the early warning data, the associated fault item corresponding to the early warning data and the history processing instruction corresponding to the associated fault item. The system then sends the target early warning processing instruction to the current operation and maintenance manager for relevant reference, and if the current operation and maintenance manager refuses to use the target early warning processing instruction, and selects the early warning processing instruction again, the system automatically stops the issuing of the current early warning processing instruction and feeds back to the upper management department.

In step S106 to step S107, if there is no correlation between the early warning data, each early warning data needs to be analyzed separately, so as to further obtain a fault calibration level corresponding to each early warning data, where the fault calibration level refers to a risk level formulated for the power grid fault risk levels corresponding to different early warning data.

For example, the early warning data is a current magnitude, and the corresponding fault calibration grades are high, medium and low risk grades. Wherein, the high risk represents excessive current, which may cause serious consequences such as short circuit, overheat, fire and the like of the equipment; the medium risk represents that the current deviates slightly from the standard value, but can still cause equipment failure and potential safety hazards; a low risk represents a smaller current ripple window and less impact on the device.

Further, according to the fault calibration level, a corresponding fault processing ordering scheme is generated as a target early warning processing instruction. The fault processing and sorting scheme is a priority processing and sorting table formed according to the level of the fault calibration level corresponding to the early warning data in order to effectively prevent the current early warning fault in time, and the higher the fault calibration level corresponding to the early warning data is, the earlier the priority processing level corresponding to the early warning data in the fault processing and sorting scheme is.

According to the power grid fault early warning method, power grid node data are obtained, fault model prediction is conducted on the power grid node data, the fault condition of the power grid node, namely, a prediction result, can be rapidly and accurately predicted, then compliance judgment is conducted on the prediction result according to a preset fault early warning standard, a target early warning item with faults in a current power system is output, through system evaluation analysis, if corresponding early warning processing indication of the target early warning item does not meet the standard, the fact that the suitability between the currently-issued early warning operation indication and the target early warning item is poor is indicated, then adaptive analysis is conducted on the target early warning item again, namely, correlation analysis is conducted on specific early warning data in the target early warning item, and the historical related processing condition of the early warning data is combined, so that the corresponding target early warning processing indication is generated. Due to the suitability evaluation of the early warning data and the corresponding early warning processing instructions of the power system, the power grid fault can be processed more quickly and scientifically by related staff, the situation of missed judgment or misjudgment is reduced, and the accuracy of the early warning instructions in the power grid fault is improved.

In one implementation manner of the present embodiment, as shown in fig. 2, step S105, that is, if there is a correlation between the early warning data, acquires a corresponding associated fault item, and generates a target early warning processing instruction by combining a history processing instruction corresponding to the associated fault item includes the following steps:

S201, judging whether the same associated fault item corresponds to a plurality of historical processing instructions or not if correlation exists among the early warning data;

s202, if the same associated fault item corresponds to a plurality of historical processing instructions, acquiring the integrity of early warning information corresponding to each historical processing instruction;

s203, if the integrity of the early warning information accords with the early warning information indication specification, acquiring a corresponding pre-selected early warning processing indication;

s204, if the number of the pre-selection early-warning processing instructions is multiple, acquiring fault repair rates corresponding to the pre-selection early-warning processing instructions;

s205, associating the fault restoration rate with a pre-selected early warning processing instruction corresponding to the fault restoration rate, and generating a corresponding target early warning processing instruction.

In step S201 to step S202, if there is a correlation between the early warning data, in order to perform deep analysis on the early warning data of the same associated fault item type, and further to make an effective fault prevention measure, it is determined whether the current same associated fault item corresponds to a plurality of historical processing instructions.

Further, if the current same associated fault item corresponds to a plurality of historical processing instructions, the integrity of the early warning information corresponding to each current historical processing instruction is obtained, wherein the integrity of the early warning information refers to the key information degree of the associated fault item contained in the early warning information. For example, if the early warning information only includes information of the type of the fault to be caused, the integrity of the early warning information is relatively low, and if the early warning information includes not only information of the type of the fault to be caused but also a specific process and cause of the type of the fault to be caused, the integrity of the early warning information is relatively high.

For example, the related fault items are frequent voltage fluctuation, the corresponding historical processing instruction comprises peak regulation control and increase of power generation capacity of the power grid, the early warning information corresponding to the peak regulation control of the power grid only indicates that the frequent voltage fluctuation can cause current abnormality, the early warning information corresponding to the increase of the power generation capacity system indicates that the frequent voltage fluctuation can cause current abnormality, and the cause of the frequent current abnormality caused by the frequent voltage fluctuation is taught, namely, the frequent occurrence of the voltage fluctuation can cause current abnormality change, especially for an alternating current power system, the running problem can be caused, the equipment is possibly overheated, and proper adjustment is needed. The early warning information integrity corresponding to the increased power generation capacity can be judged to be larger than the early warning information integrity for peak regulation control of the power grid.

In steps S203 to S205, the early warning information indication specification refers to the standard that the integrity of the corresponding early warning information should meet when the history processing indication is selected. If the integrity of the early warning information of the current historical processing instructions meets the early warning information instruction specification, the historical processing instructions are calibrated to be preselected early warning processing instructions, and if the current preselected early warning processing instructions are multiple, the fault repair rate corresponding to each current preselected early warning processing instruction is obtained in order to further optimize the preselected early warning processing instructions.

Further, in order to provide early warning processing references for relevant operation and maintenance managers more intuitively, the fault repair rate and the pre-selected early warning processing indication corresponding to the fault repair rate are associated to generate a corresponding target early warning processing indication, and the relevant operation and maintenance managers can formulate a more rigorous early warning processing scheme according to the current target early warning processing indication.

According to the grid fault early warning method provided by the embodiment, the early warning information integrity of the historical processing instructions of the associated fault items is obtained, the fault condition can be known more comprehensively, the early warning processing instructions are generated more scientifically, if the same associated fault item corresponds to a plurality of historical processing instructions, the pre-selected early warning processing instructions with the integrity meeting the specifications are screened out by obtaining the early warning information integrity corresponding to each processing instruction, and if a plurality of pre-selected early warning processing instructions exist, more suitable target early warning processing instructions are generated according to the fault repair rate corresponding to each instruction, so that the feasibility of an early warning processing scheme is improved, and the early warning accuracy and the overall efficiency are further improved.

In one implementation manner of this embodiment, as shown in fig. 3, in step S202, if the same associated fault item corresponds to a plurality of history processing instructions, the method further includes the following steps after obtaining the integrity of the early warning information corresponding to each history processing instruction:

S301, if the integrity of the early warning information does not accord with the early warning information indication specification, acquiring an information missing item corresponding to the history processing indication;

s302, identifying an information missing item, and acquiring a corresponding missing detection type;

s303, if the missing detection type is instruction missing, generating an instruction missing feedback report corresponding to an information missing item according to information to be grabbed of the instruction missing;

s304, if the missing detection type is that the instruction is not generated, determining that the non-generated early warning instruction corresponding to the information missing item is used as an instruction non-generated feedback report according to the early warning instruction retrieval standard.

In step S301 to step S302, if the integrity of the early warning information corresponding to the current historical processing instruction does not meet the early warning information instruction specification, it is indicated that the early warning information may have some artificial subjective or objective reason loss, and then the integrity of the early warning information and the early warning information instruction specification are compared and analyzed to generate an information loss item corresponding to the historical processing instruction. And then identifying the current information missing item through the power grid self-checking system, and acquiring a missing detection type corresponding to the information missing item, wherein the missing detection type refers to a specific abnormal type causing the information missing item.

It should be noted that, the grid self-checking system may trace back the log corresponding to the information missing item, where the log records the specific information missing item and the missing detection type of the information missing item in the history processing instruction.

Specifically, the deletion detection types include: the sensor fails, the power grid fault early warning system relies on the sensor to collect data, and if the sensor fails or reads in error, early warning information acquisition is lost; the data processing is wrong, the power grid fault early warning system needs a large amount of data processing, and if the processing process is wrong, early warning information is lost; the human factor, the power grid fault early warning system needs professional personnel to maintain and operate, if the human factor causes misoperation or neglects alarming, early warning information is lost; if the design of the early warning system is unreasonable, the early warning system depends on a single sensor or algorithm, and early warning information is lost.

In step S303, if the missing detection type is that the instruction is missing, it is indicated that the system has generated an acquisition instruction of the early warning information, but the corresponding data information acquisition device fails to identify the acquisition instruction, and in order to perform timely and effective feedback on the anomaly, an instruction missing feedback report corresponding to the information missing item is generated according to the information to be captured that is missing from the current instruction.

For example, because the current sensor has an instruction recognition fault, the system issues a data acquisition instruction corresponding to the current sensor, wherein the data acquisition instruction comprises the data acquisition of the current size and the current direction, and the data acquisition of the current direction is detected to be unsuccessful, namely the current sensor fails to successfully recognize the data acquisition instruction of the current direction, namely the current direction acquisition instruction is lost, so that early warning information about the current sensor cannot be completely collected, and corresponding information to be grabbed is lost according to the current direction data, namely the current direction acquisition instruction, to generate a corresponding instruction loss feedback report.

In step S304, if the missing detection type is that the instruction is not generated, it is explained that the system fails to generate the corresponding acquisition detection instruction according to the early warning instruction search criteria, where the early warning instruction search criteria is a type criterion indicating that acquisition search data is required for the early warning fault that may occur in the current power grid. For example, a data acquisition instruction of current direction and current magnitude is required to be generated according to an early warning instruction search standard, but the data acquisition instruction of current magnitude is not generated due to a temporary abnormality of the system, and according to further determining that the current magnitude is an information missing item, the data acquisition instruction of current magnitude, namely, the non-generated early warning instruction is taken as an instruction, and a feedback report is not generated.

According to the power grid fault early warning method, under the condition that the integrity of early warning information does not accord with the specification, the information missing item indicated by historical processing is further obtained, the missing detection type is identified, the reason and the specific situation of the incomplete early warning information can be better known, basic data are provided for subsequent processing, if the missing detection type is instruction missing, an instruction missing feedback report is generated according to the information to be grabbed, workers are helped to better know whether the instruction is not triggered normally, the fault processing flow is further improved, if the detection type is instruction non-generation, the non-generated early warning instruction corresponding to the missing item is determined according to the early warning instruction retrieval standard, the instruction non-generation feedback report is provided, the workers are helped to further improve the generation process of the early warning instruction, and therefore the early warning accuracy and timeliness are improved.

In one implementation manner of the present embodiment, as shown in fig. 4, in step S303, if the type of the miss detection is instruction miss, the generating an instruction miss feedback report corresponding to the information miss item according to the information to be grabbed of the instruction miss further includes the following steps:

s401, acquiring abnormal frequency of instructions corresponding to information to be grabbed;

s402, if the abnormal frequency of the instruction exceeds a preset abnormal frequency threshold, determining a corresponding information acquisition module according to the information to be grabbed;

s403, if the attribute type of the information acquisition module is single-function, associating the information to be acquired with the information acquisition module corresponding to the information to be acquired, and generating a corresponding instruction loss tracing result;

s404, if the attribute type of the information acquisition module is multifunctional, acquiring the corresponding abnormal function subunit in the information acquisition module, correlating the information to be grabbed corresponding to the abnormal function subunit, and generating a corresponding instruction loss tracing result.

In steps S401 to S402, the abnormal frequency of the instruction corresponding to the information to be grabbed refers to the number of times that the instruction corresponding to the information is executed on the device to generate an abnormality in a certain time range for a certain information to be grabbed. For example, when an application program needs to acquire network state information from a device, if an execution abnormality, such as disconnection of a network or acquisition timeout, occurs during execution of an instruction, the number of the abnormalities can be used as the abnormal frequency of the instruction corresponding to the information to be acquired.

Further, the preset abnormal frequency threshold value is the number of abnormal times allowed to occur in a certain time by the pointer corresponding to the instruction of the information to be grabbed, if the abnormal frequency of the instruction exceeds the preset abnormal frequency threshold value, the functional module for identifying the instruction is indicated to have serious identification problem, and then the corresponding information acquisition module is determined according to the information to be grabbed. The information acquisition module is a functional module for identifying and executing the instruction corresponding to the information to be grabbed.

In step S403, if the attribute type of the information acquisition module is single function, it indicates that the module is only responsible for acquiring information of a specific type, such as information of network status, battery level, etc., from the device. At this time, the system associates the information to be captured with the information acquisition module corresponding to the information to generate an instruction loss tracing result, and the instruction loss tracing result can be used for displaying the instruction loss problem possibly occurring in the process of acquiring the information.

In step S404, if the attribute type of the information acquisition module is multi-functional, the module has a plurality of functional subunits, each of which is responsible for acquiring information of a different type from the device. For example, the current anomaly analysis module comprises a current data acquisition subunit, a current data analysis subunit and a current data analysis result output subunit.

Further, when the abnormal frequency of the instruction corresponding to the information to be grabbed exceeds a preset abnormal frequency threshold, the system automatically detects and acquires the corresponding abnormal function subunit in the information acquisition module, associates the information to be grabbed corresponding to the abnormal function subunit and generates a corresponding instruction loss tracing result.

For example, a certain information acquisition module includes a plurality of subunits such as a network state, a device temperature, a battery power and the like, aiming at the occurrence of an abnormality in an instruction for acquiring the network state, a system acquires the network state subunit in the information acquisition module, associates information to be acquired corresponding to the network state subunit, and generates an instruction loss tracing result, which indicates that an instruction loss problem may occur in the process of acquiring the network state.

According to the power grid fault early warning method, aiming at the missing detection of the instruction loss type, whether the instruction is lost can be accurately judged by acquiring the instruction abnormal frequency corresponding to the information to be grabbed and comparing the instruction abnormal frequency with the preset abnormal frequency threshold. Meanwhile, the corresponding information acquisition module is determined according to the information to be grabbed, so that the problem can be positioned more quickly, and corresponding tracing results and feedback reports can be generated. In addition, if the attribute type of the information acquisition module is multifunctional, the specific position of the abnormal instruction in the multifunctional module can be displayed more clearly by acquiring the abnormal function subunit and associating the corresponding information to be grabbed, so that the early warning accuracy is improved.

In one implementation manner of this embodiment, as shown in fig. 5, step S106, that is, if there is no correlation between the early warning data, includes the following steps of:

s501, if no correlation exists among the early warning data, acquiring early warning frequency and early warning indication range corresponding to each early warning data;

s502, setting initial fault calibration grades corresponding to early warning data according to early warning frequency and early warning indication range;

s503, combining the initial fault calibration level and fault attributes corresponding to the early warning data to generate the fault calibration level corresponding to the early warning data.

In step S501, if there is no correlation between the early warning data, that is, there is no obvious correlation between them, then separate processing is required for each item of early warning data. The frequency of each item of early warning data in a specified analysis time period and the range of the normal operation of the power grid affected by the frequency, namely the early warning indication range, are obtained. The abnormal degree of hidden danger corresponding to each item of early warning data can be primarily judged through the acquisition analysis.

In step S502, an initial fault calibration level corresponding to the early warning data is set according to the obtained early warning frequency and early warning indication range. Specifically, the higher the early warning frequency is, the more frequently the corresponding fault of the early warning data appears, and the higher the initial fault calibration level is. Meanwhile, the larger the early warning indication range is, the larger the fault range indicated by the early warning data is, and the higher the initial fault calibration level is.

In step S503, the fault attribute refers to a fault condition, such as a fault type, a fault severity, etc., of the power grid indicated by the early warning data, and generally, the fault severity corresponding to different fault types is different. And combining the fault attribute and the initial fault calibration level, determining the actual fault level of the early warning data. It should be noted that, the fault attribute and the initial fault calibration level are in a direct proportion relation with the fault calibration level generated by the fault attribute and the initial fault calibration level, that is, the more serious the fault attribute is, the higher the initial fault calibration level is, and the higher the corresponding fault calibration level is.

According to the grid fault early warning method, the early warning frequency and the early warning indication range corresponding to each early warning data are obtained, the situation of each early warning data can be known more comprehensively, the initial fault calibration level is set by combining factors such as fault attributes, on the basis, the cause and the severity of fault occurrence can be judged more accurately by combining the initial fault calibration level and the fault attributes corresponding to the early warning data, and the corresponding fault calibration level is generated, so that the early warning accuracy is improved.

In one implementation manner of the present embodiment, as shown in fig. 6, step S107, that is, generating, according to the fault calibration level, a corresponding fault handling ordering scheme as a target early warning handling instruction includes the following steps:

S601, matching an early warning processing scheme corresponding to early warning data according to the fault calibration level;

s602, if a plurality of early warning processing schemes are adopted, acquiring a historical fault elimination record and a historical fault elimination rate corresponding to each early warning processing scheme;

s603, combining the historical fault elimination record and the historical fault elimination rate to generate sequencing priority corresponding to each early warning processing scheme;

s604, associating the fault calibration level with the sequencing priority, and generating a fault processing sequencing scheme corresponding to the early warning data as a target early warning processing instruction.

In step S601 to step S602, the system sequentially matches corresponding early warning processing schemes according to the fault calibration level of the early warning data. The early warning processing scheme is a prevention processing scheme of faults indicated by early warning data, and if the number of the current early warning processing schemes is multiple, in order to select a more efficient processing scheme, a historical fault elimination record and a historical fault elimination rate corresponding to each early warning processing scheme are obtained. The historical fault elimination record refers to the processing process of the corresponding faults of each early warning processing scheme history and the elimination success rate of the corresponding early warning faults.

In step S603 to step S604, the above-mentioned historical fault elimination records and the historical fault elimination rates of the respective early warning processing schemes are combined to generate the sorting priorities corresponding to the respective early warning processing schemes, and by means of the sorting priorities, the relevant operation and maintenance manager can analyze and process the early warning faults more effectively.

According to the power grid fault early warning method provided by the embodiment, the early warning processing schemes corresponding to the early warning data are matched according to the fault calibration level, if a plurality of early warning processing schemes exist, the effect of different early warning processing schemes can be better known by acquiring the historical fault elimination record and the historical fault elimination rate corresponding to each early warning processing scheme, and the sequencing priority corresponding to each early warning processing scheme is generated, so that fault processing work can be guided more accurately through the target early warning processing indication, the fault processing efficiency and the success rate are improved, better auxiliary information can be provided for the processing work, the problem solving capability of operators is improved, and the accuracy and the practicability of early warning are further improved.

In one implementation manner of this embodiment, as shown in fig. 7, in step S602, if the number of early warning processing schemes is plural, the steps of obtaining the historical fault elimination record and the historical fault elimination rate corresponding to each early warning processing scheme further include the following steps:

s701, acquiring a history implementation record corresponding to the early warning processing instruction;

s702, judging whether additional fault loss corresponding to the early warning processing instruction exists in the history implementation record;

S703, if additional fault loss corresponding to the early warning processing instruction exists in the history implementation record, associating an estimated loss rate corresponding to the additional fault loss with an instruction issuing person corresponding to the early warning processing instruction, and generating a corresponding early warning abnormal report.

In step S701, the history implementation record refers to a specific implementation situation of the early warning processing instruction, where the specific implementation situation includes an issuing situation of the early warning processing instruction and an early warning processing operation corresponding to a subsequent operation and maintenance person, and the early warning processing instruction is known to be inconsistent with the relevant early warning processing specification. If the early warning processing instruction is improper, the safety of the power system may be threatened, namely if the power grid early warning is misjudged, so that the power grid is subjected to processing or operation which does not meet the specification, and a larger range or more serious fault may be brought to the power grid.

In step S702 to step S703, the additional fault loss refers to that the early warning processing indicates that an additional fault or power grid performance loss occurs after corresponding maintenance of the corresponding early warning fault. If additional fault loss corresponding to the early warning processing instruction exists in the history implementation record, in order to trace the additional fault loss effectively, the loss rate corresponding to the current additional fault loss and the instruction issuing personnel corresponding to the early warning processing instruction are associated, and a corresponding early warning abnormal report is generated.

The loss assessment rate refers to the statistical rate of various losses such as personal casualties, property losses, economic influences and the like caused by additional fault losses, and is an important index for evaluating the risk of power grid faults and guiding power grid management. And then, the corresponding early warning abnormal report is generated by combining the indication corresponding to the early warning processing indication and issuing personnel, and the early warning processing indication which does not accord with the early warning processing specification can be traced back through the early warning abnormal report.

According to the power grid fault early warning method provided by the embodiment, after the historical implementation record corresponding to the early warning processing instruction is obtained, whether additional fault loss corresponding to the early warning processing instruction exists is judged, if the additional fault loss occurs, the loss rate corresponding to the additional fault loss and the early warning processing instruction issuing personnel are associated immediately, a corresponding early warning abnormal report is generated, and further the early warning processing condition including possible additional fault loss can be known more comprehensively through the early warning abnormal report, so that the root cause and the cause of the fault problem of the power system can be analyzed better.

The embodiment of the application discloses a power grid fault early warning system, as shown in fig. 8, comprising:

the early warning data acquisition module is used for acquiring corresponding early warning data in the target early warning items if the early warning processing instruction corresponding to the target early warning items does not accord with the early warning processing specification;

the correlation analysis module is used for judging whether correlation exists among the early warning data if the data types of the early warning data are multiple;

the fault grade calibration module is used for acquiring fault calibration grades corresponding to all the early warning data if no correlation exists between the early warning data;

and the second indication module is used for generating a corresponding fault processing ordering scheme as a target early warning processing indication according to the fault calibration level.

According to the power grid fault early warning system provided by the embodiment, the power grid node data are obtained according to the fault prediction module and are subjected to fault model prediction, the fault condition of the power grid node, namely, the prediction result, can be rapidly and accurately predicted, then the prediction result is subjected to compliance judgment according to the preset fault early warning standard through the early warning output module, the target early warning item with the fault of the current power grid is output, through system evaluation analysis, if the corresponding early warning processing indication of the target early warning item does not accord with the specification, the suitability between the currently-issued early warning operation indication and the target early warning item is poor, then the target early warning item is subjected to adaptability analysis again, namely, the correlation between specific early warning data in the target early warning item is analyzed through the early warning data acquisition module, and the corresponding target early warning processing indication is generated through the first indication module or the second indication module in combination with the historical correlation processing condition of the early warning data. Due to the suitability evaluation of the early warning data and the corresponding early warning processing instructions of the power system, the power grid fault can be processed more quickly and scientifically by related staff, the situation of missed judgment or misjudgment is reduced, and the accuracy of the early warning instructions in the power grid fault is improved.

It should be noted that, the power grid fault early warning system provided by the embodiment of the present application further includes each module and/or the corresponding sub-module corresponding to the logic function or the logic step of any one of the foregoing power grid fault early warning methods, so that the same effects as each logic function or logic step are achieved, and detailed descriptions thereof are omitted herein.

The embodiment of the application also discloses a terminal device which comprises a memory, a processor and computer instructions which are stored in the memory and can run on the processor, wherein when the processor executes the computer instructions, any power grid fault early warning method in the embodiment is adopted.

The terminal device may be a computer device such as a desktop computer, a notebook computer, or a cloud server, and the terminal device includes, but is not limited to, a processor and a memory, for example, the terminal device may further include an input/output device, a network access device, a bus, and the like.

The processor may be a Central Processing Unit (CPU), or of course, according to actual use, other general purpose processors, digital Signal Processors (DSP), application Specific Integrated Circuits (ASIC), ready-made programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., and the general purpose processor may be a microprocessor or any conventional processor, etc., which is not limited in this respect.

The memory may be an internal storage unit of the terminal device, for example, a hard disk or a memory of the terminal device, or an external storage device of the terminal device, for example, a plug-in hard disk, a Smart Memory Card (SMC), a secure digital card (SD), or a flash memory card (FC) provided on the terminal device, or the like, and may be a combination of the internal storage unit of the terminal device and the external storage device, where the memory is used to store computer instructions and other instructions and data required by the terminal device, and the memory may be used to temporarily store data that has been output or is to be output, which is not limited by the present application.

Any one of the power grid fault early warning methods in the embodiment is stored in the memory of the terminal device through the terminal device, and is loaded and executed on the processor of the terminal device, so that the power grid fault early warning method is convenient to use.

The embodiment of the application also discloses a computer readable storage medium, and the computer readable storage medium stores computer instructions, wherein when the computer instructions are executed by a processor, any power grid fault early warning method in the embodiment is adopted.

The computer instructions may be stored in a computer readable medium, where the computer instructions include computer instruction codes, where the computer instruction codes may be in a source code form, an object code form, an executable file form, or some middleware form, etc., and the computer readable medium includes any entity or device capable of carrying the computer instruction codes, a recording medium, a usb disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a Random Access Memory (RAM), an electrical carrier signal, a telecommunication signal, a software distribution medium, etc., where the computer readable medium includes but is not limited to the above components.

Any one of the power grid fault early warning methods in the above embodiments is stored in the computer readable storage medium through the computer readable storage medium, and is loaded and executed on the processor, so as to facilitate the storage and application of the method.

The above embodiments are not intended to limit the scope of the present application, so: all equivalent changes in structure, shape and principle of the application should be covered in the scope of protection of the application.

Claims

1. The power grid fault early warning method is characterized by comprising the following steps of:

2. The power grid fault early warning method according to claim 1, wherein if correlation exists between the early warning data, obtaining a corresponding associated fault item, and generating a target early warning processing indication by combining a history processing indication corresponding to the associated fault item comprises the following steps:

3. The grid fault early warning method according to claim 2, wherein after the step of obtaining the integrity of the early warning information corresponding to each of the historical processing instructions if the same associated fault item corresponds to a plurality of the historical processing instructions, the method further comprises the steps of:

4. The power grid fault early warning method according to claim 3, further comprising the following steps after generating an instruction loss feedback report corresponding to the information loss item according to the information to be captured of the instruction loss if the loss detection type is instruction loss:

5. The power grid fault early warning method according to claim 1, wherein if there is no correlation between the early warning data, obtaining the fault calibration level corresponding to each of the early warning data comprises the following steps:

6. The power grid fault early warning method according to claim 1, wherein the generating a corresponding fault handling ordering scheme as the target early warning processing indication according to the fault calibration level comprises the following steps:

7. The grid fault early warning method according to claim 6, further comprising the following steps after obtaining the early warning data corresponding to the target early warning item if the early warning processing indication corresponding to the target early warning item does not meet the early warning processing specification:

8. A grid fault early warning system, comprising:

9. A terminal device comprising a memory and a processor, wherein the memory stores computer instructions executable on the processor, and wherein the processor, when loaded and executing the computer instructions, employs a grid fault warning method according to any one of claims 1 to 7.

10. A computer readable storage medium having stored therein computer instructions, wherein the computer instructions, when loaded and executed by a processor, employ a grid fault warning method according to any one of claims 1 to 7.