CN117374976B - Electrical safety management system based on automatic line fault identification - Google Patents
Electrical safety management system based on automatic line fault identification Download PDFInfo
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Abstract
The invention relates to the technical field of electric safety management, in particular to an electric safety management system based on automatic line fault identification. The power failure state monitoring system comprises a prediction result output module and a power failure state monitoring module. According to the invention, the type of the fault data of the current power equipment, the priority of each corresponding triggering reason and the position of the power monitoring equipment are combined through the prediction result output module, the power equipment with faults and the corresponding triggering reason are predicted, the operation state of the current predicted power equipment with faults is monitored through the power fault state monitoring module, the similarity between the operation state and the triggering reason is judged, the final fault reason is determined, the prediction accuracy of the fault reason is improved, the prediction error is reduced, meanwhile, the equipment which is configured on the power equipment is started after the power equipment with faults is positioned, the response frequency of the equipment is reduced, the generation of redundant data is reduced, and the operation space of a power system is relieved.
Description
Technical Field
The invention relates to the technical field of electric safety management, in particular to an electric safety management system based on automatic line fault identification.
Background
Because of the wide application of electric energy, the electric safety has wide application, and the electric safety is not separated from the production field or the life field, and various electric safety problems can be met. The electric safety has the comprehensive characteristics, and is closely related to the electric industry, and is closely related to various industries such as construction, coal, metallurgy, petroleum, chemical industry, machinery and the like; moreover, the electric safety work has the engineering technology side and the organization management side, the electric operation is the industry with high danger and frequent accidents, and the necessary safety education for electric operators is particularly important, so that the safety guarantee measures for construction must be taken, in the electric safety management process, the operators are required to strictly execute operation specifications, meanwhile, the electric equipment is required to be overhauled and checked regularly, and the fault reasons are required to be positioned in time when the equipment faults are found.
In the operation process of the power engineering, a plurality of pieces of power equipment are required to be matched with each other, and as each piece of power equipment is mutually influenced, when one piece of power equipment breaks down, the normal operation of the whole power system is easily influenced, so that each piece of broken down power equipment is required to be checked in time to determine the fault cause, most of the existing power system checks the operation state of each piece of power equipment through the monitoring instrument which is arranged on each piece of power equipment in real time, the monitoring data generated by the power equipment are basically the same in the conventional monitoring mode, a large amount of space is required to be consumed for existence of redundant data in the operation process of the power equipment, the memory space of the power system is influenced, and meanwhile, when the abnormal data is acquired in time, the abnormal data analysis is also required to be carried out to determine the related abnormal data judgment, so that the final fault cause can be determined.
In order to address the above problems, there is a need for an electrical safety management system based on automatic line fault identification.
Disclosure of Invention
The invention aims to provide an electric safety management system based on automatic line fault identification so as to solve the problems in the background art.
In order to achieve the above purpose, an electrical safety management system based on automatic line fault identification is provided, which comprises a power fault data acquisition module, a monitoring equipment positioning module, a fault type judging module, a fault cause analysis model planning module, a prediction result output module, a power fault state monitoring module and a fault cause output module;
the power failure data acquisition module acquires power operation data by adopting power monitoring equipment and records power equipment failure data;
the output end of the power failure data acquisition module is connected with the input end of the monitoring equipment positioning module, and the monitoring equipment positioning module is used for determining the position of each power monitoring equipment and carrying out marking processing according to the position;
the output end of the power failure data acquisition module is also connected with the input end of the failure type judging module, and the failure type judging module is used for determining the type of the power equipment failure data monitored by each marked power monitoring device by combining the power equipment failure data;
the output end of the fault type judging module is connected with the input end of the fault cause analysis model planning module, the fault cause analysis model planning module is used for collecting the triggering reasons corresponding to the fault data of the power equipment of various types generated in the industry, calculating the occurrence rate of each triggering reason corresponding to the fault data of the same power equipment, determining the priority of each triggering reason according to the occurrence rate, and establishing a fault cause analysis model;
the output end of the fault cause analysis model planning module is connected with the input end of the prediction result output module, and the prediction result output module predicts the power equipment with faults and the corresponding trigger causes by combining the type of the fault data of the current power equipment, the corresponding priority of each trigger cause and the position of the power monitoring equipment;
the input end of the prediction result output module is connected with the output end of the power failure state monitoring module, the power failure state monitoring module is used for monitoring the running state of the currently predicted power equipment with failure, judging the similarity between the running state and the triggering reason, and determining the final failure reason;
the output end of the prediction result output module is connected with the input end of the fault reason output module, and the fault reason output module is used for outputting the final fault reason and transmitting the final fault reason to an maintainer.
As a further improvement of the technical scheme, the power fault data acquisition module comprises various normal detection threshold planning units and a fault data identification unit, wherein the various normal detection threshold planning units are used for determining various data thresholds in the normal operation process of various power equipment, the output ends of the various normal detection threshold planning units and the fault data identification unit plan the duration of acquired data values exceeding the data thresholds, the duration is marked as threshold time, and the acquired data values corresponding to the threshold time exceeding the duration are marked as fault data.
As a further improvement of the technical scheme, the input end of the fault data identification unit is connected with a fluctuation amplitude planning unit, and the fluctuation amplitude planning unit is used for planning a normal fluctuation range corresponding to each numerical value of the power equipment.
As a further improvement of the technical scheme, the monitoring equipment positioning module comprises a marking mode planning unit, each electric power composition classifying unit and a marking point storage unit, wherein the marking mode planning unit is used for making a corresponding electric power equipment marking scheme, the output end of the marking mode planning unit is connected with the input end of each electric power composition classifying unit, each electric power composition classifying unit classifies each electric power equipment according to the working content of each electric power equipment in the electric power system, marks each electric power equipment according to the made electric power equipment marking scheme, then marks each electric power equipment according to the position of the electric power monitoring equipment, the output end of each electric power composition classifying unit is connected with the input end of the marking point storage unit, and the marking point storage unit is used for binding each classified electric power equipment with the corresponding marking.
As a further improvement of the technical scheme, the method for marking the power monitoring equipment by the monitoring equipment positioning module comprises the following steps:
s1, determining the operation sequence of each component power equipment in a power system;
s2, sequentially marking according to the operation sequence of each component power equipment, and determining the labels of each component power equipment;
s3, positioning the positions of the power monitoring devices, and determining the adjacent power devices and corresponding labels;
and S4, carrying out adaptive labeling on the current power monitoring equipment by combining the adjacent power equipment with the labels of the power monitoring equipment.
As a further improvement of the technical scheme, the fault cause analysis model planning module comprises an initiation cause analysis unit, an initiation cause occurrence rate calculation unit and a training model planning unit, wherein the initiation cause analysis unit is used for collecting initiation causes corresponding to fault data of all types of power equipment, the output end of the initiation cause analysis unit is connected with the input end of the initiation cause occurrence rate calculation unit, the initiation cause occurrence rate calculation unit is used for calculating the occurrence rate of all the initiation causes corresponding to the fault data of the power equipment, the output end of the initiation cause occurrence rate calculation unit is connected with the input end of the training model planning unit, and the training model planning unit is used for combining the occurrence rate of all the initiation causes, training priorities corresponding to all the initiation causes and establishing a fault cause analysis model.
As a further improvement of the technical scheme, the power failure state monitoring module comprises a detection device response unit and a similarity comparison unit, wherein the detection device response unit is combined with the predicted position of the failed power device, responds to the detection device at the corresponding position, monitors the running state of the current failed power device, the output end of the detection device response unit is connected with the input end of the similarity comparison unit, and the similarity comparison unit compares the running state with the similarity of the triggering reason, determines a similarity value, plans a similarity threshold value and judges the final failure reason.
As a further improvement of the technical scheme, the similarity comparison unit compares the similarity of the running state and the triggering reason by adopting a threshold comparison algorithm, and the algorithm formula is as follows:
；
；
；
wherein the method comprises the steps ofFor each feature set corresponding to the operating state detected by the detection device,/->To->For detecting the respective characteristic of the operating state detected by the device, < > for the respective characteristic>For each feature set corresponding to the predicted cause, < ->To->For each characteristic corresponding to the predicted cause of initiation, < ->For the threshold comparison function, Q is the similarity value of the running state and the initiation reason,for the similarity threshold, when the similarity value Q of the running state and the cause is lower than the similarity threshold +.>Time thresholdValue comparison functionThe output is 0, which indicates that the initiation cause of the prediction is inaccurate, the prediction needs to be re-performed, and when the similarity value Q of the running state and the initiation cause is not lower than the similarity threshold value +.>At the time, threshold comparison function->The output is 1, which indicates that the predicted cause is accurate and is the final fault cause.
As a further improvement of the technical scheme, the output end of the fault cause output module is connected with a database storage module, and the database storage module is used for establishing a classification database and storing the prediction result and the prediction flow.
Compared with the prior art, the invention has the beneficial effects that:
in the electrical safety management system based on automatic line fault identification, the type of fault data of the current power equipment, the priority of each corresponding triggering reason and the position of the power monitoring equipment are combined through the prediction result output module, the power equipment with faults and the corresponding triggering reason are predicted, the operation state of the current predicted power equipment with faults is monitored through the power fault state monitoring module, the similarity between the operation state and the triggering reason is judged, the final fault reason is determined, the prediction accuracy of the fault reason is improved, the prediction error is reduced, meanwhile, after the power equipment with faults is positioned, the equipment to be inspected on the power equipment is started, the response frequency of the equipment to be inspected is reduced, the generation of redundant data is reduced, and the operation space of the power system is relieved.
Drawings
FIG. 1 is a block diagram of the overall structure of the present invention;
FIG. 2 is a block diagram of a power failure data collection module according to the present invention;
FIG. 3 is a block diagram of a monitoring device positioning module according to the present invention;
FIG. 4 is a block diagram of a fault cause analysis model planning module according to the present invention;
fig. 5 is a block diagram of a power failure state monitoring module according to the present invention.
The meaning of each reference sign in the figure is:
10. the power failure data acquisition module; 110. each normal detection threshold planning unit; 120. a failure data identification unit; 130. a fluctuation amplitude planning unit;
20. a monitoring device positioning module; 210. a marking mode planning unit; 220. each electric power constitutes a classification unit; 230. a mark point storage unit;
30. a fault type judging module;
40. a fault cause analysis model planning module; 410. an initiation cause analysis unit; 420. an initiation cause occurrence rate calculation unit; 430. training a model planning unit;
50. a prediction result output module;
60. a power failure state monitoring module; 610. a detection device response unit; 620. a similarity comparison unit;
70. a failure cause output module;
80. the database stores the modules.
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 to 5, an electrical safety management system based on automatic line fault identification is provided, which includes a power fault data acquisition module 10, a monitoring device positioning module 20, a fault type judging module 30, a fault cause analysis model planning module 40, a prediction result output module 50, a power fault state monitoring module 60 and a fault cause output module 70;
the power failure data acquisition module 10 acquires power operation data by adopting power monitoring equipment and records power equipment failure data;
the output end of the power failure data acquisition module 10 is connected with the input end of the monitoring equipment positioning module 20, and the monitoring equipment positioning module 20 is used for determining the position of each power monitoring equipment and carrying out marking processing according to the position;
the output end of the power failure data acquisition module 10 is also connected with the input end of the failure type judging module 30, and the failure type judging module 30 combines the power equipment failure data to determine the type of the power equipment failure data monitored by each marked power monitoring equipment;
the output end of the fault type judging module 30 is connected with the input end of the fault cause analysis model planning module 40, and the fault cause analysis model planning module 40 is used for collecting the triggering reasons corresponding to the fault data of the power equipment of various types in the industry, calculating the occurrence rate of each triggering reason corresponding to the fault data of the same power equipment, determining the priority of each triggering reason according to the occurrence rate, and establishing a fault cause analysis model;
the output end of the fault cause analysis model planning module 40 is connected with the input end of the prediction result output module 50, and the prediction result output module 50 predicts the power equipment with faults and the corresponding trigger causes by combining the type of the fault data of the current power equipment, the corresponding priority of each trigger cause and the position of the power monitoring equipment;
the input end of the prediction result output module 50 is connected with the output end of the power failure state monitoring module 60, the power failure state monitoring module 60 is used for monitoring the running state of the currently predicted power equipment with failure, judging the similarity between the running state and the initiation reason, and determining the final failure reason;
the output end of the prediction result output module 50 is connected with the input end of the failure reason output module 70, and the failure reason output module 70 is used for outputting the final failure reason and transmitting the final failure reason to an maintainer.
When the system is particularly used, a plurality of pieces of power equipment are required to be matched with each other in the operation process of the power engineering, as each piece of power equipment is mutually influenced, when one piece of power equipment breaks down and the normal operation of the whole power system is easily influenced, each piece of broken down power equipment is required to be checked in time to determine the fault cause, most of the conventional power system is provided with monitoring instruments for each piece of power equipment, the operation state of each piece of power equipment is monitored in real time through the monitoring instruments, the monitoring data generated by the power equipment are basically the same in the conventional monitoring mode, a large amount of space is required to be consumed for existence of redundant data in the operation process of the system, the memory space of the power system is influenced, and meanwhile, when abnormal data are timely acquired, abnormal data analysis is also required, and the mutual associated abnormal data judgment is determined, so that the final fault cause can be determined;
in order to cope with the above problems, during the troubleshooting process of the power engineering, firstly, the power operation data is collected by adopting the power monitoring equipment through the power failure data collection module 10, the power equipment failure data is recorded, namely, the power equipment failure data exceeds the threshold value in the normal operation process, and in order to facilitate the later positioning of the position of the failed power equipment, before the troubleshooting process of the power engineering, the positions of the power monitoring equipment are required to be determined through the monitoring equipment positioning module 20, marking processing is carried out according to the positions, the power equipment monitored by the power monitoring equipment at different positions is definite, the collected data sources can be positioned, then, the power failure data collection module 10 transmits the collected power equipment failure data to the failure type judgment module 30, and the failure type judgment module 30 combines the power equipment failure data to determine the types of the power equipment failure data monitored by the power monitoring equipment after marking, such as unstable voltage, abnormal temperature change or unstable current;
because the types of the fault data of each power equipment are different, in order to be able to predict the trigger reasons corresponding to the fault data of the power equipment, the fault analysis model planning module 40 needs to collect the trigger reasons corresponding to the fault data of each type of power equipment in the industry, calculate the occurrence rate of each trigger reason corresponding to the fault data of the same power equipment, determine the priority of each trigger reason according to the occurrence rate, establish a fault cause analysis model, for example, when the monitored power equipment has abnormal temperature, the trigger reasons causing abnormal temperature include excessive load of the power equipment, blockage of a heat dissipation port and external temperature change, collect the occurrence rate of each trigger reason of abnormal temperature in the previous monitoring process, determine the priority of each trigger reason according to the occurrence rate, take the trigger reason with the largest priority as a prediction result, and then the prediction result output module 50 combines the type of the fault data of the current power equipment, the corresponding trigger reason and the position of the power monitoring equipment to predict the power equipment with the priority and the corresponding trigger reason;
in order to verify the accuracy of the prediction result, the operation state of the currently predicted power equipment with fault needs to be monitored by the power fault state monitoring module 60, the similarity between the operation state and the cause is judged, the final fault cause is determined, the power equipment with fault can be positioned by combining the position of the power monitoring equipment, at the moment, the power equipment operation state checking equipment is started, the operation state of the currently damaged power equipment is determined, for example, the displayed values of the power equipment operation state are determined, at the moment, the operation state of the power equipment and the cause are compared, the final fault cause is determined, and the final fault cause is output by the final fault cause output module 70 and transmitted to an maintainer.
According to the invention, the type of the fault data of the current power equipment, the priority of each corresponding triggering reason and the position of the power monitoring equipment are combined through the prediction result output module 50, the power equipment with faults and the corresponding triggering reason are predicted, the operation state of the current predicted power equipment with faults is monitored through the power fault state monitoring module 60, the similarity between the operation state and the triggering reason is judged, the final fault reason is determined, the prediction accuracy of the fault reason is improved, the prediction error is reduced, meanwhile, the equipment which is configured on the power equipment is started after the power equipment with faults is positioned, the response frequency of the equipment is reduced, the generation of redundant data is reduced, and the operation space of a power system is relieved.
In addition, the power failure data collection module 10 includes each normal detection threshold planning unit 110 and a failure data identification unit 120, each normal detection threshold planning unit 110 is configured to determine each data threshold value in the normal operation process of each power device, each normal detection threshold planning unit 110 output end and the failure data identification unit 120 plan the duration of the collected data value exceeding the data threshold value, the duration of the collected data value corresponding to the exceeding threshold value is marked as the threshold value time, during the failure data collection process, each data threshold value in the normal operation process of each power device is firstly determined through each normal detection threshold planning unit 110, namely, each value change in the normal operation of the power system is maintained, then the duration of the collected data value exceeding the data threshold value is planned through the failure data identification unit 120, the collected data value corresponding to the exceeding threshold value time is marked as the failure data, thereby improving the positioning accuracy of the failure data and reducing the influence of accidental data on the failure data.
Since the operation data of each electric power equipment in the electric power system also fluctuates in a normal state, the operation data are all normal phenomena, in order to prevent the normal values from being misjudged into fault data in the later period, furthermore, the input end of the fault data identification unit 120 is connected with the fluctuation amplitude planning unit 130, the fluctuation amplitude planning unit 130 is used for planning the normal fluctuation range corresponding to each value of the electric power equipment, the normal fluctuation range corresponding to each value of the electric power equipment is required to be planned through the fluctuation amplitude planning unit 130, the acquired data values measured when the fluctuation time exceeds the normal fluctuation range are marked as fault data, and the fault data positioning accuracy is further improved.
Still further, the monitoring device positioning module 20 includes a marking mode planning unit 210, each electric power component classifying unit 220, and a marking point storage unit 230, where the marking mode planning unit 210 is used to make a corresponding marking scheme of the electric power device, an output end of the marking mode planning unit 210 is connected with an input end of each electric power component classifying unit 220, each electric power component classifying unit 220 classifies each electric power device according to the working content of each electric power device in the electric power system, and performs marking processing on each electric power device according to the formulated marking scheme of the electric power device, and then performs marking processing on each electric power device according to a position where the electric power monitoring device is located, and each electric power component classifying unit 220 output end is connected with an input end of the marking point storage unit 230, where the marking point storage unit 230 is used to bind each classified electric power device and a corresponding mark.
Specifically, the method for marking the power monitoring device by the monitoring device positioning module 20 includes the following steps:
s1, determining the operation sequence of each component power equipment in a power system;
s2, sequentially marking according to the operation sequence of each component power equipment, and determining the labels of each component power equipment;
s3, positioning the positions of the power monitoring devices, and determining the adjacent power devices and corresponding labels;
and S4, carrying out adaptive labeling on the current power monitoring equipment by combining the adjacent power equipment with the labels of the power monitoring equipment.
In the process of marking the power monitoring equipment, firstly, determining the operation sequence of each component power equipment in the power system, namely, the data transmission flow sequence, sequentially marking according to the operation sequence of each component power equipment, determining the labels of each component power equipment, then positioning the positions of each power monitoring equipment, determining the adjacent power equipment and the corresponding labels, combining the adjacent power equipment with the labels, carrying out adaptive labels on the current power monitoring equipment, for example, the current power monitoring equipment is arranged on a connecting line between two power equipment, wherein the labels of the two power equipment are respectivelyAnd +.>At this point the power monitoring device is marked +.>When two different power monitoring devices exist between the two power devices, marking the two different power monitoring devices as the power forming devices according to the operation sequence of the power forming devicesAnd +.>。
In addition, the fault cause analysis model planning module 40 includes a cause analysis unit 410, a cause occurrence rate calculation unit 420, and a training model planning unit 430, where the cause analysis unit 410 is configured to collect causes corresponding to each type of power equipment fault data, the output end of the cause analysis unit 410 is connected to the input end of the cause occurrence rate calculation unit 420, the cause occurrence rate calculation unit 420 is configured to calculate each cause occurrence rate corresponding to the power equipment fault data, the output end of the cause occurrence rate calculation unit 420 is connected to the input end of the training model planning unit 430, the training model planning unit 430 is configured to combine each cause occurrence rate, train priorities corresponding to each cause, and establish a fault cause analysis model, and in the process of establishing a fault cause analysis model, first, the cause analysis unit 410 is required to collect causes corresponding to each type of power equipment fault data, for example, the causes causing abnormal temperature include excessive load, blockage of the heat dissipation ports, and external temperature change of the power equipment, then the cause occurrence rate calculation unit 420 calculates each cause occurrence rate corresponding to the power equipment fault data, i.e., unit time, the same number of occurrence of times of power equipment fault data, calculate the number of times of the same power equipment fault data, and calculate the occurrence rate corresponding to the priority of the cause analysis model corresponding to each cause occurrence rate, and the cause analysis model is established according to the priorities of the priority of the cause occurrence rate.
Since the detection devices added to each power device are not continuously started, in order to reduce the generation of redundant data, further, the power failure state monitoring module 60 includes a detection device response unit 610 and a similarity comparison unit 620, where the detection device response unit 610 combines with the predicted position of the power device with failure, and responds to the detection device at the corresponding position, to monitor the operation state of the power device with failure currently, the output end of the detection device response unit 610 is connected with the input end of the similarity comparison unit 620, the similarity comparison unit 620 compares the operation state with the similarity of the cause, determines a similarity value, plans a similarity threshold, determines the final failure cause, combines with the predicted position of the power device with failure by the detection device response unit 610, responds to the detection device at the corresponding position, monitors the operation state of the power device with failure currently, then the similarity comparison unit 620 compares the operation state with the similarity of the cause, determines a similarity value, and plans the similarity threshold, and determines the final failure cause.
Still further, the similarity comparison unit 620 compares the running state with the similarity of the cause of the initiation by using a threshold comparison algorithm, and the algorithm formula is as follows:
；
；
；
wherein the method comprises the steps ofFor each feature set corresponding to the operating state detected by the detection device,/->To->For detecting the respective characteristics of the operating state detected by the device, such as temperature rise, current instability and voltage instability, etc. +.>For each feature set corresponding to the predicted cause, < ->To->For each characteristic corresponding to the predicted cause of initiation, < ->Q is the similarity value between the running state and the initiation cause as the threshold comparison function, and +.>For the similarity threshold, when the similarity value Q of the running state and the cause is lower than the similarity threshold +.>At the time, threshold comparison function->The output is 0, which indicates that the initiation cause of the prediction is inaccurate, the prediction needs to be re-performed, and when the similarity value Q of the running state and the initiation cause is not lower than the similarity threshold value +.>At the time, threshold comparison function->The output is 1, which indicates that the predicted cause is accurate and is the final fault cause.
In addition, the output end of the failure cause output module 70 is connected with a database storage module 80, the database storage module 80 is used for establishing a classification database to store the prediction result and the prediction flow, after the power failure cause prediction work is completed, the classification database is established through the database storage module 80 to store the monitoring data of each power monitoring device in the prediction process, the final prediction result is bound, and when the failure under the same condition is encountered in the later stage, only the data prestored in the database storage module 80 is required to be called, the prediction result is directly obtained, the system response time is reduced, and the prediction efficiency is improved.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. Electric safety management system based on line fault automatic identification, its characterized in that: the system comprises a power failure data acquisition module (10), a monitoring equipment positioning module (20), a failure type judging module (30), a failure cause analysis model planning module (40), a prediction result output module (50), a power failure state monitoring module (60) and a failure cause output module (70);
the power failure data acquisition module (10) acquires power operation data by adopting power monitoring equipment and records power equipment failure data;
the output end of the power failure data acquisition module (10) is connected with the input end of the monitoring equipment positioning module (20), and the monitoring equipment positioning module (20) is used for determining the position of each power monitoring equipment and carrying out marking processing according to the position;
the output end of the power failure data acquisition module (10) is also connected with the input end of the failure type judging module (30), and the failure type judging module (30) is used for determining the type of the power equipment failure data monitored by each marked power monitoring equipment by combining the power equipment failure data;
the output end of the fault type judging module (30) is connected with the input end of the fault cause analysis model planning module (40), the fault cause analysis model planning module (40) is used for collecting the trigger causes corresponding to the fault data of the power equipment of various types in the industry, calculating the occurrence rate of each trigger cause corresponding to the fault data of the same power equipment, determining the priority of each trigger cause according to the occurrence rate, and establishing a fault cause analysis model;
the output end of the fault cause analysis model planning module (40) is connected with the input end of the prediction result output module (50), and the prediction result output module (50) predicts the power equipment with faults and the corresponding trigger causes by combining the type of the fault data of the current power equipment, the corresponding trigger cause priority and the position of the power monitoring equipment;
the input end of the prediction result output module (50) is connected with the output end of the power failure state monitoring module (60), the power failure state monitoring module (60) is used for monitoring the running state of the currently predicted power equipment with failure, judging the similarity between the running state and the initiation reason, and determining the final failure reason;
the output end of the prediction result output module (50) is connected with the input end of the fault reason output module (70), and the fault reason output module (70) is used for outputting the final fault reason and transmitting the final fault reason to an maintainer;
the monitoring equipment positioning module (20) comprises a marking mode planning unit (210), each electric power composition classifying unit (220) and a marking point storage unit (230), wherein the marking mode planning unit (210) is used for making a corresponding electric power equipment marking scheme, the output end of the marking mode planning unit (210) is connected with the input end of each electric power composition classifying unit (220), each electric power composition classifying unit (220) classifies each electric power equipment according to the working content of each electric power equipment in the electric power system, each electric power equipment is marked according to the made electric power equipment marking scheme, then the marking is carried out on each electric power equipment according to the position where the electric power monitoring equipment is located, the output end of each electric power composition classifying unit (220) is connected with the input end of the marking point storage unit (230), and the marking point storage unit (230) is used for binding each classified electric power equipment with the corresponding marking;
the method for marking the power monitoring equipment by the monitoring equipment positioning module (20) comprises the following steps:
s1, determining the operation sequence of each component power equipment in a power system;
s2, sequentially marking according to the operation sequence of each component power equipment, and determining the labels of each component power equipment;
s3, positioning the positions of the power monitoring devices, and determining the adjacent power devices and corresponding labels;
and S4, carrying out adaptive labeling on the current power monitoring equipment by combining the adjacent power equipment with the labels of the power monitoring equipment.
2. The electrical safety management system based on automatic line fault identification of claim 1, wherein: the power fault data acquisition module (10) comprises various normal detection threshold planning units (110) and a fault data identification unit (120), wherein the various normal detection threshold planning units (110) are used for determining various data thresholds in the normal operation process of various power equipment, the output ends of the various normal detection threshold planning units (110) and the fault data identification unit (120) plan the duration of acquired data values exceeding the data thresholds, the duration is marked as threshold time, and the acquired data values corresponding to the threshold time exceeding the duration are marked as fault data.
3. The electrical safety management system based on automatic line fault identification of claim 2, wherein: the input end of the fault data identification unit (120) is connected with a fluctuation amplitude planning unit (130), and the fluctuation amplitude planning unit (130) is used for planning a normal fluctuation range corresponding to each numerical value of the power equipment.
4. The electrical safety management system based on automatic line fault identification of claim 1, wherein: the fault cause analysis model planning module (40) comprises an initiation cause analysis unit (410), an initiation cause occurrence rate calculation unit (420) and a training model planning unit (430), wherein the initiation cause analysis unit (410) is used for collecting initiation causes corresponding to fault data of various types of power equipment, the output end of the initiation cause analysis unit (410) is connected with the input end of the initiation cause occurrence rate calculation unit (420), the initiation cause occurrence rate calculation unit (420) is used for calculating various initiation cause occurrence rates corresponding to the fault data of the power equipment, the output end of the initiation cause occurrence rate calculation unit (420) is connected with the input end of the training model planning unit (430), and the training model planning unit (430) is used for combining various initiation cause occurrence rates, training priorities corresponding to various initiation causes and establishing a fault cause analysis model.
5. The electrical safety management system based on automatic line fault identification of claim 1, wherein: the power failure state monitoring module (60) comprises a detection device response unit (610) and a similarity comparison unit (620), wherein the detection device response unit (610) is combined with the predicted position of the failed power device, responds to the detection device at the corresponding position, monitors the running state of the power device which is failed currently, the output end of the detection device response unit (610) is connected with the input end of the similarity comparison unit (620), and the similarity comparison unit (620) compares the running state with the similarity of the cause, determines a similarity value, plans a similarity threshold value and judges the final failure cause.
6. The electrical safety management system based on automatic line fault identification of claim 5, wherein: the similarity comparison unit (620) adopts a threshold comparison algorithm for comparing the similarity between the running state and the cause, and the algorithm formula is as follows:
，
，
，
wherein the method comprises the steps ofFor each feature set corresponding to the operating state detected by the detection device,/->To->For detecting the respective characteristic of the operating state detected by the device, < > for the respective characteristic>For each feature set corresponding to the predicted cause, < ->To->For each characteristic corresponding to the predicted cause of initiation, < ->For threshold comparison function, ++>For the similarity value of the operating state and the cause of the initiation, < + >>For similarity threshold, when the running state and the cause similarity value are +.>Below the similarity threshold +.>At the time, threshold comparison function->The output is 0, which indicates that the predicted cause is inaccurate, the prediction needs to be re-performed, and the similarity value between the running state and the cause is +.>Not lower than the similarity threshold +.>At the time, threshold comparison function->The output is 1, which indicates that the predicted cause is accurate and is the final fault cause.
7. The electrical safety management system based on automatic line fault identification of claim 1, wherein: the output end of the failure cause output module (70) is connected with a database storage module (80), and the database storage module (80) is used for establishing a classification database and storing the prediction result and the prediction flow.
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