CN117150418B - Transformer operation detection period formulation method and system based on state characteristic fault tree - Google Patents

Transformer operation detection period formulation method and system based on state characteristic fault tree Download PDF

Info

Publication number
CN117150418B
CN117150418B CN202311421404.3A CN202311421404A CN117150418B CN 117150418 B CN117150418 B CN 117150418B CN 202311421404 A CN202311421404 A CN 202311421404A CN 117150418 B CN117150418 B CN 117150418B
Authority
CN
China
Prior art keywords
similar
fault
node
priority
abnormality
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202311421404.3A
Other languages
Chinese (zh)
Other versions
CN117150418A (en
Inventor
黄桢
钱凯
周一挺
余军威
徐蕴镠
康权
陈凯
李子楠
邵志鹏
杨硕
王健楠
钱航
鄢雨辰
贺昌
陈珈颖
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Ningbo Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Original Assignee
Ningbo Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Ningbo Power Supply Co of State Grid Zhejiang Electric Power Co Ltd filed Critical Ningbo Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Priority to CN202311421404.3A priority Critical patent/CN117150418B/en
Publication of CN117150418A publication Critical patent/CN117150418A/en
Application granted granted Critical
Publication of CN117150418B publication Critical patent/CN117150418B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • G06F18/20Analysing
    • G06F18/24Classification techniques
    • G06F18/243Classification techniques relating to the number of classes
    • G06F18/2433Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • G06F18/20Analysing
    • G06F18/21Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • G06F18/20Analysing
    • G06F18/24Classification techniques
    • G06F18/243Classification techniques relating to the number of classes
    • G06F18/24323Tree-organised classifiers

Landscapes

  • Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Artificial Intelligence (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Testing Of Short-Circuits, Discontinuities, Leakage, Or Incorrect Line Connections (AREA)

Abstract

The invention provides a method and a system for formulating a transformer operation detection period based on a state characteristic fault tree, comprising the following steps: acquiring fault information of all transformers recorded in a power grid system and classifying the fault information to obtain a first classification result; establishing a state characteristic fault tree; acquiring a recording time point of similar fault information, and acquiring a time difference value of two adjacent similar fault information to obtain a similar interval; obtaining similar fault information in each category to obtain similar quantity; obtaining failure frequency according to the similar quantity and the similar interval; adding a safety condition point to each node, judging whether the current node has priority or not according to failure frequency, and obtaining a priority sequence; introducing the abnormal information into a characteristic fault tree, judging the node to which the abnormal information belongs, and judging whether the current abnormality needs to be comprehensively examined according to the priority of the node to which the abnormal information belongs; and performing fault troubleshooting sequencing on the daily inspection according to the priority sequence from high to low. The invention makes the inspection and barrier removal of the transformer more convenient.

Description

Transformer operation detection period formulation method and system based on state characteristic fault tree
Technical Field
The invention relates to the technical field of transformers, in particular to a method and a system for formulating a transformer operation and detection period based on a state characteristic fault tree.
Background
The transformer is a core device of a transformer substation, plays roles of voltage conversion, current conversion, voltage stabilization and the like, and has the advantages of high price of a single unit, high replacement cost and great economic loss caused by frequent failure and power failure. Therefore, it is necessary to periodically carry out inspection and maintenance, and to carry out maintenance or maintenance at the initial stage of abnormality or failure, but frequent inspection and maintenance at the present stage consumes a great deal of manpower and material resources, and meanwhile, most of the current inspection is not sequential, so that time and manpower and material resources are wasted on some unimportant problems, and the inspection process is longer.
Disclosure of Invention
Therefore, the embodiment of the invention provides a method and a system for formulating the operation and detection period of the transformer based on the state characteristic fault tree, so that the inspection and obstacle removal of the transformer are more convenient.
In order to solve the above problems, the present invention provides a method for formulating a transformer operation detection period based on a state feature fault tree, comprising: acquiring fault information of all transformers recorded in a power grid system, and classifying the fault information to obtain a first classification result; establishing a state characteristic fault tree according to the first classification result; acquiring a recording time point of similar fault information in each classification in the first classification result, and acquiring a time difference value of two adjacent similar fault information according to the recording time point to obtain a similar interval; obtaining the number of similar fault information in each category to obtain the similar number; obtaining failure frequency according to the similar quantity and the similar interval; adding a safety condition point to each node in the state characteristic fault tree, and judging whether the fault information represented by the current node has priority or not according to the failure frequency from top to bottom to obtain a priority sequence; acquiring current abnormality information, introducing the abnormality information into a characteristic fault tree, judging nodes to which the abnormality information belongs, and judging whether the current abnormality needs to be comprehensively examined according to the priorities of the nodes to which the abnormality information belongs; and performing fault troubleshooting sequencing on the daily inspection according to the priority sequence from high to low.
Compared with the prior art, the technical effect achieved by adopting the technical scheme is as follows: the fault information of all transformers is acquired through setting and classifying, the fault types of the transformers are extracted, data support is conveniently provided when the transformers are checked later, meanwhile, a state characteristic fault tree is set, fault analysis is enabled to be more comprehensive and deeper, time points for acquiring similar fault information in each classification are set, adjacent time difference values are acquired, further similar intervals are acquired, the occurrence frequency of similar faults can be known, the importance of the abnormality can be further known, information support is provided for the follow-up inspection successively, reference in inspection is enabled to be more scientific and practical, meanwhile, the number of similar faults is acquired, failure frequency is obtained through the similar number and the similar intervals, further whether faults represented by each node in the fault tree have inspection priority is judged through the failure frequency, judgment of the priority is enabled to be more practical, inspection can be conducted through the priority, the inspection process is enabled to be more convenient and rapid, meanwhile, the purpose is strong, meanwhile, due to the fact that the fault tree is set to conduct inspection and statistics, and the inspection is enabled to be more comprehensive due to the fact that the fault tree is located by means of the fact that the fault tree and the priority sequence is located at present.
In one example of the present invention, obtaining fault information of all transformers recorded in a power grid system, and classifying the fault information to obtain a first classification result includes: the classification basis of the fault information comprises the following steps: abnormal sound, abnormal voltage, abnormal oil temperature and abnormal oil level.
Compared with the prior art, the technical effect achieved by adopting the technical scheme is as follows: through setting up through sound and voltage and oil temperature and oil level to the trouble information classification for the trouble information can be more orderly, be convenient for follow-up establishment fault tree, and then make with the help of the follow-up judgement of fault tree more accurate and laminating reality, ensured the efficiency of follow-up inspection.
In one example of the present invention, obtaining a recording time point of the similar fault information in each category in the first category result, and obtaining a time difference value of two adjacent similar fault information according to the recording time point, where obtaining the similar interval further includes: performing first screening calculation on the time difference value; when the time difference values are all greater than or equal to a first threshold value, adding all obtained time difference values, taking an average value, and taking the average value as a similar interval; and when the time difference value is smaller than the first threshold value, taking the minimum value in all the time difference values as a similar interval.
Compared with the prior art, the technical effect achieved by adopting the technical scheme is as follows: the obtained time difference value is subjected to first screening calculation, and different time intervals are obtained through comparison of the time difference value and the first threshold value, when the time difference value is larger than or equal to the first threshold value, the current abnormal occurrence frequency is lower, and the current abnormal occurrence frequency is normal, so that an average value is taken as a similar interval, the similar interval cannot deviate from reality, the similar interval cannot be too high, further follow-up inspection establishment is more scientific and fit reality, meanwhile, when the time difference value is smaller than the first threshold value, the current abnormal occurrence condition is indicated, so that important attention is required, the minimum value is taken as the similar interval, the priority of the minimum value is increased, the follow-up inspection establishment can focus on the abnormal occurrence, the inspection is enabled to be stronger, and the inspection efficiency is improved.
In one example of the present invention, deriving the failure frequency based on the similar number and the similar interval further comprises: failure frequency s is calculated by equation 1: s=n/t+k; where n is a similar number, t is a similar interval, k is a failure error, and k is greater than 0.
Compared with the prior art, the technical effect achieved by adopting the technical scheme is as follows: the failure frequency is calculated according to a formula, meanwhile, the failure frequency is calculated by means of the proportion of the similar quantity and the similar interval, meanwhile, the failure error is added to enable the failure frequency to be more fit with the actual situation, meanwhile, the obtained failure frequency can be better improved in priority arrangement accuracy, and inspection efficiency is guaranteed.
In one example of the present invention, adding a security condition point to each node in the state feature fault tree, and determining, from top to bottom, according to the failure frequency, whether the class of fault information represented by the current node has a priority, where obtaining the priority sequence further includes: judging whether failure frequency corresponding to failure classification represented by the current node is greater than a safety threshold value from top to bottom; if the priority sequence is greater than the safety threshold, the priority sequence is arranged from large to small according to the failure frequency corresponding to each node; and if the priority sequence is smaller than the safety threshold, arranging the priority sequence according to the checking convenience, and arranging the priority sequence after the part larger than the safety threshold.
Compared with the prior art, the technical effect achieved by adopting the technical scheme is as follows: judging whether the node of the current fault classification is greater than a safety threshold value by means of failure frequency from top to bottom, further judging the priority of the current node, meanwhile, when the failure frequency is large, the current abnormal frequency is high, and the current abnormal frequency is easy to occur, so that key investigation is needed, frequent power failure is prevented, a large amount of economic loss is caused, whether the current node needs daily inspection and the sequence during inspection can be judged at maximum efficiency by comparing the failure frequency with the safety threshold value, the inspection efficiency is higher, the purposiveness is stronger, and the normal operation of the current transformer can be guaranteed to the greatest extent.
In one embodiment of the present invention, if the priority sequence is greater than the safety threshold, the step of arranging the priority sequence from the large to the small according to the failure frequency corresponding to each node further includes: if nodes with equal failure frequencies exist, counting the number of failure frequencies larger than a safety threshold under each large category according to a first classification result to obtain a first statistical result; and comparing the magnitudes of the first statistical results under the large classification corresponding to the nodes with equal failure frequencies, and sequencing the priority sequences according to the first statistical results from large to small.
Compared with the prior art, the technical effect achieved by adopting the technical scheme is as follows: when the condition that the failure frequencies are equal is set, the judgment is carried out by comparing the number of the failure frequencies under the large classification, the classification that the number of the failure frequencies is large is carried out firstly, and the number of the failure frequencies under the changed classification is large, so that the important anomalies under the current classification are more, the investigation is preferably carried out on the classification, the probability of occurrence of multiple anomalies can be reduced to the greatest extent, the stable operation of the transformer is ensured, the inspection efficiency is also improved, the inspection is not carried out blindly, and the inspection has better purposefulness.
In one embodiment of the present invention, determining whether the current anomaly needs to be comprehensively examined according to the priority of the node to which the current anomaly belongs further includes: if the position of the priority sequence of the node where the current abnormality is located is larger than a first safety limit, judging that the current abnormality needs to be comprehensively examined, and executing a first early warning operation; if the safety threshold is smaller than the first safety threshold, the rejection is normally performed.
Compared with the prior art, the technical effect achieved by adopting the technical scheme is as follows: when an abnormality occurs, the priority sequence position of the node to which the abnormality belongs is compared with a first safety limit to judge whether to perform first early warning operation, and because the importance degree of the abnormality is different, the unimportant abnormality does not need to be checked with a lot of attention, so that manpower and material resources are wasted, the abnormality with high importance degree is considered, the occurrence of the associated abnormality is prevented, economic loss is caused, resident life is influenced, the efficiency of the abnormality is higher through the arrangement of the first safety limit, and time is not wasted.
In one embodiment of the present invention, if the location of the priority sequence of the node where the current abnormality is located is greater than the first safety limit, determining that the current abnormality needs to be comprehensively examined, and executing the first early warning operation further includes: extracting all nodes in the priority sequence which are larger than the first safety limit, and acquiring the corresponding influence range when each node is abnormal to obtain a range limit value; performing secondary priority sequence arrangement on each node according to the size of the range limit value to obtain an investigation sequence; all nodes that are greater than the first security limit are inspected in an inspection sequence.
Compared with the prior art, the technical effect achieved by adopting the technical scheme is as follows: the nodes with the priority sequence larger than the first safety limit are extracted and the influence range is acquired through setting, so that each node is ordered according to the influence range, the priority of the nodes with the importance level higher than that of the nodes with the priority sequence is guaranteed to the greatest extent when the nodes with the priority sequence larger than the first safety limit are examined, namely the nodes with the greatest influence are examined preferentially, the loss caused by abnormality is reduced to the greatest extent, the normal operation of the transformer is guaranteed, and meanwhile the inspection efficiency is improved.
In one example of the present invention, the scope of influence is defined in terms of the repair time required when the anomaly corresponding to the current node occurs.
Compared with the prior art, the technical effect achieved by adopting the technical scheme is as follows: the abnormal maintenance time is used as a judgment basis, so that the sequencing basis is more fit with the actual situation, and the influence on residents is greater as the loss is higher as the maintenance time is longer, the judgment is carried out through the maintenance time, the normal operation of the transformer can be guaranteed to the greatest extent by the sequencing basis, and the inspection is more efficient.
The invention also provides a transformer operation and detection period making system based on the state characteristic fault tree, wherein the transformer operation and detection period making method according to any one of the above is applied to the transformer operation and detection period making system, and the transformer operation and detection period making system comprises: the acquisition module is used for acquiring fault information and further establishing a state characteristic fault tree; the calculation module is used for calculating failure frequency according to the similar quantity and the similar interval; the analysis module is used for analyzing the fault information to obtain a priority sequence, judging whether the current abnormality needs to be comprehensively checked according to the priority sequence, and formulating a fault checking sequence of daily inspection.
The transformer operation and detection period making system has all the characteristics of the transformer operation and detection period making method, so that the same technical effects are achieved, and the description is omitted herein.
After the technical scheme of the invention is adopted, the following technical effects can be achieved:
(1) The fault information of all transformers is acquired through setting and classifying, so that the fault types of the transformers are extracted, data support is conveniently provided when the transformers are inspected subsequently, meanwhile, a state characteristic fault tree is set, fault analysis is more comprehensive and deeper, time points for acquiring similar fault information in each classification are set, adjacent time difference values are acquired, similar intervals are acquired, the occurrence frequency of similar faults can be known, the importance of the abnormality can be further known, information support is provided for subsequent inspection, reference during inspection is more scientific and practical, meanwhile, the number of similar faults is acquired, failure frequency is obtained through the similar number and the similar intervals, whether faults represented by each node in the fault tree have inspection priority is judged through the failure frequency, the judgment of the priority is more practical, the inspection process can be more conveniently and rapidly performed through the priority during inspection, meanwhile, the purpose is strong, and meanwhile, the inspection is more convenient and rapid due to the fact that the fault tree is set for carrying out the inspection and statistics, and the inspection is more comprehensive due to the fact that the fault tree and the priority sequence can be rapidly positioned and the inspection is required to be more comprehensive;
(2) Calculating the failure frequency according to a formula by setting the failure frequency, calculating the failure frequency by means of the proportion of the similar quantity and the similar interval, and adding the failure error to enable the failure frequency to be more fit with the actual situation, and guaranteeing that the obtained failure frequency can better improve the accuracy of priority arrangement and the inspection efficiency;
(3) Judging whether the node of the current fault classification is greater than a safety threshold value by means of failure frequency from top to bottom, further judging the priority of the current node, meanwhile, indicating that the current abnormality is high in frequency when the failure frequency is high, and easy to occur, so that key investigation is needed, frequent power failure is prevented, a large amount of economic loss is caused, whether the current node needs daily inspection and the sequence of inspection can be judged at maximum efficiency by comparing the failure frequency with the safety threshold value, the inspection efficiency is higher, the purpose is stronger, and normal operation of the current transformer can be guaranteed to the greatest extent.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings to be used in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art;
fig. 1 is a flowchart of a method for formulating a transformer operation detection period based on a state feature fault tree according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a method for making a transformer operation and detection period based on a state feature fault tree according to an embodiment of the present invention.
Reference numerals illustrate:
100 is a transformer operation and detection period making system; 110 is an acquisition module; 120 is a calculation module; 130 is an analysis module.
Detailed Description
In order to make the above objects, features and advantages of the present invention more comprehensible, embodiments accompanied with present invention are described in detail with embodiments of the present invention including only some 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.
[ first embodiment ]
Referring to fig. 1, the invention provides a method for formulating a transformer operation detection period based on a state characteristic fault tree, which comprises the following steps:
step S100: acquiring fault information of all transformers recorded in a power grid system, and classifying the fault information to obtain a first classification result; establishing a state characteristic fault tree according to the first classification result; acquiring a recording time point of similar fault information in each classification in the first classification result, and acquiring a time difference value of two adjacent similar fault information according to the recording time point to obtain a similar interval; obtaining the number of similar fault information in each category to obtain the similar number;
step S200: obtaining failure frequency according to the similar quantity and the similar interval; adding a safety condition point to each node in the state characteristic fault tree, and judging whether the fault information represented by the current node has priority or not according to the failure frequency from top to bottom to obtain a priority sequence;
step S300: acquiring current abnormality information, introducing the abnormality information into a characteristic fault tree, judging nodes to which the abnormality information belongs, and judging whether the current abnormality needs to be comprehensively examined according to the priorities of the nodes to which the abnormality information belongs; and performing fault troubleshooting sequencing on the daily inspection according to the priority sequence from high to low.
Specifically, a fault information database is established, maintenance records about transformers recorded in all power grid systems are firstly obtained, fault information in the transformer is extracted, such as reasons of faults, maintenance time of the faults, areas of the faults and the like, and extraction contents can be set according to actual conditions. After the acquisition is completed, all fault information in the database is classified according to the reasons of faults, such as key faults including abnormal sound, abnormal voltage, abnormal oil temperature, abnormal oil level and the like, and the key faults are classified into a plurality of major categories, namely large categories, the major categories are respectively set as top events of a state characteristic fault tree, the rest of tiny branch contents are used as refinement events, such as part abnormality, wiring abnormality and the like in the voltage abnormality, the lower events are more detailed reasons, the tiny branch contents are classified into nodes in an enlarged mode, and the fault tree and the node are established according to the major categories. The method particularly refines branches according to maintenance reasons recorded in the database, so that the reasons under the classification are more complete and deep, the fault reasons are analyzed to be the most fundamental, and the accuracy and the efficiency of positioning according to the fault tree when the subsequent abnormality occurs are improved.
Further, the definition of priority is a definition value used for ranking the chronological order in the priority sequence.
Preferably, fault information of all transformers is acquired through setting and classified, so that fault types of the transformers are extracted, data support is conveniently provided when the transformers are inspected, a state characteristic fault tree is set, fault analysis is more comprehensive and deeper, a time point for acquiring similar fault information in each classification is set, adjacent time difference values are acquired, further similar intervals are acquired, the occurrence frequency of similar faults can be obtained, the importance of the abnormality can be obtained, information support is provided for the sequence of subsequent inspection, reference during inspection is more scientific and practical, meanwhile, the number of similar faults is acquired, failure frequency is obtained through the similar number and the similar intervals, and then whether faults represented by each node in the fault tree have inspection priority is judged through the failure frequency, so that the priority judgment is more practical, inspection can be performed through the priority, the inspection process is more convenient and rapid, meanwhile, the purpose is strong, and meanwhile, due to the fact that the fault tree is set for carrying out the inspection and statistics, the fault tree and the priority sequence can be used for positioning and the inspection at present more conveniently and comprehensively judging whether the faults represented by the failure tree have the priority.
Specifically, obtaining fault information of all transformers recorded in a power grid system, classifying the fault information, and obtaining a first classification result includes: the classification basis of the fault information comprises the following steps: abnormal sound, abnormal voltage, abnormal oil temperature and abnormal oil level.
Specifically, the abnormal acquisition is performed through a corresponding sensor, the sensor is a common corresponding sensor, and the sensor is installed at a detection position or a generation position corresponding to the abnormal to perform real-time monitoring and is in remote signal connection with the system to transmit detection contents in real time.
Preferably, fault information is classified through sound, voltage, oil temperature and oil level, so that the fault information can be more orderly, a fault tree can be conveniently built subsequently, further, the follow-up judgment of the fault tree is more accurate and practical, and the follow-up inspection efficiency is guaranteed.
Specifically, obtaining the recording time point of the similar fault information in each classification in the first classification result, and obtaining the time difference value of two adjacent similar fault information according to the recording time point, where obtaining the similar interval further includes: performing first screening calculation on the time difference value; when the time difference values are all greater than or equal to a first threshold value, adding all obtained time difference values, taking an average value, and taking the average value as a similar interval; and when the time difference value is smaller than the first threshold value, taking the minimum value in all the time difference values as a similar interval.
Specifically, the first threshold is a set value, and is obtained through experimental data and calculation, and can be adaptively adjusted according to actual conditions. For example, the time differences are 7 days, 10 days, 12 days, 14 days, and 7 days, respectively, and the first threshold is set to 7 days, and the similar interval is set to 10 days. The time difference value is equal to or greater than the first threshold value, at this time, the occurrence frequency of the abnormality is not high, and when one day is 4 days in the time difference value, it is indicated that two adjacent abnormality occurrence intervals are too short, and important attention is required, and the time interval is taken to be 4 days.
Preferably, the first screening calculation is performed on the obtained time difference value, and different time intervals are obtained through comparison of the time difference value and the first threshold value, and when the time difference value is larger than or equal to the first threshold value, the frequency of occurrence of the current abnormality is lower, and the current abnormality is indicated to be normal, so that the average value is taken as a similar interval, the similar interval cannot deviate from reality, the similar interval cannot be too high, the follow-up inspection is made more scientifically and more practical, meanwhile, when the time difference value is smaller than the first threshold value, the current abnormality is indicated to have high frequency occurrence, so that important attention is required, the minimum value is taken as the similar interval, the priority of the current abnormality is increased, the follow-up inspection can be ensured to focus on the abnormality, the inspection purpose is stronger, and the inspection efficiency is improved.
Specifically, obtaining the failure frequency according to the similar number and the similar interval further includes: failure frequency s is calculated by equation 1: s=n/t+k; where n is a similar number, t is a similar interval, k is a failure error, and k is greater than 0.
Specifically, the failure error is an experimental value obtained according to actual conditions, and can be adaptively adjusted. For example, the number of similarities n=10, the interval of similarity t=10, taking k=0.5, s=1.5. And comparing the priority with a safety threshold value to judge whether the current node has the priority and the position in the priority sequence.
Preferably, the failure frequency is calculated according to a formula by setting the failure frequency, meanwhile, the failure frequency is calculated by means of the proportion of the similar quantity and the similar interval, meanwhile, the failure error is added to enable the failure frequency to be more fit with the actual condition, meanwhile, the obtained failure frequency can be better improved in priority arrangement accuracy, and the inspection efficiency is guaranteed.
Specifically, adding a security condition point to each node in the state feature fault tree, and judging whether the fault information represented by the current node has priority or not according to the failure frequency from top to bottom, and obtaining the priority sequence further includes: judging whether failure frequency corresponding to failure classification represented by the current node is greater than a safety threshold value from top to bottom; if the priority sequence is greater than the safety threshold, the priority sequence is arranged from large to small according to the failure frequency corresponding to each node; and if the priority sequence is smaller than the safety threshold, arranging the priority sequence according to the checking convenience, and arranging the priority sequence after the part larger than the safety threshold.
Specifically, the safety threshold is an artificial set value, and can be adaptively adjusted according to actual conditions.
Preferably, whether the node of the current fault classification is larger than a safety threshold value or not is judged by means of failure frequency from top to bottom, the priority of the current node is further judged, meanwhile, the current abnormal frequency is high when the failure frequency is large, the abnormality is easy to occur, so that important investigation is needed, frequent power failure is prevented, a large amount of economic loss is caused, whether the current node needs daily inspection and the sequence during inspection can be judged at maximum efficiency through comparison of the failure frequency and the safety threshold value, the inspection efficiency is higher, the purpose is stronger, and normal operation of the current transformer can be guaranteed to the greatest extent.
Specifically, if the priority sequence is greater than the safety threshold, the priority sequence is arranged from large to small according to the failure frequency corresponding to each node, and the priority sequence further comprises: if nodes with equal failure frequencies exist, counting the number of failure frequencies larger than a safety threshold under each large category according to a first classification result to obtain a first statistical result; and comparing the magnitudes of the first statistical results under the large classification corresponding to the nodes with equal failure frequencies, and sequencing the priority sequences according to the first statistical results from large to small.
Specifically, the large classification is classified according to the classification vertex under the first classification result, such as sound abnormality, voltage abnormality, oil temperature abnormality, and oil level abnormality, which are the large classifications.
Preferably, when the failure frequency is equal, the number of failure frequencies under the larger classification is compared to judge, and the classification with the larger number of failure frequencies is carried out first, and because the number of failure frequencies under the changed classification is larger, the important abnormality under the current classification is more, so that the classification is preferably checked, the probability of occurrence of multiple abnormalities can be reduced to the greatest extent, the stable operation of the transformer is ensured, the inspection efficiency is also improved, the inspection can not be carried out blindly, and the inspection has better purpose.
Specifically, judging whether the current abnormality needs to be comprehensively examined according to the priority of the node to which the current abnormality belongs further includes: if the position of the priority sequence of the node where the current abnormality is located is larger than a first safety limit, judging that the current abnormality needs to be comprehensively examined, and executing a first early warning operation; if the safety threshold is smaller than the first safety threshold, the rejection is normally performed.
Specifically, the first safety limit is an artificial set value, and can be adjusted according to actual conditions.
Specifically, when a new abnormality is acquired, that is, a new abnormality which does not exist in all the currently established state feature fault trees, the system sends an alarm signal to inform maintenance personnel of going to the system, after the maintenance of the maintenance personnel is completed, a maintenance record is uploaded to the system, and the system analyzes according to the fault information content recorded by the power grid system to judge whether the current abnormality needs to be established as a new state feature fault tree.
Specifically, the system analyzes according to the content of the fault information specifically includes: and comparing the maintenance time in the recorded fault information with the minimum value of the range limit values of all nodes in the first safety limit, if the maintenance time is greater than the minimum value, establishing a new state characteristic fault tree for the new abnormality, otherwise, arranging the new state characteristic fault tree into the priority sequence of the nodes smaller than the first safety limit according to the maintenance time.
Preferably, when an abnormality occurs, the priority sequence position of the node to which the abnormality belongs is compared with the first safety limit to determine whether to perform the first early warning operation, and because the importance degree of the abnormality is different, the unimportant abnormality does not need to be checked with great expense and chapters, manpower and material resources are wasted, the abnormality with high importance degree is considered, the comprehensive check is performed, the occurrence of the associated abnormality is prevented, the economic loss is caused, the life of residents is influenced, the efficiency of the abnormality is higher through the arrangement of the first safety limit, and the time is not wasted.
Specifically, if the position of the priority sequence of the node where the current abnormality is located is greater than the first safety limit, judging that the current abnormality needs to be comprehensively examined, and executing the first early warning operation further includes: extracting all nodes in the priority sequence which are larger than the first safety limit, and acquiring the corresponding influence range when each node is abnormal to obtain a range limit value; performing secondary priority sequence arrangement on each node according to the size of the range limit value to obtain an investigation sequence; all nodes that are greater than the first security limit are inspected in an inspection sequence.
Preferably, the nodes which are larger than the first safety limit in the priority sequence are extracted and the influence range is acquired through setting, so that each node is ordered according to the influence range, the priority of the nodes with high importance level is ensured to the greatest extent even when the nodes which are larger than the first safety limit are inspected, namely the nodes with the greatest influence are ensured to be inspected preferentially, the loss caused by abnormality is reduced to the greatest extent, the normal operation of the transformer is ensured, and the inspection efficiency is improved.
Specifically, the scope of influence is defined according to the required maintenance time when the abnormality corresponding to the current node occurs.
Preferably, the abnormal maintenance time is used as a judgment basis, so that the sequencing basis is more practical, and the longer the maintenance time is, the higher the loss is, the larger the influence on residents is, and therefore the judgment is performed through the maintenance time, the normal operation of the transformer can be guaranteed to the greatest extent by the sequencing basis, and the inspection is more efficient.
[ second embodiment ]
Referring to fig. 2, in the present invention, a system 100 for making a transformer operation and detection cycle based on a state feature fault tree is further provided, where the method for making a transformer operation and detection cycle according to any one of the above is applied to the system 100 for making a transformer operation and detection cycle, the system 100 for making a transformer operation and detection cycle includes: the acquisition module 110 is used for acquiring fault information, and further establishing a state characteristic fault tree; the calculating module 120, the calculating module 120 is used for calculating the failure frequency according to the similar quantity and the similar interval; the analysis module 130 is used for analyzing the fault information to obtain a priority sequence, judging whether the current abnormality needs to be comprehensively checked according to the priority sequence, and making a fault checking sequence of daily inspection.
In a specific embodiment, the obtaining module 110, the calculating module 120, and the analyzing module 130 cooperate to implement the method for making the operation and inspection cycle of the transformer as described above, which is not described herein.
The transformer operation cycle making system 100 has all the features of the above-mentioned transformer operation cycle making method, so that the same technical effects are achieved, and will not be described in detail herein.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (8)

1. A method for formulating a transformer operation detection period based on a state characteristic fault tree is characterized by comprising the following steps:
acquiring fault information of all transformers recorded in a power grid system, and classifying the fault information to obtain a first classification result;
establishing a state characteristic fault tree according to the first classification result;
acquiring a recording time point of similar fault information in each classification in the first classification result, and acquiring a time difference value of two adjacent similar fault information according to the recording time point to obtain a similar interval;
performing first screening calculation on the time difference value;
when the time difference values are all larger than or equal to a first threshold value, adding all obtained time difference values, and taking an average value as the similar interval;
when the time difference value is smaller than a first threshold value, taking the minimum value of all the time difference values as the similar interval;
obtaining the number of similar fault information in each category to obtain the similar number;
obtaining failure frequency according to the similar quantity and the similar interval;
the failure frequency s passes through equation 1: s=n/t+k;
wherein n is the similar number, t is the similar interval, k is a failure error, and k is greater than 0;
adding a safety condition point to each node in the state characteristic fault tree, and judging whether the fault information represented by the current node has priority or not according to the failure frequency from top to bottom to obtain a priority sequence;
acquiring current abnormality information, introducing the abnormality information into the characteristic fault tree, judging nodes to which the abnormality information belongs, and judging whether the current abnormality needs to be comprehensively examined according to the priorities of the nodes to which the abnormality information belongs;
and performing fault troubleshooting sequencing on the daily inspection according to the priority sequence from high to low.
2. The method for formulating the operational testing period of the transformer based on the state characteristic fault tree according to claim 1, wherein the steps of obtaining fault information of all transformers recorded in the power grid system, classifying the fault information, and obtaining a first classification result include:
the classification basis of the fault information comprises the following steps: abnormal sound, abnormal voltage, abnormal oil temperature and abnormal oil level.
3. The method for formulating the operational cycle of the transformer based on the state feature fault tree according to claim 1, wherein adding a safety condition point to each node in the state feature fault tree, and judging whether a class of fault information represented by the current node has priority according to the failure frequency from top to bottom, and obtaining the priority sequence further comprises:
judging whether failure frequency corresponding to failure classification represented by the current node is greater than a safety threshold value from top to bottom;
if so, arranging priority sequences from large to small according to the corresponding failure frequency of each node in the part larger than the safety threshold value; and if the priority sequence is smaller than the safety threshold, arranging the priority sequence according to the checking convenience, and arranging the priority sequence after the part larger than the safety threshold.
4. The method for formulating a transformer operation and detection cycle based on a state feature fault tree according to claim 3, wherein the step of arranging the priority sequence from large to small according to the failure frequency corresponding to each node in the portion larger than the safety threshold if the failure frequency is larger than the safety threshold further comprises:
if nodes with equal failure frequencies exist, counting the number of failure frequencies larger than the safety threshold under each large classification according to the first classification result to obtain a first statistical result;
and comparing the magnitudes of the first statistical results under the large classification corresponding to the nodes with equal failure frequencies, and sequencing the priority sequences according to the first statistical results from large to small.
5. The method for formulating the operational cycle of the transformer based on the state characteristic fault tree according to claim 1, wherein the step of judging whether the current abnormality needs to be comprehensively examined according to the priority of the node to which the current abnormality belongs further comprises:
if the position of the priority sequence of the node where the current abnormality is located is larger than a first safety limit, judging that the current abnormality needs to be comprehensively examined, and executing a first early warning operation;
if the first safety limit is smaller than the first safety limit, the rejection is normally carried out.
6. The method for formulating a transformer operation detection period based on a state feature fault tree according to claim 5, wherein if the location of the priority sequence of the node where the current abnormality is located is greater than a first safety limit, determining that the current abnormality needs to be comprehensively examined, and performing a first early warning operation further comprises:
extracting all nodes larger than a first safety limit in the priority sequence, and acquiring an influence range corresponding to each node when abnormality occurs, so as to obtain a range limit value;
performing secondary priority sequence arrangement on each node according to the size of the range limit value to obtain an investigation sequence;
and checking all nodes larger than the first safety limit according to the checking sequence.
7. The method for formulating the operational cycle of the transformer based on the state characteristic fault tree according to claim 6, wherein the influence range is defined according to a maintenance time required when an abnormality corresponding to the current node occurs.
8. A transformer operation and detection cycle making system based on a state characteristic fault tree, wherein the transformer operation and detection cycle making method according to any one of claims 1 to 7 is applied to the transformer operation and detection cycle making system, and the transformer operation and detection cycle making system comprises:
the acquisition module is used for acquiring fault information and further establishing the state characteristic fault tree;
the calculating module is used for calculating the failure frequency according to the similar quantity and the similar interval;
the analysis module is used for analyzing the fault information to obtain the priority sequence, judging whether the current abnormality needs to be comprehensively checked according to the priority sequence, and formulating the fault checking sequence of daily inspection.
CN202311421404.3A 2023-10-31 2023-10-31 Transformer operation detection period formulation method and system based on state characteristic fault tree Active CN117150418B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202311421404.3A CN117150418B (en) 2023-10-31 2023-10-31 Transformer operation detection period formulation method and system based on state characteristic fault tree

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202311421404.3A CN117150418B (en) 2023-10-31 2023-10-31 Transformer operation detection period formulation method and system based on state characteristic fault tree

Publications (2)

Publication Number Publication Date
CN117150418A CN117150418A (en) 2023-12-01
CN117150418B true CN117150418B (en) 2024-01-26

Family

ID=88897228

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202311421404.3A Active CN117150418B (en) 2023-10-31 2023-10-31 Transformer operation detection period formulation method and system based on state characteristic fault tree

Country Status (1)

Country Link
CN (1) CN117150418B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117520999B (en) * 2024-01-08 2024-04-05 浙江德塔森特数据技术有限公司 Intelligent operation and maintenance method and system for edge data center equipment

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104778370A (en) * 2015-04-20 2015-07-15 北京交通大学 Risk analyzing method based on Monte-Carlo simulation solution dynamic fault tree model
CN106533754A (en) * 2016-11-08 2017-03-22 北京交通大学 Fault diagnosis method and expert system for college teaching servers
CN107544462A (en) * 2017-09-07 2018-01-05 新疆金风科技股份有限公司 For the method and system for the failure for diagnosing wind power generating set
CN109872084A (en) * 2019-03-18 2019-06-11 国电联合动力技术有限公司 The failure rate statistical method and system of Wind turbines
CN115511136A (en) * 2022-11-01 2022-12-23 北京磁浮有限公司 Equipment fault auxiliary diagnosis method and system based on hierarchical analysis and fault tree
CN115687315A (en) * 2022-09-20 2023-02-03 北京天泽智云科技有限公司 Fault diagnosis and health management system of fan
CN116483865A (en) * 2022-11-15 2023-07-25 中国煤炭科工集团太原研究院有限公司 Hydraulic fault diagnosis expert system with priority
CN116542655A (en) * 2023-07-04 2023-08-04 北京智盟信通科技有限公司 Event tree-based power transformation equipment fault mechanism deduction method

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR2933512B1 (en) * 2008-07-07 2010-08-27 Eurocopter France DIAGNOSTIC METHOD FOR LOCATING A FAILURE IN A COMPLEX SYSTEM AND DEVICE FOR CARRYING OUT SAID METHOD
JP7454775B2 (en) * 2020-07-31 2024-03-25 三菱重工サーマルシステムズ株式会社 Abnormality diagnosis device, abnormality diagnosis method, and program

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104778370A (en) * 2015-04-20 2015-07-15 北京交通大学 Risk analyzing method based on Monte-Carlo simulation solution dynamic fault tree model
CN106533754A (en) * 2016-11-08 2017-03-22 北京交通大学 Fault diagnosis method and expert system for college teaching servers
CN107544462A (en) * 2017-09-07 2018-01-05 新疆金风科技股份有限公司 For the method and system for the failure for diagnosing wind power generating set
CN109872084A (en) * 2019-03-18 2019-06-11 国电联合动力技术有限公司 The failure rate statistical method and system of Wind turbines
CN115687315A (en) * 2022-09-20 2023-02-03 北京天泽智云科技有限公司 Fault diagnosis and health management system of fan
CN115511136A (en) * 2022-11-01 2022-12-23 北京磁浮有限公司 Equipment fault auxiliary diagnosis method and system based on hierarchical analysis and fault tree
CN116483865A (en) * 2022-11-15 2023-07-25 中国煤炭科工集团太原研究院有限公司 Hydraulic fault diagnosis expert system with priority
CN116542655A (en) * 2023-07-04 2023-08-04 北京智盟信通科技有限公司 Event tree-based power transformation equipment fault mechanism deduction method

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Reliability analysis of dynamic fault trees with Priority-AND gates based on irrelevance coverage model;Siwei Zhou 等;《Reliability Engineering & System Safety》;第224卷;1-15 *
基于可靠性和风险评估的电力变压器状态维修决策方法研究;王有元;《中国博士学位论文全文数据库 工程科技Ⅱ辑》;第2009年卷(第6期);C042-16 *
通信网络故障数据挖掘与溯源算法研究;赵泽玲;《中国优秀硕士学位论文全文数据库 信息科技辑》;第2023年卷(第7期);I136-326 *

Also Published As

Publication number Publication date
CN117150418A (en) 2023-12-01

Similar Documents

Publication Publication Date Title
CN111539550B (en) Method, device, equipment and storage medium for determining working state of photovoltaic array
CN111507376B (en) Single-index anomaly detection method based on fusion of multiple non-supervision methods
Catterson et al. Online conditional anomaly detection in multivariate data for transformer monitoring
CN107885642A (en) Business monitoring method and system based on machine learning
CN112561736A (en) Fault diagnosis system and method for relay protection device of intelligent substation
CN117150418B (en) Transformer operation detection period formulation method and system based on state characteristic fault tree
CN112925279A (en) Fault comprehensive analysis system based on MES system
GB2522926A (en) Method of identifying anomalies
CN113902241A (en) Power grid equipment maintenance strategy system and method based on comprehensive state evaluation
CN112987696A (en) Regional power distribution network equipment management platform and operation method thereof
CN111666978B (en) Intelligent fault early warning system for IT system operation and maintenance big data
CN110580492A (en) Track circuit fault precursor discovery method based on small fluctuation detection
CN113962308A (en) Aviation equipment fault prediction method
CN115437358A (en) Intelligent state monitoring and fault diagnosis system and fault diagnosis method for industrial robot
CN114487705A (en) Power grid equipment fault positioning detection method
CN113313365A (en) Degradation early warning method and device for primary air fan
CN112803587A (en) Intelligent inspection method for state of automatic equipment based on diagnosis decision library
CN108613820A (en) A kind of online allophone monitoring algorithm for GIS bulk mechanicals defect diagonsis and positioning
Catterson et al. On-line transformer condition monitoring through diagnostics and anomaly detection
CN114167282B (en) Motor fault diagnosis and degradation trend prediction system
CN114492636B (en) Transformer winding state signal acquisition system
CN116381380A (en) Dynamic fault supervision system for intelligent substation fault recorder
CN115600695A (en) Fault diagnosis method of metering equipment
CN113836203A (en) Network data diagnosis detection analysis system
CN114760332A (en) Efficient equipment monitoring and analyzing method

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant