CN112580498A - Low-voltage contact cabinet fault analysis method and device, computer equipment and storage medium - Google Patents
Low-voltage contact cabinet fault analysis method and device, computer equipment and storage medium Download PDFInfo
- Publication number
- CN112580498A CN112580498A CN202011493177.1A CN202011493177A CN112580498A CN 112580498 A CN112580498 A CN 112580498A CN 202011493177 A CN202011493177 A CN 202011493177A CN 112580498 A CN112580498 A CN 112580498A
- Authority
- CN
- China
- Prior art keywords
- low
- fault
- voltage contact
- monitoring data
- cabinet
- 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.)
- Pending
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 68
- 238000012544 monitoring process Methods 0.000 claims abstract description 170
- 238000003062 neural network model Methods 0.000 claims abstract description 60
- 238000000034 method Methods 0.000 claims abstract description 44
- 230000002159 abnormal effect Effects 0.000 claims description 31
- 238000004891 communication Methods 0.000 claims description 30
- 238000004590 computer program Methods 0.000 claims description 13
- 238000013528 artificial neural network Methods 0.000 abstract description 7
- 238000010586 diagram Methods 0.000 description 7
- 238000012549 training Methods 0.000 description 5
- 238000013024 troubleshooting Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 230000005856 abnormality Effects 0.000 description 3
- 238000012856 packing Methods 0.000 description 3
- 239000000779 smoke Substances 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/2431—Multiple classes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K17/00—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations
- G06K17/0022—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations arrangements or provisions for transferring data to distant stations, e.g. from a sensing device
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Computational Linguistics (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Biophysics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Evolutionary Biology (AREA)
- Primary Health Care (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Signal Processing (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The application relates to a low-voltage contact cabinet fault analysis method and device, computer equipment and a storage medium. The method comprises the following steps: and acquiring monitoring data of a plurality of low-voltage contact cabinets, and inputting the monitoring data of each low-voltage contact cabinet into a preset neural network model for fault analysis to obtain a fault result of each low-voltage contact cabinet. By adopting the method, the monitoring data of each low-voltage contact cabinet can be automatically acquired, and the monitoring data is input into the preset neural network to directly obtain the fault result, so that the fault is not required to be checked and analyzed by workers on site, and the efficiency of fault analysis of the low-voltage contact cabinets is improved.
Description
Technical Field
The application relates to the technical field of power distribution cabinets, in particular to a low-voltage contact cabinet fault analysis method and device, computer equipment and a storage medium.
Background
With the rapid development of science and technology, various electric facilities emerge endlessly. Meanwhile, the reliability and stability of the power supply of the electric appliance are required to be higher and higher. Therefore, the failure cause of the power distribution cabinet in the power supply system needs to be analyzed when the power distribution cabinet fails, and the reliability and the stability of the power distribution cabinet are improved by aiming at the failure cause.
The low-voltage interconnection cabinet is the final-stage equipment of the power distribution system, is usually used in the occasions with dispersed loads and less loops, distributes the electric energy of a certain circuit of the upper-stage power distribution equipment to the nearby coincidence and provides protection, monitoring and control. The low-voltage contact cabinet on the existing market has low informatization degree, can only give an alarm when a fault is detected, and after receiving an alarm signal, a worker needs to manually perform troubleshooting and analyze the fault reason on the spot.
Therefore, the low-voltage contact cabinet fault analysis method in the prior art has the problem of low efficiency.
Disclosure of Invention
In view of the above, it is necessary to provide a method and an apparatus for analyzing a fault of a low-voltage contact cabinet, a computer device, and a storage medium, which can quickly perform fault analysis.
In a first aspect, the present application provides a method for analyzing a fault of a low-voltage contact cabinet, including:
acquiring monitoring data of a plurality of low-voltage contact cabinets;
and respectively inputting the monitoring data of each low-voltage contact cabinet into a preset neural network model for fault analysis to obtain a fault result of each low-voltage contact cabinet, wherein the fault result is used for indicating whether the low-voltage contact cabinet has a fault and the fault type when the low-voltage contact cabinet has the fault.
In one embodiment, if the fault result indicates that the low-voltage communication cabinet has a fault, the method further includes:
acquiring equipment identification in the monitoring data with faults;
and determining the position information of the failed low-voltage contact cabinet according to the equipment identification.
In one embodiment, the step of inputting the monitoring data of each low-voltage contact cabinet into a preset neural network model for fault analysis to obtain fault results of each low-voltage contact cabinet includes:
and when a fault signal is received, respectively inputting the monitoring data of the low-voltage contact cabinets at the current moment into a preset neural network model for fault analysis to obtain fault results of the low-voltage contact cabinets.
In one embodiment, if the fault result indicates that the low-voltage communication cabinet has not failed, the method further includes:
determining a target threshold corresponding to the monitoring data according to a preset threshold curve; the threshold value curve is used to represent the correspondence between each monitored data and the threshold value,
and determining whether the low-voltage communication cabinet is abnormal within a preset time period or not according to the monitoring data and the target threshold value.
In one embodiment, predicting whether the low-voltage communication cabinet is abnormal or not according to the monitoring data and the target threshold comprises the following steps:
calculating a difference between the value of the monitoring data and a target threshold;
if the difference value is within the preset difference value range, determining that the low-voltage contact cabinet is abnormal within a preset time period, and outputting an early warning signal;
and if the difference value is out of the preset difference value range, determining that the low-voltage communication cabinet is not abnormal in the preset time period.
In one embodiment, determining a target threshold corresponding to the monitoring data according to a preset threshold curve includes:
acquiring each data type in the monitoring data and a numerical value corresponding to each data type;
acquiring a target threshold corresponding to each data type according to the threshold curve;
calculating a difference between the value of the monitored data and a target threshold, comprising:
and calculating the difference between the numerical value corresponding to each data type and the target threshold.
In one embodiment, the method further comprises:
acquiring historical fault data and corresponding sample fault types of a plurality of low-voltage contact cabinets;
inputting historical fault data into an initial neural network model for fault analysis to obtain a predicted fault type;
and adjusting parameters of the initial neural network model according to the loss between the sample fault type and the predicted fault type until a preset convergence condition is reached to obtain the neural network model.
In a second aspect, the present application provides a low-voltage contact cabinet fault analysis device, which includes:
the acquisition module is used for acquiring monitoring data of the plurality of low-voltage contact cabinets;
and the analysis module is used for inputting the monitoring data of each low-voltage contact cabinet into a preset neural network model for fault analysis to obtain a fault result of each low-voltage contact cabinet, and the fault result is used for indicating whether the low-voltage contact cabinet breaks down or not and the fault type when the low-voltage contact cabinet breaks down.
In a third aspect, the present application provides a computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the method in any one of the above embodiments of the first aspect when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method in any of the embodiments of the first aspect described above.
According to the method and the device for analyzing the faults of the low-voltage contact cabinets, the computer equipment and the storage medium, the monitoring data of the low-voltage contact cabinets are obtained, the monitoring data of the low-voltage contact cabinets are respectively input into the preset neural network model for fault analysis, the fault result of each low-voltage contact cabinet is obtained, and the fault result is used for indicating whether the low-voltage contact cabinet breaks down or not and the fault type when the low-voltage contact cabinet breaks down. Because the computer equipment can automatically acquire the monitoring data of each low-voltage contact cabinet and input the monitoring data into the preset neural network according to the monitoring data, a fault result is directly obtained, a worker does not need to perform troubleshooting on the site and analyze the fault reason, and the efficiency of fault analysis of the low-voltage contact cabinets is improved.
Drawings
FIG. 1 is a diagram of an exemplary embodiment of a method for fault analysis of a low-voltage contact cabinet;
FIG. 2 is a schematic flow chart of a method for analyzing faults of a low-voltage contact cabinet in one embodiment;
FIG. 3 is a schematic flow chart of a method for analyzing faults of a low-voltage communication cabinet in another embodiment;
FIG. 4 is a schematic flow chart of a method for analyzing faults of a low-voltage communication cabinet in another embodiment;
FIG. 5 is a schematic diagram of a threshold curve in one embodiment;
FIG. 6 is a schematic flow chart of a method for analyzing faults of a low-voltage communication cabinet in another embodiment;
FIG. 7 is a schematic flow chart of a method for analyzing faults of a low-voltage contact cabinet in another embodiment;
FIG. 8 is a schematic flow chart of a method for analyzing faults of a low-voltage contact cabinet in another embodiment;
FIG. 9 is a schematic flow chart of a method for analyzing faults of a low-voltage communication cabinet in another embodiment;
FIG. 10 is a block diagram showing the structure of a fault analysis apparatus for a low-voltage interconnection cabinet according to an embodiment;
FIG. 11 is a block diagram showing the structure of a fault analysis apparatus for a low-voltage interconnection cabinet according to an embodiment;
FIG. 12 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The low-voltage contact cabinet fault analysis method can be applied to the application environment shown in fig. 1. The application environment includes a plurality of low voltage contact cabinets 11 and computer equipment 12. Wherein each low voltage contact cabinet 11 communicates with a computer device 12 via a network. The computer equipment monitors the plurality of low-voltage contact cabinets in real time through a network, acquires monitoring data of each low-voltage contact cabinet in real time, and inputs the monitoring data of each low-voltage contact cabinet into a preset neural network model for fault analysis to obtain a fault result of each low-voltage contact cabinet. The computer device 12 may be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices, among others.
In one embodiment, as shown in fig. 2, there is provided a method for analyzing a fault of a low-voltage communication cabinet, which is described by taking the method as an example for being applied to the computer device in fig. 1, and includes the following steps:
s202, acquiring monitoring data of a plurality of low-voltage contact cabinets.
The monitoring data refers to internal environment data of the low-voltage contact cabinet in the operation process, electric parameter data of internal electric equipment and the like. For example, the monitoring data may include temperature data, current data, voltage data, and the like, without limitation. For example, the current data may be further refined into three-phase average line current, phase current a, phase current B, phase current C; the temperature data can be further refined into cable joint temperature data, temperature data in a low-voltage contact cabinet, cable core temperature data and the like. Optionally, the monitoring data may also include monitoring data of other electrical or non-electrical parameters, such as humidity data, smoke concentration data, water level data, etc., in the low-voltage communication cabinet, which is not limited herein.
Specifically, in the operation process of each low-voltage contact cabinet, the computer equipment receives the monitoring data of each low-voltage contact cabinet in real time. Wherein, each low pressure contact cabinet can pack respectively each low pressure contact cabinet's monitoring data when sending monitoring data, and computer equipment can be the monitoring data of the low pressure contact cabinet of a plurality of packings of simultaneous reception, also can be the monitoring data of each low pressure contact cabinet of packing of receiving in proper order, does not restrict here. Illustratively, multiple types of monitoring data collected corresponding to the current timestamp of the a low-voltage contact cabinet are packaged into one set of data S1, and the set of data S1 includes all types of monitoring data corresponding to the a low-voltage contact cabinet at the current timestamp.
And S204, respectively inputting the monitoring data of each low-voltage contact cabinet into a preset neural network model for fault analysis to obtain a fault result of each low-voltage contact cabinet, wherein the fault result is used for indicating whether the low-voltage contact cabinet has a fault and the fault type when the low-voltage contact cabinet has the fault.
The preset neural network model may be a BP network model or a Hopfield network model, which is not limited herein. The fault result of each low-voltage communication cabinet refers to whether each low-voltage communication cabinet has a fault or not. For example, if no fault occurs, the fault result is that no fault occurs; if the fault occurs, the fault result is the fault and the fault type when the fault occurs. The type of fault when the fault occurs may be over-temperature, over-current, over-voltage, etc., and is not limited herein.
Specifically, after the computer device acquires the monitoring data of each low-voltage contact cabinet, the monitoring data of each low-voltage contact cabinet is respectively input into a preset neural network model, corresponding fault analysis is carried out, and a corresponding fault result is output. In an example, firstly, the monitoring data a of the low-voltage communication cabinet 1 is input into a preset neural network, and the fault result of the low-voltage communication cabinet 1 is output as that no fault occurs; and inputting the monitoring data b of the low-voltage communication cabinet 2 into a preset neural network, and outputting a fault result of the low-voltage communication cabinet 2 as a fault, wherein the fault type is overhigh temperature.
According to the low-voltage contact cabinet fault analysis method, the monitoring data of the low-voltage contact cabinets are acquired, and the monitoring data of the low-voltage contact cabinets are respectively input into the preset neural network model for fault analysis, so that fault results of the low-voltage contact cabinets are obtained. Because the computer equipment can automatically acquire the monitoring data of each low-voltage contact cabinet and input the monitoring data into the preset neural network according to the monitoring data, a fault result is directly obtained, a worker does not need to perform troubleshooting on the site and analyze the fault reason, and the efficiency of fault analysis of the low-voltage contact cabinets is improved.
In one embodiment, as shown in fig. 3, if the fault result indicates that the low-voltage communication cabinet has a fault, the method further includes:
s302, acquiring the equipment identifier in the monitoring data with the fault.
The equipment identification means the identification information of the unique corresponding low-voltage contact cabinet which can be identified. The equipment identification can comprise position information, a unique identification code, a unique factory code and the like of the low-voltage contact cabinet, the unique factory code is usually set when the low-voltage contact cabinet leaves a factory and used for recording factory information and the like of the low-voltage contact cabinet, and the equipment identification can be traced back to the information and has uniqueness. The unique identification code can be a random code configured by later workers for each low-voltage contact cabinet, and is also unique. The unique identification code may be expressed in the form of a two-dimensional code, a bar code, a character, etc., without limitation.
Specifically, because can bind the equipment identification of its corresponding low pressure contact cabinet when packing each low pressure contact cabinet various monitoring data that correspond, generate the monitoring data of each low pressure contact cabinet. And when the monitoring data with faults is acquired, automatically identifying the equipment identification in the monitoring data.
And S304, determining the position information of the failed low-voltage contact cabinet according to the equipment identifier.
Specifically, the device identifier may be device location information, and after the device identifier is obtained, the location information may be automatically screened out and displayed on a display interface; or there may be a corresponding relationship between the device identifier and the position information of the low-voltage contact cabinet, and after the device identifier is obtained, the position information of the low-voltage contact cabinet is determined according to the corresponding relationship between the device identifier and the position information of the low-voltage contact cabinet, which is not limited herein.
In this embodiment, the location information of the failed low-voltage contact cabinet is determined according to the device identifier by obtaining the device identifier in the failed monitoring data. Because the position information of the corresponding low-voltage contact cabinet with the fault can be accurately positioned according to the equipment identification in the monitoring data with the fault, the staff can conveniently find the corresponding low-voltage contact cabinet to overhaul according to the position information, and the working efficiency is improved.
In one embodiment, the step of inputting the monitoring data of each low-voltage contact cabinet into a preset neural network model for fault analysis to obtain a fault result of each low-voltage contact cabinet includes:
and when a fault signal is received, respectively inputting the monitoring data of the low-voltage contact cabinets at the current moment into a preset neural network model for fault analysis to obtain fault results of the low-voltage contact cabinets.
The fault signal is that when the low-voltage contact cabinet breaks down, an alarm signal is automatically sent out, the alarm signal can be sent out by the low-voltage contact cabinet through an alarm lamp, or the alarm signal can be sent out by an alarm, and the fault signal is not limited.
Specifically, when the alarm signal of the low-voltage contact cabinet is received, the packed monitoring data of each low-voltage contact cabinet at the current moment can be respectively input into a preset neural network model for fault analysis, and the fault result of each low-voltage contact cabinet is obtained.
For example, the low-voltage contact cabinet E packages the monitoring data at the current time into an aggregate data E1, where the aggregate data E1 includes all types of monitoring data corresponding to the low-voltage contact cabinet E at the current time, such as voltage data, current data, and temperature data. Meanwhile, the low-voltage contact cabinet R packages the monitoring data of the current moment into an aggregate data R1, and the aggregate data R1 includes all types of monitoring data corresponding to the low-voltage contact cabinet R at the current moment, such as voltage data, current data, temperature data, and the like. After receiving the alarm signal of the low-voltage contact cabinet, respectively inputting the monitoring data in E1 at the current moment into the neural network model, outputting the corresponding fault result, and after the detection of the monitoring data of the low-voltage contact cabinet E at the current moment is finished, respectively inputting the monitoring data in r1 into the neural network model, and outputting the corresponding fault result.
In this embodiment, when receiving fault signals, the monitoring data of each low-voltage contact cabinet at the current moment are respectively input into a preset neural network model for fault analysis, so that fault results of each low-voltage contact cabinet are obtained, which one of the low-voltage contact cabinets has a fault can be accurately determined, the reason of the fault is quickly analyzed, and the accuracy and the analysis efficiency of the fault analysis are improved.
In one embodiment, as shown in fig. 4, if the fault result indicates that the low-voltage communication cabinet has not failed, the method further includes:
s402, determining a target threshold corresponding to the monitoring data according to a preset threshold curve; the threshold value curve is used for representing the corresponding relation between each monitoring data and the threshold value.
The preset threshold curve is a threshold curve generated according to various monitoring data in the historical data, for example, the threshold curve may be formed according to current and temperature in the historical data, as shown in fig. 5, an X axis is a current value, and a Y axis is a temperature value; it may also be a threshold curve of voltage versus temperature, which is not limited herein.
The threshold curve comprises two different types of monitoring data, and the target threshold refers to a specific numerical value of the other type of monitoring data corresponding to a specific numerical value inquired in the threshold curve according to the specific numerical value in the one type of monitoring data, namely the target threshold. For example, if the monitored data is current data and temperature data, and the corresponding threshold curve is a temperature-current curve, then according to the current a and the temperature-current curve in the monitored data, the temperature b corresponding to the current a is queried, and the temperature b is a temperature threshold, that is, a target threshold; the current d corresponding to the temperature c may also be queried according to the temperature data c and the temperature-current curve in the monitoring data, where the current d is a current threshold, and is not limited herein.
Optionally, when the fault result indicates that no fault occurs in the low-voltage contact cabinets, the computer device may also obtain various types of monitoring data of each low-voltage contact cabinet, input the various types of monitoring data into the neural network model, may determine a fault type in which a fault may occur, search for a threshold curve having a monitoring data type consistent with that of the fault type, compare the monitoring data causing the fault type with the same type of data in a preset threshold curve, and determine respective corresponding target thresholds. Illustratively, the fault type is a cable burnout fault type, the cable burnout fault type includes cable connector temperature monitoring data of current monitoring data, a threshold curve having a monitoring data type consistent with that corresponding to the fault type, that is, a current-temperature threshold curve, is searched at this time, when the current monitoring data is a P1 value, the cable connector temperature is a B1 value, and the cable connector temperature corresponding to the P1 value in the query current-temperature threshold curve is a B2 value, the B2 value is a target threshold.
S404, determining whether the low-voltage contact cabinet is abnormal within a preset time period or not according to the monitoring data and the target threshold value.
The preset time period is a time period from the current time to a future time.
Specifically, whether the low-voltage contact cabinet is abnormal within a preset time period is determined according to the monitoring data and the target threshold, and since the target threshold refers to the monitoring data value when a fault occurs, the closer the monitoring data value is to the target threshold means that the low-voltage contact cabinet is more likely to be abnormal. The monitoring data and the target threshold value are differentiated, and if the difference value is within a preset range, the abnormality of the low-voltage contact cabinet at a certain moment possibly within a preset time period can be determined; if the difference value is out of the preset range, the low-voltage communication cabinet can be determined not to be abnormal at a certain moment in the preset time period. Or the monitoring data and the target threshold value are subjected to quotient, if the quotient value is in a preset range, the fact that the low-voltage contact cabinet is abnormal at a certain moment possibly in a preset time period can be determined; if the quotient value is out of the preset range, it can be determined that the low-voltage contact cabinet is not abnormal at a certain moment in the preset time period.
Illustratively, the monitoring data 11 and the target threshold 12 are differentiated, if the preset range is 0-1.5, and if the difference is 1 within the preset range, it can be determined that the low-voltage communication cabinet is abnormal at a certain moment within the preset time period; and (3) performing difference on the monitoring data 5 and the target threshold value 12, wherein the preset range is 0-1.5, and if the difference value is out of the preset range, determining that the low-voltage contact cabinet is not abnormal at a certain moment in a preset time period.
In this embodiment, a target threshold corresponding to the monitoring data is determined according to a preset threshold curve; and determining whether the low-voltage communication cabinet is abnormal within a preset time period or not according to the monitoring data and the target threshold value. By the method, the relation between the monitoring data and the target threshold value can be accurately determined, so that whether the low-voltage contact cabinet is abnormal at a certain time in the future or not can be accurately judged, early warning can be given, a worker can overhaul the low-voltage contact cabinet according to early warning information, and the safety of the low-voltage contact cabinet is improved.
In one embodiment, as shown in fig. 6, predicting whether the low-voltage contact cabinet is abnormal or not according to the monitoring data and the target threshold comprises:
s602, calculating a difference value between the value of the monitoring data and the target threshold value. If the difference value is within the preset difference value range, executing step S604; if the difference is outside the preset difference range, step S606 is executed.
Specifically, the value of the monitoring data is subtracted from the target threshold, so that a difference between the value of the monitoring data and the target threshold can be obtained. Illustratively, the monitoring data is current data, the value of the current data is 5 amperes, the target threshold value is 6 amperes, and the current data is subtracted from the target threshold value to obtain a difference value of 1 ampere.
And S604, if the difference value is within the preset difference value range, determining that the low-voltage contact cabinet is abnormal within a preset time period, and outputting an early warning signal.
The preset difference range refers to a maximum range where the value of the monitoring data and the target threshold can be accessed.
Illustratively, the preset difference range is 0-2, the monitoring data is voltage, the voltage value is 224V, the voltage value of the target threshold is 225V, the difference is made, if the difference is 1, the difference is within the preset difference range, the low-voltage contact cabinet is abnormal at the next moment, and an early warning signal is output.
And S606, if the difference value is out of the preset difference value range, determining that the low-voltage communication cabinet is not abnormal in the preset time period.
Illustratively, the preset difference range is 0-3, the monitoring data is voltage, the voltage value is 220V, the voltage value of the target threshold is 225V, the difference is made, if the difference is 5, the difference is out of the preset difference range, and the low-voltage communication cabinet is not abnormal at the next moment.
In this embodiment, by calculating a difference between the value of the monitoring data and the target threshold, if the difference is within a preset difference range, it is determined that the low-voltage interconnection cabinet is abnormal within a preset time period, and an early warning signal is output, and if the difference is outside the preset difference range, it is determined that the low-voltage interconnection cabinet is not abnormal within the preset time period. The difference value of the monitoring data and the target threshold value is compared with the preset difference value range, whether the low-voltage contact cabinet is abnormal in the preset time period can be reflected, and early warning information is made when the abnormality is judged, so that a worker can overhaul the low-voltage contact cabinet in advance according to the early warning information, and the occurrence of faults is avoided.
In one embodiment, as shown in fig. 7, determining the target threshold corresponding to the monitoring data according to the preset threshold curve includes:
s702, acquiring each data type in the monitoring data and a numerical value corresponding to each data type.
Specifically, when the low-voltage contact cabinet works normally, the computer equipment can collect the monitoring data of the low-voltage contact cabinet in real time, and the numerical values corresponding to the data types in the monitoring data, and store the monitoring data and the values of the data of the types in the monitoring data. When the low-voltage contact cabinet needs to be monitored in an early warning mode, all data types in the monitoring data and numerical values corresponding to all the data types are obtained from the database. The data type of the monitoring data may include input/output voltage, input/output current, internal temperature of the low-voltage interconnection cabinet, internal humidity of the low-voltage interconnection cabinet, generated smoke content, and the like, which is not limited herein.
And S704, acquiring target threshold values corresponding to the data types according to the threshold value curve.
Specifically, after various types of monitoring data of each low-voltage contact cabinet are acquired, the various types of monitoring data are input into the neural network model, a threshold curve having the same type as that of the monitoring data corresponding to the fault type can be searched by determining the fault type of the fault, the monitoring data causing the fault type are compared with the same type of data in a preset threshold curve, and the corresponding target threshold values are determined. Illustratively, the fault type is a cable burnout fault type, the cable burnout fault type includes cable connector temperature monitoring data of current monitoring data, a threshold curve having a monitoring data type consistent with that corresponding to the fault type, that is, a current-temperature threshold curve, is searched, when the current monitoring data is H, the cable connector temperature is M1, and the cable connector temperature corresponding to H is M2, which is M2 is a target threshold value, is queried in the current-temperature threshold curve.
S706, calculating a difference between the value of the monitoring data and the target threshold, including: and calculating the difference between the numerical value corresponding to each data type and the target threshold.
Specifically, the difference between the value of the monitored data and the target threshold may be obtained by subtracting the value corresponding to each data type from the target threshold corresponding to the data type. Exemplarily, the monitoring data type is current data, the value of the current data is 4 amperes, the target threshold value is 5 amperes, and the value of the current data is differentiated from the target threshold value to obtain a difference value of 1 ampere; the monitoring data type is voltage data, the value of the voltage data is 220V, the target threshold value is 225V, and the value of the voltage data is differed from the target threshold value to obtain a difference value of 5V.
In the embodiment, each data type in the monitoring data and the value corresponding to each data type are obtained, the target threshold corresponding to each data type is obtained according to the threshold curve, and the difference between the value corresponding to each data type and the target threshold is calculated, so that whether the low-voltage contact cabinet is abnormal or not can be accurately reflected, a worker can obtain early warning information in advance and can overhaul the low-voltage contact cabinet, and the occurrence of faults is avoided.
In one embodiment, as shown in fig. 8, the method for analyzing the fault of the low-voltage contact cabinet further includes:
s802, historical fault data and corresponding sample fault types of the low-voltage contact cabinets are obtained.
The historical fault data refers to the stored monitoring data when each low-voltage contact cabinet fails. The corresponding sample fault type refers to a fault type corresponding to various types of monitoring data stored when faults occur in each low-voltage contact cabinet in the past.
Specifically, since the historical fault data and the corresponding sample fault types are stored in the storage module in the computer device, the historical fault data and the corresponding sample fault types of the plurality of low-voltage contact cabinets can be acquired through real-time calling.
And S804, inputting the historical fault data into the initial neural network model for fault analysis to obtain a predicted fault type.
The historical fault data may include, among other things, current data, voltage data, temperature data, humidity data, and the like. The initial neural network model may be a CNN network model, a BP network model, or the like. The resulting predicted fault type may be cable burn, switch trip, etc. Optionally, one type of data in the historical fault data may correspond to one fault type, or one fault type may correspond to multiple types of historical fault data.
Specifically, the historical fault data is input to the initial neural network model for fault analysis to obtain the predicted fault type, and the predicted fault type may be obtained by respectively inputting various types of monitoring data in the historical fault data to the neural network model and outputting the fault type corresponding to each type of monitoring data.
And S806, adjusting parameters of the initial neural network model according to the loss between the sample fault type and the predicted fault type until a preset convergence condition is reached, and obtaining the neural network model.
Wherein the preset convergence condition may be that the training reaches a preset number of iterations; or calculating loss values of the sample fault type and the predicted fault type by using a loss function, and reaching a preset convergence condition when the loss values reach a preset standard.
For example, the historical fault data is input into the initial neural network model, and the corresponding sample fault type is output, where the training is completed when the preset convergence condition is that the iteration number reaches 500 times, and the obtained neural network model is the current neural network model, that is, when the iteration number is 500 times, the current neural network model is used as the trained neural network model. Or calculating the loss values of the sample fault type and the predicted fault type corresponding to the historical fault data by using a loss function, wherein the loss values reach a preset standard value, and the establishment of the neural network model is determined to be completed. Taking input fault data as an example, when training is carried out for the 10 th time, the input current value is 10A, the output sample fault type current is too large, the loss value of the sample fault type and the predicted fault type calculated through the loss function is 0.2, the input current data is adjusted, the input current value is 11A, when training is carried out for the 11 th time, the loss value of the sample fault type and the predicted fault type calculated through the loss function is 0.1, the preset standard value is 0.1, at this time, training is finished, and the neural network model is established.
In the embodiment, historical fault data and corresponding sample fault types of a plurality of low-voltage contact cabinets are obtained, the historical fault data are input into an initial neural network model for fault analysis, a predicted fault type is obtained, parameters of the initial neural network model are adjusted according to loss between the sample fault type and the predicted fault type until a preset convergence condition is reached, and the neural network model is obtained. Because the neural network model is built, real-time monitoring data of the low-voltage contact cabinet can be input into the neural network model, whether the low-voltage contact cabinet fails at a future moment is predicted, the staff can overhaul the low-voltage contact cabinet in advance according to the early warning information, the failure is avoided, and the safety of the low-voltage contact cabinet is improved.
In order to facilitate understanding of those skilled in the art, the method for analyzing the fault of the low-voltage contact cabinet provided by the present application is described in detail below, and as shown in fig. 9, the method may include:
and S901, acquiring historical fault data and corresponding sample fault types of a plurality of low-voltage contact cabinets.
And S902, inputting the historical fault data into an initial neural network model for fault analysis to obtain a predicted fault type.
And S903, adjusting the parameters of the initial neural network model according to the loss between the sample fault type and the predicted fault type until a preset convergence condition is reached, so as to obtain the neural network model.
And S904, acquiring the monitoring data of the plurality of low-voltage contact cabinets.
S905, when a fault signal is received, respectively inputting the monitoring data of the low-voltage contact cabinets at the current moment into a preset neural network model for fault analysis, judging whether the low-voltage contact cabinets have faults or not, and if so, executing steps S906-S907; if not, steps S908-S912 are executed.
S906, acquiring the equipment identification in the monitoring data with the fault.
And S907, determining the position information of the failed low-voltage contact cabinet according to the equipment identifier.
S908, obtaining each data type in the monitoring data and a value corresponding to each data type.
And S909, acquiring target threshold values corresponding to the data types according to the threshold value curves.
S910, calculating a difference value between the value of the monitoring data and the target threshold value, judging whether the difference value is within a preset range, and if so, executing the step S911; if not, step S912 is executed.
And S911, determining that the low-voltage communication cabinet is abnormal in a preset time period, and outputting an early warning signal.
And S912, determining that the low-voltage communication cabinet is not abnormal in a preset time period.
In this embodiment, the monitoring data of the plurality of low-voltage contact cabinets is acquired, and the monitoring data of each low-voltage contact cabinet is respectively input into a preset neural network model for fault analysis, so as to obtain a fault result of each low-voltage contact cabinet, and fault analysis is performed according to the fault result or whether early warning is possible to occur in the low-voltage contact cabinet at a future time. Because the computer equipment can acquire the monitoring data of each low-voltage contact cabinet automatically, and according to the monitoring data input in the neural network of predetermineeing, directly reachs the fault result, need not the staff and carry out troubleshooting and analysis failure reason on the scene, improved low-voltage contact cabinet failure analysis's efficiency, can monitor and carry out the early warning to the low-voltage contact cabinet that does not break down simultaneously, improved the fail safe nature of low-voltage contact cabinet operation.
It should be understood that although the various steps in the flow charts of fig. 2-9 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-9 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 10, there is provided a low-voltage communication cabinet fault analysis device, including: an acquisition module 101 and an analysis module 102, wherein:
the first obtaining module 101 is configured to obtain monitoring data of a plurality of low-voltage contact cabinets.
And the analysis module 102 is configured to input the monitoring data of each low-voltage contact cabinet into a preset neural network model for fault analysis, so as to obtain a fault result of each low-voltage contact cabinet, where the fault result is used to indicate whether the low-voltage contact cabinet fails and a fault type when the low-voltage contact cabinet fails.
In this embodiment, the first obtaining module obtains the monitoring data of the plurality of low-voltage contact cabinets, and the analyzing module inputs the monitoring data of each low-voltage contact cabinet into the preset neural network model for fault analysis, so as to obtain a fault result of each low-voltage contact cabinet. Because the computer equipment can automatically acquire the monitoring data of each low-voltage contact cabinet and input the monitoring data into the preset neural network according to the monitoring data, a fault result is directly obtained, a worker does not need to perform troubleshooting on the site and analyze the fault reason, and the efficiency of fault analysis of the low-voltage contact cabinets is improved.
In one embodiment, as shown in fig. 11, the low-voltage interconnection cabinet fault analysis apparatus further includes:
and a second obtaining module 103, configured to obtain a device identifier in the monitoring data that has the fault.
And the first determination module 104 is used for determining the position information of the failed low-voltage contact cabinet according to the equipment identification.
In one embodiment, the analysis module is specifically configured to, when the fault signal is received, input the current-time monitoring data of each low-voltage contact cabinet into a preset neural network model for fault analysis, and obtain a fault result of each low-voltage contact cabinet.
In one embodiment, referring to fig. 11, the low-voltage interconnection cabinet fault analysis apparatus further includes:
a second determining module 105, configured to determine a target threshold corresponding to the monitoring data according to a preset threshold curve; the threshold curve is used for representing the corresponding relation between each monitoring data and the threshold;
and the third determining module 106 is configured to determine whether the low-voltage contact cabinet is abnormal within a preset time period according to the monitoring data and the target threshold.
In one embodiment, referring to fig. 11, the third determining module 106 includes:
the calculating unit 1061 is configured to calculate a difference between the value of the monitoring data and the target threshold.
The first determining unit 1062 is configured to determine that the low-voltage interconnection cabinet is abnormal in a preset time period if the difference is within a preset difference range, and output an early warning signal.
The second determining unit 1063 is configured to determine that no abnormality occurs in the low-voltage interconnection cabinet within the preset time period if the difference is outside the preset difference range.
In one embodiment, referring to fig. 11, the second determination module 105 includes:
the first obtaining unit 1051 is configured to obtain each data type in the monitoring data and a value corresponding to each data type.
A second obtaining unit 1052, configured to obtain a target threshold corresponding to each data type according to the threshold curve.
A calculation unit 1053 that calculates a difference between the value of the monitoring data and the target threshold, including: and calculating the difference between the numerical value corresponding to each data type and the target threshold.
In one embodiment, referring to fig. 11, the low-voltage interconnection cabinet fault analysis apparatus further includes:
and the third obtaining module 107 is used for obtaining historical fault data and corresponding sample fault types of the plurality of low-voltage contact cabinets.
The input module 108 is used for inputting the historical fault data into the initial neural network model for fault analysis to obtain a predicted fault type;
and the adjusting module 109 is configured to adjust parameters of the initial neural network model according to the loss between the sample fault type and the predicted fault type until a preset convergence condition is reached, so as to obtain the neural network model.
For specific limitations of the low-voltage interconnection cabinet fault analysis device, reference may be made to the above limitations of the low-voltage interconnection cabinet fault analysis method, and details are not repeated here. All or part of the modules in the low-voltage communication cabinet fault analysis device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 12. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer equipment is used for monitoring data of the low-voltage contact cabinet. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize a low-voltage contact cabinet fault analysis method.
Those skilled in the art will appreciate that the architecture shown in fig. 12 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
the method comprises the steps of obtaining monitoring data of a plurality of low-voltage contact cabinets, inputting the monitoring data of each low-voltage contact cabinet into a preset neural network model respectively for fault analysis, and obtaining fault results of each low-voltage contact cabinet, wherein the fault results are used for indicating whether the low-voltage contact cabinet breaks down or not and fault types when the low-voltage contact cabinet breaks down.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
the method comprises the steps of obtaining monitoring data of a plurality of low-voltage contact cabinets, inputting the monitoring data of each low-voltage contact cabinet into a preset neural network model respectively for fault analysis, and obtaining fault results of each low-voltage contact cabinet, wherein the fault results are used for indicating whether the low-voltage contact cabinet breaks down or not and fault types when the low-voltage contact cabinet breaks down.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A low-voltage contact cabinet fault analysis method is characterized by comprising the following steps:
acquiring monitoring data of a plurality of low-voltage contact cabinets;
and respectively inputting the monitoring data of each low-voltage contact cabinet into a preset neural network model for fault analysis to obtain a fault result of each low-voltage contact cabinet, wherein the fault result is used for indicating whether the low-voltage contact cabinet has a fault and the fault type when the low-voltage contact cabinet has the fault.
2. The method of claim 1, wherein if the fault result indicates that the low voltage contact cabinet is faulty, the method further comprises:
acquiring equipment identification in the monitoring data with faults;
and determining the position information of the failed low-voltage contact cabinet according to the equipment identification.
3. The method as claimed in claim 1 or 2, wherein the step of inputting the monitoring data of each low-voltage contact cabinet into a preset neural network model for fault analysis to obtain a fault result of each low-voltage contact cabinet comprises:
and when a fault signal is received, respectively inputting the monitoring data of the low-voltage contact cabinets at the current moment into a preset neural network model for fault analysis to obtain fault results of the low-voltage contact cabinets.
4. The method of claim 1, wherein if the fault result indicates that the low voltage contact cabinet is not faulty, the method further comprises:
determining a target threshold corresponding to the monitoring data according to a preset threshold curve; the threshold curve is used for representing the corresponding relation between each monitoring data and the threshold;
and determining whether the low-voltage communication cabinet is abnormal within a preset time period or not according to the monitoring data and the target threshold value.
5. The method of claim 4, wherein predicting whether the low-voltage contact cabinet is abnormal or not according to the monitoring data and the target threshold comprises:
calculating a difference between the value of the monitoring data and the target threshold;
if the difference value is within a preset difference value range, determining that the low-voltage contact cabinet is abnormal within a preset time period, and outputting an early warning signal;
and if the difference value is out of the preset difference value range, determining that the low-voltage communication cabinet is not abnormal in a preset time period.
6. The method according to claim 5, wherein the determining the target threshold corresponding to the monitoring data according to a preset threshold curve comprises:
acquiring each data type in the monitoring data and a numerical value corresponding to each data type;
acquiring a target threshold corresponding to each data type according to the threshold curve;
the calculating a difference between the value of the monitoring data and the target threshold comprises: and calculating the difference between the numerical value corresponding to each data type and the target threshold value.
7. The method of claim 1, further comprising:
acquiring historical fault data and corresponding sample fault types of a plurality of low-voltage contact cabinets;
inputting the historical fault data into an initial neural network model for fault analysis to obtain a predicted fault type;
and adjusting parameters of the initial neural network model according to the loss between the sample fault type and the predicted fault type until a preset convergence condition is reached to obtain the neural network model.
8. A low-voltage interconnection cabinet fault analysis device, comprising:
the acquisition module is used for acquiring monitoring data of the plurality of low-voltage contact cabinets;
and the analysis module is used for inputting the monitoring data of each low-voltage contact cabinet into a preset neural network model for fault analysis to obtain a fault result of each low-voltage contact cabinet, and the fault result is used for indicating whether the low-voltage contact cabinet breaks down or not and the fault type when the low-voltage contact cabinet breaks down.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011493177.1A CN112580498A (en) | 2020-12-17 | 2020-12-17 | Low-voltage contact cabinet fault analysis method and device, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011493177.1A CN112580498A (en) | 2020-12-17 | 2020-12-17 | Low-voltage contact cabinet fault analysis method and device, computer equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112580498A true CN112580498A (en) | 2021-03-30 |
Family
ID=75135757
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011493177.1A Pending CN112580498A (en) | 2020-12-17 | 2020-12-17 | Low-voltage contact cabinet fault analysis method and device, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112580498A (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109816161A (en) * | 2019-01-14 | 2019-05-28 | 中国电力科学研究院有限公司 | A kind of power distribution network operation computer-aided decision support System and its application method |
-
2020
- 2020-12-17 CN CN202011493177.1A patent/CN112580498A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109816161A (en) * | 2019-01-14 | 2019-05-28 | 中国电力科学研究院有限公司 | A kind of power distribution network operation computer-aided decision support System and its application method |
Non-Patent Citations (2)
| Title |
|---|
| 丰保民: "智能型箱式变电站故障诊断系统的研究", 中国优秀博硕士学位论文全文数据库（硕士） 工程科技Ⅱ辑, pages 042 - 38 * |
| 郑树泉等: "工业智能技术与应用", vol. 1, 31 January 2019, 上海科学技术出版社, pages: 284 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108537426B (en) | Power equipment running state estimation method and device and computer equipment | |
| CN111860900A (en) | BIM-based digital twin intelligent machine room management method, device, equipment and medium | |
| CN108445410A (en) | A kind of method and device of monitoring accumulator group operating status | |
| CN105205394B (en) | Data detection method and device for intrusion detection | |
| US11152126B2 (en) | Abnormality diagnosis system and abnormality diagnosis method | |
| CN112884199B (en) | Hydropower station equipment fault prediction method, hydropower station equipment fault prediction device, computer equipment and storage medium | |
| CN108471114B (en) | Power transmission line state evaluation method and device, computer equipment and storage medium | |
| CN110956288A (en) | Underground cable fault early warning method and device based on three-dimensional modeling | |
| CN112182981B (en) | Electrolytic capacitor life prediction method, device, equipment and storage medium | |
| CN110930075A (en) | Power equipment fault positioning method and device, computer equipment and storage medium | |
| CN108549980B (en) | Panoramic visual-based power quality monitoring method and system and computer equipment | |
| CN113222531A (en) | Data storage and monitoring method, system, computer equipment and storage medium | |
| CN113237565A (en) | Terminal temperature measuring method and device, computer equipment and storage medium | |
| CN110544031B (en) | Method and device for predicting reliability of electric energy meter, computer equipment and storage medium | |
| CN112580498A (en) | Low-voltage contact cabinet fault analysis method and device, computer equipment and storage medium | |
| CN113092855A (en) | Temperature monitoring method and device, double-core intelligent ammeter and storage medium | |
| CN117110794A (en) | Intelligent diagnosis system and method for cable faults | |
| CN111123087A (en) | Power distribution network switching equipment fault detection method and device and computer equipment | |
| CN115684829A (en) | Power secondary circuit fault detection early warning method and system | |
| CN115223344A (en) | Monitoring alarm method and device for meter equipment, electronic equipment and storage medium | |
| CN109470975B (en) | Compression joint detection method and device for strain clamp, computer equipment and storage medium | |
| CN114943297A (en) | Intelligent operation and maintenance method and device for generator outlet circuit breaker and computer equipment | |
| CN114487731A (en) | Alternating current system fault arc detection method and device and computer equipment | |
| CN113268844A (en) | Fault equipment acquisition method, device and equipment for power line | |
| CN110954780A (en) | Underground cable fault detection method and device based on block chain |
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 |