CN117706258B - Fault detection system based on big data processing - Google Patents
Fault detection system based on big data processing Download PDFInfo
- Publication number
- CN117706258B CN117706258B CN202410166009.3A CN202410166009A CN117706258B CN 117706258 B CN117706258 B CN 117706258B CN 202410166009 A CN202410166009 A CN 202410166009A CN 117706258 B CN117706258 B CN 117706258B
- Authority
- CN
- China
- Prior art keywords
- load
- power distribution
- distribution cabinet
- fault
- coefficient
- 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
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 34
- 238000012545 processing Methods 0.000 title claims abstract description 14
- 238000000034 method Methods 0.000 claims description 61
- 230000036962 time dependent Effects 0.000 claims description 20
- 238000011156 evaluation Methods 0.000 claims description 10
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Landscapes
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The invention relates to the technical field of fault detection and discloses a fault detection system based on big data processing.
Description
Technical Field
The invention relates to the technical field of fault detection, in particular to a fault detection system based on big data processing.
Background
With the construction and development of power systems, the distribution, control and protection demands of electric energy are increasing. The distribution, control and protection of electric energy are required to involve a plurality of complex technical problems, and the importance of the power distribution cabinet is also increasingly highlighted as one of the technical problems.
The traditional power distribution cabinet fault detection mainly comprises manual work for periodically overhauling the power distribution cabinet, wherein most of the overhauling is performed after faults occur, the faults can be detected, certain hysteresis quality is provided, the faults of the power distribution cabinet can not be detected in real time, moreover, even if the faults of the power distribution cabinet can be detected in real time, the existing fault detection system can only detect the faults after the fault lamps are on, but the internal load of the power distribution cabinet is at the fault edge and still does not reach the fault alarm threshold value, and potential faults exist, meanwhile, the overall faults of the power distribution cabinet are related to load operation parameters and the internal overall environment of the power distribution cabinet, and the existing fault detection system can not detect the faults in real time.
Disclosure of Invention
The invention aims to provide a fault detection system based on big data processing, which solves the technical problems.
The aim of the invention can be achieved by the following technical scheme:
a big data processing based fault detection system, the system comprising:
the working parameter acquisition module is used for acquiring load parameters of the working process of the power distribution cabinet;
The environment parameter acquisition module is used for acquiring temperature parameters in the power distribution cabinet;
The analysis module is used for analyzing the data acquired by the working parameter acquisition module and the environment parameter module;
The fault evaluation module is used for evaluating the fault state of the power distribution cabinet according to the analysis result of the analysis module;
The remote control module is used for remotely controlling the power distribution cabinet according to the evaluation result of the fault evaluation module;
The working process of the detection system comprises the following steps:
Step S1, numbering all loads in the power distribution cabinet, wherein the numbers are as follows: 1. 2 … n;
s2, sequentially collecting working parameters of each load through a working parameter collecting module, and analyzing fault states of each load through an analyzing module;
step S3, if a load is found to have a fault, immediately closing the load through a remote control module to avoid affecting the work of the whole power distribution cabinet, and if the load is not found to have the fault, entering a step S4;
S4, predicting potential faults of the load, and if the potential faults are predicted, immediately closing the load through the remote control module;
and S5, acquiring the internal temperature of the power distribution cabinet through an environment parameter acquisition module, and then analyzing the internal environment of the power distribution cabinet through an analysis module to detect whether the whole power distribution cabinet fails or not.
As a further description of the solution of the present invention, the specific working process of step S3 includes:
And acquiring real-time load currents of all loads, comparing the real-time load currents of all loads with a set target threshold value, if any one real-time load current is larger than the target threshold value, indicating that the load fails, immediately closing the load through a remote control module, and otherwise, carrying out consistency fault analysis of all loads of the power distribution cabinet and load integrity fault analysis of the power distribution cabinet.
As a further description of the scheme of the invention, the specific working process of analyzing the consistency faults of each load of the power distribution cabinet comprises the following steps:
Acquiring time-dependent data of load current of the ith load, and fitting a time-dependent curve of load current of the ith load ；
The consistency fault coefficient of the ith load of the power distribution cabinet is calculated through the following steps of：
；（1）
In the method, in the process of the invention,For the consistent fault coefficient of the ith load, n is the number of loads in the power distribution cabinet, i is [1, n ],/>For historical detection time points,/>K is a weight coefficient for the current detection time point;
Consistency failure coefficient of ith load Consistency fault coefficient with i-th load preset threshold/>Comparing, if the consistency fault coefficient/>, of the ith loadConsistency fault coefficient preset threshold value greater than ith load/>And immediately carrying out early warning, and closing the ith load through the remote control module.
As a further description of the scheme of the invention, the specific working process of the load integrity fault analysis of the power distribution cabinet comprises the following steps:
calculating the load integrity fault coefficient of the power distribution cabinet through the following steps ：
；（2）
In the method, in the process of the invention,For the weight coefficient of each load,/>The load integrity fault coefficient of the power distribution cabinet;
substituting formula (1) into formula (2) to obtain overall fault coefficient of load of power distribution cabinet And then the load integrity fault coefficient/> of the power distribution cabinetFailure coefficient threshold/>, integral with power distribution cabinet loadComparing, if the load integrity fault coefficient of the power distribution cabinetGreater than the power distribution cabinet load integrity fault coefficient threshold/>And immediately early warning is carried out, and the whole power distribution cabinet is closed through the remote control module.
As a further description of the solution of the present invention, the specific working process of step S4 includes:
Acquiring time-dependent data of load current of the ith load, and fitting a time-dependent curve of load current of the ith load ；
Obtaining a standard curve of load current of an ith load along with timeConstructing a rectangular coordinate system xoy;
generating load current time-dependent curves of the ith load in the rectangular coordinate system xoy And the load current versus time standard curve/>, of the ith load；
Obtaining load current time-dependent curves of i loads in rectangular coordinate system xoyArea enclosed with x-axis/>Obtaining a standard curve/>, of load current of an ith load over timeArea enclosed with x-axis/>；
Calculating a potential failure coefficient of the ith load by：
；（3）
In the method, in the process of the invention,For potential failure coefficient of ith load,/>For historical detection time points,/>The current detection time point;
potential failure coefficient of ith load Preset threshold/>, with potential failure coefficient of the ith loadComparing, if potential failure coefficient/>, of the ith loadPotential failure coefficient preset threshold value greater than ith load/>And immediately carrying out early warning, and closing the ith load through the remote control module.
As a further description of the solution of the present invention, the specific working process of step S4 further includes:
Calculating the potential fault coefficient of the load integrity of the power distribution cabinet through the following steps ：
；（4）
In the method, in the process of the invention,For the weight coefficient of each load,/>Potential fault coefficients for the load integrity of the power distribution cabinet;
Substituting formula (3) into formula (4) to obtain potential fault coefficients of load integrity of power distribution cabinet The potential failure coefficient/>, of the load integrity of the power distribution cabinetPotential failure coefficient threshold/>, integral with power distribution cabinet loadComparing, if the potential failure coefficient/>, of the load integrity of the power distribution cabinetGreater than a power distribution cabinet load integrity latent fault coefficient threshold/>And immediately early warning is carried out, and the whole power distribution cabinet is closed through the remote control module.
As a further description of the solution of the present invention, the specific working process of step S5 includes:
Integral fault coefficient of power distribution cabinet is calculated through the following steps ：
；（5）
In the method, in the process of the invention,And/>Respectively are weight coefficients,/>As a load influencing function,/>As a function of temperature influence,/>The fault coefficient is the integral fault coefficient of the power distribution cabinet;
integral fault coefficient of power distribution cabinet Integral fault coefficient threshold value/> with power distribution cabinetComparing, if the overall fault coefficient of the power distribution cabinet/>Is greater than the integral fault coefficient threshold value/>And immediately early warning is carried out, and the whole power distribution cabinet is closed through the remote control module.
As a further description of the solution of the present invention, the following is mentionedThe acquisition process of (1) comprises:
Acquiring time-varying data of internal temperature of power distribution cabinet, and fitting time-varying curve of internal temperature of power distribution cabinet ；
Calculated by：
；
In the method, in the process of the invention,Is the actual temperature in the power distribution cabinet,/>And (5) standard temperature inside the power distribution cabinet.
The invention has the beneficial effects that: according to the invention, all loads in the power distribution cabinet are numbered, then the working parameters of all loads are sequentially collected through the working parameter collection module, the internal temperature of the power distribution cabinet is collected through the environment parameter collection module, then whether all loads in the power distribution cabinet have faults and potential faults or not is monitored in real time through the working parameters of the loads, and fault evaluation is carried out on the whole of all loads.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a schematic diagram of a portion of the workflow of a big data processing based fault detection system provided by the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a fault detection system based on big data processing, the system includes:
the working parameter acquisition module is used for acquiring load parameters of the working process of the power distribution cabinet;
The environment parameter acquisition module is used for acquiring temperature parameters in the power distribution cabinet;
The analysis module is used for analyzing the data acquired by the working parameter acquisition module and the environment parameter module;
The fault evaluation module is used for evaluating the fault state of the power distribution cabinet according to the analysis result of the analysis module;
The remote control module is used for remotely controlling the power distribution cabinet according to the evaluation result of the fault evaluation module;
The working process of the detection system comprises the following steps:
Step S1, numbering all loads in the power distribution cabinet, wherein the numbers are as follows: 1. 2 … n;
s2, sequentially collecting working parameters of each load through a working parameter collecting module, and analyzing fault states of each load through an analyzing module;
step S3, if a load is found to have a fault, immediately closing the load through a remote control module to avoid affecting the work of the whole power distribution cabinet, and if the load is not found to have the fault, entering a step S4;
S4, predicting potential faults of the load, and if the potential faults are predicted, immediately closing the load through the remote control module;
and S5, acquiring the internal temperature of the power distribution cabinet through an environment parameter acquisition module, and then analyzing the internal environment of the power distribution cabinet through an analysis module to detect whether the whole power distribution cabinet fails or not.
Through the technical scheme, all loads in the power distribution cabinet are numbered, working parameters of all loads are sequentially collected through the working parameter collection module, the temperature in the power distribution cabinet is collected through the environment parameter collection module, whether all loads in the power distribution cabinet have faults and potential faults or not is monitored in real time through the working parameters of the loads, fault assessment is carried out on the whole of all the loads, and finally fault assessment is carried out on the whole of the power distribution cabinet through the working parameters and the temperature in the power distribution cabinet.
As a further description of the solution of the present invention, the specific working process of step S3 includes:
And acquiring real-time load currents of all loads, comparing the real-time load currents of all loads with a set target threshold value, if any one real-time load current is larger than the target threshold value, indicating that the load fails, immediately closing the load through a remote control module, and otherwise, carrying out consistency fault analysis of all loads of the power distribution cabinet and load integrity fault analysis of the power distribution cabinet.
As a further description of the scheme of the invention, the specific working process of analyzing the consistency faults of each load of the power distribution cabinet comprises the following steps:
Acquiring time-dependent data of load current of the ith load, and fitting a time-dependent curve of load current of the ith load ；
The consistency fault coefficient of the ith load of the power distribution cabinet is calculated through the following steps of：
；（1）
In the method, in the process of the invention,For the consistent fault coefficient of the ith load, n is the number of loads in the power distribution cabinet, i is [1, n ],/>For historical detection time points,/>K is a weight coefficient for the current detection time point;
Consistency failure coefficient of ith load Consistency fault coefficient with i-th load preset threshold/>Comparing, if the consistency fault coefficient/>, of the ith loadConsistency fault coefficient preset threshold value greater than ith load/>And immediately carrying out early warning, and closing the ith load through the remote control module.
By the technical scheme, the embodiment obtains the time-dependent data of the load current of the ith load and fits the time-dependent curve of the load current of the ith loadThen according to the formulaCalculating the consistency fault coefficient/>, of the ith load of the power distribution cabinetConsistency failure coefficient/>, of ith loadConsistency fault coefficient with i-th load preset threshold/>Comparing, if the consistency fault coefficient/>, of the ith loadConsistency fault coefficient preset threshold value greater than ith load/>And immediately carrying out early warning, and closing the ith load through the remote control module.
As a further description of the scheme of the invention, the specific working process of the load integrity fault analysis of the power distribution cabinet comprises the following steps:
calculating the load integrity fault coefficient of the power distribution cabinet through the following steps ：
；（2）
In the method, in the process of the invention,For the weight coefficient of each load,/>The load integrity fault coefficient of the power distribution cabinet;
substituting formula (1) into formula (2) to obtain overall fault coefficient of load of power distribution cabinet And then the load integrity fault coefficient/> of the power distribution cabinetFailure coefficient threshold/>, integral with power distribution cabinet loadComparing, if the load integrity fault coefficient of the power distribution cabinetGreater than the power distribution cabinet load integrity fault coefficient threshold/>And immediately early warning is carried out, and the whole power distribution cabinet is closed through the remote control module.
Through the technical scheme, the method and the device adopt the formulaCalculating load integrity fault coefficient of power distribution cabinetAnd then the load integrity fault coefficient/> of the power distribution cabinetFailure coefficient threshold/>, integral with power distribution cabinet loadComparing, if the load integrity fault coefficient/> of the power distribution cabinetGreater than the power distribution cabinet load integrity fault coefficient threshold/>And immediately early warning is carried out, and the whole power distribution cabinet is closed through the remote control module.
As a further description of the solution of the present invention, the specific working process of step S4 includes:
Acquiring time-dependent data of load current of the ith load, and fitting a time-dependent curve of load current of the ith load ；
Obtaining a standard curve of load current of an ith load along with timeConstructing a rectangular coordinate system xoy;
generating load current time-dependent curves of the ith load in the rectangular coordinate system xoy And the load current versus time standard curve/>, of the ith load；
Obtaining load current time-dependent curves of i loads in rectangular coordinate system xoyArea enclosed with x-axis/>Obtaining a standard curve/>, of load current of an ith load over timeArea enclosed with x-axis/>；
Calculating a potential failure coefficient of the ith load by：
；（3）
In the method, in the process of the invention,For potential failure coefficient of ith load,/>For historical detection time points,/>The current detection time point;
potential failure coefficient of ith load Preset threshold/>, with potential failure coefficient of the ith loadComparing, if potential failure coefficient/>, of the ith loadPotential failure coefficient preset threshold value greater than ith load/>And immediately carrying out early warning, and closing the ith load through the remote control module.
As a further description of the solution of the present invention, the specific working process of step S4 further includes:
Calculating the potential fault coefficient of the load integrity of the power distribution cabinet through the following steps ：
；（4）
In the method, in the process of the invention,For the weight coefficient of each load,/>Potential fault coefficients for the load integrity of the power distribution cabinet;
Substituting formula (3) into formula (4) to obtain potential fault coefficients of load integrity of power distribution cabinet The potential failure coefficient/>, of the load integrity of the power distribution cabinetPotential failure coefficient threshold/>, integral with power distribution cabinet loadComparing, if the potential failure coefficient/>, of the load integrity of the power distribution cabinetGreater than a power distribution cabinet load integrity latent fault coefficient threshold/>And immediately early warning is carried out, and the whole power distribution cabinet is closed through the remote control module.
Through the technical scheme, the rectangular coordinate system xoy is constructed, and load current change curves of i loads along with time are obtained in the rectangular coordinate system xoyArea enclosed with x-axis/>Obtaining a standard curve/>, of load current of an ith load over timeArea enclosed with x-axis/>By the formula/>Calculating potential failure coefficient of ith loadPotential failure coefficient/>, of the ith loadPreset threshold/>, with potential failure coefficient of the ith loadComparing, if potential failure coefficient/>, of the ith loadPotential failure coefficient preset threshold value greater than ith load/>Early warning is immediately carried out, the ith load is closed through the remote control module, and then the formula/>Calculating potential failure coefficient/> of load integrity of power distribution cabinetPotential failure coefficient/> of load integrity of power distribution cabinetPotential failure coefficient threshold/>, integral with power distribution cabinet loadComparing, if the potential failure coefficient/>, of the load integrity of the power distribution cabinetGreater than a power distribution cabinet load integrity latent fault coefficient threshold/>And immediately early warning is carried out, and the whole power distribution cabinet is closed through the remote control module.
As a further description of the solution of the present invention, the specific working process of step S5 includes:
Integral fault coefficient of power distribution cabinet is calculated through the following steps ：
；（5）
In the method, in the process of the invention,And/>Respectively are weight coefficients,/>As a load influencing function,/>As a function of temperature influence,/>The fault coefficient is the integral fault coefficient of the power distribution cabinet;
integral fault coefficient of power distribution cabinet Integral fault coefficient threshold value/> with power distribution cabinetComparing, if the overall fault coefficient of the power distribution cabinet/>Is greater than the integral fault coefficient threshold value/>And immediately early warning is carried out, and the whole power distribution cabinet is closed through the remote control module.
As a further description of the solution of the present invention, the following is mentionedThe acquisition process of (1) comprises:
Acquiring time-varying data of internal temperature of power distribution cabinet, and fitting time-varying curve of internal temperature of power distribution cabinet ；
Calculated by：
；
In the method, in the process of the invention,Is the actual temperature in the power distribution cabinet,/>And (5) standard temperature inside the power distribution cabinet.
Through the technical scheme, the invention adopts the following steps ofCalculating integral fault coefficient/> of power distribution cabinetThe integral fault coefficient/> of the power distribution cabinetIntegral fault coefficient threshold value/> with power distribution cabinetComparing, if the overall fault coefficient of the power distribution cabinet/>Is greater than the integral fault coefficient threshold value/>And immediately early warning is carried out, and the whole power distribution cabinet is closed through the remote control module.
An embodiment of the present invention has been described in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (6)
1. A big data processing based fault detection system, the system comprising:
the working parameter acquisition module is used for acquiring load parameters of the working process of the power distribution cabinet;
The environment parameter acquisition module is used for acquiring temperature parameters in the power distribution cabinet;
The analysis module is used for analyzing the data acquired by the working parameter acquisition module and the environment parameter module;
The fault evaluation module is used for evaluating the fault state of the power distribution cabinet according to the analysis result of the analysis module;
The remote control module is used for remotely controlling the power distribution cabinet according to the evaluation result of the fault evaluation module;
The working process of the detection system comprises the following steps:
Step S1, numbering all loads in the power distribution cabinet, wherein the numbers are as follows: 1. 2 … n;
s2, sequentially collecting working parameters of each load through a working parameter collecting module, and analyzing fault states of each load through an analyzing module;
step S3, if a load is found to have a fault, immediately closing the load through a remote control module to avoid affecting the work of the whole power distribution cabinet, and if the load is not found to have the fault, entering a step S4;
S4, predicting potential faults of the load, and if the potential faults are predicted, immediately closing the load through the remote control module;
the specific working process of the step S4 includes:
Acquiring time-dependent data of load current of the ith load, and fitting a time-dependent curve of load current of the ith load ；
Obtaining a standard curve of load current of an ith load along with timeConstructing a rectangular coordinate system xoy;
generating load current time-dependent curves of the ith load in the rectangular coordinate system xoy And the load current versus time standard curve/>, of the ith load；
Obtaining load current time-dependent curves of i loads in rectangular coordinate system xoyArea enclosed with x-axis/>Obtaining a standard curve/>, of load current of an ith load over timeArea enclosed with x-axis/>；
Calculating a potential failure coefficient of the ith load by：
；（3）
In the method, in the process of the invention,For potential failure coefficient of ith load,/>For historical detection time points,/>The current detection time point;
potential failure coefficient of ith load Preset threshold/>, with potential failure coefficient of the ith loadComparing, if potential failure coefficient/>, of the ith loadPotential failure coefficient preset threshold value greater than ith load/>Immediately early warning is carried out, and the ith load is closed through the remote control module;
and S5, acquiring the internal temperature of the power distribution cabinet through an environment parameter acquisition module, and then analyzing the internal environment of the power distribution cabinet through an analysis module to detect whether the whole power distribution cabinet fails or not.
2. The fault detection system based on big data processing according to claim 1, wherein the specific operation of step S3 includes:
And acquiring real-time load currents of all loads, comparing the real-time load currents of all loads with a set target threshold value, if any one real-time load current is larger than the target threshold value, indicating that the load fails, immediately closing the load through a remote control module, and otherwise, carrying out consistency fault analysis of all loads of the power distribution cabinet and load integrity fault analysis of the power distribution cabinet.
3. The fault detection system based on big data processing according to claim 2, wherein the specific working process of the analysis of the consistency faults of each load of the power distribution cabinet comprises:
Acquiring time-dependent data of load current of the ith load, and fitting a time-dependent curve of load current of the ith load ；
The consistency fault coefficient of the ith load of the power distribution cabinet is calculated through the following steps of：
+k/>；（1）
In the method, in the process of the invention,For the consistent fault coefficient of the ith load, n is the number of loads in the power distribution cabinet, i is [1, n ],/>For historical detection time points,/>K is a weight coefficient for the current detection time point;
Consistency failure coefficient of ith load Consistency fault coefficient with i-th load preset threshold/>Comparing, if the consistency fault coefficient/>, of the ith loadConsistency fault coefficient preset threshold value greater than ith load/>And immediately carrying out early warning, and closing the ith load through the remote control module.
4. The fault detection system based on big data processing according to claim 2, wherein the specific working process of the load integrity fault analysis of the power distribution cabinet comprises:
calculating the load integrity fault coefficient of the power distribution cabinet through the following steps ：
；（2）
In the method, in the process of the invention,For the weight coefficient of each load,/>The load integrity fault coefficient of the power distribution cabinet;
substituting formula (1) into formula (2) to obtain overall fault coefficient of load of power distribution cabinet And then the load integrity fault coefficient/> of the power distribution cabinetFailure coefficient threshold/>, integral with power distribution cabinet loadComparing, if the load integrity fault coefficient/> of the power distribution cabinetGreater than the power distribution cabinet load integrity fault coefficient threshold/>And immediately early warning is carried out, and the whole power distribution cabinet is closed through the remote control module.
5. The fault detection system based on big data processing according to claim 1, wherein the specific operation of step S4 further comprises:
Calculating the potential fault coefficient of the load integrity of the power distribution cabinet through the following steps ：
；（4）
In the method, in the process of the invention,For the weight coefficient of each load,/>Potential fault coefficients for the load integrity of the power distribution cabinet;
Substituting formula (3) into formula (4) to obtain potential fault coefficients of load integrity of power distribution cabinet The potential failure coefficient/>, of the load integrity of the power distribution cabinetPotential failure coefficient threshold/>, integral with power distribution cabinet loadComparing, if the potential failure coefficient/>, of the load integrity of the power distribution cabinetGreater than a power distribution cabinet load integrity latent fault coefficient threshold/>And immediately early warning is carried out, and the whole power distribution cabinet is closed through the remote control module.
6. The fault detection system based on big data processing according to claim 1, wherein the specific operation of step S5 includes:
Integral fault coefficient of power distribution cabinet is calculated through the following steps ：
；（5）
In the method, in the process of the invention,And/>Respectively are weight coefficients,/>As a load influencing function,/>As a function of temperature influence,/>The fault coefficient is the integral fault coefficient of the power distribution cabinet;
The said The acquisition process of (1) comprises:
Acquiring time-varying data of internal temperature of power distribution cabinet, and fitting time-varying curve of internal temperature of power distribution cabinet ；
Calculated by：
；
In the method, in the process of the invention,Is the actual temperature in the power distribution cabinet,/>The internal standard temperature of the power distribution cabinet;
integral fault coefficient of power distribution cabinet Integral fault coefficient threshold value/> with power distribution cabinetComparing, if the overall fault coefficient of the power distribution cabinet/>Is greater than the integral fault coefficient threshold value/>And immediately early warning is carried out, and the whole power distribution cabinet is closed through the remote control module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410166009.3A CN117706258B (en) | 2024-02-06 | 2024-02-06 | Fault detection system based on big data processing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410166009.3A CN117706258B (en) | 2024-02-06 | 2024-02-06 | Fault detection system based on big data processing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117706258A CN117706258A (en) | 2024-03-15 |
| CN117706258B true CN117706258B (en) | 2024-05-10 |
Family
ID=90155677
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410166009.3A Active CN117706258B (en) | 2024-02-06 | 2024-02-06 | Fault detection system based on big data processing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117706258B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN206804743U (en) * | 2017-01-04 | 2017-12-26 | 国网河南新野县供电公司 | Switch cabinet state monitoring device and system |
| CN111272225A (en) * | 2020-03-04 | 2020-06-12 | 四川瑞霆电力科技有限公司 | Switch cabinet comprehensive state monitoring system |
| CN112611940A (en) * | 2020-12-10 | 2021-04-06 | 国网辽宁省电力有限公司丹东供电公司 | Power distribution cabinet cable joint loosening early warning method based on real-time data acquisition |
| CN113612306A (en) * | 2020-05-18 | 2021-11-05 | 海南美亚电能有限公司 | Distributed power distribution cabinet and control system thereof |
| WO2022007013A1 (en) * | 2020-07-08 | 2022-01-13 | 南京东创信通物联网研究院有限公司 | Online monitoring and fault prediction system for high-voltage electrical device |
| CN114692433A (en) * | 2022-04-28 | 2022-07-01 | 中原环保股份有限公司 | Fault analysis method for power distribution cabinet surface temperature inspection |
| CN115327369A (en) * | 2022-09-13 | 2022-11-11 | 国网安徽省电力有限公司宿州供电公司 | On-line monitoring and analyzing system for opening and closing current of switch cabinet circuit breaker |
| CN115792442A (en) * | 2022-11-23 | 2023-03-14 | 大全集团有限公司 | Data-driven comprehensive fault diagnosis method for direct-current switch cabinet for rail transit |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102018130690B3 (en) * | 2018-12-03 | 2020-03-26 | Bender Gmbh & Co. Kg | Magnetic field measuring device and method for detecting a localization current in a branched AC power supply system |
-
2024
- 2024-02-06 CN CN202410166009.3A patent/CN117706258B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN206804743U (en) * | 2017-01-04 | 2017-12-26 | 国网河南新野县供电公司 | Switch cabinet state monitoring device and system |
| CN111272225A (en) * | 2020-03-04 | 2020-06-12 | 四川瑞霆电力科技有限公司 | Switch cabinet comprehensive state monitoring system |
| CN113612306A (en) * | 2020-05-18 | 2021-11-05 | 海南美亚电能有限公司 | Distributed power distribution cabinet and control system thereof |
| WO2022007013A1 (en) * | 2020-07-08 | 2022-01-13 | 南京东创信通物联网研究院有限公司 | Online monitoring and fault prediction system for high-voltage electrical device |
| CN112611940A (en) * | 2020-12-10 | 2021-04-06 | 国网辽宁省电力有限公司丹东供电公司 | Power distribution cabinet cable joint loosening early warning method based on real-time data acquisition |
| CN114692433A (en) * | 2022-04-28 | 2022-07-01 | 中原环保股份有限公司 | Fault analysis method for power distribution cabinet surface temperature inspection |
| CN115327369A (en) * | 2022-09-13 | 2022-11-11 | 国网安徽省电力有限公司宿州供电公司 | On-line monitoring and analyzing system for opening and closing current of switch cabinet circuit breaker |
| CN115792442A (en) * | 2022-11-23 | 2023-03-14 | 大全集团有限公司 | Data-driven comprehensive fault diagnosis method for direct-current switch cabinet for rail transit |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117706258A (en) | 2024-03-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109444791B (en) | Error state evaluation method and system for capacitor voltage transformer | |
| CN116069079B (en) | Intelligent heat dissipation control method and system for intelligent switch cabinet | |
| CN114252749B (en) | Transformer partial discharge detection method and device based on multiple sensors | |
| CN113763667B (en) | Fire disaster early warning and state monitoring device and method based on 5G edge calculation | |
| CN115979349B (en) | Power station space environment monitoring method and system | |
| CN112149877B (en) | Multi-source data driven fault prediction method and system for multi-element complex urban power grid | |
| CN114895163A (en) | Cable inspection positioning device and method based on cable insulation performance | |
| CN114740303A (en) | Fault monitoring system of wireless passive high-voltage switch cabinet | |
| CN117269655B (en) | Transformer substation power equipment temperature anomaly monitoring method, system, terminal and medium | |
| CN117706258B (en) | Fault detection system based on big data processing | |
| CN106325258B (en) | Relay protection device state evaluation method based on online monitoring information | |
| CN117192369A (en) | Traction motor monitoring and diagnosing method based on digital twin technology | |
| CN116827264A (en) | Early warning system for photovoltaic power generation | |
| CN112989573B (en) | Metering cabinet state detection method, device, equipment and medium | |
| CN115632379A (en) | Multi-way detection arc light protection system for vcs isolating switch box | |
| CN115684829A (en) | Power secondary circuit fault detection early warning method and system | |
| CN113486535A (en) | Power grid information detection and analysis method based on environmental information | |
| CN111352365B (en) | Dustproof ventilation type electric power and electrical equipment cabinet and control method | |
| CN114399074A (en) | Power grid fault early warning method, device, equipment and storage medium based on big data | |
| CN117851910B (en) | Self-sensing method and system for hooking and dismantling novel intelligent grounding device of power transmission line | |
| CN116150666B (en) | Energy storage system fault detection method and device and intelligent terminal | |
| CN116449898B (en) | Remote temperature and humidity control system for switch cabinet | |
| CN114336612B (en) | Power station and electric quantity loss calculation method of fault equipment of power station and related application equipment | |
| CN114924171A (en) | Cable insulation performance fault early warning device and method | |
| Wang et al. | Environmental risk perception and warning strategy of power metering laboratory based on LSTM network |
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 |