CN116482460A - Fault diagnosis method for power grid equipment and related equipment - Google Patents
Fault diagnosis method for power grid equipment and related equipment Download PDFInfo
- Publication number
- CN116482460A CN116482460A CN202310440029.0A CN202310440029A CN116482460A CN 116482460 A CN116482460 A CN 116482460A CN 202310440029 A CN202310440029 A CN 202310440029A CN 116482460 A CN116482460 A CN 116482460A
- Authority
- CN
- China
- Prior art keywords
- fault diagnosis
- data
- analysis
- power grid
- grid equipment
- 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
- 238000003745 diagnosis Methods 0.000 title claims abstract description 49
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000001514 detection method Methods 0.000 claims abstract description 16
- 238000012545 processing Methods 0.000 claims abstract description 12
- 238000004458 analytical method Methods 0.000 claims description 21
- 238000007781 pre-processing Methods 0.000 claims description 13
- 238000007621 cluster analysis Methods 0.000 claims description 12
- 238000000605 extraction Methods 0.000 claims description 12
- 238000001914 filtration Methods 0.000 claims description 12
- 238000000513 principal component analysis Methods 0.000 claims description 12
- 238000012216 screening Methods 0.000 claims description 12
- 238000012706 support-vector machine Methods 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 8
- 238000007405 data analysis Methods 0.000 claims description 6
- 230000000007 visual effect Effects 0.000 claims description 4
- 238000012423 maintenance Methods 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 230000009471 action Effects 0.000 description 3
- 238000013473 artificial intelligence Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000007418 data mining Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The patent discloses a power grid equipment fault diagnosis method and related equipment. The system comprises a plurality of fault detection modules, a data processing module, a fault diagnosis module and a display output module. The fault detection module is used for detecting faults of the power grid equipment and collecting real-time operation data; the data processing module preprocesses the collected real-time operation data to improve the accuracy of fault diagnosis; the fault diagnosis module analyzes the preprocessed data, diagnoses possible faults of the power grid equipment, and helps a user to timely carry out equipment maintenance. The patent also discloses a power grid equipment fault diagnosis method which can efficiently diagnose the faults of the power grid equipment and improve the reliability and stability of the power grid equipment.
Description
Technical Field
The disclosure relates to the technical field of power grids, in particular to a power grid equipment fault diagnosis method and related equipment.
Background
At present, with the continuous development of a power system and the improvement of the intelligent degree, the fault diagnosis technology of power grid equipment is widely focused. The traditional power grid equipment fault diagnosis method mainly depends on experience and manual judgment, and has the problems of low diagnosis precision, low efficiency and the like. With the rapid development of artificial intelligence technology, the power grid equipment fault diagnosis method based on machine learning and data mining gradually becomes a research hot spot.
With the wide application of internet and internet of things, more and more power grid devices can be remotely monitored and controlled through a network and a sensor. This provides more data sources and means for fault diagnosis of electrical equipment. The collected data is analyzed and processed by utilizing the technologies of machine learning, data mining, artificial intelligence and the like, so that the fault type and position of power grid equipment can be accurately diagnosed, and support is provided for rapid fault repair.
Therefore, developing a high-efficiency, accurate and intelligent technology-based power grid equipment fault diagnosis method and system becomes an important research direction in the power industry.
Disclosure of Invention
The embodiment of the disclosure provides a power grid equipment fault diagnosis method and related equipment, which can at least solve part of problems in the prior art.
In a first aspect of embodiments of the present disclosure,
the utility model provides a power grid equipment fault diagnosis method, which comprises the following steps:
performing fault detection on power grid equipment, and collecting real-time operation data;
preprocessing the collected real-time operation data to improve the accuracy of fault diagnosis;
and analyzing the preprocessed data to diagnose possible faults of the power grid equipment.
In an alternative embodiment, the preprocessing includes data filtering, feature extraction and feature screening, and the preprocessing the collected real-time operation data includes:
and carrying out data filtering, feature extraction and feature screening on the acquired real-time operation data.
In an alternative embodiment, wherein the data analysis includes cluster analysis, principal component analysis, and support vector machine analysis, the analyzing the preprocessed data includes:
and analyzing the preprocessed data through the cluster analysis, the principal component analysis and the support vector machine analysis.
In an alternative embodiment, the method further comprises:
the analysis results are summarized and presented to the user in a visual manner.
In a second aspect of the embodiments of the present disclosure,
there is provided a grid equipment fault diagnosis system, the system comprising:
the fault detection module is used for carrying out fault detection on the power grid equipment and collecting real-time operation data;
the data processing module is used for preprocessing the collected real-time operation data so as to improve the accuracy of fault diagnosis;
and the fault diagnosis module is used for analyzing the preprocessed data and diagnosing possible faults of the power grid equipment.
In an alternative embodiment, the preprocessing includes data filtering, feature extraction and feature screening, and the data processing module is specifically configured to perform data filtering, feature extraction and feature screening on the collected real-time operation data, so as to improve accuracy of the fault diagnosis.
In an alternative embodiment, the data analysis includes cluster analysis, principal component analysis and support vector machine analysis, and the fault diagnosis module analyzes the preprocessed data, and specifically configured to:
and analyzing the preprocessed data through the cluster analysis, the principal component analysis and the support vector machine analysis.
In an alternative embodiment, the display device further comprises a display output module;
and the display output module is used for summarizing the analysis results and presenting the analysis results to a user in a visual mode.
In a third aspect of the embodiments of the present disclosure,
there is provided an electronic device including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the instructions stored in the memory to perform the aforementioned grid equipment fault diagnosis method.
In a fourth aspect of embodiments of the present disclosure,
there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the aforementioned grid plant fault diagnosis method.
The application provides a power grid equipment fault diagnosis method and related equipment. The system comprises a plurality of fault detection modules, a data processing module, a fault diagnosis module and a display output module. The fault detection module is used for detecting faults of the power grid equipment and collecting real-time operation data; the data processing module preprocesses the collected real-time operation data to improve the accuracy of fault diagnosis; the fault diagnosis module analyzes the preprocessed data, diagnoses possible faults of the power grid equipment, and helps a user to timely carry out equipment maintenance. The patent also discloses a power grid equipment fault diagnosis method which can efficiently diagnose the faults of the power grid equipment and improve the reliability and stability of the power grid equipment.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings may be obtained according to the provided drawings without inventive effort to a person skilled in the art.
Fig. 1 is a schematic flow chart of a fault diagnosis method for power grid equipment according to an embodiment of the disclosure;
fig. 2 is a schematic structural diagram of a power grid equipment fault diagnosis system according to an embodiment of the disclosure.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are only some embodiments of the present disclosure, not all embodiments. Based on the embodiments in this disclosure, all other embodiments that a person of ordinary skill in the art would obtain without making any inventive effort are within the scope of protection of this disclosure.
The technical scheme of the present disclosure is described in detail below with specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
Fig. 1 is a schematic flow chart of a fault diagnosis method for power grid equipment according to an embodiment of the disclosure, as shown in fig. 1, the method may include:
s101, performing fault detection on power grid equipment, and collecting real-time operation data;
s102, preprocessing the collected real-time operation data to improve the accuracy of fault diagnosis;
s103, analyzing the preprocessed data, and diagnosing possible faults of the power grid equipment.
In an alternative embodiment, where the preprocessing includes data filtering, feature extraction and feature screening, step S102 shown in fig. 1 may specifically include:
and carrying out data filtering, feature extraction and feature screening on the acquired real-time operation data.
In an alternative embodiment, where the data analysis includes cluster analysis, principal component analysis and support vector machine analysis, step S103 shown in fig. 1 may specifically include:
and analyzing the preprocessed data through the cluster analysis, the principal component analysis and the support vector machine analysis.
In a second aspect of the embodiments of the present disclosure,
there is provided a grid equipment fault diagnosis system, as shown in fig. 2, which may include:
the fault detection modules 100 are used for detecting faults of power grid equipment and collecting real-time operation data; the method comprises the steps of carrying out a first treatment on the surface of the
The data processing module 110 is configured to pre-process the collected real-time operation data to improve accuracy of fault diagnosis;
the fault diagnosis module 120 is configured to analyze the preprocessed data, and diagnose possible faults of the power grid device.
In an alternative embodiment, the fault detection module may include a temperature detection module, a vibration detection module, a current detection module, and the like.
In an alternative embodiment, the preprocessing includes data filtering, feature extraction and feature screening, and the data processing module 110 is specifically configured to perform data filtering, feature extraction and feature screening on the collected real-time operation data, so as to improve accuracy of the fault diagnosis.
In an alternative embodiment, where the data analysis includes cluster analysis, principal component analysis, and support vector machine analysis, the fault diagnosis module 120 analyzes the preprocessed data, specifically configured to:
and analyzing the preprocessed data through the cluster analysis, the principal component analysis and the support vector machine analysis.
In an alternative embodiment, the system may be applied to an electrical power system, an electrical power plant manufacturer, or an electrical power plant operation and maintenance company.
In an alternative embodiment, the system may further comprise a display output module;
and the display output module is used for summarizing the analysis results and presenting the analysis results to a user in a visual mode.
In a third aspect of the embodiments of the present disclosure,
there is provided an electronic device including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the instructions stored in the memory to perform the method described previously.
In a fourth aspect of embodiments of the present disclosure,
there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method as described above.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, the device includes one or more processors (CPUs), memory, and a bus. The device may also include input/output interfaces, network interfaces, and the like.
The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
In this specification, each embodiment is described in a related manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments in part.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (10)
1. A method for diagnosing a power grid device fault, the method comprising:
performing fault detection on power grid equipment, and collecting real-time operation data;
preprocessing the collected real-time operation data to improve the accuracy of fault diagnosis;
and analyzing the preprocessed data to diagnose possible faults of the power grid equipment.
2. The method of claim 1, wherein preprocessing comprises data filtering, feature extraction, and feature screening, the preprocessing the collected real-time operational data comprising:
and carrying out data filtering, feature extraction and feature screening on the acquired real-time operation data.
3. The grid plant fault diagnosis method according to claim 1 or 2, wherein the data analysis includes cluster analysis, principal component analysis, and support vector machine analysis, and the analyzing the preprocessed data includes:
and analyzing the preprocessed data through the cluster analysis, the principal component analysis and the support vector machine analysis.
4. The method as recited in claim 1, further comprising:
and summarizing the analysis results and visually presenting the analysis results to a user.
5. A power grid equipment fault diagnosis system, the system comprising:
the fault detection module is used for carrying out fault detection on the power grid equipment and collecting real-time operation data;
the data processing module is used for preprocessing the collected real-time operation data so as to improve the accuracy of fault diagnosis;
and the fault diagnosis module is used for analyzing the preprocessed data and diagnosing possible faults of the power grid equipment.
6. The grid plant fault diagnosis system according to claim 5, wherein preprocessing comprises data filtering, feature extraction and feature screening, and the data processing module is specifically configured to perform data filtering, feature extraction and feature screening on the collected real-time operation data to improve accuracy of the fault diagnosis.
7. The grid plant fault diagnosis system according to claim 5 or 6, wherein the data analysis comprises a cluster analysis, a principal component analysis and a support vector machine analysis, and the fault diagnosis module analyzes the preprocessed data, and is specifically configured to:
and analyzing the preprocessed data through the cluster analysis, the principal component analysis and the support vector machine analysis.
8. The system of claim 5, further comprising a display output module;
and the display output module is used for summarizing the analysis results and presenting the analysis results to a user in a visual mode.
9. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the instructions stored by the memory to perform the grid equipment fault diagnosis method of any of claims 1 to 4.
10. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the grid equipment fault diagnosis method of any of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310440029.0A CN116482460A (en) | 2023-04-21 | 2023-04-21 | Fault diagnosis method for power grid equipment and related equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310440029.0A CN116482460A (en) | 2023-04-21 | 2023-04-21 | Fault diagnosis method for power grid equipment and related equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116482460A true CN116482460A (en) | 2023-07-25 |
Family
ID=87224637
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310440029.0A Pending CN116482460A (en) | 2023-04-21 | 2023-04-21 | Fault diagnosis method for power grid equipment and related equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116482460A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117272152A (en) * | 2023-11-16 | 2023-12-22 | 晶科储能科技有限公司 | Energy storage system fault diagnosis method, system, electronic equipment and storage medium |
-
2023
- 2023-04-21 CN CN202310440029.0A patent/CN116482460A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117272152A (en) * | 2023-11-16 | 2023-12-22 | 晶科储能科技有限公司 | Energy storage system fault diagnosis method, system, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP7069269B2 (en) | Semi-supervised methods and systems for deep anomaly detection for large industrial surveillance systems based on time series data using digital twin simulation data | |
| de la Hermosa González | Wind farm monitoring using Mahalanobis distance and fuzzy clustering | |
| CN112393931A (en) | Detection method, detection device, electronic equipment and computer readable medium | |
| CN111767957B (en) | Log abnormality detection method and device, storage medium and electronic equipment | |
| CN116482460A (en) | Fault diagnosis method for power grid equipment and related equipment | |
| CN113742993A (en) | Method, device, equipment and storage medium for predicting life loss of dry-type transformer | |
| CN112926636A (en) | Method and device for detecting abnormal temperature of traction converter cabinet body | |
| CN112904148A (en) | Intelligent cable operation monitoring system, method and device | |
| CN114925905A (en) | Industrial energy consumption allocation method, equipment and medium based on identification analysis | |
| Du et al. | RUL prediction based on GAM–CNN for rotating machinery | |
| CN116611006B (en) | Fault identification method and device of electric kettle based on user feedback | |
| Al-Dahidi et al. | A novel ensemble clustering for operational transients classification with application to a nuclear power plant turbine | |
| CN116820821A (en) | Disk failure detection method, apparatus, electronic device and computer readable storage medium | |
| CN112527610A (en) | Method and device for monitoring operation state of equipment | |
| CN116089891A (en) | Method and system for diagnosing safety condition of pile foundation structure | |
| CN115510998A (en) | Transaction abnormal value detection method and device | |
| CN113283076A (en) | Method and device for researching electricity utilization abnormity and computer readable storage medium | |
| CN110879537A (en) | Method and device for processing transient stability of intelligent household energy system | |
| CN113468384B (en) | Processing method, device, storage medium and processor for network information source information | |
| CN116361351A (en) | Data mining method for health management of industrial equipment | |
| CN114330569A (en) | Method and equipment for detecting fan unit component fault and storage medium | |
| CN113539352A (en) | Solid state disk hidden fault detection method and related equipment | |
| CN113098390A (en) | New energy grid-connected automatic alarm and visualization method and device based on fuzzy mathematics | |
| CN113139332A (en) | Automatic model construction method, device and equipment | |
| CN112379656A (en) | Processing method, device, equipment and medium for detecting abnormal data of industrial system |
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 |