CN115587016A - Power equipment operation analysis method - Google Patents
Power equipment operation analysis method Download PDFInfo
- Publication number
- CN115587016A CN115587016A CN202211300224.5A CN202211300224A CN115587016A CN 115587016 A CN115587016 A CN 115587016A CN 202211300224 A CN202211300224 A CN 202211300224A CN 115587016 A CN115587016 A CN 115587016A
- Authority
- CN
- China
- Prior art keywords
- data
- equipment
- fault
- state
- model
- 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 28
- 238000000034 method Methods 0.000 claims abstract description 22
- 238000003745 diagnosis Methods 0.000 claims abstract description 17
- 238000012544 monitoring process Methods 0.000 claims abstract description 13
- 238000001514 detection method Methods 0.000 claims abstract description 9
- 230000008859 change Effects 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims abstract description 8
- 238000003860 storage Methods 0.000 claims abstract description 7
- 238000004140 cleaning Methods 0.000 claims abstract description 6
- 238000013500 data storage Methods 0.000 claims abstract description 4
- 230000007547 defect Effects 0.000 claims description 7
- 238000009826 distribution Methods 0.000 claims description 4
- 238000012300 Sequence Analysis Methods 0.000 claims description 3
- 230000004931 aggregating effect Effects 0.000 claims description 3
- 238000012098 association analyses Methods 0.000 claims description 3
- 238000009434 installation Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 238000007726 management method Methods 0.000 claims description 2
- 238000011156 evaluation Methods 0.000 abstract description 6
- 230000002159 abnormal effect Effects 0.000 abstract description 4
- 238000007418 data mining Methods 0.000 abstract description 4
- 238000005516 engineering process Methods 0.000 abstract description 4
- 230000000694 effects Effects 0.000 abstract description 3
- 230000010354 integration Effects 0.000 abstract description 3
- 238000006243 chemical reaction Methods 0.000 abstract description 2
- 238000000605 extraction Methods 0.000 abstract description 2
- 238000007689 inspection Methods 0.000 abstract description 2
- 238000013441 quality evaluation Methods 0.000 abstract 1
- 238000011161 development Methods 0.000 description 5
- 238000012423 maintenance Methods 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000010248 power generation Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 230000002542 deteriorative effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 230000001502 supplementing effect Effects 0.000 description 1
- 208000024891 symptom Diseases 0.000 description 1
- 239000002023 wood Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3447—Performance evaluation by modeling
-
- 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)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Marketing (AREA)
- Health & Medical Sciences (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Strategic Management (AREA)
- Bioinformatics & Computational Biology (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Biology (AREA)
- Computer Hardware Design (AREA)
- Life Sciences & Earth Sciences (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention belongs to the technical field of power equipment, and particularly relates to a power equipment operation analysis method. The method comprises the following steps: the method comprises the steps of multi-source heterogeneous equipment state data integration, state data quality evaluation and data cleaning, equipment state data storage, equipment state data processing, equipment state change and fault prediction. The method is combined with the popularization and application of the charged detection, on-line monitoring and intelligent inspection technologies in recent years, massive state detection data are collected, the integration, conversion, cleaning, storage, processing and data mining of multi-source heterogeneous data are realized, the accuracy and adaptability of the operation analysis of the power equipment are effectively improved, and good effects are realized in the aspects of application scenes such as historical knowledge extraction, personalized evaluation, abnormal rapid detection, intelligent fault diagnosis and state prediction.
Description
Technical Field
The invention relates to a power equipment operation analysis method.
Background
With the continuous development of social economy, the running power grid is continuously developed and strengthened, the development of electricity directly influences the development of economy, and meanwhile, the improvement of the living standard of people is promoted. If the electric power system is unstable in operation, great inconvenience is brought to production and life, absolute stability does not exist, and the stability of the electric power system can be influenced by various factors such as load change of the electric power system, influence of natural factors, stability of electric power equipment, service life and the like, so that the operation condition of the electric power equipment can be known in time, and analysis and processing can be carried out in time, the stability of the electric power system is maintained, and the safe operation of the electric power system is ensured, so that the electric power system is very important work.
At present, the operation analysis of the power equipment mainly adopts an on-line monitoring and fault accurate positioning mode, and the two technical means are the main technical basis of the development of an intelligent power technology circuit. The online monitoring technology mainly finds out hidden trouble faults in the operation of power equipment, processes the hidden trouble in time, and quickly judges the fault reason by maintenance personnel, accurately positions the equipment and lines after the faults occur, and then carries out fault recovery processing, thereby not influencing the normal power utilization of social production. However, in actual operation of the power equipment, the method is mainly based on a small number of state parameters and a unified diagnostic standard, the determination of the parameters and the threshold values is mainly based on statistical analysis and subjective experience of data, the analysis result is one-sided, the objective rule between fault evolution and performance characteristics cannot be comprehensively reflected, and the fixed threshold value judgment method of the unified standard is difficult to guarantee applicability to different equipment.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide the power equipment operation analysis method which can improve the stability and accuracy of the power equipment operation analysis.
The above purpose is realized by the following technical scheme:
an operation analysis method of power equipment comprises the following steps:
step 1: integrating state data of multi-source heterogeneous equipment, and acquiring related data from a PMS (permanent magnet system), an EMS (energy management system), an equipment state monitoring system, a mobile interconnection system and a meteorological information system to form an equipment panoramic information base;
step 2: evaluating the quality of state data and cleaning the data, removing useless error data and filling up key missing data through statistical distribution, clustering, association analysis and time sequence analysis;
and 3, step 3: storing equipment state data, and aggregating data with correlation in a cluster according to the equipment main attribute, the timestamp and the correlation coefficient;
and 4, step 4: processing equipment state data, and statistically constructing a current state model, a historical knowledge model, a fault diagnosis model and a multi-parameter prediction model;
and 5: and by analyzing the historical knowledge model and combining the multi-parameter prediction model and the fault diagnosis model, the fault occurrence probability, the fault type and the fault position of the power equipment are predicted in real time.
As a preferable aspect of the above technical solution, the method further includes: the data acquired in the equipment state monitoring system comprise state monitoring, live detection, test, operation, equipment defects, operation years, installation areas, fault records and the like.
As a preferable aspect of the above technical solution, the method further includes: the storage and retrieval of the equipment state data storage adopt a query method and a secondary index of multi-data source parallel connection.
As a preferable aspect of the above technical solution, the method further includes: the fault diagnosis model generates at least one fault state model respectively according to different fault types and multi-period time fault attributes of each device.
As a preferable aspect of the above technical solution, the method further includes: the analysis early warning model is used for comparing the historical knowledge model, the multi-parameter prediction model, the fault diagnosis model and the current state model so as to evaluate the running state of the wind turbine generator according to the similarity and early warn the fault of the wind turbine generator.
The invention has the following beneficial effects:
the method is combined with the popularization and application of the charged detection, on-line monitoring and intelligent inspection technologies in recent years, massive state detection data are collected, the integration, conversion, cleaning, storage, processing and data mining of multi-source heterogeneous data are realized, the accuracy and adaptability of the operation analysis of the power equipment are effectively improved, and good effects are realized in the aspects of application scenes such as historical knowledge extraction, personalized evaluation, abnormal rapid detection, intelligent fault diagnosis and state prediction. The method has the advantages that the intelligent equipment fault diagnosis model is established based on enough defect, fault and site interference sample data, similar defect or fault cases can be searched through big data matching and a correlation algorithm, basis is provided for equipment fault analysis, certain universality is achieved, and the method is suitable for actual operation requirements of power production.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of the overall structure of the present invention;
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
Due to the distributivity of the power equipment and the complexity of the power grid, comprehensive and accurate state evaluation needs to be performed on the power equipment, and data information of different sources such as power grid operation, equipment state and meteorological environment needs to be considered, and meanwhile, comprehensive analysis is performed by combining the current state change and the historical state change of the equipment.
As shown in fig. 1, the method for analyzing the operation of the power equipment includes the following steps:
step 1: and integrating the state data of the multi-source heterogeneous equipment, and acquiring related data from a PMS, an EMS, an equipment state monitoring system, a mobile interconnection system and a meteorological information system to form an equipment panoramic information base. The information base can reveal the statistical distribution rule of the state change of the power equipment, the occurrence rule of the equipment defects and faults and the association change rule of the equipment state in multiple layers and multiple dimensions, so that a historical knowledge base based on mass data mining analysis is formed, support is provided for the state evaluation, fault diagnosis and prediction of the power equipment, and a scientific basis can be provided for state maintenance auxiliary decision-making.
Step 2: and (3) evaluating the quality of the state data and cleaning the data, removing useless error data and filling up key missing data through statistical distribution, clustering, association analysis and time sequence analysis, wherein the data quality is a precondition for ensuring the data mining analysis effect. The method can effectively solve the problems that related data have a plurality of sources, heterogeneous information, a large number, a plurality of attributes and the like, and avoids the influence on the analysis and evaluation results of the equipment state caused by various invalid abnormal values in the monitored data.
And step 3: and storing the device state data, namely aggregating the data with correlation in a cluster according to the device main attribute, the timestamp and the correlation coefficient, and generally adopting a distributed file storage database and a distributed column storage database.
And 4, step 4: and (3) processing equipment state data, counting and constructing a current state model, a historical knowledge model, a fault diagnosis model and a multi-parameter prediction model, intelligently learning by using a large amount of sample wood data, and correcting, supplementing and perfecting the existing state evaluation and fault diagnosis physical and mathematical models.
And 5: the equipment state prediction is to search rules from the existing state data, predict future states or states which cannot be observed by using the rules, and realize the real-time prediction of the fault occurrence probability, the fault type and the fault position of the power equipment and establish an analysis early warning model by analyzing the historical knowledge model and combining the multi-parameter prediction model and the fault diagnosis model.
The power equipment can be influenced by bad working conditions and events such as overload, overvoltage, sudden short circuit, severe weather and insulation degradation in the actual operation process to enable the equipment state to change abnormally, and if the abnormal operation state is discovered and taken measures in time, equipment failure can be caused and huge economic loss can be caused. The method is one of the important advantages of big data analysis of the equipment state. The data acquired in the equipment state monitoring system comprise state monitoring, live detection, test, operation, equipment defect, operation age, installation area, fault record and the like.
The storage and retrieval of the equipment state data storage adopt a query method and a secondary index of parallel connection of multiple data sources.
And the fault diagnosis model is used for respectively generating at least one fault state model aiming at different fault types and multi-period time fault attributes of each device.
And the analysis early warning model is used for comparing the historical knowledge model, the multi-parameter prediction model, the fault diagnosis model and the current state model so as to evaluate the running state of the wind turbine generator according to the similarity and carry out early warning on the fault of the wind turbine generator. The fault nature, the severity and the development trend of the latent fault with the fault or the symptom are accurately judged, so that operation and maintenance personnel can conveniently make a targeted maintenance strategy, and the equipment state is prevented from further deteriorating.
By evaluating and evaluating the prediction result, the effectiveness of the provided data method and the accuracy of the comprehensive prediction method can be fully verified. The wind power generation capacity prediction analysis method can provide a feasible solution for wind power generation capacity prediction.
While the present invention has been described with reference to the particular illustrative embodiments, it is not to be restricted by the embodiments but only by the appended claims. It is to be understood by those skilled in the art that variations and modifications of the embodiments of the present invention may be made without departing from the scope and spirit of the invention.
Claims (5)
1. An operation analysis method of power equipment comprises the following steps:
step 1: integrating state data of multi-source heterogeneous equipment, and acquiring related data from a PMS (permanent magnet system), an EMS (energy management system), an equipment state monitoring system, a mobile interconnection system and a meteorological information system to form an equipment panoramic information base;
step 2: evaluating the quality of state data and cleaning the data, removing useless error data and filling up key missing data through statistical distribution, clustering, association analysis and time sequence analysis;
and step 3: storing equipment state data, and aggregating data with correlation in a cluster according to the equipment main attribute, the timestamp and the correlation coefficient;
and 4, step 4: processing equipment state data, and statistically constructing a current state model, a historical knowledge model, a fault diagnosis model and a multi-parameter prediction model;
and 5: and (3) predicting the state change and the fault of the equipment, and realizing the real-time prediction of the fault occurrence probability, the fault type and the fault part of the power equipment and establishing an analysis early warning model by analyzing the historical knowledge model and combining the multi-parameter prediction model and the fault diagnosis model.
2. The power equipment operation analysis method according to claim 1, characterized in that: the data acquired in the equipment state monitoring system comprise state monitoring, live detection, test, operation, equipment defects, operation years, installation areas, fault records and the like.
3. The power equipment operation analysis method according to claim 1, characterized in that: the storage and retrieval of the equipment state data storage adopt a query method and a secondary index of multi-data source parallel connection.
4. The power equipment operation analysis method according to claim 1, characterized in that: the fault diagnosis model generates at least one fault state model respectively according to different fault types and multi-period time fault attributes of each device.
5. The power equipment operation analysis method according to claim 1, characterized in that: the analysis early warning model is used for comparing the historical knowledge model, the multi-parameter prediction model, the fault diagnosis model and the current state model so as to evaluate the running state of the wind turbine generator according to the similarity and carry out early warning on the fault of the wind turbine generator.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211300224.5A CN115587016A (en) | 2022-10-09 | 2022-10-09 | Power equipment operation analysis method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211300224.5A CN115587016A (en) | 2022-10-09 | 2022-10-09 | Power equipment operation analysis method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115587016A true CN115587016A (en) | 2023-01-10 |
Family
ID=84781457
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211300224.5A Pending CN115587016A (en) | 2022-10-09 | 2022-10-09 | Power equipment operation analysis method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115587016A (en) |
-
2022
- 2022-10-09 CN CN202211300224.5A patent/CN115587016A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106019084B (en) | Medium-voltage power distribution network disconnection fault diagnosis method based on power distribution and utilization data correlation | |
| CN107358366B (en) | Distribution transformer fault risk monitoring method and system | |
| AU2022204116A1 (en) | Verification method for electrical grid measurement data | |
| CN114267178B (en) | Intelligent operation maintenance method and device for station | |
| CN108919044B (en) | Active identification method for unit distribution power grid faults based on mutual verification mechanism | |
| CN113325308B (en) | Power supply fault detection method for data center | |
| CN110221173B (en) | Power distribution network intelligent diagnosis method based on big data drive | |
| CN115603459A (en) | Digital twin technology-based power distribution network key station monitoring method and system | |
| CN110794254A (en) | Power distribution network fault prediction method and system based on reinforcement learning | |
| CN116506339A (en) | Network security real-time monitoring and analyzing system for power industry | |
| CN116861503A (en) | Method for constructing digital twin model of power transformer based on big data | |
| CN114487705A (en) | Power grid equipment fault positioning detection method | |
| CN117150418B (en) | Transformer operation detection period formulation method and system based on state characteristic fault tree | |
| CN111391694B (en) | Multi-level rapid data monitoring method and system for operation and maintenance of charging station | |
| CN110674240B (en) | GIS-based distributed multistage intelligent fault diagnosis system for power equipment | |
| CN117031201A (en) | Multi-scene topology anomaly identification method and system for power distribution network | |
| CN115587016A (en) | Power equipment operation analysis method | |
| CN116644361A (en) | Overhead transmission line fault diagnosis method and computer readable medium | |
| CN114168662A (en) | Power distribution network problem combing and analyzing method and system based on multiple data sources | |
| Rakhimov et al. | AI-based power transformer condition assessment | |
| Liangzhi et al. | Research on fault prediction and diagnosis of power equipment based on big data | |
| CN118300526A (en) | Photovoltaic module fault diagnosis method | |
| CN118050600B (en) | Distribution network fault positioning system based on distributed traveling wave measurement | |
| CN117007912B (en) | Distribution network line power failure analysis method, device, equipment and storage medium | |
| CN115239058A (en) | Generator state detection evaluation method and system based on DCS data deep mining |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication |