CN108537344B - Secondary equipment intelligent operation and maintenance method based on closed-loop knowledge management - Google Patents
Secondary equipment intelligent operation and maintenance method based on closed-loop knowledge management Download PDFInfo
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- 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
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- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
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- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention discloses a secondary equipment intelligent operation and maintenance method based on closed-loop knowledge management, which comprises the following steps: the method comprises the following steps: establishing a substation monitoring signal database which comprises the equipment, the signal name, the signal source, the signal type, the signal meaning, the associated signal, the generation reason and the processing principle; step two: establishing a typical defect library of secondary equipment according to the existing data; step three: establishing a signal closed loop analysis process; step four: and establishing a secondary equipment defect knowledge base closed-loop management process. The method takes the secondary equipment operation and maintenance data information as clues and two-stage closed-loop knowledge management of a regulation center and a transformer substation as a method, establishes an intelligent operation and maintenance knowledge base, accumulates samples through operation and maintenance treatment, analyzes and creates knowledge data afterwards, and realizes automatic defect statistics based on the sample base, so that the initiative and preventive operation and maintenance transition of the secondary equipment operation and maintenance to data driving is supported, and the defects of the existing means are overcome.
Description
Technical Field
The invention relates to the technical field of power equipment defect analysis and operation and maintenance, in particular to an intelligent operation and maintenance method for secondary equipment based on closed-loop knowledge management.
Background
The method strengthens the operation and maintenance management of the power grid equipment, and is an important measure for improving the operation management level of the equipment and ensuring the safe production. Compared with the primary equipment of the power grid, the state evaluation, the defect analysis and the operation and maintenance technical research of the secondary equipment are relatively lagged, the knowledge has larger growth space in the width and depth, and the following problems exist:
1) the characteristic quantity reflecting the state of the secondary equipment is small, the evaluation method of the state of the secondary equipment and the maintenance strategy are greatly different from those of the primary equipment, and the intelligent operation and maintenance technology of the secondary equipment needs to be researched.
2) The PMS provides a standard information model for defect statistics, and modeling of secondary equipment, defect types, reasons and other factors needs to be further expanded and refined to meet the requirement of supporting intelligent operation and maintenance analysis.
3) The EMS, PMS and other systems focus on monitoring of the production process and management and assessment of the process, and the problem analysis method, field treatment experience and mining, storage and sharing of operation and maintenance knowledge are lacked, so that the experience knowledge of scheduling operators and operation and maintenance maintainers is difficult to standardize, systematize and continue.
4) The accumulation of effective samples of defects and faults is insufficient, and the requirements of research and development and application of operation and maintenance big data technology are not met.
5) The operation and maintenance of the secondary equipment are mainly based on the post analysis of fault alarm, the prior early warning means is lacked, the regularity commonality problems such as family defects and the like are difficult to be automatically analyzed and identified from the occurred defect records, and the predictability and the initiative of the maintenance work need to be improved.
6) The operation and maintenance of the secondary equipment mainly depends on manual analysis and decision-making, along with the improvement of the complexity of the secondary operation and maintenance work, the analysis and judgment of the reasons and the influences of the alarm signals and the analysis of the reasons and the disposal strategies of the defects all need to be supported by a data-driven automatic analysis technology, so that the operation and maintenance personnel can comprehensively and accurately master information and make a reasonable decision quickly.
In order to solve the problems, the project takes secondary equipment operation and maintenance data information as clues, takes two-stage dynamic closed-loop knowledge management of a regulation center and a transformer substation as a method, researches and utilizes big data and an artificial intelligence technology, strengthens command decision-making capability of regulation and control personnel, improves execution capacity of the operation and maintenance personnel, and realizes the transition from fault alarm post-analysis to pre-warning elimination of deficiency in secondary operation and maintenance.
Disclosure of Invention
In view of this, the present invention provides an intelligent operation and maintenance method for secondary equipment based on closed-loop knowledge management, which realizes the transition from failure alarm post analysis to pre-warning elimination.
The purpose of the invention is realized by the following technical scheme.
The invention discloses a secondary equipment intelligent operation and maintenance method based on closed-loop knowledge management, which comprises the following steps:
the method comprises the following steps: establishing a substation monitoring signal database which comprises the equipment, the signal name, the signal source, the signal type, the signal meaning, the associated signal, the generation reason and the processing principle;
step two: establishing a typical defect library of secondary equipment according to the existing data, wherein the typical defect library comprises a defect type, an equipment type, a detection means, a judgment basis, a manufacturer, a defect map, a defect reason and a processing scheme;
step three: establishing a signal closed loop analysis process, wherein the process comprises the following steps: monitoring personnel obtain a reason list of signal generation through retrieval according to a received monitoring signal and inform substation operation and maintenance personnel of the investigation, the investigation result is fed back to a monitoring center on site, if the site result is inconsistent with the reason list obtained through retrieval, the regulating personnel edits and adds a new reason text in a 'generation reason' attribute corresponding to the signal, if the monitoring personnel does not retrieve related monitoring signal information, the signal content, the corresponding generation reason and a processing principle can be induced, the 'signal knowledge addition' is selected, a new monitoring signal record is added, and the new monitoring signal record comprises the affiliated equipment, the signal name, the signal source, the signal type, the signal meaning, the associated signal, the generation reason and the processing principle;
step four: establishing a secondary equipment defect knowledge base closed-loop management process, wherein the process comprises the following substeps:
step 4.1: the transformer substation operation and maintenance personnel gather and upload secondary equipment faults, time, place, specific equipment, equipment ledger, cause occurrence pass, defect types, detection methods and field inspection information to an operation and maintenance local platform through a PC terminal to form a local fault case library;
step 4.2: the method comprises the steps that on-site operation and maintenance selects data types to be uploaded to a monitoring center at a PC (personal computer) end, wherein the data types comprise defect equipment types, equipment manufacturers, defect types and defect reasons, and are uploaded in a gathering mode, the monitoring center obtains the probability of different defects of different equipment through sorting of a working log and a case base and statistical analysis, induces the reasons of the occurrence of faults, establishes the corresponding relation of the defects, the faults and factors and gives an equipment inspection priority list;
step 4.3: downloading data summarized by the monitoring center to a PC or an intelligent terminal in an operation and maintenance site, identifying the priority of the defect factors conforming to the site of the transformer substation by combining the defect characteristic factors in the operation and maintenance defect sample library of the transformer substation and based on a Bayesian inference of an inspection knowledge list and characteristics, and planning an inspection task according to the factor list;
step 4.4: according to the inspection factor list, the equipment defect name and the defect reason are fed back to the control center through the intelligent terminal on site, the inspection video is filed or the picture is taken for evidence collection, the control center forms a sample record of the defect name and the defect reason according to the on-site feedback result, and the correlation among the factors, the fault and the defect is corrected.
Further, the method also comprises the steps of analyzing the defects of the secondary equipment by adopting an intelligent statistical analysis algorithm, forming analysis data and storing the analysis data into a typical defect library of the secondary equipment, wherein the analysis types specifically comprise the following analysis types:
(1) threshold analysis: diagnosing technical application guide rules according to different detection means, and analyzing the defect types of the equipment;
(2) family defects: summarizing equipment defect and fault information, counting and analyzing the probability of the same type of defect produced by the same manufacturer in different models, different specifications and different series during operation, and identifying family defects;
(3) and (3) evaluating the equipment state: establishing an equipment state evaluation system, establishing an evaluation rule based on a substation equipment state evaluation guide rule, forming a rating table, automatically rating by combining equipment operation information under different time scales, and evaluating the equipment state;
(4) and (3) risk evaluation: on the basis of equipment state evaluation, equipment with an abnormal evaluation result is evaluated, a risk evaluation model is established, equipment assets, asset loss degree and probability factors of equipment failure are comprehensively considered, risks faced by the equipment and possibly caused by the equipment are quantitatively evaluated, and early warning is carried out when the risk evaluation guide rule threshold value is exceeded.
The method further comprises the steps that the monitoring center adopts a defect intelligent analysis algorithm to identify the key state quantity of the equipment state and push the information of the operation risk, wherein the identification of the key state quantity of the equipment state is to identify relevant factors of the equipment fault based on distance correlation, dynamically sequence the relevant relations of fault variables, identify the fault indexes of the equipment, discover hidden relations existing among phenomena and events, and analyze and identify the key state quantity of the corresponding state of different equipment; and the information pushing of the operation risk is to analyze and calculate the equipment maintenance priority indexes of all levels in a certain area by combining the evaluation results of the bad working conditions and the equipment states, and to issue the equipment maintenance items, the maintenance levels, the maintenance time and the maintenance sequence to the intelligent terminal to generate the inspection plan.
Further, in step 1, the device classification includes: the device comprises a 220kV transformer, 220kV outgoing equipment, 220kV bus equipment, a 110kV transformer, 110kV outgoing equipment, 110kV bus equipment, 35kV outgoing equipment, a 35kV and below segmented circuit breaker, 10kV outgoing equipment, a capacitor, a reactor, an arc extinguishing device, a station transformer, public equipment, an automatic device, a fault recorder and a direct current system.
Further, in step 1, the signal types include: accident signals of transformer substations and interval accident signals; a switch position signal, a switch position signal; protecting fault signals and action signals; a reclosing signal; a stable control device outlet signal; primary equipment failure and alarm signals; secondary equipment or loop faults and alarm signals; warning signals of AC and DC power supplies in the station; a reactive voltage control exit signal; and tripping an outlet signal of the low-frequency low-voltage joint cutting device.
The invention has the beneficial effects that: the method takes the secondary equipment operation and maintenance data information as clues and two-stage closed-loop knowledge management of a regulation center and a transformer substation as a method, establishes an intelligent operation and maintenance knowledge base, accumulates samples through operation and maintenance treatment, analyzes and creates knowledge data afterwards, and realizes automatic defect statistics based on the sample base, so that the initiative and preventive operation and maintenance transition of the secondary equipment operation and maintenance to data driving is supported, and the defects of the existing means are overcome.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof.
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In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of closed loop analysis of a signal;
FIG. 2 is a knowledge base hierarchy and flow chart.
Detailed Description
Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the preferred embodiments are illustrative of the invention only and are not limiting upon the scope of the invention.
The intelligent operation and maintenance method of the secondary equipment based on closed-loop knowledge management comprises the following steps:
the method comprises the following steps: establishing a substation monitoring signal database which comprises the equipment, the signal name, the signal source, the signal type, the signal meaning, the associated signal, the generation reason and the processing principle; wherein the device classification includes: the system comprises a 220kV transformer, 220kV outgoing equipment, 220kV bus equipment, a 110kV transformer, 110kV outgoing equipment, 110kV bus equipment, 35kV outgoing equipment, a 35kV and below segmented circuit breaker, 10kV outgoing equipment, a capacitor, a reactor, an arc extinction device, a station transformer, public equipment, an automatic device, a fault recorder and a direct current system; the signal types include: accident signals of transformer substations and interval accident signals; a switch position signal, a switch position signal; protecting fault signals and action signals; a reclosing signal; a stable control device outlet signal; primary equipment failure and alarm signals; secondary equipment or loop faults and alarm signals; warning signals of AC and DC power supplies in the station; a reactive voltage control exit signal; and tripping an outlet signal of the low-frequency low-voltage joint cutting device.
Step two: establishing a typical defect library of secondary equipment according to the existing data, wherein the typical defect library comprises a defect type, an equipment type, a detection means, a judgment basis, a manufacturer, a defect map, a defect reason and a processing scheme;
step three: a signal closed-loop analysis process is established, as shown in fig. 1, the process is as follows: monitoring personnel obtain a reason list of signal generation through retrieval according to a received monitoring signal and inform substation operation and maintenance personnel of the investigation, the investigation result is fed back to a monitoring center on site, if the site result is inconsistent with the reason list obtained through retrieval, the regulating personnel edits and adds a new reason text in a 'generation reason' attribute corresponding to the signal, if the monitoring personnel does not retrieve related monitoring signal information, the signal content, the corresponding generation reason and a processing principle can be induced, the 'signal knowledge addition' is selected, a new monitoring signal record is added, and the new monitoring signal record comprises the affiliated equipment, the signal name, the signal source, the signal type, the signal meaning, the associated signal, the generation reason and the processing principle;
step four: establishing a secondary equipment defect knowledge base closed-loop management process, as shown in fig. 2, the process includes the following sub-steps:
step 4.1: the transformer substation operation and maintenance personnel gather and upload secondary equipment faults, time, place, specific equipment, equipment ledger, cause occurrence pass, defect types, detection methods and field inspection information to an operation and maintenance local platform through a PC terminal to form a local fault case library;
step 4.2: the method comprises the steps that on-site operation and maintenance selects data types to be uploaded to a monitoring center at a PC (personal computer) end, wherein the data types comprise defect equipment types, equipment manufacturers, defect types and defect reasons, and are uploaded in a gathering mode, the monitoring center obtains the probability of different defects of different equipment through sorting of a working log and a case base and statistical analysis, induces the reasons of the occurrence of faults, establishes the corresponding relation of the defects, the faults and factors and gives an equipment inspection priority list;
step 4.3: downloading data summarized by the monitoring center to a PC or an intelligent terminal in an operation and maintenance site, identifying the priority of the defect factors conforming to the site of the transformer substation by combining the defect characteristic factors in the operation and maintenance defect sample library of the transformer substation and based on a Bayesian inference of an inspection knowledge list and characteristics, and planning an inspection task according to the factor list;
step 4.4: according to the inspection factor list, the equipment defect name and the defect reason are fed back to the control center through the intelligent terminal on site, the inspection video is filed or the picture is taken for evidence collection, the control center forms a sample record of the defect name and the defect reason according to the on-site feedback result, and the correlation among the factors, the fault and the defect is corrected.
It should be noted that the method further includes analyzing the secondary device defects by using an intelligent statistical analysis algorithm, and forming analysis data to be stored in a secondary device typical defect library, specifically including the following analysis types:
(1) threshold analysis: diagnosing technical application guide rules according to different detection means, and analyzing the defect types of the equipment;
(2) family defects: summarizing equipment defect and fault information, counting and analyzing the probability of the same type of defect produced by the same manufacturer in different models, different specifications and different series during operation, and identifying family defects;
(3) and (3) evaluating the equipment state: establishing an equipment state evaluation system, establishing an evaluation rule based on a substation equipment state evaluation guide rule, forming a rating table, automatically rating by combining equipment operation information under different time scales, and evaluating the equipment state;
(4) and (3) risk evaluation: on the basis of equipment state evaluation, equipment with an abnormal evaluation result is evaluated, a risk evaluation model is established, equipment assets, asset loss degree and probability factors of equipment failure are comprehensively considered, risks faced by the equipment and possibly caused by the equipment are quantitatively evaluated, and early warning is carried out when the risk evaluation guide rule threshold value is exceeded.
In addition, the invention also comprises a monitoring center which adopts a defect intelligent analysis algorithm to identify the key state quantity of the equipment state and push the information of the operation risk, wherein the identification of the key state quantity of the equipment state is to identify the relevant factors of the equipment fault based on the distance correlation, dynamically sequence the relevant relations of the fault variables, identify the fault indexes of the equipment, discover the hidden relations between the phenomena and the events, and analyze and identify the key state quantity corresponding to different equipment; and the information pushing of the operation risk is to analyze and calculate the equipment maintenance priority indexes of all levels in a certain area by combining the evaluation results of the bad working conditions and the equipment states, and to issue the equipment maintenance items, the maintenance levels, the maintenance time and the maintenance sequence to the intelligent terminal to generate the inspection plan.
The invention realizes the on-line learning of the machine, the risk early warning, the assistant expert command decision and the elimination of the defects of operation and maintenance based on sample data and on-line data analysis. Effective sample accumulation is realized through flow optimization and closed loop management, so that knowledge experience develops from static generation to dynamic development and from dispersion, non-continuability to concentration, shareability and continuability, and the data-intensive operation and maintenance decision and field operation under the support of artificial intelligence are perfected.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (5)
1. The intelligent operation and maintenance method of the secondary equipment based on closed-loop knowledge management is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: establishing a substation monitoring signal database which comprises the equipment, the signal name, the signal source, the signal type, the signal meaning, the associated signal, the generation reason and the processing principle;
step two: establishing a secondary equipment typical defect library according to the existing data, wherein the secondary equipment typical defect library comprises a defect type, an equipment type, a detection means, a judgment basis, a manufacturer, a defect map, a defect reason and a processing scheme, and is used as a knowledge base of a monitoring center;
step three: establishing a signal closed loop analysis process, wherein the process comprises the following steps: monitoring personnel obtain a reason list of signal generation through retrieval according to a received monitoring signal and inform substation operation and maintenance personnel of the investigation, the investigation result is fed back to a monitoring center on site, if the site result is inconsistent with the reason list obtained through retrieval, the regulating personnel edits and adds a new reason text in a 'generation reason' attribute corresponding to the signal, if the monitoring personnel does not retrieve related monitoring signal information, the signal content, the corresponding generation reason and a processing principle can be induced, the 'signal knowledge addition' is selected, a new monitoring signal record is added, and the new monitoring signal record comprises the affiliated equipment, the signal name, the signal source, the signal type, the signal meaning, the associated signal, the generation reason and the processing principle;
step four: establishing a secondary equipment defect knowledge base closed-loop management process, wherein the process comprises the following substeps:
step 4.1: the transformer substation operation and maintenance personnel gather and upload secondary equipment faults, time, place, specific equipment, equipment ledger, cause occurrence pass, defect types, detection methods and field inspection information to an operation and maintenance local platform through a PC terminal to form a local fault case library;
step 4.2: the method comprises the steps that on-site operation and maintenance selects data types to be uploaded to a monitoring center at a PC (personal computer) end, wherein the data types comprise defect equipment types, equipment manufacturers, defect types and defect reasons, and are uploaded in a gathering mode, the monitoring center obtains the probability of different defects of different equipment through sorting of a working log and a case base and statistical analysis, induces the reasons of the occurrence of faults, establishes the corresponding relation of the defects, the faults and factors and gives an equipment inspection priority list;
step 4.3: downloading data summarized by the monitoring center to a PC or an intelligent terminal in an operation and maintenance site, identifying the priority of the defect factors conforming to the site of the transformer substation by combining the defect characteristic factors in the operation and maintenance defect sample library of the transformer substation and based on a Bayesian inference of an inspection knowledge list and characteristics, and planning an inspection task according to the factor list;
step 4.4: according to the inspection factor list, the equipment defect name and the defect reason are fed back to the control center through the intelligent terminal on site, the inspection video is filed or the picture is taken for evidence collection, the control center forms a sample record of the defect name and the defect reason according to the on-site feedback result, and the correlation among the factors, the fault and the defect is corrected.
2. The intelligent operation and maintenance method for secondary equipment based on closed-loop knowledge management as claimed in claim 1, wherein: the method further comprises the steps of analyzing the defects of the secondary equipment by adopting an intelligent statistical analysis algorithm, forming analysis data and storing the analysis data into a typical defect library of the secondary equipment, wherein the analysis types specifically comprise the following analysis types:
(1) threshold analysis: diagnosing technical application guide rules according to different detection means, and analyzing the defect types of the equipment;
(2) family defects: summarizing equipment defect and fault information, counting and analyzing the probability of the same type of defect produced by the same manufacturer in different models, different specifications and different series during operation, and identifying family defects;
(3) and (3) evaluating the equipment state: establishing an equipment state evaluation system, establishing an evaluation rule based on a substation equipment state evaluation guide rule, forming a rating table, automatically rating by combining equipment operation information under different time scales, and evaluating the equipment state;
(4) and (3) risk evaluation: on the basis of equipment state evaluation, equipment with an abnormal evaluation result is evaluated, a risk evaluation model is established, equipment assets, asset loss degree and probability factors of equipment failure are comprehensively considered, risks faced by the equipment and possibly caused by the equipment are quantitatively evaluated, and early warning is carried out when the risk evaluation guide rule threshold value is exceeded.
3. The intelligent operation and maintenance method for the secondary equipment based on the closed-loop knowledge management as claimed in claim 1 or 2, wherein: the method also comprises the steps of identifying the key state quantity of the equipment state and pushing operation risk information by adopting a defect intelligent analysis algorithm, wherein the identification of the key state quantity of the equipment state is to identify relevant factors of the equipment fault based on distance correlation, dynamically sequence relevant relations of fault variables, identify fault indexes of the equipment, discover hidden relations existing between phenomena and events, and analyze and identify the key state quantity of the corresponding state of different equipment; and the information pushing of the operation risk is to analyze and calculate the equipment maintenance priority indexes of all levels in each area by combining the evaluation results of the bad working conditions and the equipment states, and to issue the equipment maintenance items, the maintenance levels, the maintenance time and the maintenance sequence to the intelligent terminal to generate the inspection plan.
4. The intelligent operation and maintenance method for secondary equipment based on closed-loop knowledge management as claimed in claim 1, wherein: in the first step, the device classification includes: the device comprises a 220kV transformer, 220kV outgoing equipment, 220kV bus equipment, a 110kV transformer, 110kV outgoing equipment, 110kV bus equipment, 35kV outgoing equipment, a 35kV and below segmented circuit breaker, 10kV outgoing equipment, a capacitor, a reactor, an arc extinguishing device, a station transformer, public equipment, an automatic device, a fault recorder and a direct current system.
5. The intelligent operation and maintenance method for secondary equipment based on closed-loop knowledge management as claimed in claim 1, wherein: in the first step, the signal types include: accident signals of transformer substations and interval accident signals; a switch position signal, a switch position signal; protecting fault signals and action signals; a reclosing signal; a stable control device outlet signal; primary equipment failure and alarm signals; secondary equipment or loop faults and alarm signals; warning signals of AC and DC power supplies in the station; a reactive voltage control exit signal; and tripping an outlet signal of the low-frequency low-voltage joint cutting device.
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