CN114389359A - Intelligent operation and maintenance method of centralized control type relay protection equipment based on cloud edge cooperation - Google Patents
Intelligent operation and maintenance method of centralized control type relay protection equipment based on cloud edge cooperation Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
- H02J13/00034—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for the elements or equipment being or involving an electric power substation
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- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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Abstract
The invention belongs to the technical field of relay protection, and particularly relates to an intelligent operation and maintenance method of centralized control type relay protection equipment based on cloud-edge cooperation. The method utilizes mass wave recording data in a wave recording master station system database to realize remote abnormity screening, inference analysis and state evaluation of secondary equipment of the power system, exerts longitudinal disturbance discrimination and large data normalized monitoring of transverse comparison and investigation, locks common faults of the wave recorder, improves the wave recorder maintenance from 'detection before expiration' to 'detection after inspection', achieves full plan before leaving the station, realizes indexes of one-time maintenance success rate improvement, maintenance time shortening and the like, reduces the workload of leaving the station, and realizes personnel reduction and efficiency improvement.
Description
Technical Field
The invention belongs to the technical field of relay protection, and particularly relates to an intelligent operation and maintenance method of centralized control type relay protection equipment based on cloud-edge cooperation.
Background
Along with the expansion of the scale of a power grid, the number of intelligent substations and oscillographs is increased rapidly for a long time, the contradiction between the increasing operation and maintenance workload and the limited operation and maintenance personnel is more and more prominent, the traditional operation and maintenance method depends on manpower to regularly go to the station for inspection, the labor intensity is high, the working efficiency is not high, the operation and maintenance personnel have no effective way to sense the current state of secondary equipment, and the accuracy of troubleshooting is difficult to guarantee. Therefore, the southern power grid provides a development idea of intelligent operation and maintenance, personnel reduction and efficiency improvement.
The support of relay protection on scheduling decisions is an important specialty for ensuring the safe and stable operation of a power system, and the scheduling master station is an indispensable tool means for accident diagnosis and analysis of the relay protection specialty. The strategic practice of the south power grid on the relay protection intellectualization is already stepping into the deep water area, and the main problems are that the lean management requirement is high, the informatization level is low, the shortage of human and material resources is difficult to cope with the continuous expansion of the scale of the power grid equipment, the intellectualization level and the management order of system maintenance are delayed, the operation and maintenance work is difficult and serious, and the practical effect is almost blank particularly in the aspect of the operation and inspection guidance of the system on the station side relay protection equipment. The above-mentioned varieties seriously restrict the decision quality and guarantee level of the operation and maintenance of the relay protection equipment in the power system industry in China, also aggravate the work passivity of the equipment, and are necessary to develop the research of the advanced intervention type intelligent operation and maintenance guidance technology.
In the power system, a main station system in a communication machine room of a regulation and control mechanism analyzes power grid faults by monitoring information of secondary equipment, the secondary equipment is deployed in scattered substations, the health state and the operation level of the secondary equipment relate to the sensing and control capacity of the main station system on the power grid faults, the operation and maintenance quality directly relates to the stability degree of the main station system reflecting the power grid faults, and the operation and maintenance of the secondary equipment with huge number is very critical.
In order to ensure that the operation and maintenance work of the relay protection equipment is normally carried out and secondary equipment faults are eliminated in advance, the traditional operation and maintenance adopts a regular inspection operation mode, but the inspection process consumes time and labor and is long in period, the reliability and the real-time performance of an inspection result are poor, quick response and effective processing can not be carried out in the first time after the secondary equipment faults occur, and the continuous stability of the relay protection equipment is difficult to ensure. Specific problems are summarized as follows:
(1) the transport and inspection personnel are seriously insufficient: the protection device of southern electric wire netting when operation surpasses 70000 sets, and the oscillograph is nearly 7000 sets, and equipment quantity increases rapidly, and the fortune examines personnel and can't increase in step, and the traditional regular inspection mode that uses manpower as the owner exposes huge manpower breach problem, is difficult to support the fortune dimension work of relay protection equipment.
(2) The operation and maintenance labor intensity is large: the number of operation and maintenance equipment is increasing day by day, and manufacturers, models, structures and functions are different, so that the equipment inspection project is complicated, the load of operation and inspection personnel in the regular inspection task distribution is heavier and heavier, the field labor intensity is high, the operation time is long, the psychological and physical quality of the personnel is seriously examined, and the service quality of the operation and maintenance is difficult to guarantee in the past.
(3) The operation and maintenance work efficiency is low: the operation and inspection personnel fill in the result after inspection in a form according to the distributed inspection task and the operation detailed rules, the real-time performance of the fault of the reaction equipment is poor, inspection guidance and side emphasis are avoided, and the problems of low operation and maintenance level and low maintenance efficiency are easily caused by adopting a universal scanning mode of all projects.
(4) The problems are difficult to find in time: the regular inspection has the fixity in time, the fault found in the regular inspection can be recorded in a case, a defect removing list is issued for tracking, but the potential problem with the degradation trend or the fault after the regular inspection is difficult to be found out in the regular inspection or in time after the fault is found out, the hidden danger or the data collection failure of the normal operation of the relay protection equipment is possible to be buried, and the timeliness of the fault finding of the equipment and the accuracy and the integrity of the problem finding are difficult to be ensured by a manual regular inspection mode.
The remote oscillograph maintenance guidance based on the oscillograph master station is always a short board in the relay protection intelligent field, and in order to ensure the authority and the rigor of the protection specialty of a regulation and control mechanism, a large amount of verification work is usually required in advance when a maintenance unit issues a deletion order, so that the remote oscillograph maintenance guidance is very complicated and low in efficiency.
Disclosure of Invention
In order to solve the problems, the invention provides an intelligent operation and maintenance method of centralized control type relay protection equipment based on cloud-edge cooperation, which is used for researching accurate operation and maintenance decision guidance of a power grid-level large-scale wave recorder. The advantages of high data collection speed, complete information and accurate fault judgment of the existing wave recording main station are utilized, and the characteristics of rich and multi-source data collected by distributed secondary equipment are utilized, the wave recording main station and the edge intelligent equipment adopt a cloud-edge cooperative mode, comprehensively analyze fault data, evaluate the running state of the equipment, formulate a corresponding maintenance strategy, provide information auxiliary support for timely discovering secondary equipment faults and intelligent operation and maintenance, and guarantee practicality, reliability and effectiveness. The specific technical scheme is as follows:
an intelligent operation and maintenance method for centralized control type relay protection equipment based on cloud edge cooperation comprises the following steps:
s1: the intelligent equipment on the station side acquires data of all wave recorders in the transformer substation and uploads the acquired data to a wave recording master station through a scheduling data network;
s2: the wave recording master station uniformly preprocesses heterogeneous data on wave recorder data from different sources, and specifically comprises uniform information modeling processing, friendly fault-tolerant processing, uniform data format processing, safe transcoding processing and transparent access processing;
s3: carrying out knowledge mining by utilizing the preprocessed data, establishing logical relations between different defect faults and fault characteristics, and constructing a knowledge base; abnormal case screening is carried out on the recording data received in real time, case reasoning analysis is carried out by utilizing the established knowledge base, and the knowledge base is continuously updated and iterated;
s4: the wave recording master station screens equipment fault stations discovered by case reasoning in a whole network range in a global angle, evaluates fault coverage and influence degree, outputs a secondary equipment state evaluation result as a reference basis of secondary equipment overhaul urgency degree, classifies alarms by grading the evaluation result, pushes the most important alarm content to the front end, generates a defect elimination list, and directionally issues information to inform corresponding operation and inspection personnel to process faults in time.
Preferably, the data of the oscillograph in the substation acquired by the intelligent device at the station side in the step S1 includes oscillograph topology structure information, ledger information, device state information, link state information, configuration file information, and overhaul record information.
Preferably, the unified information modeling processing in step S2 specifically includes the following steps:
s211: in a wave recording master station system development stage, according to the definition in the IEC61850 standard, measuring the difference between the parameters of the special-shaped wave recorder model by using the parameter difference degree index, and establishing an information model for converting a non-standard protocol to a standard protocol to form a protocol conversion rule base;
s212: all tasks enter a queue in sequence according to arrival time;
s213: the communication scheduler marks the data by the stipulation rule characteristic and starts to mark the next data in the queue at the same time;
s214: according to the protocol rule feature labels, automatically matching and loading the corresponding IEC61850 information model from the protocol conversion rule library;
s215: matching multi-source data to a data channel of IEC61850 information model unified parameter description based on the IEC61850 information model to realize parameter unification;
s216: the communication scheduler preferentially allocates tasks to idle communication links, and preferentially allocates tasks to communication links which have processed the same type of information models last time when a plurality of communication links are in an idle state;
s217: the communication scheduler will close the long-term idle link, release the system resources, and open as needed.
Preferably, the friendly fault tolerance processing in step S2 specifically includes: after unified information modeling processing is carried out on the data parameters of the oscillographs from different sources, standard parameter configuration information is generated by utilizing a general compensation list or a mode of calling a primary fixed value model, and data completion or repeated data elimination is carried out on functions which are not complete or redundant and are matched with data calling.
Preferably, the unified data format processing in step S2 includes the following steps:
s231: data structured difference recognition: carrying out the difference analysis of the same data structure on the data of the oscillographs of different types, and identifying the difference of the data structures;
s232: establishing a data format conversion template: establishing template middleware objects for the differences identified by the data structures, and performing granularity processing on the differentially expressed data by adopting a least conversion principle and calling corresponding functions;
s233: the data format is unified: and converting the data into logic expressions with the same format to form a data storage directory with the same type of data parameters and uniform format.
Preferably, the secure transcoding processing in step S2 specifically includes: and the waveform data of the wave recorder is subjected to Hilbert-yellow transform smoothing filtering to reduce noise interference, the association among the waveform data is found by utilizing a configuration file, and the waveform is drawn in parallel to form a graphical waveform file for a wave recording master station to directly take, so that lossless and safe transcoding of the waveform data is realized.
Preferably, the transparent access processing in step S2 is specifically: and the intelligent equipment on the plant station side performs centralized management on the data access interface, so that transparent access of the secondary equipment data on the edge plant station side is realized.
Preferably, the step S3 of the wave recording master station building a knowledge base according to the preprocessed data mining specifically includes the following steps:
s311: extracting secondary equipment fault cases from historical fault data and reports;
s312: each case is recorded in a structural organization mode in a standard way;
s313: converting the fault information into case samples to meet the requirement of secondary equipment defect data mining;
s314: reducing the fault characteristic attribute of the case sample by using the rough set, and cleaning the redundancy attribute;
s315: using the case samples after the clustering analysis classification reduction to form initial clusters;
s316: counting initial clusters and merging similar clusters;
s317: typical case samples in each cluster are extracted to form a simplified knowledge base.
Preferably, the step S3 of the wave recording master station performing real-time analysis of the operation state of the secondary device, updating the knowledge base, and evaluating the state specifically includes the following steps:
s321: analyzing real-time wave recording data sent by the edge intelligent equipment based on case reasoning, wherein the real-time wave recording data comprises exception screening of a current case and reasoning analysis of an exception case;
the current case exception screening specifically comprises the following steps:
(1) and (3) transversely comparing multiple sources with fault data: collecting wave recording files generated by wave recorders on two sides of the same fault line and a protective device acquisition unit, comparing the same sampling channel data, and generating an abnormal alarm when the difference of waveform data, fixed value parameters and clock signals reaches a certain threshold value so as to find the abnormality in a secondary equipment sampling loop;
(2) and comparing the monitoring data of the same object longitudinally: locking equipment serving as an analysis object, performing parameter and information proofreading on data and reports provided by edges and historical data, finding out a fault judgment logic supporting point, converting the data into value information, primarily judging the state of the equipment, and predicting whether a degradation trend exists according to past routing inspection records;
the reasoning analysis of the abnormal case comprises the following steps:
(1) and (3) retrieval: inputting keyword information of a current case, and retrieving similar typical cases from a knowledge base;
(2) calling: obtaining an analysis process and a scheme from the retrieved case, and judging whether the solution requirements are met; if the current case actual situation is met, directly calling a merging solution of the scheme or the schemes, otherwise, correcting according to the actual situation of the current case;
(3) and (3) correction: adjusting the solution of the similar cases to meet the solution requirements of the current case, and obtaining a new solution meeting the characteristics of the current abnormal case;
(4) and (3) storage: submitting the new scheme and the solving basis thereof according to the expression of the specification;
(5) updating: upgrading and perfecting a knowledge base;
s322: establishing a state evaluation model and evaluation indexes, carrying out secondary equipment running state evaluation, calculating the evaluation indexes by using a qualitative and quantitative evaluation method to sense the running situation of the equipment, and carrying out information directional release and equipment full life cycle normalized management according to the equipment state evaluation result and the priority sequence.
The invention has the beneficial effects that: based on a cloud-edge cooperative mode of a wave recording main station and edge-side intelligent equipment, the invention performs knowledge mining on the collected and preprocessed mass wave recording data by a data mining technical means, realizes remote abnormity screening, reasoning analysis and state evaluation of secondary equipment of a power system, exerts large data normalized monitoring of longitudinal disturbance judgment and transverse comparison investigation, locks common faults of a wave recorder, such as hard disk jam, fixed value deviation, clock desynchronization, starting failure, zero sequence reversal, plug-in damage, channel instability, starting failure, background crash and the like, performs classification according to the weight and urgency, improves the wave recorder overhaul from 'detection at expiration' in the past to 'detection at the moment', achieves the purposes of fully pre-planning before the station, realizing the improvement of one-time overhaul success rate, shortening of overhaul time and the like. The recording master station realizes the operation state, the alarm information, the constant value management, the clock comparison, the polarity monitoring and the like of the normally automatic inspection transformer substation recorder, supports the non-inductive inspection and the defect report of the full-network recorder, reduces the workload of operation and maintenance personnel when the substation recorder is out of station, and realizes the reduction of personnel and the improvement of efficiency.
In practical application, the intelligent operation and maintenance mode of data-supported equipment situation perception and advanced intervention type 'current inspection and detection' is used, the advantages of deep excavation and big data analysis of cloud-edge cooperative mass recording data are effectively exerted, the intelligent operation and maintenance-oriented power grid equipment state modeling and evaluation technology is fully utilized, and the goals of full-life-cycle normalized management and intelligent operation and maintenance of the relay protection equipment are achieved.
The invention not only can globally monitor the overall operation of the secondary equipment and simplify the workload of maintenance, but also can provide big data evaluation and evidence support for subsequent equipment type selection by summarizing the operation management experience of the equipment.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a schematic design of the present invention;
FIG. 2 is a flow chart of the present invention;
FIG. 3 is a flow chart of data preprocessing;
FIG. 4 is a flow chart of knowledge base establishment;
fig. 5 is a flow of abnormal case reasoning analysis.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
The invention researches an intelligent operation and maintenance principle, a method and a flow, and realizes the purposes of normalized management and intelligent operation and maintenance of the whole network wave recorder equipment by means of data collection, preprocessing, value mining, knowledge base establishment, abnormal case screening, state evaluation model establishment and evaluation grade index establishment.
The wave recording master station separates non-core and normalized services from the centralized control monitoring of the master station by using the resource advantages of the secondary equipment wave recording networking, distributes the non-core and normalized services to the edge secondary equipment for processing, carries out information collection, comprehensive diagnosis and whole-network screening after the edge processing is finished by the master station, and enables evaluation results to react on the edge secondary equipment for scientific and reasonable operation and maintenance, so that the data service quality of the intelligent equipment is improved, and a mode of cooperative operation between the master station and the secondary equipment is formed, wherein the principle is shown in figure 1.
1) The intelligent equipment at the plant station side acquires secondary equipment data in the station and processes non-core data services, and provides data support for the main station side in real time; and the master station side utilizes multi-source multi-dimensional data to develop deep mining of data characteristic association relation facing different defects, comprehensively analyzes and infers abnormal conditions and perceives the operation situation of the equipment.
2) The main station side carries out anomaly screening of the whole network by utilizing an equipment state evaluation result obtained by analyzing mass recording data, and carries out trend analysis and state management on secondary equipment at the side of the edge plant station in a centralized manner so as to realize the target of main station centralized control type intelligent operation and maintenance; the secondary equipment on the station side continuously, stably and healthily operates to provide more excellent data acquisition and non-core service data service for the main station side.
And the cloud edges cooperate to form benign interaction of mutual information feedback between the main station and the secondary equipment. The data service and the non-core service of the edge secondary intelligent equipment are processed in a coordinated mode, so that the overall optimized utilization of the whole network equipment resources can be realized, and the comprehensive analysis and fault early warning capabilities of the main station are improved.
Based on the mass wave recording data and the fault report obtained by cloud edge collaborative analysis, mining incidence relations and case samples among historical mass wave recording data by using a big data technology and facing object fault characteristics to form a knowledge base; screening abnormal data, carrying out case reasoning analysis, providing abnormal reasons matched with the abnormal data and a processing plan, and continuously upgrading and perfecting a knowledge base; and establishing a state evaluation model and grading levels to evaluate the state of the equipment, controlling the state of the equipment in a grading way, directionally releasing information and realizing the normalized management of the whole life cycle of the equipment.
The invention provides an intelligent operation and maintenance method of centralized control type relay protection equipment based on cloud edge cooperation, which comprises four steps of data collection, preprocessing, data analysis and equipment state evaluation, and finally realizes intelligent operation and maintenance and reduces the workload of overhauling a substation, and specifically comprises the following steps:
s1: the intelligent equipment on the side of the station performs unified management on all wave recorders in the station, collects data of all wave recorders in the substation, including wave recorder topological structure information, ledger information, equipment state information, link state information, configuration file information and maintenance record information, and uploads the collected data to a wave recorder main station through a scheduling data network, so that a wave recorder equipment data edge rapid aggregation mode using the substation as a minimum unit is formed.
S2: as shown in fig. 3, the wave recording master station performs uniform heterogeneous data preprocessing on the wave recorder data from different sources, specifically including uniform information modeling processing, friendly fault-tolerant processing, uniform data format processing, secure transcoding processing, and transparent access processing.
The unified information modeling processing is the unique global universal standard IEC61850 in the field of electric power system automation, a communication module and a communication server are not required to be fixed under the structure, any communication server can process the information as long as resources are available, and the communication module can recombine a non-standard protocol according to an information model defined by a standard, so that the self-adaption of any type of communication protocol is realized. The method specifically comprises the following steps:
s211: in a wave recording master station system development stage, according to the definition in the IEC61850 standard, measuring the difference between the parameters of the special-shaped wave recorder model by using the parameter difference degree index, and establishing an information model for converting a non-standard protocol to a standard protocol to form a protocol conversion rule base;
s212: all tasks enter a queue in sequence according to arrival time;
s213: the communication scheduler marks the data by the stipulation rule characteristic and starts to mark the next data in the queue at the same time;
s214: according to the protocol rule feature labels, automatically matching and loading the corresponding IEC61850 information model from the protocol conversion rule library;
s215: matching multi-source data to a data channel of IEC61850 information model unified parameter description based on the IEC61850 information model to realize parameter unification;
s216: the communication scheduler preferentially allocates tasks to idle communication links, and preferentially allocates tasks to communication links which have processed the same type of information models last time when a plurality of communication links are in an idle state;
s217: the communication scheduler will close the long-term idle link, release the system resources, and open as needed.
The friendly fault-tolerant processing specifically comprises the following steps: after unified information modeling processing is carried out on the data parameters of the oscillographs from different sources, standard parameter configuration information is generated by utilizing a general compensation list or a mode of calling a primary fixed value model, and data completion or repeated data elimination is carried out on functions which are not complete or redundant and are matched with data calling. Comprises the following steps:
(1) the main information of the wave recording data is researched, the wave recorder starts to record voltage and current signals collected by the transformer substations on the two sides of the transmission line M and N in real time, the signals are converted into secondary current and voltage sampling instantaneous values through PT/CT transformation ratio, and the secondary current and voltage sampling instantaneous values are stored by the wave recorder in the station to form static wave recording data. When the transmission line breaks down, the oscillographs at the two ends of the line record the sampling instantaneous values of three-phase fault voltage and current.
(2) Under the condition of data loss such as CT/PT, line length, positive sequence impedance, zero sequence impedance, wave recorder sampling rate and the like, a three-phase fault voltage and current sampling instantaneous value curve graph cannot be generated effectively, a general compensation list can be established according to the data which is frequently lost in the wave recording data of the traditional transformer substation, a supplement function is used for automatically backfilling the lost data, a fixed value list is called for supplementing the special data which is lost outside the compensation list, and then complete data in a fault wave recording model is obtained.
(3) Some files have multiple copies or a large number of data repeats in the file, such as repeated occurrences of a current disturbance or periodic data of the current. If only one instance is reserved for all the same data blocks, the actually stored data quantity and the data processing level are greatly reduced, a set of frequent items with the occurrence frequency reaching a certain threshold value in a plurality of sets is mined, and the frequent items can be deleted when the matching rate of certain feature information is high and reaches the threshold value.
The unified data format processing comprises the following steps:
s231: data structured difference recognition: carrying out the difference analysis of the same data structure on the data of the oscillographs of different types, and identifying the difference of the data structures;
s232: establishing a data format conversion template: establishing template middleware objects for the differences identified by the data structures, and performing granularity processing on the differentially expressed data by adopting a least conversion principle and calling corresponding functions;
s233: the data format is unified: and converting the data into logic expressions with the same format to form a data storage directory with the same type of data parameters and uniform format.
The safe transcoding treatment specifically comprises the following steps: and the waveform data of the wave recorder is subjected to Hilbert-yellow transform smoothing filtering to reduce noise interference and improve the smoothness of the data, the association among the waveform data is found by utilizing a configuration file, the waveform is drawn in parallel, and a graphical waveform file is formed for a wave recording master station to directly take, so that lossless and safe transcoding of the waveform data is realized.
The transparent access processing specifically includes: and the intelligent equipment on the plant station side performs centralized management on the data access interface, so that transparent access of the secondary equipment data on the edge plant station side is realized.
S3: carrying out knowledge mining by utilizing the preprocessed data, establishing logical relations between different defect faults and fault characteristics, and constructing a knowledge base; and carrying out abnormal case screening on the recording data received in real time, and carrying out case reasoning analysis by using the established knowledge base to realize continuous updating and iteration of the knowledge base.
Data Mining (DM), also known as database Knowledge Discovery (KDD), can search information hidden in a model algorithm by using massive Data through logic setting in the model algorithm, and is widely applied to equipment operation and maintenance work. The method comprises the steps of establishing a knowledge base through historical data mining, wherein the knowledge base comprises multiple hidden defects of secondary equipment, such as intermittent GPS desynchronization, fixed value deviation, local failure of software module functions and the like, extracting fault characteristics related to different defects, establishing a bottom layer logic relation between the fault characteristics, forming the knowledge base by case samples facing intelligent operation and maintenance objects, and comprehensively establishing by using technologies such as rough sets, clustering and the like, wherein the establishing flow of the knowledge base is shown in figure 4, and the establishing of the knowledge base by a wave recording master station according to preprocessed data mining specifically comprises the following steps:
s311: extracting secondary equipment fault cases from historical fault data and reports;
s312: each case is recorded in a structural organization mode in a standard way;
s313: converting the fault information into case samples to meet the requirement of secondary equipment defect data mining;
s314: reducing the fault characteristic attribute of the case sample by using the rough set, and cleaning the redundancy attribute;
s315: using the case samples after the clustering analysis classification reduction to form initial clusters;
s316: counting initial clusters and merging similar clusters;
s317: typical case samples in each cluster are extracted to form a simplified knowledge base.
And a knowledge base is established to form a comprehensive and organized knowledge cluster, and the existing item query and solution guidance can be provided for the same or similar abnormal cases.
The real-time analysis and knowledge base updating of the running state of the secondary equipment and state evaluation of the wave recording master station specifically comprise the following steps:
s321: analyzing real-time wave recording data sent by the edge intelligent equipment based on case reasoning, wherein the real-time wave recording data comprises exception screening of a current case and reasoning analysis of an exception case;
the current case exception screening specifically comprises the following steps:
(1) and (3) transversely comparing multiple sources with fault data: collecting wave recording files generated by wave recorders on two sides of the same fault line and a protective device acquisition unit, comparing the same sampling channel data, and generating an abnormal alarm when the difference of waveform data, fixed value parameters and clock signals reaches a certain threshold value so as to find the abnormality in a secondary equipment sampling loop;
(2) and comparing the monitoring data of the same object longitudinally: locking equipment serving as an analysis object, utilizing data such as equipment state, link state, ledger information, configuration files and maintenance records uploaded by a recorder, performing parameter and information proofreading on data and reports provided by edges and historical data, finding a fault judgment logic support point, converting the data into value information, primarily judging the equipment state, and predicting whether a degradation trend exists according to past routing inspection records;
Case-Based Reasoning (CBR) is a method for searching typical cases in a knowledge base by using keywords of a current Case, and guiding the current Case to solve through a historical Case analysis process and processing experience, and tends to analyze new situations and solve new problems by using past experience. The upgrading of the knowledge base is realized by continuously accumulating the abnormal cases, and the situation awareness capacity of the secondary equipment is continuously improved along with the continuous improvement of the knowledge base. Case reasoning flow as shown in fig. 5, the reasoning analysis of the abnormal case includes the following steps:
(1) and (3) retrieval: inputting keyword information of a current case, and retrieving similar typical cases from a knowledge base;
(2) calling: obtaining an analysis process and a scheme from the retrieved case, and judging whether the solution requirements are met; if the current case actual situation is met, directly calling a merging solution of the scheme or the schemes, otherwise, correcting according to the actual situation of the current case;
(3) and (3) correction: adjusting the solution of the similar cases to meet the solution requirements of the current case, and obtaining a new solution meeting the characteristics of the current abnormal case;
(4) and (3) storage: submitting the new scheme and the solving basis thereof according to the expression of the specification;
(5) updating: and 4, upgrading and perfecting the knowledge base.
Collecting information such as description of the current abnormal case, abnormal frequency, recurrence steps and the like, searching whether the current abnormal case belongs to the coverage range of the knowledge base by case reasoning, comparing the current abnormal case with items of the classical case, reasoning the matching degree of the existing solving and processing scheme of the abnormality, carrying out investigation of possible reasons, exploration of an effective scheme and adjustment of the existing processing scheme, and finally forming continuous updating and iteration of the knowledge base.
S322: establishing a state evaluation model and evaluation indexes, carrying out secondary equipment running state evaluation, calculating the evaluation indexes by using a qualitative and quantitative evaluation method to sense the running situation of the equipment, and carrying out information directional release and equipment full life cycle normalized management according to the equipment state evaluation result and the priority sequence.
S4: the wave recording master station screens equipment fault stations discovered by case reasoning in a whole network range in a global angle, evaluates fault coverage and influence degree, outputs a secondary equipment state evaluation result as a reference basis of secondary equipment overhaul urgency degree, classifies alarms by grading the evaluation result, pushes the most important alarm content to the front end, generates a defect elimination list, and directionally issues information to inform corresponding operation and inspection personnel to process faults in time.
The fault data and the obtained report of cloud-side cooperative analysis are the basis of equipment state evaluation of the power grid with an incidence relation, secondary equipment operation state evaluation is carried out by establishing object-oriented different state models, defects can be efficiently and accurately captured by the equipment state evaluation, and the method has an important effect on the research of the normalized management and the intelligent operation and maintenance of the full life cycle of the secondary equipment.
An evaluation model is introduced in the equipment state evaluation as shown in table 1, a potential implicit rule is discovered while the known data parameter information is acquired, the evaluation model is used for describing the incidence relation between the known information and the implicit defect, the computer technology is used for performing bottom layer logic comparison analysis between fault characteristics, and the real-time evaluation of the running state of the secondary equipment is performed.
In order to evaluate the influence degree and range of the equipment defects, equipment state grades are introduced as shown in table 2, the discovered equipment defects are screened in a full-network range from point to surface by utilizing cloud edge cooperation, and a qualitative and quantitative evaluation method is used for grading and grading. The master station system has a remote centralized inspection function, can derive the equipment state evaluation report in real time, and provides reason analysis, processing plan and scoring result with reference value in a non-real-time targeted manner. Through data analysis and state evaluation, transparent display and clear cognition are performed on the current equipment defects, the state perception capability of the secondary equipment is improved, the operation and inspection personnel can be supported to arrange a reasonable plan and perform early intervention and defect repair, and corresponding maintenance strategies are provided as maintenance guidance.
TABLE 1 evaluation model of secondary equipment operating conditions
TABLE 2 Equipment State class Table
| Status rating | Difference (D) | In | Good wine | Superior food |
| Score range | 0～0.30 | 0.31～0.60 | 0.61～0.80 | 0.81～1 |
| Maintenance strategy | Immediate overhaul | Overhaul as soon as possible | Scheduled maintenance | Delayed maintenance |
The qualitative assessment is specifically as follows:
according to the equipment state indexes qualitatively described by environmental factors, overhaul records, configuration files, ledger information, link on-off, influence ranges and the like, state scoring of abnormal equipment is carried out in an expert investigation mode, the state grade thrown by the highest proportion rho is taken as a final result, and the expression is as follows:
the quantitative evaluation was specifically as follows:
quantitative analysis is used as quantification and verification of qualitative analysis, and a concept of relative deterioration degree is introduced to represent the degree of the abnormal state of the secondary equipment, wherein the degree is a quantitative score with a value range of [0,1 ].
For the more and more optimal indexes, a half-trapezoidal model is adopted, and the scoring expression is as follows:
for smaller and more optimal indexes, a half-reduced trapezoidal model is adopted, and the evaluation expression is as follows:
in the formula, xmaxAnd xminThe actual measurement value and the maximum and minimum limit values of the state index are respectively. x is the number ofmaxAnd xminCan be obtained through the channels of equipment specifications, specification indexes, testing technologies and the like.
The method has the advantages that corresponding data receiving authorities are opened for dispatching, operating and overhauling personnel, abnormal related information of the equipment is directionally issued, maximum sharing and effective utilization of the information of the equipment in the whole network are achieved, communication cost can be reduced, information transmission omission is avoided, and quick response and response processing are carried out at the first time of equipment failure.
Because the information quantity of the abnormal case state evaluation report is large, only partial equipment state evaluation results are displayed as shown in table 3, table 3 realizes classified display of the abnormal case defect equipment states, assists the operation and inspection personnel to complete preliminary preparation work such as full pre-planning before the operation and inspection, pre-positioning of spare parts and tools and the like, carries out equipment operation and inspection tasks in order, and converts secondary equipment inspection into a planned and recyclable 'inspection-as-inspection mode'.
TABLE 3 Power grid abnormal case inference analysis and State evaluation results
The degree of urgency of equipment maintenance is determined according to the equipment state scoring result, the most important alarm content is pushed to the forefront end to generate a defect elimination list, and the corresponding operation and inspection personnel are informed of fault timely processing and closed-loop through directional release information, so that the normalized management of the whole life cycle of the equipment and the intelligent operation and maintenance target of 'inspection when inspection is performed' are realized.
Those of ordinary skill in the art will appreciate that the elements of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the components of the examples have been described above generally in terms of their functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present application, it should be understood that the division of the unit is only one division of logical functions, and other division manners may be used in actual implementation, for example, multiple units may be combined into one unit, one unit may be split into multiple units, or some features may be omitted.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (9)
1. An intelligent operation and maintenance method for centralized control type relay protection equipment based on cloud edge cooperation is characterized by comprising the following steps: the method comprises the following steps:
s1: the intelligent equipment on the station side acquires data of all wave recorders in the transformer substation and uploads the acquired data to a wave recording master station through a scheduling data network;
s2: the wave recording master station uniformly preprocesses heterogeneous data on wave recorder data from different sources, and specifically comprises uniform information modeling processing, friendly fault-tolerant processing, uniform data format processing, safe transcoding processing and transparent access processing;
s3: carrying out knowledge mining by utilizing the preprocessed data, establishing logical relations between different defect faults and fault characteristics, and constructing a knowledge base; abnormal case screening is carried out on the recording data received in real time, case reasoning analysis is carried out by utilizing the established knowledge base, and the knowledge base is continuously updated and iterated;
s4: the wave recording master station screens equipment fault stations discovered by case reasoning in a whole network range in a global angle, evaluates fault coverage and influence degree, outputs a secondary equipment state evaluation result as a reference basis of secondary equipment overhaul urgency degree, classifies alarms by grading the evaluation result, pushes the most important alarm content to the front end, generates a defect elimination list, and directionally issues information to inform corresponding operation and inspection personnel to process faults in time.
2. The intelligent operation and maintenance method for the centralized control type relay protection equipment based on cloud edge coordination as claimed in claim 1, wherein: in the step S1, the data of the oscillograph in the substation acquired by the station side intelligent device includes oscillograph topology structure information, ledger information, device state information, link state information, configuration file information, and overhaul record information.
3. The intelligent operation and maintenance method for the centralized control type relay protection equipment based on cloud edge coordination as claimed in claim 1, wherein: the unified information modeling processing in step S2 specifically includes the following steps:
s211: in a wave recording master station system development stage, according to the definition in the IEC61850 standard, measuring the difference between the parameters of the special-shaped wave recorder model by using the parameter difference degree index, and establishing an information model for converting a non-standard protocol to a standard protocol to form a protocol conversion rule base;
s212: all tasks enter a queue in sequence according to arrival time;
s213: the communication scheduler marks the data by the stipulation rule characteristic and starts to mark the next data in the queue at the same time;
s214: according to the protocol rule feature labels, automatically matching and loading the corresponding IEC61850 information model from the protocol conversion rule library;
s215: matching multi-source data to a data channel of IEC61850 information model unified parameter description based on the IEC61850 information model to realize parameter unification;
s216: the communication scheduler preferentially allocates tasks to idle communication links, and preferentially allocates tasks to communication links which have processed the same type of information models last time when a plurality of communication links are in an idle state;
s217: the communication scheduler will close the long-term idle link, release the system resources, and open as needed.
4. The intelligent operation and maintenance method for the centralized control type relay protection equipment based on cloud edge coordination as claimed in claim 1, wherein: the friendly fault-tolerant processing in step S2 specifically includes: after unified information modeling processing is carried out on the data parameters of the oscillographs from different sources, standard parameter configuration information is generated by utilizing a general compensation list or a mode of calling a primary fixed value model, and data completion or repeated data elimination is carried out on functions which are not complete or redundant and are matched with data calling.
5. The intelligent operation and maintenance method for the centralized control type relay protection equipment based on cloud edge coordination as claimed in claim 1, wherein: the unified data format processing in step S2 includes the following steps:
s231: data structured difference recognition: carrying out the difference analysis of the same data structure on the data of the oscillographs of different types, and identifying the difference of the data structures;
s232: establishing a data format conversion template: establishing template middleware objects for the differences identified by the data structures, and performing granularity processing on the differentially expressed data by adopting a least conversion principle and calling corresponding functions;
s233: the data format is unified: and converting the data into logic expressions with the same format to form a data storage directory with the same type of data parameters and uniform format.
6. The intelligent operation and maintenance method for the centralized control type relay protection equipment based on cloud edge coordination as claimed in claim 1, wherein: the secure transcoding processing in step S2 specifically includes: and the waveform data of the wave recorder is subjected to Hilbert-yellow transform smoothing filtering to reduce noise interference, the association among the waveform data is found by utilizing a configuration file, and the waveform is drawn in parallel to form a graphical waveform file for a wave recording master station to directly take, so that lossless and safe transcoding of the waveform data is realized.
7. The intelligent operation and maintenance method for the centralized control type relay protection equipment based on cloud edge coordination as claimed in claim 1, wherein: the transparent access processing in step S2 specifically includes: and the intelligent equipment on the plant station side performs centralized management on the data access interface, so that transparent access of the secondary equipment data on the edge plant station side is realized.
8. The intelligent operation and maintenance method for the centralized control type relay protection equipment based on cloud edge coordination as claimed in claim 1, wherein: the construction of the knowledge base by the wave recording master station according to the preprocessed data mining in the step S3 specifically comprises the following steps:
s311: extracting secondary equipment fault cases from historical fault data and reports;
s312: each case is recorded in a structural organization mode in a standard way;
s313: converting the fault information into case samples to meet the requirement of secondary equipment defect data mining;
s314: reducing the fault characteristic attribute of the case sample by using the rough set, and cleaning the redundancy attribute;
s315: using the case samples after the clustering analysis classification reduction to form initial clusters;
s316: counting initial clusters and merging similar clusters;
s317: typical case samples in each cluster are extracted to form a simplified knowledge base.
9. The intelligent operation and maintenance method for the centralized control type relay protection equipment based on cloud edge coordination as claimed in claim 1, wherein: the step S3 of the wave recording master station performing real-time analysis of the running state of the secondary device, updating the knowledge base, and evaluating the state specifically includes the following steps:
s321: analyzing real-time wave recording data sent by the edge intelligent equipment based on case reasoning, wherein the real-time wave recording data comprises exception screening of a current case and reasoning analysis of an exception case;
the current case exception screening specifically comprises the following steps:
(1) and (3) transversely comparing multiple sources with fault data: collecting wave recording files generated by wave recorders on two sides of the same fault line and a protective device acquisition unit, comparing the same sampling channel data, and generating an abnormal alarm when the difference of waveform data, fixed value parameters and clock signals reaches a certain threshold value so as to find the abnormality in a secondary equipment sampling loop;
(2) and comparing the monitoring data of the same object longitudinally: locking equipment serving as an analysis object, performing parameter and information proofreading on data and reports provided by edges and historical data, finding out a fault judgment logic supporting point, converting the data into value information, primarily judging the state of the equipment, and predicting whether a degradation trend exists according to past routing inspection records;
the reasoning analysis of the abnormal case comprises the following steps:
(1) and (3) retrieval: inputting keyword information of a current case, and retrieving similar typical cases from a knowledge base;
(2) calling: obtaining an analysis process and a scheme from the retrieved case, and judging whether the solution requirements are met; if the current case actual situation is met, directly calling a merging solution of the scheme or the schemes, otherwise, correcting according to the actual situation of the current case;
(3) and (3) correction: adjusting the solution of the similar cases to meet the solution requirements of the current case, and obtaining a new solution meeting the characteristics of the current abnormal case;
(4) and (3) storage: submitting the new scheme and the solving basis thereof according to the expression of the specification;
(5) updating: upgrading and perfecting a knowledge base;
s322: establishing a state evaluation model and evaluation indexes, carrying out secondary equipment running state evaluation, calculating the evaluation indexes by using a qualitative and quantitative evaluation method to sense the running situation of the equipment, and carrying out information directional release and equipment full life cycle normalized management according to the equipment state evaluation result and the priority sequence.
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