CN115580028A - Power quality monitoring method and system for power management - Google Patents
Power quality monitoring method and system for power management Download PDFInfo
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- CN115580028A CN115580028A CN202211451647.7A CN202211451647A CN115580028A CN 115580028 A CN115580028 A CN 115580028A CN 202211451647 A CN202211451647 A CN 202211451647A CN 115580028 A CN115580028 A CN 115580028A
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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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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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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
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/001—Methods to deal with contingencies, e.g. abnormalities, faults or failures
- H02J3/0012—Contingency detection
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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
- 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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- 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
- 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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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- 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
- Y04S10/52—Outage or fault management, e.g. fault detection or location
Abstract
The invention provides a power quality monitoring method and a system for power management, relates to the technical field of intelligent power grids, and aims to construct a tree-shaped circuit distribution network for a preset power grid area, collect a power grid fault event set, acquire a power quality monitoring index and a power quality deviation factor set, generate a plurality of groups of power quality influence factor sets by frequent mining, determine a threshold value interval of the plurality of groups of influence factors, construct a power quality abnormity prediction model based on the threshold value interval, perform model analysis to perform prediction management on the power quality of the preset power grid area, solve the technical problem that the efficiency of power management is influenced due to poor early warning performance because the current monitoring method has certain limitation when power quality monitoring and early warning are performed in the prior art, and perform intelligent monitoring and analysis on real-time power operation by enhancing monitoring rigidness, and perform targeted adjustment on the basis of ensuring abnormity early warning accuracy.
Description
Technical Field
The invention relates to the technical field of smart power grids, in particular to a power quality monitoring method and system for power management.
Background
In the operation process of an electric power system, the influence of various uncontrollable factors can be avoided, so that the electric energy quality is influenced, the electric energy quality is not only related to the safe and economic operation of a power grid enterprise, but also influences the use safety and quality of users to electric power equipment, and the operation and maintenance monitoring of the electric energy quality is limited due to the distributed arrangement of the power grid.
In the prior art, when power quality monitoring and early warning are carried out, due to certain limitation of the current monitoring method, the early warning performance is poor, so that the early warning timeliness is influenced, and further the efficiency of power management is influenced.
Disclosure of Invention
The application provides a power quality monitoring method and system for power management, which are used for solving the technical problems that when power quality monitoring and early warning are carried out in the prior art, the early warning performance is poor due to certain limitation of the current monitoring method, the early warning timeliness is influenced, and further the efficiency of power management is influenced.
In view of the foregoing, the present application provides a power quality monitoring method and system for power management.
In a first aspect, the present application provides a power quality monitoring method for power management, the method comprising: constructing a tree-shaped circuit distribution network according to the child-parent-level circuit relation of a preset power grid area; collecting a power grid fault event set in a preset time granularity of the preset power grid region according to the tree circuit distribution network; acquiring a power quality monitoring index and a power quality deviation factor set; performing frequent item mining on the power quality monitoring indexes and the power quality deviation factor set according to the power grid fault event set to generate a plurality of groups of power quality influence factor sets; traversing the multiple groups of electric energy quality influence factor sets to obtain multiple groups of influence factor set and threshold value intervals; according to the multiple groups of influence factors and a threshold interval, constructing a power quality abnormity prediction model; and carrying out prediction management on the electric energy quality of the preset power grid area according to the electric energy quality abnormity prediction model.
In a second aspect, the present application provides a power quality monitoring system for power management, the system comprising: the network construction module is used for constructing a tree-shaped circuit distribution network according to the child-parent-level circuit relation of a preset power grid area; the event set construction module is used for collecting a power grid fault event set in a preset time granularity of the preset power grid region according to the tree circuit distribution network; the parameter acquisition module is used for acquiring a power quality monitoring index and a power quality deviation factor set; the influence factor acquisition module is used for performing frequent item mining on the electric energy quality monitoring index and the electric energy quality deviation factor set according to the power grid fault event set to generate a plurality of groups of electric energy quality influence factor sets; the interval acquisition module is used for traversing the multiple groups of electric energy quality influence factor sets to acquire multiple groups of influence factor combination threshold intervals; the model construction module is used for constructing an electric energy quality abnormity prediction model according to the multiple groups of influence factors and the threshold interval; and the quality management module is used for carrying out prediction management on the electric energy quality of the preset power grid region according to the electric energy quality abnormity prediction model.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the electric energy quality monitoring method for power management, a tree-shaped circuit distribution network is built according to the child-parent-level circuit relation of a preset power grid area, a power grid fault event set in the preset time granularity of the preset power grid area is collected, an electric energy quality monitoring index and an electric energy quality deviation factor set are obtained, frequent item mining is carried out on the electric energy quality monitoring index and the electric energy quality deviation factor set according to the power grid fault event set, multiple groups of electric energy quality influence factor sets are generated and combined to determine threshold intervals of the multiple groups of influence factor sets, an electric energy quality abnormity prediction model is built on the basis of the threshold intervals, prediction management is carried out on the electric energy quality of the preset power grid area through model analysis, the technical problem that when power quality monitoring and early warning are carried out in the prior art is solved, due to the fact that the current monitoring method has certain limitation, early warning performance is poor, early warning timeliness is influenced, and further technical problems of influencing efficiency of power management are solved, and intelligent monitoring and analysis in real-time can be carried out through targeted adjustment on the basis of guaranteeing the abnormity early warning accuracy.
Drawings
Fig. 1 is a schematic flow chart of a power quality monitoring method for power management according to the present application;
FIG. 2 is a schematic diagram illustrating a process for constructing a grid fault event set in a power quality monitoring method for power management according to the present application;
fig. 3 is a flow chart illustrating a plurality of sets of power quality influence factors in a power quality monitoring method for power management according to the present application;
fig. 4 is a schematic diagram of a power quality monitoring system for power management according to the present application.
Description of the reference numerals: the system comprises a network construction module 11, an event set construction module 12, a parameter acquisition module 13, an influence factor acquisition module 14, an interval acquisition module 15, a model construction module 16 and a quality management module 17.
Detailed Description
The application provides an electric energy quality monitoring method and system for power management, a tree-shaped circuit distribution network is built for a preset power grid area, a power grid fault event set is collected, an electric energy quality monitoring index and an electric energy quality deviation factor set are obtained, a plurality of groups of electric energy quality influence factor sets are generated by frequent item mining, a threshold value section of the plurality of groups of influence factor sets is determined, an electric energy quality abnormity prediction model is built based on the electric energy quality abnormity prediction model, model analysis is carried out to carry out prediction management on the electric energy quality of the preset power grid area, and the electric energy quality monitoring method and the system are used for solving the technical problems that when electric energy quality monitoring early warning is carried out in the prior art, the early warning performance is poor due to certain limitation of the current monitoring method, the early warning timeliness is influenced, and the efficiency of power management is influenced.
Example one
As shown in fig. 1, the present application provides a power quality monitoring method for power management, the method comprising:
step S100: constructing a tree-shaped circuit distribution network according to the child-parent-level circuit relation of a preset power grid area;
specifically, electric power is used as a main energy source for maintaining normal operation of the society, when the electric energy quality does not reach the standard, a series uncontrollable result is possibly caused, and therefore the method is particularly important for monitoring the quality of the electric power.
Further, the step S100 of constructing a tree-like circuit distribution network according to a child-parent-level circuit relationship of a preset power grid area further includes:
step S110: acquiring a circuit topology structure diagram according to the preset power grid area;
step S120: constructing a child-parent line table according to the circuit topology structure chart;
step S130: extracting a first-level line set, a second-level line set and an Nth-level line set according to the child-parent-level line table;
step S140: traversing the first, second, and nth sets of hierarchy lines, matching first, second, and nth sets of hierarchy power devices up to an nth set of hierarchy power devices;
step S150: constructing a first level root node set according to the first level line set and the first level power equipment set;
step S160: constructing an Nth-level leaf node set according to the Nth-level line set and the Nth-level power equipment set;
step S170: and based on the child-parent-level line table, connecting the first level root node set until the Nth level leaf node set to generate the tree circuit distribution network.
Specifically, a power grid distribution area is used as the preset power grid area, a corresponding circuit structure system is determined based on the guidance of electric signals in a power grid, the circuit topology structure diagram is determined based on the reference, multi-level circuit lines are determined according to the electric signal guidance of the circuit topology diagram, the sub-parent line list is constructed based on the connection relation of the multi-level circuit lines, further, multi-level line extraction is carried out according to the sub-parent line list, the first-level line set, the second-level line set and the nth-level line set are obtained, further, power equipment matching is respectively carried out on the multi-level line sets, and a plurality of power equipment covered by the multi-level lines are obtained, wherein the power equipment comprises the first-level power equipment set, the second-level power equipment set and the nth-level power equipment set.
Further, a tree-shaped circuit distribution network is built according to a matched multi-level power equipment set, the first-level root node set of the tree-shaped circuit distribution network is built on the basis of the first-level line set and the first-level power equipment set, a plurality of root nodes can be formed, the construction of multi-level leaf node sets is performed in sequence, the Nth-level leaf node set is built on the basis of the Nth-level line set and the Nth-level power equipment set, the node set is built at the moment, the child parent-level circuit table is matched with the first-level root node set until the Nth-level leaf node set, the arrangement mode of the multi-level node sets is determined, the multi-level node sets are connected, the tree-shaped power distribution network is generated, the tree-shaped power distribution network is made to be adaptive to an actual circuit system, and subsequent power quality analysis is facilitated.
Step S200: collecting a power grid fault event set in a preset time granularity of the preset power grid region according to the tree circuit distribution network;
specifically, through right predetermine the electric wire netting area and carry out the circuit connection analysis, generate tree circuit distribution network acquires predetermine the time granularity, predetermine the time granularity and carry out the event interval that power equipment trouble incident was gathered for carrying out, based on tree circuit distribution network is right predetermine the electric wire netting area and divide, wherein, the division result is corresponding with the multilevel network, further respectively to the region that the multilevel network corresponds based on predetermine the time granularity and carry out trouble incident and gather, wherein probably contain multiple equipment trouble and induce, further classify the integration to the trouble incident of gathering, acquire multiregional electric wire netting trouble incident, add it and advance in the electric wire netting trouble incident set, the information that electric wire netting trouble incident set contains is comparatively complete, will electric wire netting trouble incident set is as waiting to analyze the data source, has tamped the basis for the follow-up electric energy quality monitoring analysis.
Further, as shown in fig. 2, the collecting, according to the tree circuit distribution network, a set of grid fault events within a preset time granularity of the preset grid region further includes:
step S210: acquiring a first level circuit region, a second level circuit region and an Nth level circuit region according to the tree circuit distribution network;
step S220: traversing the first hierarchical circuit area, and collecting a first regional power grid fault event set;
step S230: traversing the Nth-level circuit area and collecting an Nth area power grid fault event set;
step S240: adding the first regional power grid fault event set to the Nth regional power grid fault event set.
Specifically, the tree-shaped circuit distribution network is constructed based on the preset power grid region, region division is performed based on multi-level nodes of the tree-shaped circuit distribution network, the first-level circuit region, the second-level circuit region and the Nth-level circuit region are obtained, the preset time granularity is set, namely event intervals for collecting power equipment fault events are obtained, the power equipment corresponding to the multi-level regions are respectively subjected to fault event collection based on the preset time granularity, the first-level circuit region is traversed, the power equipment covered by the region is subjected to fault event collection, labels are generated based on fault time, fault events, fault equipment and the like so as to be identified and distinguished quickly, the first-region power grid fault event set is obtained, the Nth-region power grid fault event set is obtained by traversing the Nth-level circuit region in sequence, the first-region power grid fault event set and the Nth-region power grid fault event set are further classified and integrated, the first-region power grid fault event set and the Nth-region power grid fault event set are added into the power grid fault event set, completeness and orderliness of the power grid fault event set can be effectively guaranteed, and subsequent analysis efficiency is improved.
Step S300: acquiring a power quality monitoring index and a power quality deviation factor set;
further, the step S300 of obtaining the power quality monitoring index and the power quality deviation factor set further includes:
step S310: the power quality monitoring indexes comprise voltage quality indexes, frequency quality indexes and waveform quality indexes;
step S320: the electric energy quality deviation factor set comprises an environment deviation factor set and an electric power equipment deviation factor set.
Specifically, voltage deviation grading is carried out based on the power supply voltage, for example, the sum of absolute values of voltage deviation of the power supply voltage of more than 35kV does not exceed 10% of the rated voltage, the allowable deviation of the power supply voltage of 20kV and less is +/-7% of the rated voltage, and the voltage quality index, namely a plurality of relevant parameters for expressing the voltage quality, including the allowable deviation scales of the power supply voltages of different grades, is determined; taking the degree of frequency deviation in a normal operation state as the frequency quality index, for example, the frequency deviation ± 0.2% hz; and taking the measured harmonic voltage content and harmonic distortion rate as the waveform quality index, and adding the voltage quality index, the frequency quality index and the waveform quality index into the power quality monitoring index.
Furthermore, the corresponding power consumption requirements are different in different power consumption environments, the corresponding power quality requirements are different, for example, household power consumption, factory power consumption and the like, meanwhile, different power consumption scenes can also cause certain influences, such as sudden change of weather and the like, meanwhile, equipment aging, line damage and the like can also cause power quality deviation, different inducements are different, corresponding power quality deviation results are different, the factors are classified and integrated, the environment deviation factor set and the power equipment deviation factor set are generated and added into the power quality deviation factor set, and in the equipment operation process, the electric equipment can be guaranteed to normally operate in the specified power index quality, meanwhile, the electric equipment also has the capability of bearing operation exceeding standards in a short time and can be subjected to fine adjustment on the power quality indexes, and the acquisition of the power quality monitoring index and the power quality deviation factor set provides a basic basis for the subsequent determination on the power quality influence factors.
Step S400: performing frequent item mining on the power quality monitoring indexes and the power quality deviation factor set according to the power grid fault event set to generate a plurality of sets of power quality influence factor sets;
step S500: traversing the multiple groups of electric energy quality influence factor sets to obtain multiple groups of influence factor set and threshold value intervals;
specifically, traversing the power quality monitoring indicators, including the voltage quality monitoring indicators, the frequency quality monitoring indicators, and the waveform quality monitoring indicators, respectively performing fault inducement analysis on the power quality deviation factor set and the power quality deviation factor set based on the power grid fault event set, where power grid faults are mostly caused by inappropriate power quality and application scenes, power quality needs to be strictly controlled, multiple influencing factors causing power grid fault events are determined, accidental factors are eliminated, the influencing factors having universality are integrated to generate the multiple groups of power quality influencing factor sets, further traversing the multiple groups of power quality influencing factor sets, respectively setting threshold intervals, namely dynamic fluctuation intervals of the influencing factors, for each influencing factor, when the threshold intervals are met, the power grid is in a normal operation state, respectively setting the threshold intervals for the multiple groups of influencing factors, generating the multiple groups of influencing factor set threshold intervals, and taking the multiple groups of influencing factor set threshold intervals as the power quality evaluation criteria, thereby providing a basis for subsequent power quality anomaly monitoring analysis.
Step S600: constructing a power quality abnormity prediction model according to the multiple groups of influence factors and the threshold interval;
step S700: and carrying out prediction management on the electric energy quality of the preset power grid area according to the electric energy quality abnormity prediction model.
Specifically, an integral construction framework of the power quality abnormity prediction model is constructed based on a machine learning algorithm, the power quality abnormity prediction model is an auxiliary tool for power quality analysis, multiple groups of influence factors are input into the power quality abnormity prediction model in a threshold interval, model completion is achieved by model learning, the power quality abnormity prediction model is a multi-level network layer and comprises an information identification layer, an analysis prediction layer and an abnormity early warning layer, preferably, model training and verification can be performed based on big data acquisition associated data serving as sample data to improve the simulation accuracy of the model until the accuracy reaches a preset standard, further, real-time operation data acquisition is performed on the preset power grid area and is input into the power quality abnormity prediction model, element identification is performed based on the information identification layer, an identification result is transmitted to the analysis prediction layer, evaluation and analysis are performed based on the corresponding threshold interval to determine whether the abnormity exists, abnormity grade evaluation is performed on the abnormity elements, operation prediction is performed based on the abnormity grade, a prediction result is obtained and transmitted to the abnormity early warning layer, output information based on the abnormity factors and the prediction result is output, further, the subsequent operation management result is objectively evaluated, and the effective prediction model can be evaluated.
Further, as shown in fig. 3, the mining the power quality monitoring index and the power quality deviation factor set according to the grid fault event set to generate a plurality of sets of power quality influencing factor sets, where step S600 in the present application further includes:
step S610: according to the power grid fault event set, traversing the voltage quality indexes to perform frequent item mining on the environment deviation factor set and the power equipment deviation factor set to generate a plurality of groups of voltage quality influence factor sets;
step S620: according to the power grid fault event set, traversing the frequency quality indexes to perform frequent item mining on the environment deviation factor set and the power equipment deviation factor set to generate a plurality of groups of frequency quality influence factor sets;
step S630: according to the power grid fault event set, traversing the waveform quality indexes to perform frequent item mining on the environment deviation factor set and the power equipment deviation factor set to generate a plurality of groups of waveform quality influence factor sets;
step S640: and adding the multiple groups of voltage quality influence factor sets, the multiple groups of frequency quality influence factor sets and the multiple groups of waveform quality influence factor sets into the multiple groups of electric energy quality influence factor sets.
Specifically, the method includes the steps of acquiring a power grid fault event set by collecting fault events of the preset power grid area, traversing the voltage quality indexes, respectively performing fault inducement analysis based on the power grid fault event set, performing adaptability comparison with the environment deviation factor set and the power equipment deviation factor set, performing frequent item mining, namely voltage influencing factors inducing power grid faults, performing induced frequency statistics on the factors, and meanwhile, when the influence degree of a single influencing factor is insufficient, combining the single influencing factor with other similar influencing factors respectively, performing comprehensive influence evaluation, taking multiple groups of induced factors meeting preset frequency requirements as the frequent items, wherein the frequent items can be single influencing factors or combinations of multiple influencing factors, and integrating the single influencing factors or combinations of multiple influencing factors to generate the multiple groups of voltage quality influencing factor sets.
Similarly, traversing the frequency quality indexes, performing fault cause analysis on the environment deviation factor set and the power equipment deviation factor set based on the power grid fault event set, and determining a plurality of induction factors influencing the frequency quality under different scenes by performing frequent item mining, wherein all frequent item mining methods are the same, and performing integration processing on the frequent item mining methods to generate a plurality of groups of frequency quality influence factors; traversing the waveform quality indexes, performing fault inducement analysis on the environment deviation factors and the electrical equipment deviation factor set based on the grid fault event set, determining a plurality of induction factors with fault universality influencing the waveform quality by performing frequent item mining, integrating and processing the induction factors to generate a plurality of groups of waveform quality influencing factor sets, and further adding the plurality of groups of voltage quality influencing factor sets, the plurality of groups of frequency quality influencing factor sets and the plurality of groups of waveform quality influencing factor sets into the plurality of groups of power quality influencing factor sets in a combining manner, so that the completeness and the orderliness of the plurality of groups of power quality influencing factor sets can be effectively guaranteed, and a foundation is laid for the subsequent power monitoring tamping.
Further, the traversing the voltage quality index according to the grid fault event set performs frequent item mining on the environment deviation factor set and the electrical equipment deviation factor set to generate a plurality of sets of voltage quality influence factor sets, and step S620 of the present application further includes:
step S621: according to the power grid fault event set, the voltage quality index carries out frequent item mining on the environment deviation factor set and the power equipment deviation factor set to generate a voltage quality influence factor set;
step S622: according to the power grid fault event set and the environment deviation factor set, performing m items of frequent item mining on the power equipment deviation factor set by using voltage quality indexes to generate m items of voltage quality influence factor sets;
step S623: combining the one set of voltage quality impact factors up to the m set of voltage quality impact factors, adding into the plurality of sets of voltage quality impact factors.
Specifically, the grid fault event set and the voltage quality index are subjected to correlation analysis, corresponding voltage quality index data under different grid fault events are determined, for example, the grid fault event set can be classified, multiple groups of fault event types are determined, for one group of fault event types, the environment deviation factor set and the power equipment deviation factor set are subjected to deviation factor analysis, deviation fault factors causing grid faults are determined, and deviation fault factors meeting a preset frequency are used as the voltage quality influence factor set; based on the above analysis steps of the influence factors, the multiple sets of fault event types are respectively mined frequently based on the voltage quality indexes, the environmental deviation factor set and the power equipment deviation factor set, wherein the specific power supply environment is different from the power equipment, and the corresponding voltage quality influence factors may have differences, m voltage quality influence factor sets are obtained, the voltage quality influence factor set is further combined until the m voltage quality influence factor sets are subjected to integration identification, the multiple sets of voltage quality influence factor sets are added, and frequent mining is performed based on actual index data, so that the conformity between the determined quality influence factors and the actual conditions can be effectively guaranteed, and the accuracy of subsequent monitoring is improved.
Further, according to the grid fault event set, the voltage quality index performs m frequent mining on the environmental deviation factor set and the electrical equipment deviation factor set to generate m voltage quality influence factor sets, where step S622 of the present application further includes:
step S6221: constructing an m-term confidence evaluation formula:
wherein, the first and the second end of the pipe are connected with each other,
characterize the confidence that type k to type k + m bias factors co-occur,
the nth group of fault characteristic values of the power quality index with the characteristic type of A,
characterizing the kth type deviation factor corresponding to the nth set of fault data,
representing the frequency of the common occurrence of the power quality index A and the deviation factors from the kth type to the kth + m type, and when the characteristic value is 0, not counting the frequency calculation, wherein m belongs to an integer;
step S6222: constructing an m-term lifting degree evaluation formula:
wherein, the first and the second end of the pipe are connected with each other,
characterizing two sets of deviation factors of only a single type
Event-by-event correspondence
The amount of change is such that,
is characterized by only
The power quality index variation amount corresponding to the changed event,
the representation of the preset groups is only
Of varying events
The average value of the average value is calculated,
representing the promotion degree of the common occurrence of deviation factors from the kth type to the kth + m type;
step S6223: according to the m confidence coefficient evaluation formulas and the m promotion degree evaluation formulas, performing confidence coefficient calculation on the environment deviation factor set and the power equipment deviation factor set to generate a confidence coefficient calculation result;
step S6224: when the confidence coefficient calculation result meets a confidence coefficient threshold value, carrying out lifting degree calculation to generate a lifting degree calculation result;
step S6225: and when the calculation result of the lifting degree meets a threshold value of the lifting degree, adding m deviation factors into the m voltage quality influence factor sets.
Specifically, an m-term confidence evaluation formula is constructed:
wherein, in the step (A),
characterize the confidence that type k to type k + m bias factors co-occur,
the nth group of fault characteristic values of the power quality index with the characteristic type of A,
characterizing a kth type bias factor corresponding to the nth set of fault data,
representing the frequency of common occurrence of the power quality index A and deviation factors from the kth type to the kth + m type, when the characteristic value is 0, not counting frequency calculation, wherein m is an integer, the related factors are obtained through statistical analysis, performing confidence coefficient calculation on the combination of the environment deviation factor set and the power equipment deviation factors respectively based on the m confidence coefficient evaluation formulas, wherein the overall confidence coefficient of one or more deviation factors is included, the common occurrence frequency of the voltage quality monitoring index and the power quality deviation factors is determined through performing confidence coefficient calculation, when the frequency is too low, the current factors are lower in contingency or influence degree and can be properly ignored, and when the frequency is higher, the association degree of the two factors is indicatedHigher, sets of associated combinations that determine the loudness attainment by performing confidence calculations, e.g., voltage deviation versus one or more influencing factors.
Further, an m-term lifting degree evaluation formula is constructed:
、
wherein, in the step (A),
characterizing two sets of deviation factors of only a single type
Event-to-event correspondence
The amount of change is such that,
is characterized by only
The power quality index variation amount corresponding to the changed event,
characterizing a predetermined group only
Of varying events
The average value of the average value is calculated,
representing the promotion degree of the common occurrence of the deviation factors from the kth type to the kth + m type, and promoting the determined multiple groups of association combinations based on the m-term promotion degree evaluation formulaThe method includes the steps of calculating a boost degree, namely calculating a change rate of an electric energy quality index based on change of deviation factors, exemplarily, dividing a plurality of deviation factor combinations, and sequentially calculating the boost degree along with gradual increase of the deviation factors, wherein the larger the change rate is, the larger the corresponding boost degree is, setting a boost degree threshold, and when the calculated boost degree is larger than the boost degree threshold, indicating that the influence degrees of a plurality of closely related and corresponding deviation factors reach a standard, adding m deviation factors into the m voltage quality influence factor sets, wherein a single voltage quality influence factor comprises one or more voltage quality influence factors, and wherein generation manners of the plurality of frequency quality influence factor sets and the plurality of waveform quality influence factor sets are consistent with generation steps of the plurality of voltage quality influence factor sets.
Example two
Based on the same inventive concept as one of the power quality monitoring methods for power management in the foregoing embodiments, as shown in fig. 4, the present application provides a power quality monitoring system for power management, the system including:
the network construction module 11 is used for constructing a tree-shaped circuit distribution network according to the child-parent circuit relationship of a preset power grid area;
the event set constructing module 12 is configured to collect a power grid fault event set within a preset time granularity of the preset power grid region according to the tree circuit distribution network;
the parameter acquisition module 13 is used for acquiring a power quality monitoring index and a power quality deviation factor set;
an influence factor obtaining module 14, where the influence factor obtaining module 14 is configured to perform frequent item mining on the power quality monitoring index and the power quality deviation factor set according to the grid fault event set, and generate a plurality of sets of power quality influence factors;
an interval obtaining module 15, where the interval obtaining module 15 is configured to traverse the multiple sets of power quality influence factor sets to obtain multiple sets of influence factor combination threshold intervals;
the model building module 16 is used for building an electric energy quality abnormity prediction model according to the multiple groups of influence factors and the threshold interval;
and the quality management module 17 is configured to perform prediction management on the power quality of the preset power grid region according to the power quality anomaly prediction model.
Further, the system further comprises:
the topological graph constructing module is used for acquiring a circuit topological structure diagram according to the preset power grid area;
the circuit topology structure diagram constructing module is used for constructing a circuit topology structure diagram according to the circuit topology structure diagram;
the line extraction module is used for extracting a first-level line set, a second-level line set and an Nth-level line set according to the child-parent-level line table;
a device matching module to traverse the first, second, and nth sets of hierarchical lines, matching first, second, and nth sets of hierarchical power devices up to an nth set of hierarchical power devices;
a root node construction module to construct a first hierarchy root node set from the first hierarchy line set and the first hierarchy power device set;
a leaf node construction module for constructing an Nth level leaf node set according to the Nth level line set and the Nth level power equipment set;
a network generation module configured to generate the tree circuit distribution network by connecting the first-level root node set to the nth-level leaf node set based on the child parent-level line table.
Further, the system further comprises:
the region division module is used for acquiring a first-level circuit region, a second-level circuit region and an Nth-level circuit region according to the tree-shaped circuit distribution network;
the first area fault event acquisition module is used for traversing the first hierarchical circuit area and acquiring a first area power grid fault event set;
the Nth region fault event acquisition module is used for traversing the Nth layer circuit region and acquiring an Nth region power grid fault event set;
an event adding module for adding the first regional power grid fault event set to the Nth regional power grid fault event set into the power grid fault event set.
Further, the system further comprises:
the index splitting module is used for enabling the electric energy quality monitoring indexes to comprise voltage quality indexes, frequency quality indexes and waveform quality indexes;
a factor splitting module for the set of power quality deviation factors to include a set of environmental deviation factors and a set of power equipment deviation factors.
Further, the system further comprises:
the voltage quality influence factor generation module is used for traversing the voltage quality indexes to perform frequent item mining on the environment deviation factor set and the power equipment deviation factor set according to the power grid fault event set to generate a plurality of groups of voltage quality influence factor sets;
the frequency quality influence factor generation module is used for traversing the frequency quality indexes to perform frequent item mining on the environment deviation factor set and the power equipment deviation factor set according to the power grid fault event set to generate a plurality of groups of frequency quality influence factor sets;
the waveform quality influence factor generation module is used for traversing the waveform quality indexes to perform frequent item mining on the environment deviation factor set and the power equipment deviation factor set according to the power grid fault event set to generate a plurality of groups of waveform quality influence factor sets;
and the influence factor adding module is used for adding the multiple groups of voltage quality influence factor sets, the multiple groups of frequency quality influence factor sets and the multiple groups of waveform quality influence factor sets into the multiple groups of electric energy quality influence factor sets.
Further, the system further comprises:
the single voltage quality influence factor generation module is used for performing one frequent item mining on the power equipment deviation factor set according to the power grid fault event set and the voltage quality index on the environment deviation factor set to generate a voltage quality influence factor set;
the m-item voltage quality influence factor generation module is used for performing m-item frequent item mining on the power equipment deviation factor set according to the power grid fault event set and the voltage quality index on the environment deviation factor set to generate m-item voltage quality influence factor sets;
a voltage quality impact factor adding module, configured to add the one voltage quality impact factor to the multiple sets of voltage quality impact factors by combining the m voltage quality impact factors up to the m voltage quality impact factor sets.
Further, the system further comprises:
the confidence evaluation formula building module is used for building m confidence evaluation formulas:
wherein, the first and the second end of the pipe are connected with each other,
characterize the confidence that type k to type k + m bias factors co-occur,
the nth group of fault characteristic values of the power quality index with the characteristic type of A,
characterizing the kth type deviation factor corresponding to the nth set of fault data,
representing the frequency of the common occurrence of the power quality index A and the deviation factors from the kth type to the kth + m type, and when the characteristic value is 0, not counting the frequency calculation, wherein m belongs to an integer;
the lifting degree evaluation formula building module is used for building m lifting degree evaluation formulas:
wherein, the first and the second end of the pipe are connected with each other,
characterizing two sets of deviation factors of only a single type
Event-to-event correspondence
The amount of change is such that,
is characterized by only
The power quality index variation amount corresponding to the changed event,
the representation of the preset groups is only
Of varying events
The average value of the average values is calculated,
representing the degree of promotion of the common occurrence of deviation factors from the kth type to the kth + m type;
the confidence coefficient calculation module is used for performing confidence coefficient calculation on the environment deviation factor set according to the m confidence coefficient evaluation formulas and the m promotion degree evaluation formulas to generate a confidence coefficient calculation result;
the lifting degree calculation module is used for calculating the lifting degree when the confidence degree calculation result meets a confidence degree threshold value to generate a lifting degree calculation result;
and the deviation factor adding module is used for adding m deviation factors into the m voltage quality influence factor sets when the lifting degree calculation result meets a lifting degree threshold value.
In the present specification, through the foregoing detailed description of the power quality monitoring method for power management, it is clear to those skilled in the art that a power quality monitoring method and system for power management in the present embodiment are disclosed.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A power quality monitoring method for power management, comprising:
constructing a tree-shaped circuit distribution network according to the child-parent-level circuit relation of a preset power grid area;
collecting a power grid fault event set in a preset time granularity of the preset power grid area according to the tree-shaped circuit distribution network;
acquiring a power quality monitoring index and a power quality deviation factor set;
performing frequent item mining on the power quality monitoring indexes and the power quality deviation factor set according to the power grid fault event set to generate a plurality of sets of power quality influence factor sets;
traversing the multiple groups of electric energy quality influence factor sets to obtain multiple groups of influence factor sets and threshold intervals;
constructing a power quality abnormity prediction model according to the multiple groups of influence factors and the threshold interval;
and carrying out prediction management on the electric energy quality of the preset power grid area according to the electric energy quality abnormity prediction model.
2. The method of claim 1, wherein constructing a tree-like circuit distribution network according to the child-parent-level circuit relationship of the preset grid area comprises:
acquiring a circuit topology structure diagram according to the preset power grid area;
constructing a child-parent line table according to the circuit topology structure chart;
extracting a first-level line set, a second-level line set and an Nth-level line set according to the child-parent-level line table;
traversing the first, second, and nth sets of hierarchical lines matching first, second, and nth sets of power devices up to an nth set of power devices;
constructing a first level root node set according to the first level line set and the first level power equipment set;
constructing an Nth-level leaf node set according to the Nth-level line set and the Nth-level power equipment set;
and based on the child parent line table, connecting the first level root node set until the Nth level leaf node set to generate the tree circuit distribution network.
3. The method as claimed in claim 2, wherein the collecting the grid fault event set within the preset time granularity of the preset grid area according to the tree circuit distribution network comprises:
acquiring a first level circuit region, a second level circuit region and an Nth level circuit region according to the tree circuit distribution network;
traversing the first hierarchical circuit area, and collecting a first area power grid fault event set;
traversing the Nth-level circuit area and collecting an Nth area power grid fault event set;
adding the first regional power grid fault event set to the Nth regional power grid fault event set.
4. The method of claim 1, wherein obtaining the set of power quality monitoring indicators and power quality bias factors comprises:
the power quality monitoring indexes comprise voltage quality indexes, frequency quality indexes and waveform quality indexes;
the electric energy quality deviation factor set comprises an environment deviation factor set and an electric power equipment deviation factor set.
5. The method of claim 4, wherein the mining the set of power quality monitoring metrics and the set of power quality deviation factors for frequent items according to the set of grid fault events to generate a plurality of sets of power quality influencing factors comprises:
according to the power grid fault event set, traversing the voltage quality indexes to perform frequent item mining on the environment deviation factor set and the power equipment deviation factor set to generate a plurality of groups of voltage quality influence factor sets;
according to the power grid fault event set, traversing the frequency quality indexes to perform frequent item mining on the environment deviation factor set and the power equipment deviation factor set to generate a plurality of groups of frequency quality influence factor sets;
according to the power grid fault event set, traversing the waveform quality indexes to perform frequent item mining on the environment deviation factor set and the power equipment deviation factor set to generate a plurality of groups of waveform quality influence factor sets;
adding the multiple groups of voltage quality influence factor sets, the multiple groups of frequency quality influence factor sets and the multiple groups of waveform quality influence factor sets into the multiple groups of power quality influence factor sets.
6. The method of claim 5, wherein the traversing the voltage quality indicators from the set of grid fault events to frequently mine the set of environmental bias factors and the set of power equipment bias factors to generate a plurality of sets of voltage quality impact factors comprises:
according to the power grid fault event set, the voltage quality index carries out frequent item mining on the environment deviation factor set and the power equipment deviation factor set to generate a voltage quality influence factor set;
according to the power grid fault event set and the environment deviation factor set, performing m items of frequent item mining on the power equipment deviation factor set by using voltage quality indexes to generate m items of voltage quality influence factor sets;
combining the one set of voltage quality impact factors up to the m set of voltage quality impact factors, adding into the plurality of sets of voltage quality impact factors.
7. The method of claim 6, wherein said mining m frequent terms of said set of environmental bias factors, said set of power equipment bias factors, and said set of voltage quality indicators from said set of grid fault events to generate a set of m voltage quality impact factors, comprises:
constructing an m-term confidence evaluation formula:
wherein, the first and the second end of the pipe are connected with each other,
characterizing the confidence that type k to type k + m bias factors co-occur,A n the nth group of fault characteristic values of the power quality index with the characteristic type of A,X nk characterizing the kth type deviation factor corresponding to the nth set of fault data,
representing the frequency of the common occurrence of the deviation factors from the power quality index A and the k type to the k + m type, when the characteristic value is 0, not counting the frequency calculation, and m belongs toAt least one of an integer;
constructing an m-term lifting degree evaluation formula:
wherein, the first and the second end of the pipe are connected with each other,∆X i characterizing two sets of deviation factors of only a single typeX i Event-to-event correspondenceX i The amount of change is such that,∆Ais characterized by onlyX i The power quality index variation corresponding to the changed event,
characterizing a predetermined group onlyX i Of changing events
The average value of the average value is calculated,
representing the degree of promotion of the common occurrence of deviation factors from the kth type to the kth + m type;
according to the m confidence coefficient evaluation formulas and the m promotion degree evaluation formulas, performing confidence coefficient calculation on the environment deviation factor set and the power equipment deviation factor set to generate a confidence coefficient calculation result;
when the confidence coefficient calculation result meets a confidence coefficient threshold value, carrying out lifting degree calculation to generate a lifting degree calculation result;
and when the lifting degree calculation result meets a lifting degree threshold value, adding m deviation factors into the m voltage quality influence factor sets.
8. A power quality monitoring system for power management, the system comprising:
the network construction module is used for constructing a tree-shaped circuit distribution network according to the child-parent-level circuit relation of a preset power grid area;
the event set construction module is used for collecting a power grid fault event set in a preset time granularity of the preset power grid region according to the tree circuit distribution network;
the parameter acquisition module is used for acquiring a power quality monitoring index and a power quality deviation factor set;
the influence factor acquisition module is used for performing frequent item mining on the electric energy quality monitoring indexes and the electric energy quality deviation factor set according to the power grid fault event set to generate a plurality of groups of electric energy quality influence factor sets;
the interval acquisition module is used for traversing the multiple groups of electric energy quality influence factor sets and acquiring multiple groups of influence factor combination threshold intervals;
the model construction module is used for constructing an electric energy quality abnormity prediction model according to the multiple groups of influence factors and the threshold interval;
and the quality management module is used for carrying out prediction management on the electric energy quality of the preset power grid region according to the electric energy quality abnormity prediction model.
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