CN116996363A - Fault early warning method and related device for power distribution network - Google Patents
Fault early warning method and related device for power distribution network Download PDFInfo
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- CN116996363A CN116996363A CN202310981983.0A CN202310981983A CN116996363A CN 116996363 A CN116996363 A CN 116996363A CN 202310981983 A CN202310981983 A CN 202310981983A CN 116996363 A CN116996363 A CN 116996363A
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Classifications
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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
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0654—Management of faults, events, alarms or notifications using network fault recovery
- H04L41/0668—Management of faults, events, alarms or notifications using network fault recovery by dynamic selection of recovery network elements, e.g. replacement by the most appropriate element after failure
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/12—Discovery or management of network topologies
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Abstract
The invention discloses a fault early warning method and a related device of a power distribution network, wherein the method comprises the steps of obtaining network topology information, historical fault information and historical maintenance information of the power distribution network; determining the fault frequency and maintenance frequency of each network unit based on the network topology information, the historical fault information and the historical maintenance information; selecting network units with the fault frequency larger than a preset fault frequency threshold as candidate fault units, and selecting network units with the maintenance frequency larger than a preset maintenance frequency threshold as candidate maintenance units; carrying out association judgment on each candidate fault unit and each candidate maintenance unit to obtain association coefficients; calculating early warning coefficients of all network units according to the fault frequency, the maintenance frequency and the association coefficient; the method can realize effective early warning of faults caused among the network units and improve the accuracy of early warning results.
Description
Technical Field
The invention relates to the technical field of power distribution networks, in particular to a fault early warning method and a related device for a power distribution network.
Background
At present, with the development of technology, the power industry and people's life are more compact. For power users, the stability of the power distribution network is crucial to the power quality, and the power distribution network is more complicated due to the fact that the power distribution network is provided with a plurality of devices, and is more prone to faults.
For the power maintenance department, the units in the power distribution network can be pre-warned through the fault frequency of each network unit, and maintenance resources can be arranged in a targeted mode.
However, the failure cause of the distribution network is complex, the failure of the network element is not necessarily self-cause, the failure of the network element can be caused between the network elements, the existing early warning method cannot early warn the failure, the accuracy of early warning results is further affected, and the occurrence probability of the failure cannot be effectively reduced.
Disclosure of Invention
The invention provides a fault early warning method and a related device of a power distribution network, which solve the technical problems that the existing early warning method cannot early warn faults caused among network units, further influence the accuracy of early warning results and cannot effectively reduce the occurrence probability of faults.
The invention provides a fault early warning method of a power distribution network, which comprises the following steps:
acquiring network topology information, historical fault information and historical maintenance information of a power distribution network; the power distribution network comprises a plurality of network elements;
Determining a failure frequency and a maintenance frequency of each of the network elements based on the network topology information, the historical failure information, and the historical maintenance information;
selecting network units with the fault frequency larger than a preset fault frequency threshold as candidate fault units, and selecting network units with the maintenance frequency larger than a preset maintenance frequency threshold as candidate maintenance units;
performing association judgment on each candidate fault unit and each candidate maintenance unit to obtain association coefficients;
calculating early warning coefficients of the network units according to the fault frequency, the maintenance frequency and the association coefficients;
and selecting a network unit with an early warning coefficient larger than a preset early warning threshold as a target early warning unit, and generating and outputting early warning information of the target early warning unit.
Optionally, before the step of determining the failure frequency and the maintenance frequency of each network element based on the network topology information, the historical failure information and the historical maintenance information, the method includes:
responding to a fault feedback request of the power distribution network, and acquiring to-be-processed fault information of the power distribution network;
and when the state of the fault information to be processed is changed to processed, acquiring updated historical fault information.
Optionally, before the step of determining the failure frequency and the maintenance frequency of each network element based on the network topology information, the historical failure information and the historical maintenance information, the method further includes:
responding to a historical maintenance information update request of a power distribution network, and acquiring update time of the historical maintenance information;
if the power distribution network fails within the preset time length after the updating time, updated historical maintenance information and historical failure information are obtained;
and acquiring updated historical maintenance information if the power distribution network fails within a preset time length after the updating time.
Optionally, the step of performing association discrimination on each candidate fault unit and each candidate maintenance unit to obtain an association coefficient includes:
load coincidence degree calculation is carried out on each candidate fault unit and each candidate maintenance unit, and a unit coincidence coefficient is obtained;
carrying out data extraction on a preset fault level table according to the fault cause information of the candidate fault units, and determining the fault level coefficient of each candidate fault unit;
and calculating the association coefficient of each candidate fault unit and each candidate maintenance unit according to the unit coincidence coefficient and the fault grade coefficient.
Optionally, the step of extracting data from a preset fault level table according to the fault cause information of the candidate fault units and determining the fault level coefficient of each candidate fault unit includes:
dividing the fault cause information of the candidate fault units into a device cause fault set and a non-device cause fault set;
based on the preset fault level table, respectively extracting a device reason fault level coefficient corresponding to the device reason fault set and a non-device reason fault level coefficient corresponding to the non-device reason fault set;
determining a first grade coefficient of a device cause fault set through all the device cause fault grade coefficients;
determining a second hierarchical coefficient of the non-equipment cause fault set through a preset environment coefficient and all the non-equipment cause fault hierarchical coefficients;
and selecting the maximum value of the first level coefficient and the second level coefficient as the fault level coefficient of the candidate fault unit.
Optionally, the early warning coefficient is:
wherein: q (Q) i For the early warning coefficient of the network element i, alpha is the fault frequency of the network element i, beta is the maintenance frequency of the network element i,for the association coefficient of network element i with network element τ, U represents the set of all network elements in the distribution network.
The invention also provides a fault early warning device of the power distribution network, which comprises:
the information acquisition module is used for acquiring network topology information, historical fault information and historical maintenance information of the power distribution network; the power distribution network comprises a plurality of network elements;
a frequency determining module, configured to determine a failure frequency and a maintenance frequency of each network element based on the network topology information, the historical failure information, and the historical maintenance information;
the candidate unit selecting module is used for selecting network units with the fault frequency larger than a preset fault frequency threshold as candidate fault units and selecting network units with the maintenance frequency larger than a preset maintenance frequency threshold as candidate maintenance units;
the association judging module is used for carrying out association judgment on each candidate fault unit and each candidate maintenance unit to obtain an association coefficient;
the early warning coefficient calculation module is used for calculating the early warning coefficient of each network unit according to the fault frequency, the maintenance frequency and the association coefficient;
and the early warning information output module is used for selecting a network unit with an early warning coefficient larger than a preset early warning threshold value as a target early warning unit, and generating and outputting early warning information of the target early warning unit.
Optionally, the association discriminating module includes:
the unit superposition coefficient calculation sub-module is used for carrying out load superposition degree calculation on each candidate fault unit and each candidate maintenance unit to obtain a unit superposition coefficient;
the fault grade coefficient determining submodule is used for extracting data from a preset fault grade table according to the fault cause information of the candidate fault units and determining the fault grade coefficient of each candidate fault unit;
and the association coefficient calculating sub-module is used for calculating the association coefficient of each candidate fault unit and each candidate maintenance unit according to the unit coincidence coefficient and the fault grade coefficient.
The invention also provides an electronic device, comprising: the fault early warning system comprises a processor and a memory, wherein the memory stores computer readable instructions, and when the computer readable instructions are executed by the processor, the fault early warning method of the power distribution network is operated.
The invention also provides a storage medium, on which a computer program is stored, which runs the fault early warning method of the power distribution network when being executed by the processor.
From the above technical scheme, the invention has the following advantages:
Acquiring network topology information, historical fault information and historical maintenance information of a power distribution network; the power distribution network comprises a plurality of network units; determining the fault frequency and maintenance frequency of each network unit based on the network topology information, the historical fault information and the historical maintenance information; selecting network units with the fault frequency larger than a preset fault frequency threshold as candidate fault units, and selecting network units with the maintenance frequency larger than a preset maintenance frequency threshold as candidate maintenance units; carrying out association judgment on each candidate fault unit and each candidate maintenance unit to obtain association coefficients; calculating early warning coefficients of all network units according to the fault frequency, the maintenance frequency and the association coefficient; selecting a network unit with an early warning coefficient larger than a preset early warning threshold as a target early warning unit, and generating and outputting early warning information of the target early warning unit; according to the invention, by predicting the relation between the current maintenance resource arrangement and the fault frequency, the influence factors of the effectiveness of the maintenance resource arrangement are added in the early warning judgment process, and the early warning information of the output target early warning unit has better reference, so that the technical problems that the existing early warning method cannot early warn faults caused among network units, further influence the accuracy of early warning results and cannot effectively reduce the occurrence probability of faults are solved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained from these drawings without inventive faculty for a person skilled in the art.
Referring to fig. 1, fig. 1 is a flowchart illustrating steps of a fault early warning method for a power distribution network according to an embodiment of the present invention;
referring to fig. 2, fig. 2 is a flowchart illustrating steps of a fault early warning method for a power distribution network according to an alternative embodiment of the present invention;
referring to fig. 3, fig. 3 is a block diagram illustrating a fault early warning device for a power distribution network according to an embodiment of the present invention;
referring to fig. 4, fig. 4 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a fault early warning method and system for a power distribution network, which are used for solving the technical problems that the existing early warning method cannot early warn faults caused among network units, further influence the accuracy of early warning results and cannot effectively reduce the occurrence probability of faults.
In order to make the objects, features and advantages of the present application more comprehensible, the technical solutions in the embodiments of the present application are described in detail below with reference to the accompanying drawings, and it is apparent that the embodiments described below are only some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, fig. 1 is a flowchart illustrating steps of a fault early warning method for a power distribution network according to an embodiment of the present application.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology; wherein artificial intelligence (Artificial Intelligence, AI) is the intelligence of a person simulated, extended, and expanded using a digital computer or a machine controlled by a digital computer, the theory, method, technique, and application system that perceives the environment, obtains knowledge, and uses the knowledge to obtain the best results;
artificial intelligence basic technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, electromechanical integration, and the like; the artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions;
The fault early warning method provided by the embodiment of the application can be applied to a terminal, a server side, software running in the terminal or the server side, and the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer and the like; the server side can be configured as an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, basic cloud computing services such as big data and artificial intelligent platforms and the like; the software can be application of a fault early warning method for realizing the power distribution network, and the like, but is not limited to the above form;
the application is operational with numerous general purpose or special purpose computer system environments or configurations, such as: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like; the application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types, the application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network; in a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The application provides a fault early warning method of a power distribution network, which comprises the following steps:
step 101, acquiring network topology information, historical fault information and historical maintenance information of a power distribution network; the power distribution network comprises a plurality of network elements.
In the embodiment of the application, the network units represent nodes of the power distribution network, the network topology information comprises topology information of all network units, the historical fault information represents fault information of all network units in a certain period, and the historical maintenance information represents maintenance information of all network units in a certain period;
it can be understood that the specific information acquisition mode is various, for example, the information acquisition mode can be called from an interface of the power distribution network monitoring system or can be manually input, and a person skilled in the art can determine the specific information acquisition mode according to actual conditions, so that the application is not limited to the specific information acquisition mode;
in particular, because the power distribution maintenance work is complex, the maintenance requirements are different, and certain network units can only carry out periodical maintenance, and certain network units need to carry out specific maintenance measures in a certain period after a fault occurs; based on the above, the method and the system for acquiring the historical maintenance information of the power distribution network comprise periodic maintenance or inspection of the network unit and maintenance measures which are executed in a certain period when the network unit has a problem but does not have a fault or has a fault, but does not comprise emergency repair after the network unit has a fault, so that the corresponding maintenance times of the network unit are larger than the fault times.
Step 102, determining the fault frequency and maintenance frequency of each network element based on the network topology information, the historical fault information and the historical maintenance information.
It should be noted that, the fault frequency of each network element may be determined based on the network topology information and the historical fault information, and the maintenance frequency of each network element may be determined based on the network topology information and the historical maintenance information;
for example, if the network element a has the sub-network element B, the network element B fails 2 times and maintains 4 times in N months, and the network element a fails 1 time and maintains 4 times in N months, the failure frequency of the network element B is 2/N times per month, and the maintenance frequency is 4/N times per month; the fault frequency of the network unit A is 1/N times of month, and the maintenance frequency is 4/N times of month;
the frequency time unit corresponding to the fault frequency and the maintenance frequency can be N times per year or N times per month, the specific time setting can be selected according to practical application, and meanwhile, if the frequency is obtained in a decimal condition, in order to improve the subsequent calculation precision and the calculation efficiency of a server or a terminal, the frequency time unit can be converted into an integer, so that the influence caused by the decimal is reduced.
Step 103, selecting a network unit with the fault frequency greater than a preset fault frequency threshold as a candidate fault unit, and selecting a network unit with the maintenance frequency greater than a preset maintenance frequency threshold as a candidate maintenance unit.
It should be noted that, after obtaining the failure frequency and the maintenance frequency of the network element, comparing the failure frequency of the network element with a preset failure frequency threshold, comparing the maintenance frequency of the network element with a preset maintenance frequency, selecting a network element with a failure frequency greater than the preset failure frequency threshold as a candidate failure element, and selecting a network element with a maintenance frequency greater than the preset maintenance frequency threshold as a candidate maintenance element.
And 104, carrying out association judgment on each candidate fault unit and each candidate maintenance unit to obtain association coefficients.
In the embodiment of the application, whether a causal relationship exists between faults and maintenance can be determined by carrying out association judgment on each candidate fault unit and each candidate maintenance unit to obtain the association coefficient; specifically, the feature information of the candidate fault unit and the candidate maintenance unit can be extracted, and the similarity between the feature information of the candidate fault unit and the feature information of the candidate maintenance unit is calculated to be used as a technical means of association discrimination.
And 105, calculating early warning coefficients of all network units according to the fault frequency, the maintenance frequency and the association coefficients.
The method comprises the steps of calculating early warning degrees according to the fault frequency, the maintenance frequency and the association coefficient of a network unit to obtain an early warning coefficient, and predicting the relation between the current maintenance resource arrangement and the fault frequency so that the early warning judging process adds effective influencing factors of the maintenance resource arrangement;
Since the association coefficient indicates the relationship between the candidate fault unit and the candidate maintenance unit, the early warning coefficient actually indicates the relationship between the network unit and all other network units, for example, if the network unit a performs early warning calculation on the association coefficient of the network unit B according to the fault frequency and the maintenance frequency of the network unit a, the influence of the fault of the network unit B or the maintenance on the network unit a is quantified through the association coefficient between A, B, the fractional coefficient of the B to the a is obtained, and further, the early warning coefficient of the network unit a is obtained through all the fractional coefficients.
And 106, selecting a network unit with an early warning coefficient larger than a preset early warning threshold as a target early warning unit, and generating and outputting early warning information of the target early warning unit.
The method includes that the early warning information of the target early warning unit comprises maintenance information of the target early warning unit, the fault type ratio and the like, and staff performs maintenance resource arrangement on the target early warning unit according to the output early warning information, so that the occurrence probability of the power distribution network fault is reduced.
The embodiment of the application provides a fault early warning method of a power distribution network, which comprises the steps of obtaining network topology information, historical fault information and historical maintenance information of the power distribution network; the power distribution network comprises a plurality of network units; determining the fault frequency and maintenance frequency of each network unit based on the network topology information, the historical fault information and the historical maintenance information; selecting network units with the fault frequency larger than a preset fault frequency threshold as candidate fault units, and selecting network units with the maintenance frequency larger than a preset maintenance frequency threshold as candidate maintenance units; carrying out association judgment on each candidate fault unit and each candidate maintenance unit to obtain association coefficients; calculating early warning coefficients of all network units according to the fault frequency, the maintenance frequency and the association coefficient; and selecting a network unit with an early warning coefficient larger than a preset early warning threshold value as a target early warning unit, and generating and outputting early warning information of the target early warning unit, so that effective early warning of faults caused among the network units is realized, the accuracy of early warning results is improved, and the occurrence probability of the faults is effectively reduced.
Referring to fig. 2, fig. 2 is a flowchart illustrating steps of a fault early warning method for a power distribution network according to an alternative embodiment of the present application.
The application provides a fault early warning method of a power distribution network, which comprises the following steps:
step 201, obtaining network topology information, historical fault information and historical maintenance information of a power distribution network; the power distribution network comprises a plurality of network elements.
In the embodiment of the present application, the implementation process of step 201 is similar to that of step 101, and will not be repeated here.
Step 202, determining the failure frequency and maintenance frequency of each network element based on the network topology information, the historical failure information and the historical maintenance information.
It should be noted that, before performing step 202, the method includes: responding to a fault feedback request of the power distribution network, and acquiring to-be-processed fault information of the power distribution network; when the state of the fault information to be processed is changed to processed, updated historical fault information is obtained;
specifically, when a power distribution network system fails, a power distribution network monitoring system can obtain failure feedback to obtain to-be-processed failure information, a power distribution department can carry out emergency repair on the failure according to the to-be-processed failure information, state monitoring is carried out on the to-be-processed failure, and when the state is processed, the to-be-processed failure information is updated to historical failure information; for a certain network element, the failure frequency of the network element is insufficient to determine the network element as a candidate failure element before the network element fails, and after the network element fails, the failure frequency is sufficient to determine the network element as a candidate failure element, and whether the network element is determined as the candidate failure element can influence subsequent early warning judgment, so that the priority of failure information needs to be improved, and the latest historical failure information is acquired; responding to the updating of the historical fault information, so that the influence of the fault frequency critical of the network unit on the accuracy of the early warning information is weakened, and the accuracy of early warning and the effectiveness of resource allocation are improved;
Prior to performing step 202, further comprising: responding to a historical maintenance information update request of the power distribution network, and acquiring update time of the historical maintenance information; if the power distribution network fails within a preset time length after the updating time, updated historical maintenance information and historical failure information are obtained; acquiring updated historical maintenance information if the power distribution network fails within a preset time length after the updating time;
specifically, because the historical maintenance information needs to be uploaded by a maintainer, the maintainer can upload the historical maintenance information to the system only after a certain time is delayed from a preset time due to various factors, for a certain network unit, the maintenance frequency of the network unit is insufficient to determine the network unit as a candidate maintenance unit before the maintenance is performed, after the maintenance is performed, the maintenance frequency is sufficient to determine the network unit as the candidate maintenance unit, and whether the network unit is determined as the candidate maintenance unit also affects the subsequent early warning judgment, so that after the response of maintenance update, the maintenance update time needs to be acquired, and the maintainer is given a certain buffer space for uploading the maintenance information with the update time as a starting point, thereby reducing the incomplete influence of the maintenance information;
Further, in a preset time length after the updating time, historical fault information updating is generated, namely after the power distribution network is maintained for the current period length, a certain network unit generates a fault, after the historical fault information updating, the influence of the fault is weakened by the subsequently uploaded maintenance information, the early warning accuracy is reduced, and in order to avoid the situation that the influence on the subsequent maintenance resource configuration is caused, the historical fault information needs to be brought into the execution process of the early warning method step of this time and buffering waiting needs to be stopped; in the preset time length, if the power distribution network does not feed back the fault, the stability of the power distribution network is proved, the collection degree of the historical maintenance information is prolonged through the preset time length, and the integrity of the historical maintenance information and the early warning accuracy are improved;
it will be appreciated that if the historical fault information update is generated during the execution of step 202, the updated historical fault information is not used as the historical fault information in the execution of the present method, but is used as the historical fault information in the execution of the next method.
In step 203, network units with failure frequencies greater than a preset failure frequency threshold are selected as candidate failure units, and network units with maintenance frequencies greater than a preset maintenance frequency threshold are selected as candidate maintenance units.
In the alternative embodiment of the application, the pertinence of target determination is improved by setting the preset fault frequency threshold and the preset maintenance frequency threshold, and the candidate units are screened out by the threshold, so that the target quantity is reduced, the target prediction precision is improved, and the configuration effectiveness of the maintenance resources of the power distribution network is improved.
And 204, calculating the load coincidence degree of each candidate fault unit and each candidate maintenance unit to obtain a unit coincidence coefficient.
Specifically, the unit coincidence coefficient refers to the equipment coincidence degree between the candidate fault unit and the candidate maintenance unit, when the unit coincidence coefficient is higher, the load coincidence degree between the equipment associated with the candidate fault unit and the equipment associated with the candidate maintenance unit is higher, and when the unit coincidence coefficient is 100%, the candidate fault unit and the candidate maintenance unit are the same network unit;
under the simple network condition, the calculation efficiency can be improved by carrying out similarity comparison on the equipment coincidence quantity of the candidate fault unit and the candidate maintenance unit and taking the ratio as a unit coincidence coefficient.
And 205, extracting data from a preset fault level table according to the fault cause information of the candidate fault units, and determining the fault level coefficient of each candidate fault unit.
It should be noted that the failure level coefficient indicates the severity of a failure caused by a candidate maintenance unit by a candidate failure unit;
in an alternative embodiment of the present application, step 205 comprises the steps of:
step S1, dividing fault cause information of candidate fault units into a device cause fault set and a non-device cause fault set;
it should be noted that, there should be a plurality of fault reasons of the candidate fault units, and the plurality of fault reason messages are classified into a device reason fault set and a non-device reason fault set.
Step S2, based on a preset fault level table, respectively extracting a device reason fault level coefficient corresponding to a device reason fault set and a non-device reason fault level coefficient corresponding to a non-device reason fault set;
step S3, determining a first grade coefficient of the equipment cause fault set through all equipment cause fault grade coefficients;
specifically, after all the equipment cause fault level coefficients are extracted, the first level coefficients of the equipment cause fault set are determined by adding all the equipment cause fault level coefficients, and the first level coefficients are unique to the equipment cause fault set.
S4, determining a second level coefficient of the non-equipment cause fault set through a preset environment coefficient and all non-equipment cause fault level coefficients;
It should be noted that, because some network units are in the severe environment area, the faults caused by environmental causes cannot be avoided, the preset environmental coefficient indicates the severe degree of the environment where the network unit is located, the comprehensiveness of the target early warning unit and the early warning accuracy are improved by introducing the environmental preset coefficient, and the equipment cause fault set has a unique second level coefficient.
S5, selecting the maximum value of the first level coefficient and the second level coefficient as the fault level coefficient of the candidate fault unit;
it should be noted that, by setting the first level coefficient and the second level coefficient, the effect of influencing parameters of a certain fault type can be improved, so that the early warning result is biased to a target early warning unit which is easy to cause faults by equipment or to a target early warning unit which is easy to cause faults by non-equipment, the differentiation degree of the early warning judgment result is improved, the attribute between the target early warning units is more similar, and the purpose bias and accuracy of early warning are improved.
And 206, calculating the association coefficient of each candidate fault unit and each candidate maintenance unit according to the unit coincidence coefficient and the fault level coefficient.
After obtaining the unit coincidence coefficient and the fault grade coefficient, multiplying the unit coincidence coefficient and the fault grade coefficient to obtain the association coefficient of each candidate fault unit and each candidate maintenance unit;
Furthermore, the candidate fault units and the candidate maintenance units are connected in series in the fault dimension through the unit superposition coefficient and the fault grade coefficient, so that cause and effect factors of 'fault-maintenance' are introduced in subsequent early warning coefficient calculation, and early warning accuracy is improved;
it will be appreciated that when a network element is not a candidate faulty element and a candidate maintenance element, the association coefficient of that network element should be 0.
Step 207, calculating the early warning coefficient of each network unit according to the fault frequency, the maintenance frequency and the association coefficient.
The calculation formula of the early warning coefficient is as follows:
wherein: q (Q) i For the early warning coefficient of the network element i, alpha is the fault frequency of the network element i, beta is the maintenance frequency of the network element i,for the association coefficient of network element i with network element τ, U represents the set of all network elements in the distribution network.
And step 208, selecting a network unit with an early warning coefficient larger than a preset early warning threshold as a target early warning unit, and generating and outputting early warning information of the target early warning unit.
The application provides a power distribution network fault early warning method, which comprises the steps of obtaining network topology information, historical fault information and historical maintenance information of a power distribution network; the power distribution network comprises a plurality of network units; determining the fault frequency and maintenance frequency of each network unit based on the network topology information, the historical fault information and the historical maintenance information; selecting network units with the fault frequency larger than a preset fault frequency threshold as candidate fault units, and selecting network units with the maintenance frequency larger than a preset maintenance frequency threshold as candidate maintenance units; load coincidence ratio calculation is carried out on each candidate fault unit and each candidate maintenance unit, and a unit coincidence coefficient is obtained; carrying out data extraction on a preset fault level table according to fault cause information of the candidate fault units, and determining fault level coefficients of the candidate fault units; calculating the association coefficient of each candidate fault unit and each candidate maintenance unit according to the unit coincidence coefficient and the fault grade coefficient; calculating early warning coefficients of all network units according to the fault frequency, the maintenance frequency and the association coefficient; and selecting a network unit with an early warning coefficient larger than a preset early warning threshold value as a target early warning unit, and generating and outputting early warning information of the target early warning unit, so that faults caused among the network units are effectively early warned, the accuracy of early warning results is improved, and the occurrence probability of the faults is effectively reduced.
Referring to fig. 3, fig. 3 is a block diagram illustrating a fault early warning device for a power distribution network according to an embodiment of the present invention.
The invention also provides a fault early warning system of the power distribution network, which comprises:
the information acquisition module 301 is configured to acquire network topology information, historical fault information, and historical maintenance information of the power distribution network; the power distribution network comprises a plurality of network units;
a frequency determining module 302, configured to determine a failure frequency and a maintenance frequency of each network element based on the network topology information, the historical failure information, and the historical maintenance information;
a candidate unit selection module 303, configured to select a network unit with a failure frequency greater than a preset failure frequency threshold as a candidate failure unit, and select a network unit with a maintenance frequency greater than a preset maintenance frequency threshold as a candidate maintenance unit;
the association discriminating module 304 is configured to perform association discrimination on each candidate fault unit and each candidate maintenance unit to obtain an association coefficient;
the early warning coefficient calculation module 305 is configured to calculate early warning coefficients of each network element according to the fault frequency, the maintenance frequency and the association coefficient;
the early warning information output module 306 is configured to select a network unit with an early warning coefficient greater than a preset early warning threshold as a target early warning unit, and generate and output early warning information of the target early warning unit.
The apparatus further comprises:
the fault response module is used for responding to a fault feedback request of the power distribution network and acquiring to-be-processed fault information of the power distribution network;
the first acquisition module is used for acquiring updated historical fault information when the state of the fault information to be processed is changed to be processed.
The apparatus further comprises:
the maintenance response module is used for responding to the historical maintenance information update request of the power distribution network and acquiring the update time of the historical maintenance information;
the second acquisition module is used for acquiring updated historical maintenance information and historical fault information if the power distribution network fails within a preset time length after the updating time;
and the third acquisition module is used for acquiring updated historical maintenance information if the power distribution network fails within a preset time length after the updating time.
The association discriminating module includes:
the unit coincidence coefficient calculating sub-module is used for carrying out load coincidence degree calculation on each candidate fault unit and each candidate maintenance unit to obtain a unit coincidence coefficient;
the fault grade coefficient determining submodule is used for extracting data from a preset fault grade table according to fault cause information of the candidate fault units and determining fault grade coefficients of the candidate fault units;
And the association coefficient calculating sub-module is used for calculating the association coefficient of each candidate fault unit and each candidate maintenance unit according to the unit coincidence coefficient and the fault level coefficient.
The fault level coefficient determination subunit is specifically configured to:
dividing fault cause information of the candidate fault units into a device cause fault set and a non-device cause fault set;
based on a preset fault level table, respectively extracting a device reason fault level coefficient corresponding to the device reason fault set and a non-device reason fault level coefficient corresponding to the non-device reason fault set;
determining a first grade coefficient of a device cause fault set through all device cause fault grade coefficients;
determining a second hierarchical coefficient of the non-equipment cause fault set through a preset environment coefficient and all non-equipment cause fault hierarchical coefficients;
and selecting the maximum value of the first level coefficient and the second level coefficient as the fault level coefficient of the candidate fault unit.
The embodiment of the application provides a fault early warning system of a power distribution network, which comprises the following components: the information acquisition module is used for acquiring network topology information, historical fault information and historical maintenance information of the power distribution network; the power distribution network comprises a plurality of network units; the frequency determining module is used for determining the fault frequency and maintenance frequency of each network unit based on the network topology information, the historical fault information and the historical maintenance information; the candidate unit selecting module is used for selecting network units with the fault frequency larger than a preset fault frequency threshold as candidate fault units and selecting network units with the maintenance frequency larger than a preset maintenance frequency threshold as candidate maintenance units; the association judging module is used for carrying out association judgment on each candidate fault unit and each candidate maintenance unit to obtain association coefficients; the early warning coefficient calculation module is used for calculating early warning coefficients of all network units according to the fault frequency, the maintenance frequency and the association coefficient; the early warning information output module is used for selecting the network units with the early warning coefficients larger than the preset early warning threshold as target early warning units, generating and outputting early warning information of the target early warning units, realizing effective early warning on faults caused among the network units, improving the accuracy of early warning results and effectively reducing the occurrence probability of the faults.
Referring to fig. 4, the present application further provides an electronic device 900, including a processor 901 and a memory 902;
the memory 902 is used for storing program codes and transmitting the program codes to the processor 901;
the processor 901 is configured to execute the fault early warning method of the power distribution network in the method embodiment according to the instruction in the program code;
further, the Memory 902 may be implemented in the form of a Read-Only Memory (ROM), a static storage device, a dynamic storage device, or a random access Memory (Random Access Memory, RAM); the memory 902 may store an operating system and other application programs, and when the technical solution provided in the embodiments of the present disclosure is implemented by software or firmware, relevant program codes are stored in the memory 902, and the processor 901 invokes the early warning method for executing the embodiments of the present disclosure;
the processor 901 may be implemented by using a general-purpose CPU (Central Processing Unit), a microprocessor, an Application-specific integrated circuit (ASIC), or one or more integrated circuits, etc. to execute related programs to implement the technical solution provided by the embodiments of the present application;
An input/output interface 903 for inputting and outputting information;
the communication interface 904 is configured to implement communication interaction between the device and other devices, and may implement communication in a wired manner (e.g. USB, network cable, etc.), or may implement communication in a wireless manner (e.g. mobile network, WIFI, bluetooth, etc.);
a bus 905 that transfers information between the various components of the device (e.g., the processor 901, the memory 902, the input/output interface 903, and the communication interface 904);
wherein the processor 901, the memory 902, the input/output interface 903 and the communication interface 904 are communicatively coupled to each other within the device via a bus 905.
The application also provides a computer readable storage medium, which is used for storing program codes, and the program codes are used for executing the fault early warning method of the power distribution network in the embodiment of the method;
further, the memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs and non-transitory computer executable programs; further, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device; the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the processor through a network; examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
It will be clear to those skilled in the art that, for convenience and brevity of description, reference may be made to the corresponding process in the foregoing method embodiment for the specific working process of the above-described system, which is not described herein again.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A fault early warning method for a power distribution network, the method comprising:
acquiring network topology information, historical fault information and historical maintenance information of a power distribution network; the power distribution network comprises a plurality of network elements;
determining a failure frequency and a maintenance frequency of each of the network elements based on the network topology information, the historical failure information, and the historical maintenance information;
selecting network units with the fault frequency larger than a preset fault frequency threshold as candidate fault units, and selecting network units with the maintenance frequency larger than a preset maintenance frequency threshold as candidate maintenance units;
performing association judgment on each candidate fault unit and each candidate maintenance unit to obtain association coefficients;
Calculating early warning coefficients of the network units according to the fault frequency, the maintenance frequency and the association coefficients;
and selecting a network unit with an early warning coefficient larger than a preset early warning threshold as a target early warning unit, and generating and outputting early warning information of the target early warning unit.
2. The fault pre-warning method of a power distribution network according to claim 1, characterized in that before the step of determining the fault frequency and the maintenance frequency of each of the network elements based on the network topology information, the historical fault information and the historical maintenance information, it comprises:
responding to a fault feedback request of the power distribution network, and acquiring to-be-processed fault information of the power distribution network;
and when the state of the fault information to be processed is changed to processed, acquiring updated historical fault information.
3. The method of claim 1, wherein prior to the step of determining the failure frequency and maintenance frequency of each of the network elements based on the network topology information, the historical failure information, and the historical maintenance information, further comprising:
responding to a historical maintenance information update request of a power distribution network, and acquiring update time of the historical maintenance information;
If the power distribution network fails within the preset time length after the updating time, updated historical maintenance information and historical failure information are obtained;
and acquiring updated historical maintenance information if the power distribution network fails within a preset time length after the updating time.
4. The fault early warning method of a power distribution network according to claim 1, wherein the step of performing association discrimination on each candidate fault unit and each candidate maintenance unit to obtain an association coefficient includes:
load coincidence degree calculation is carried out on each candidate fault unit and each candidate maintenance unit, and a unit coincidence coefficient is obtained;
carrying out data extraction on a preset fault level table according to the fault cause information of the candidate fault units, and determining the fault level coefficient of each candidate fault unit;
and calculating the association coefficient of each candidate fault unit and each candidate maintenance unit according to the unit coincidence coefficient and the fault grade coefficient.
5. The fault early warning method of a power distribution network according to claim 4, wherein the step of extracting data from a preset fault level table according to the fault cause information of the candidate fault units and determining the fault level coefficient of each candidate fault unit includes:
Dividing the fault cause information of the candidate fault units into a device cause fault set and a non-device cause fault set;
based on the preset fault level table, respectively extracting a device reason fault level coefficient corresponding to the device reason fault set and a non-device reason fault level coefficient corresponding to the non-device reason fault set;
determining a first grade coefficient of a device cause fault set through all the device cause fault grade coefficients;
determining a second hierarchical coefficient of the non-equipment cause fault set through a preset environment coefficient and all the non-equipment cause fault hierarchical coefficients;
and selecting the maximum value of the first level coefficient and the second level coefficient as the fault level coefficient of the candidate fault unit.
6. The fault pre-warning method for a power distribution network according to claim 1, wherein the pre-warning coefficient is:
wherein: q (Q) i For the early warning coefficient of the network element i, alpha is the fault frequency of the network element i, beta is the maintenance frequency of the network element i,for the association coefficient of network element i with network element τ, U represents the set of all network elements in the distribution network.
7. The utility model provides a trouble early warning device of distribution network which characterized in that includes:
The information acquisition module is used for acquiring network topology information, historical fault information and historical maintenance information of the power distribution network; the power distribution network comprises a plurality of network elements;
a frequency determining module, configured to determine a failure frequency and a maintenance frequency of each network element based on the network topology information, the historical failure information, and the historical maintenance information;
the candidate unit selecting module is used for selecting network units with the fault frequency larger than a preset fault frequency threshold as candidate fault units and selecting network units with the maintenance frequency larger than a preset maintenance frequency threshold as candidate maintenance units;
the association judging module is used for carrying out association judgment on each candidate fault unit and each candidate maintenance unit to obtain an association coefficient;
the early warning coefficient calculation module is used for calculating the early warning coefficient of each network unit according to the fault frequency, the maintenance frequency and the association coefficient;
and the early warning information output module is used for selecting a network unit with an early warning coefficient larger than a preset early warning threshold value as a target early warning unit, and generating and outputting early warning information of the target early warning unit.
8. The power distribution network fault early warning device according to claim 7, wherein the association discriminating module includes:
The unit superposition coefficient calculation sub-module is used for carrying out load superposition degree calculation on each candidate fault unit and each candidate maintenance unit to obtain a unit superposition coefficient;
the fault grade coefficient determining submodule is used for extracting data from a preset fault grade table according to the fault cause information of the candidate fault units and determining the fault grade coefficient of each candidate fault unit;
and the association coefficient calculating sub-module is used for calculating the association coefficient of each candidate fault unit and each candidate maintenance unit according to the unit coincidence coefficient and the fault grade coefficient.
9. An electronic device comprising a processor and a memory, the memory storing computer readable instructions that, when executed by the processor, operate the fault warning method of the power distribution network of any one of claims 1-6.
10. A storage medium having stored thereon a computer program which when executed by a processor performs a method of fault warning of an electrical distribution network according to any one of claims 1 to 6.
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CN117254597A (en) * | 2023-11-14 | 2023-12-19 | 国网四川省电力公司信息通信公司 | Method and system for business analysis and monitoring of power operation data |
CN117254597B (en) * | 2023-11-14 | 2024-01-30 | 国网四川省电力公司信息通信公司 | Method and system for business analysis and monitoring of power operation data |
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