CN111832827A - Distribution network fault early warning method and device, readable medium and electronic equipment - Google Patents
Distribution network fault early warning method and device, readable medium and electronic equipment Download PDFInfo
- Publication number
- CN111832827A CN111832827A CN202010687377.4A CN202010687377A CN111832827A CN 111832827 A CN111832827 A CN 111832827A CN 202010687377 A CN202010687377 A CN 202010687377A CN 111832827 A CN111832827 A CN 111832827A
- Authority
- CN
- China
- Prior art keywords
- distribution network
- current
- fault
- data
- historical
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 195
- 238000000034 method Methods 0.000 title claims abstract description 51
- 238000011156 evaluation Methods 0.000 claims abstract description 29
- 238000012423 maintenance Methods 0.000 claims description 30
- 230000008859 change Effects 0.000 claims description 18
- 238000010586 diagram Methods 0.000 description 12
- 238000004590 computer program Methods 0.000 description 8
- 238000003860 storage Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 6
- 238000012545 processing Methods 0.000 description 6
- 230000002159 abnormal effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/088—Aspects of digital computing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The invention discloses a distribution network fault early warning method, a distribution network fault early warning device, a readable medium and electronic equipment, wherein the method comprises the following steps: dividing a distribution network to obtain sub-distribution networks; the method comprises the steps of periodically obtaining historical operation data and historical influence data of a sub-distribution network; adjusting the historical fault threshold according to the acquired historical operation data, historical influence data and a pre-acquired threshold adjusting rule, and determining a current fault threshold corresponding to the sub-distribution network; acquiring current operation data and current influence data of the sub-distribution network; determining current evaluation data corresponding to the sub-distribution network according to the current operation data, the current influence data and the weight coefficients corresponding to all indexes in the operation data and the influence data which are obtained in advance; and judging whether to generate early warning information or not according to the current fault threshold and the current evaluation data respectively corresponding to the sub-distribution network. According to the distribution network fault early warning method provided by the invention, the distribution network is divided, and the current fault threshold of the sub-distribution network is periodically adjusted, so that the accuracy of the fault early warning result is improved.
Description
Technical Field
The invention belongs to the technical field of electric power, and particularly relates to a distribution network fault early warning method, a distribution network fault early warning device, a readable medium and electronic equipment.
Background
The distribution network is a power network which receives electric energy from a power transmission network or a regional power plant, distributes the electric energy locally or distributes the electric energy to various users step by step according to voltage through power distribution setting, and is used as a final link for supplying power to the users by the power network, so that the normal production and life order of the society of people is directly influenced, and accurate early warning on the fault of the distribution network is very important.
In the prior art, whether a distribution network fault occurs is often determined by monitoring the load state of a distribution line, however, the distribution network fault is caused by a plurality of factors, and therefore the accuracy of the early warning result determined only by monitoring the load state of the distribution line is poor.
Disclosure of Invention
The invention provides a distribution network fault early warning method, a distribution network fault early warning device, a readable medium and electronic equipment.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the invention provides a distribution network fault early warning method, which comprises the steps of
The distribution network is divided according to a preset division rule to obtain at least one sub-distribution network;
the method comprises the steps that historical operation data and historical influence data of at least one sub-distribution network in a first preset time period are obtained periodically;
adjusting a historical fault threshold value according to the latest acquired historical operation data, historical influence data and a pre-acquired threshold value adjusting rule, and determining current fault threshold values respectively corresponding to the at least one sub-distribution network in a current period, wherein the period corresponding to the historical fault threshold value is a historical period adjacent to the current period;
acquiring current operation data and current influence data respectively corresponding to the at least one sub-distribution network at the current moment;
determining current evaluation data respectively corresponding to the at least one sub-distribution network at the current moment according to the current operation data, the current influence data and the weight coefficients corresponding to all indexes in the operation data and the influence data acquired in advance;
and judging whether to generate early warning information or not according to the current fault threshold and the current evaluation data respectively corresponding to the at least one sub-distribution network.
Preferably, the adjusting the historical fault threshold according to the latest acquired historical operating data, historical influence data, and a pre-acquired threshold adjustment rule, and determining the current fault threshold respectively corresponding to the at least one sub-distribution network in the current period includes:
determining numerical values of fault indexes in operation data and influence data corresponding to historical fault thresholds, wherein the fault indexes are determined in indexes corresponding to the operation data and the influence data according to threshold adjustment rules acquired in advance;
determining the change value of the fault index in the historical operation data and the historical influence data compared with the value of the fault index corresponding to the historical fault threshold;
and adjusting the historical fault threshold according to the change value and the pre-acquired threshold adjustment rule, and determining current fault thresholds respectively corresponding to the at least one sub-distribution network in the current period.
Preferably, the method comprises:
acquiring predicted data of the at least one sub-distribution network in a second preset time period, wherein the predicted data correspond to predictable indexes in the influence data;
the adjusting the historical fault threshold according to the latest acquired historical operation data, historical influence data, and a pre-acquired threshold adjustment rule, and determining the current fault threshold respectively corresponding to the at least one sub-distribution network in the current period includes:
and adjusting a historical fault threshold according to the latest acquired historical operation data, historical influence data, prediction data and a pre-acquired threshold adjusting rule, and determining current fault thresholds respectively corresponding to the at least one sub-distribution network in the current period.
Preferably, after the distribution network is divided according to a preset division rule to obtain at least one sub-distribution network, the method further includes:
determining the sub-distribution network codes respectively corresponding to the at least one sub-distribution network;
and judging whether to generate early warning information according to the current fault threshold and the current evaluation data respectively corresponding to the at least one sub-distribution network, wherein the early warning information comprises a sub-distribution network code to be early warned.
Preferably, the method further comprises:
determining index maintenance corresponding relation in operation data and influence data according to historical fault data and historical fault maintenance data of the distribution network;
then, after the step of determining whether to generate the warning information according to the current failure threshold and the current evaluation data respectively corresponding to the at least one sub-distribution network, the method further includes:
and if the early warning information is judged to be generated, determining maintenance strategy information according to the corresponding relation between the current evaluation data and the index maintenance.
Preferably, the preset division rule is determined according to the geographical environment, the service life, the equipment model, the operation parameter setting, the failure times and the maintenance and management times of the distribution network.
In a second aspect, the present invention provides a distribution network fault early warning device, including:
the distribution network dividing module is used for dividing the distribution network according to a preset dividing rule to obtain at least one sub-distribution network;
the first acquisition module is used for periodically acquiring historical operation data and historical influence data of the at least one sub-distribution network in a first preset time period;
the threshold adjusting module is used for adjusting a historical fault threshold according to the latest acquired historical operation data, historical influence data and a pre-acquired threshold adjusting rule, and determining current fault thresholds respectively corresponding to the at least one sub-distribution network in a current period, wherein the period corresponding to the historical fault threshold is a historical period adjacent to the current period;
the second obtaining module is used for obtaining current operation data and current influence data which respectively correspond to the at least one sub-distribution network at the current moment;
the data determining module is used for determining current evaluation data corresponding to each index of the at least one sub-distribution network at the current moment according to the current operation data, the current influence data and the weight coefficients corresponding to all indexes in the operation data and the influence data acquired in advance;
and the early warning judgment module is used for judging whether to generate early warning information according to the current fault threshold and the current evaluation data respectively corresponding to the at least one sub-distribution network.
Preferably, the threshold adjusting module includes:
the numerical value determining unit is used for determining the numerical values of fault indexes in the operation data and the influence data corresponding to the historical fault threshold, and the fault indexes are determined in the indexes corresponding to the operation data and the influence data according to a threshold adjusting rule acquired in advance;
the change value determining unit is used for determining the change value of the fault index in the historical operation data and the historical influence data compared with the value of the fault index corresponding to the historical fault threshold;
and the threshold adjusting unit is used for adjusting the historical fault threshold according to the change value and the pre-acquired threshold adjusting rule, and determining the current fault threshold respectively corresponding to the at least one sub-distribution network in the current period.
In a third aspect, the invention provides a readable medium comprising executable instructions, which when executed by a processor of an electronic device, perform the method according to any of the first aspect.
In a fourth aspect, the present invention provides an electronic device, comprising a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor performs the method according to any one of the first aspect.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a distribution network fault early warning method, a distribution network fault early warning device, a readable medium and electronic equipment, wherein a distribution network is divided by presetting a division rule to obtain one or more sub-distribution networks, then, historical operation data and historical influence data of each sub-distribution network in a first preset time are periodically acquired, and adjusting the historical fault threshold corresponding to the last historical period adjacent to the current period according to the acquired historical operation data, historical influence data and a threshold adjustment rule acquired in advance to determine the current fault threshold corresponding to each sub-distribution network in the current period respectively, the distribution network is divided, and the fault threshold corresponding to each sub-distribution network is determined, so that the fine management of the distribution network can be realized, and periodically determining the current fault threshold of each sub-distribution network, so that the current fault threshold can indicate the latest fault bearing capacity of the sub-distribution network. After the current evaluation data is determined according to the current operation data and the current influence data corresponding to each sub-distribution network at the current moment, and the weight coefficients corresponding to the indexes in the operation data and the influence data which are obtained in advance, different importance degrees of the indexes are reflected due to the existence of the weight coefficients, so that the determined current evaluation data can accurately represent the operation state of the sub-distribution network at the current moment, and when the current evaluation data which accurately represents the operation state of the sub-distribution network is compared with the current fault threshold which accurately represents the fault bearing capacity of the sub-distribution network, an accurate early warning result can be obtained.
Further effects of the above-mentioned unconventional preferred modes will be described below in conjunction with specific embodiments.
Drawings
In order to more clearly illustrate the embodiments or the prior art solutions of the present invention, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
Fig. 1 is a schematic flowchart of a first distribution network fault early warning method according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a second distribution network fault early warning method according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of a third method for early warning a distribution network fault according to an embodiment of the present invention;
fig. 4 is a schematic flowchart of a fourth method for early warning a distribution network fault according to an embodiment of the present invention;
fig. 5 is a schematic flowchart of a fifth distribution network fault early warning method according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a distribution network fault early warning apparatus according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a threshold adjustment module in a distribution network fault early warning apparatus according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail and completely with reference to the following embodiments and accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a distribution network fault early warning method, including:
103, adjusting a historical fault threshold according to the latest acquired historical operation data, historical influence data and a pre-acquired threshold adjusting rule, and determining current fault thresholds respectively corresponding to the at least one sub-distribution network in a current period, wherein the period corresponding to the historical fault threshold is a historical period adjacent to the current period;
104, acquiring current operation data and current influence data respectively corresponding to the at least one sub-distribution network at the current moment;
105, determining current evaluation data respectively corresponding to the at least one sub-distribution network at the current moment according to the current operation data, the current influence data and the weight coefficients corresponding to all indexes in the operation data and the influence data acquired in advance;
and 106, judging whether to generate early warning information or not according to the current fault threshold and the current evaluation data respectively corresponding to the at least one sub-distribution network.
In the distribution network fault early warning method provided in the embodiment shown in fig. 1, the distribution network is divided by a preset division rule to obtain one or more sub-distribution networks, then, historical operation data and historical influence data of each sub-distribution network in a first preset time are periodically acquired, and adjusting the historical fault threshold corresponding to the last historical period adjacent to the current period according to the acquired historical operation data, historical influence data and a threshold adjustment rule acquired in advance to determine the current fault threshold corresponding to each sub-distribution network in the current period respectively, the distribution network is divided, and the fault threshold corresponding to each sub-distribution network is determined, so that the fine management of the distribution network can be realized, and periodically determining the current fault threshold of each sub-distribution network, so that the current fault threshold can indicate the latest fault bearing capacity of the sub-distribution network. After the current evaluation data is determined according to the current operation data and the current influence data corresponding to each sub-distribution network at the current moment, and the weight coefficients corresponding to the indexes in the operation data and the influence data which are obtained in advance, different importance degrees of the indexes are reflected due to the existence of the weight coefficients, so that the determined current evaluation data can accurately represent the operation state of the sub-distribution network at the current moment, and when the current evaluation data which accurately represents the operation state of the sub-distribution network is compared with the current fault threshold which accurately represents the fault bearing capacity of the sub-distribution network, an accurate early warning result can be obtained.
In a possible implementation manner, the preset division rule is determined according to the geographical environment, the service life, the equipment model, the operation parameter setting, the number of faults and the number of maintenance and management times of the distribution network. The above factors all have a certain influence on the fault threshold of the distribution network, wherein the geographic environment may be the longitude and latitude and the installation environment of the distribution network, for example, the distribution network is installed in a city or a rural area. And comprehensively considering the factors, then determining a division rule, and determining one or more sub-distribution networks after division according to the division rule, wherein different sub-distribution networks have corresponding fault thresholds. For example, in the same geographic environment, distribution networks in areas with the same equipment model, similar operation parameter settings, similar failure times and similar maintenance and management times can be divided into one sub-distribution network.
Specifically, historical operation data and historical influence data of each sub-distribution network in the last 30 days are obtained every 15 days, wherein the historical influence data comprise indexes such as service life, equipment model, failure times, maintenance and management times, weather conditions and seasons. After the latest historical operation data and historical influence data are obtained, the historical fault threshold value is adjusted according to a threshold value adjusting rule obtained in advance, then the current fault threshold value of the sub-distribution network is determined, after 15 days, the current fault threshold value is updated again, and the current fault threshold value determined last time becomes the historical fault threshold value.
It should be noted that the pre-obtained threshold adjustment rule and the pre-obtained weight coefficients corresponding to each index in the operation data and the influence data can be obtained by analyzing the big data of the distribution network.
As shown in fig. 2, in one embodiment of the present invention, the step 103 includes:
In the above embodiment, as the fault tolerance of the distribution network is not constant along with the change of time, the service life of the equipment is different, the service environments are different, the service parameters are different, and different sub-distribution networks correspond to different fault thresholds, and the change of the maintenance times of the same sub-distribution network also causes different fault tolerances due to the change of seasons, and for the fault thresholds adopted in the evaluation of different fault tolerances of the same sub-distribution network in this embodiment, the corresponding relationship between the change condition of each index in the operation data and the influence data and the threshold change is determined by analyzing the big data of the sub-distribution network, so as to determine the threshold adjustment rule, which is the upper and lower adjustment values of the corresponding thresholds when a certain index in the operation data and the influence data changes. For example, when the age of a device in the impact data changes from 2 years to 3 years, the historical failure threshold may need to be adjusted down by 0.05 when adjusting the current failure threshold. Wherein, because not all indexes in the operation data and the influence data are related to the adjustment of the threshold value, the indexes related to the threshold value adjustment in the operation data and the influence data are determined as fault indexes.
After the threshold adjustment rule is predetermined, the fault threshold of the sub-distribution network can be periodically adjusted according to the threshold adjustment rule. Specifically, determining operation data and influence data corresponding to a historical fault threshold value, and determining numerical values of fault indexes in the operation data and the influence data; and then determining values corresponding to the fault indexes in the historical operation data and the historical influence data, further determining a value change value of the historical operation data and the historical influence data, which corresponds to the fault index corresponding to the historical fault threshold, then determining an adjustment value corresponding to the change value according to a threshold adjustment rule obtained in advance, and adjusting the historical fault threshold by using the adjustment value to obtain a current fault threshold corresponding to the current period of the sub-distribution network. The periodic adjustment of the fault threshold ensures that the current fault threshold presents the latest fault bearing capacity of the sub-distribution network, and is beneficial to accurately judging the subsequent early warning result.
As shown in fig. 3, in one embodiment of the invention, the method comprises:
then the step 103 includes:
In the above embodiment, when the distribution network fault is researched, it is found that predictable indexes such as weather factors will cause the distribution network fault, and therefore the predictable indexes in the influence data are taken into consideration in the adjustment of the current fault threshold. Specifically, prediction data corresponding to a predictable index of each sub-distribution network in a second preset time period is obtained, for example, weather prediction data in 15 days in the future is obtained, then a historical fault threshold needs to be adjusted according to the latest obtained historical operation data, historical influence data, prediction data and a pre-obtained threshold adjustment rule, a current fault threshold corresponding to the sub-distribution network in the current period is determined, and at this time, the pre-obtained threshold adjustment rule includes an adjustment rule related to the prediction data. Therefore, the current fault threshold value not only considers the historical factors influencing the fault bearing capacity of the distribution network, but also considers the future factors possibly influencing the fault bearing capacity of the distribution network, and the accuracy of the determined current fault threshold value is higher through comprehensive consideration.
As shown in fig. 4, in an embodiment of the present invention, after the distribution network is divided according to a preset division rule to obtain at least one sub-distribution network, the method further includes:
in step 106, the early warning information includes the child distribution network code to be early warned.
In the embodiment, after the distribution network is divided according to the preset division rule to obtain at least one sub-distribution network, the corresponding sub-distribution network code is determined for each sub-distribution network, so that when the early warning information is determined, the early warning information includes the sub-distribution network code to be early warned, the position of the pre-warned sub-distribution network can be quickly located according to the early warning information, and the purpose of saving time can be achieved.
As shown in fig. 5, in one embodiment of the invention, the method further comprises:
then, after step 106, the method further comprises:
and step 110, if the early warning information is judged to be generated, determining maintenance strategy information according to the current evaluation data and the index maintenance corresponding relation.
In the above embodiment, the historical failure data is operation data and influence data of the distribution network in a fixed time period when and before the failure occurs, and may be, for example, operation data and influence data at the time of the failure and in 72 hours before the failure occurs, and the historical failure maintenance data is a maintenance log of maintenance personnel after the failure occurs in the distribution network, that is, what measure is taken to solve the failure. Specifically, historical fault data and historical fault maintenance data of the distribution network are obtained in advance, the historical fault data and the historical maintenance data are analyzed, and index maintenance corresponding relations in the operation data and the influence data are determined, wherein the index maintenance corresponding relations refer to corresponding relations between the abnormal state of one or more indexes and maintenance means for solving the abnormal state. Therefore, when the early warning information is needed to be generated, maintenance strategy information is determined according to the historical operation data corresponding to the current evaluation data and the numerical values of all indexes corresponding to the historical influence data and the index maintenance corresponding relation, and the maintenance strategy information and the early warning information are sent to the appointed service end together, so that maintenance personnel at the service end can determine the early warning solving means as soon as possible, time is saved, the occurrence of faults is avoided, and various losses caused by the occurrence of the faults are reduced.
As shown in fig. 6, an embodiment of the present invention provides a distribution network fault early warning apparatus, including:
the distribution network dividing module 61 is configured to divide the distribution network according to a preset dividing rule to obtain at least one sub-distribution network;
a first obtaining module 62, configured to periodically obtain historical operation data and historical influence data of the at least one sub-distribution network in a first preset time period;
a threshold adjusting module 63, configured to adjust a historical fault threshold according to the latest acquired historical operating data, the historical influence data, and a threshold adjusting rule acquired in advance, and determine current fault thresholds corresponding to the at least one sub-distribution network in a current cycle, where a cycle corresponding to the historical fault threshold is a historical cycle adjacent to the current cycle;
a second obtaining module 64, configured to obtain current operation data and current influence data respectively corresponding to the at least one sub-distribution network at the current time;
a data determining module 65, configured to determine, according to the current operation data, the current influence data, and weight coefficients corresponding to indexes in the operation data and the influence data that are obtained in advance, current evaluation data that respectively correspond to the at least one sub-distribution network at a current time;
and an early warning judgment module 66, configured to judge whether to generate early warning information according to the current fault threshold and the current evaluation data respectively corresponding to the at least one sub-distribution network.
As shown in fig. 7, in an embodiment of the present invention, the threshold adjusting module 63 includes:
a numerical value determining unit 631, configured to determine numerical values of fault indexes in the operation data and the influence data corresponding to historical fault thresholds, where the fault indexes are determined in indexes corresponding to the operation data and the influence data according to threshold adjustment rules acquired in advance;
a variation value determining unit 632, configured to determine a variation value of the fault indicator in the historical operating data and the historical influence data, compared with a value of the fault indicator corresponding to the historical fault threshold;
a threshold adjusting unit 633, configured to adjust the historical failure threshold according to the variation value and a threshold adjusting rule acquired in advance, and determine current failure thresholds corresponding to the at least one sub-distribution network in a current period respectively.
For convenience of description, the above device embodiments are described with functions divided into various units or modules, and the functions of the units or modules may be implemented in one or more software and/or hardware when implementing the present invention.
Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. On the hardware level, the electronic device comprises a processor and optionally an internal bus, a network interface and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (peripheral component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 8, but that does not indicate only one bus or one type of bus.
And the memory is used for storing the execution instruction. In particular, a computer program that can be executed by executing instructions. The memory may include both memory and non-volatile storage and provides execution instructions and data to the processor.
In a possible implementation manner, the processor reads the corresponding execution instruction from the nonvolatile memory to the memory and then runs the execution instruction, and may also obtain the corresponding execution instruction from other devices, so as to form the distribution network fault early warning device on a logic level. The processor executes the execution instruction stored in the memory, so that the distribution network fault early warning method provided by any embodiment of the invention is realized through the executed execution instruction.
The method executed by the distribution network fault early warning device according to the embodiments shown in fig. 6 and fig. 7 of the present invention may be applied to a processor, or implemented by the processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
An embodiment of the present invention further provides a readable storage medium, where the readable storage medium stores an execution instruction, and when the stored execution instruction is executed by a processor of an electronic device, the electronic device can execute the distribution network fault early warning method provided in any embodiment of the present invention, and is specifically configured to execute the method shown in fig. 1, fig. 2, fig. 3, fig. 4, or fig. 5.
The electronic device described in the foregoing embodiments may be a computer.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A distribution network fault early warning method is characterized by comprising the following steps:
the distribution network is divided according to a preset division rule to obtain at least one sub-distribution network;
the method comprises the steps that historical operation data and historical influence data of at least one sub-distribution network in a first preset time period are obtained periodically;
adjusting a historical fault threshold value according to the latest acquired historical operation data, historical influence data and a pre-acquired threshold value adjusting rule, and determining current fault threshold values respectively corresponding to the at least one sub-distribution network in a current period, wherein the period corresponding to the historical fault threshold value is a historical period adjacent to the current period;
acquiring current operation data and current influence data respectively corresponding to the at least one sub-distribution network at the current moment;
determining current evaluation data respectively corresponding to the at least one sub-distribution network at the current moment according to the current operation data, the current influence data and the weight coefficients corresponding to all indexes in the operation data and the influence data acquired in advance;
and judging whether to generate early warning information or not according to the current fault threshold and the current evaluation data respectively corresponding to the at least one sub-distribution network.
2. The distribution network fault early warning method according to claim 1, wherein the step of adjusting the historical fault threshold according to the latest acquired historical operating data, historical influence data and a pre-acquired threshold adjustment rule to determine the current fault threshold respectively corresponding to the at least one sub-distribution network in the current period comprises:
determining numerical values of fault indexes in operation data and influence data corresponding to historical fault thresholds, wherein the fault indexes are determined in indexes corresponding to the operation data and the influence data according to threshold adjustment rules acquired in advance;
determining the change value of the fault index in the historical operation data and the historical influence data compared with the value of the fault index corresponding to the historical fault threshold;
and adjusting the historical fault threshold according to the change value and the pre-acquired threshold adjustment rule, and determining current fault thresholds respectively corresponding to the at least one sub-distribution network in the current period.
3. The distribution network fault early warning method according to claim 1, wherein the method comprises the following steps:
acquiring predicted data of the at least one sub-distribution network in a second preset time period, wherein the predicted data correspond to predictable indexes in the influence data;
the adjusting the historical fault threshold according to the latest acquired historical operation data, historical influence data, and a pre-acquired threshold adjustment rule, and determining the current fault threshold respectively corresponding to the at least one sub-distribution network in the current period includes:
and adjusting a historical fault threshold according to the latest acquired historical operation data, historical influence data, prediction data and a pre-acquired threshold adjusting rule, and determining current fault thresholds respectively corresponding to the at least one sub-distribution network in the current period.
4. The distribution network fault early warning method according to claim 1, wherein the distribution network is divided according to a preset division rule to obtain at least one sub-distribution network, and the method further comprises the following steps:
determining the sub-distribution network codes respectively corresponding to the at least one sub-distribution network;
and judging whether to generate early warning information according to the current fault threshold and the current evaluation data respectively corresponding to the at least one sub-distribution network, wherein the early warning information comprises a sub-distribution network code to be early warned.
5. The distribution network fault early warning method according to claim 1, further comprising:
determining index maintenance corresponding relation in operation data and influence data according to historical fault data and historical fault maintenance data of the distribution network;
then, after the step of determining whether to generate the warning information according to the current failure threshold and the current evaluation data respectively corresponding to the at least one sub-distribution network, the method further includes:
and if the early warning information is judged to be generated, determining maintenance strategy information according to the corresponding relation between the current evaluation data and the index maintenance.
6. The distribution network fault pre-warning method according to any one of claims 1-5,
the preset division rule is determined according to the geographical environment, the service life, the equipment model, the operation parameter setting, the failure times and the maintenance and management times of the distribution network.
7. The utility model provides a join in marriage net fault early warning device which characterized in that includes:
the distribution network dividing module is used for dividing the distribution network according to a preset dividing rule to obtain at least one sub-distribution network;
the first acquisition module is used for periodically acquiring historical operation data and historical influence data of the at least one sub-distribution network in a first preset time period;
the threshold adjusting module is used for adjusting a historical fault threshold according to the latest acquired historical operation data, historical influence data and a pre-acquired threshold adjusting rule, and determining current fault thresholds respectively corresponding to the at least one sub-distribution network in a current period, wherein the period corresponding to the historical fault threshold is a historical period adjacent to the current period;
the second obtaining module is used for obtaining current operation data and current influence data which respectively correspond to the at least one sub-distribution network at the current moment;
the data determining module is used for determining current evaluation data corresponding to each index of the at least one sub-distribution network at the current moment according to the current operation data, the current influence data and the weight coefficients corresponding to all indexes in the operation data and the influence data acquired in advance;
and the early warning judgment module is used for judging whether to generate early warning information according to the current fault threshold and the current evaluation data respectively corresponding to the at least one sub-distribution network.
8. The distribution network fault warning device of claim 7, wherein the threshold adjustment module comprises:
the numerical value determining unit is used for determining the numerical values of fault indexes in the operation data and the influence data corresponding to the historical fault threshold, and the fault indexes are determined in the indexes corresponding to the operation data and the influence data according to a threshold adjusting rule acquired in advance;
the change value determining unit is used for determining the change value of the fault index in the historical operation data and the historical influence data compared with the value of the fault index corresponding to the historical fault threshold;
and the threshold adjusting unit is used for adjusting the historical fault threshold according to the change value and the pre-acquired threshold adjusting rule, and determining the current fault threshold respectively corresponding to the at least one sub-distribution network in the current period.
9. A readable medium comprising executable instructions which, when executed by a processor of an electronic device, cause the electronic device to perform the method of any of claims 1 to 6.
10. An electronic device comprising a processor and a memory storing execution instructions, the processor performing the method of any of claims 1-6 when the processor executes the execution instructions stored by the memory.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010687377.4A CN111832827A (en) | 2020-07-16 | 2020-07-16 | Distribution network fault early warning method and device, readable medium and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010687377.4A CN111832827A (en) | 2020-07-16 | 2020-07-16 | Distribution network fault early warning method and device, readable medium and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111832827A true CN111832827A (en) | 2020-10-27 |
Family
ID=72924177
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010687377.4A Pending CN111832827A (en) | 2020-07-16 | 2020-07-16 | Distribution network fault early warning method and device, readable medium and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111832827A (en) |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112340609A (en) * | 2020-11-25 | 2021-02-09 | 广州三叠纪元智能科技有限公司 | Parameter information configuration method, electronic box, server and storage medium |
| CN112650660A (en) * | 2020-12-28 | 2021-04-13 | 北京中大科慧科技发展有限公司 | Early warning method and device for power system of data center |
| CN112802313A (en) * | 2020-12-31 | 2021-05-14 | 新奥数能科技有限公司 | Alarm method and device of equipment |
| CN113190416A (en) * | 2021-05-27 | 2021-07-30 | 中国工商银行股份有限公司 | Database execution plan early warning method and device, electronic equipment and storage medium |
| CN113536206A (en) * | 2021-07-19 | 2021-10-22 | 国网陕西省电力公司 | Power distribution network area early warning method and system, terminal equipment and readable storage medium |
| CN114295940A (en) * | 2022-01-04 | 2022-04-08 | 珠海市鸿瑞信息技术股份有限公司 | Distribution network fault state monitoring system and method based on smart city |
| CN114414938A (en) * | 2021-12-22 | 2022-04-29 | 南通联拓信息科技有限公司 | Dynamic response method and system for power distribution network fault |
| CN114705430A (en) * | 2022-04-15 | 2022-07-05 | 广东石油化工学院 | Rolling bearing fault monitoring method, device, equipment and storage medium |
| CN115701890A (en) * | 2022-12-14 | 2023-02-14 | 深圳富联富桂精密工业有限公司 | Method for adjusting alarm rule and related equipment |
| CN115933841A (en) * | 2022-12-23 | 2023-04-07 | 南方电网大数据服务有限公司 | Liquid cooling CDU prediction system based on historical information analysis |
| CN116260235A (en) * | 2023-03-11 | 2023-06-13 | 傲视恒安科技（北京）有限公司 | Power supply switching method and device, electronic equipment and readable storage medium |
| CN116559598A (en) * | 2023-07-10 | 2023-08-08 | 武汉创星空间科技发展有限公司 | Smart distribution network fault positioning method and system |
-
2020
- 2020-07-16 CN CN202010687377.4A patent/CN111832827A/en active Pending
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112340609A (en) * | 2020-11-25 | 2021-02-09 | 广州三叠纪元智能科技有限公司 | Parameter information configuration method, electronic box, server and storage medium |
| CN112340609B (en) * | 2020-11-25 | 2023-06-16 | 广州三叠纪元智能科技有限公司 | Parameter information configuration method, electronic box, server and storage medium |
| CN112650660A (en) * | 2020-12-28 | 2021-04-13 | 北京中大科慧科技发展有限公司 | Early warning method and device for power system of data center |
| CN112650660B (en) * | 2020-12-28 | 2024-05-03 | 北京中大科慧科技发展有限公司 | Early warning method and device for data center power system |
| CN112802313A (en) * | 2020-12-31 | 2021-05-14 | 新奥数能科技有限公司 | Alarm method and device of equipment |
| CN113190416A (en) * | 2021-05-27 | 2021-07-30 | 中国工商银行股份有限公司 | Database execution plan early warning method and device, electronic equipment and storage medium |
| CN113536206A (en) * | 2021-07-19 | 2021-10-22 | 国网陕西省电力公司 | Power distribution network area early warning method and system, terminal equipment and readable storage medium |
| CN113536206B (en) * | 2021-07-19 | 2023-12-01 | 国网陕西省电力公司 | Power distribution network area early warning method, system, terminal equipment and readable storage medium |
| CN114414938A (en) * | 2021-12-22 | 2022-04-29 | 南通联拓信息科技有限公司 | Dynamic response method and system for power distribution network fault |
| CN114414938B (en) * | 2021-12-22 | 2023-06-16 | 哈尔滨光宇电气自动化有限公司 | Dynamic response method and system for power distribution network faults |
| CN114295940B (en) * | 2022-01-04 | 2022-07-15 | 珠海市鸿瑞信息技术股份有限公司 | Distribution network fault state monitoring system and method based on smart city |
| CN114295940A (en) * | 2022-01-04 | 2022-04-08 | 珠海市鸿瑞信息技术股份有限公司 | Distribution network fault state monitoring system and method based on smart city |
| CN114705430B (en) * | 2022-04-15 | 2022-09-20 | 广东石油化工学院 | Rolling bearing fault monitoring method, device, equipment and storage medium |
| CN114705430A (en) * | 2022-04-15 | 2022-07-05 | 广东石油化工学院 | Rolling bearing fault monitoring method, device, equipment and storage medium |
| CN115701890A (en) * | 2022-12-14 | 2023-02-14 | 深圳富联富桂精密工业有限公司 | Method for adjusting alarm rule and related equipment |
| CN115933841B (en) * | 2022-12-23 | 2023-11-07 | 南方电网大数据服务有限公司 | Liquid cooling CDU prediction system based on historical information analysis |
| CN115933841A (en) * | 2022-12-23 | 2023-04-07 | 南方电网大数据服务有限公司 | Liquid cooling CDU prediction system based on historical information analysis |
| CN116260235B (en) * | 2023-03-11 | 2023-08-22 | 傲视恒安科技（北京）有限公司 | Power supply switching method and device, electronic equipment and readable storage medium |
| CN116260235A (en) * | 2023-03-11 | 2023-06-13 | 傲视恒安科技（北京）有限公司 | Power supply switching method and device, electronic equipment and readable storage medium |
| CN116559598A (en) * | 2023-07-10 | 2023-08-08 | 武汉创星空间科技发展有限公司 | Smart distribution network fault positioning method and system |
| CN116559598B (en) * | 2023-07-10 | 2023-09-15 | 武汉创星空间科技发展有限公司 | Smart distribution network fault positioning method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111832827A (en) | Distribution network fault early warning method and device, readable medium and electronic equipment | |
| CN108920336B (en) | Service abnormity prompting method and device based on time sequence | |
| CN113380005A (en) | Flood prevention consultation monitoring device and method based on Internet of things | |
| US10282968B2 (en) | Intelligent monitoring method and apparatus | |
| CN111414999B (en) | Method and device for monitoring running state of equipment | |
| CN109886477B (en) | Water pollution prediction method and device and electronic equipment | |
| CN112311611A (en) | Data anomaly monitoring method and device and electronic equipment | |
| CN108061841A (en) | A kind of method of electric network fault event class automatic identification | |
| CN109525036B (en) | Method, device and system for monitoring mains supply state of communication equipment | |
| CN113992602B (en) | Cable monitoring data uploading method, device, equipment and storage medium | |
| CN112162174A (en) | Distribution room fault positioning method and system based on marketing and distribution integration | |
| CN116961217A (en) | Power equipment multistage cooperative early warning method and system based on state monitoring | |
| CN116662829A (en) | Standard power curve definition rule and deviation verification method for field group fan | |
| CN112802313B (en) | Alarm method and device of equipment | |
| CN115422504A (en) | Power distribution equipment fault risk identification method and device | |
| CN114743703A (en) | Reliability analysis method, device, equipment and storage medium for nuclear power station unit | |
| CN110990449A (en) | Time series processing method, device, storage medium and processor | |
| CN112432307A (en) | Water pump evaluation method and device, air conditioning system, storage medium and electronic equipment | |
| CN110879770A (en) | Terminal performance evaluation and field fault self-detection method and system | |
| CN111817305B (en) | Power grid operation efficiency post-evaluation method and system for capacity increase of power transmission and transformation equipment | |
| CN110808894B (en) | Data transmission method and device based on CAN bus | |
| CN116307866B (en) | Carbon emission metering method, equipment and carbon metering system | |
| CN116990578B (en) | Direct current detection method and system based on intelligent power | |
| CN110568315B (en) | Bus fault probability calculation method based on running state abnormal information | |
| CN111880506B (en) | Method for computer-assisted processing of status messages in an automation installation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |