CN116961217A - Power equipment multistage cooperative early warning method and system based on state monitoring - Google Patents

Power equipment multistage cooperative early warning method and system based on state monitoring Download PDF

Info

Publication number
CN116961217A
CN116961217A CN202310752506.7A CN202310752506A CN116961217A CN 116961217 A CN116961217 A CN 116961217A CN 202310752506 A CN202310752506 A CN 202310752506A CN 116961217 A CN116961217 A CN 116961217A
Authority
CN
China
Prior art keywords
early warning
information
fault
transformer substation
equipment
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
Application number
CN202310752506.7A
Other languages
Chinese (zh)
Inventor
王峰
毕建刚
毛光辉
程养春
孙杨
王申强
袁帅
杨圆
弓艳朋
单敬
王广真
于浩
季严松
姜金鹏
纪晓艺
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
North China Electric Power University
Original Assignee
State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
North China Electric Power University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by State Grid Corp of China SGCC, China Electric Power Research Institute Co Ltd CEPRI, North China Electric Power University filed Critical State Grid Corp of China SGCC
Priority to CN202310752506.7A priority Critical patent/CN116961217A/en
Publication of CN116961217A publication Critical patent/CN116961217A/en
Pending legal-status Critical Current

Links

Classifications

    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression 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
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • 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
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Economics (AREA)
  • Health & Medical Sciences (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Power Engineering (AREA)
  • Computational Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • Probability & Statistics with Applications (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Operations Research (AREA)
  • Evolutionary Biology (AREA)
  • Algebra (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Human Resources & Organizations (AREA)
  • Marketing (AREA)
  • Primary Health Care (AREA)
  • Strategic Management (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Remote Monitoring And Control Of Power-Distribution Networks (AREA)

Abstract

The invention discloses a multistage cooperative early warning method and system for power equipment based on state monitoring, and belongs to the technical field of power equipment state evaluation. The method of the invention comprises the following steps: determining a risk value and a risk early warning level of a single power device in the transformer substation, and generating reporting information of any single power device in the transformer substation based on the state monitoring data, the risk value and the risk early warning level; acquiring whole network operation information in the transformer substation and meteorological information in a transformer substation area, and determining early warning information in the transformer substation based on the reported information, the whole network operation information and the meteorological information; and generating shared early warning information based on the early warning information and transmitting the shared early warning information to the power equipment in the transformer substation so as to finish multistage collaborative fault early warning of the power equipment in the transformer substation. The invention improves the early warning accuracy and macroscopicity of the substation equipment.

Description

Power equipment multistage cooperative early warning method and system based on state monitoring
Technical Field
The invention relates to the technical field of power equipment state evaluation, in particular to a power equipment multistage cooperative early warning method and system based on state monitoring.
Background
At present, the source of early warning information of primary equipment of power transformation is basically limited to the state information of a single equipment. The utility company shares equipment risk information over the whole network in a simple manner such as notifying equipment family defects over the whole network, inspecting equipment status before a special condition or extreme weather comes. The risk information sharing among all equipment in the transformer substation is lacked, and the risk information sharing among multiple transformer substations in the regional power grid is lacked, so that the early warning information is incomplete and the early warning result is inaccurate.
In addition, the current early warning object is limited to a single device, lacks early warning of macroscopic targets such as whole station devices, regional power grid devices and the like, and is difficult to meet the requirements of different levels of reliability in intelligent operation of the power grid.
Disclosure of Invention
Aiming at the problems, the invention provides a power equipment multistage cooperative early warning method based on state monitoring, which comprises the following steps:
performing state monitoring on single electric equipment in a transformer substation to obtain state monitoring data, calculating the fault probability of the single electric equipment based on the state monitoring data and a probability density function of a historical single electric equipment fault, determining a risk value and a risk early warning level of the single electric equipment in the transformer substation based on the fault probability, a historical importance coefficient and family defect analysis data of the transformer substation, and generating reporting information of any single electric equipment in the transformer substation based on the state monitoring data, the risk value and the risk early warning level;
acquiring whole network operation information in the transformer substation and meteorological information in a transformer substation area, and determining early warning information in the transformer substation based on the reported information, the whole network operation information and the meteorological information;
and generating shared early warning information based on the early warning information and transmitting the shared early warning information to the power equipment in the transformer substation so as to finish multistage collaborative fault early warning of the power equipment in the transformer substation.
Optionally, the reporting information of the single power device includes the following steps: fault location, fault type, fault state quantity, ledger, load current and content information of dissolved gas in equipment oil at fault moment of single power equipment.
Optionally, the early warning information includes the following: fault device, fault location of the fault device, and fault state quantity of the fault device.
Optionally, generating shared early warning information based on the early warning information includes:
and determining a probability density distribution function of a single power device in the transformer substation under the fault condition based on the early warning information, calculating a load current coefficient of the single power device in the transformer substation based on the report information, and generating shared early warning information based on a calculation result of the load current coefficient, the family defect analysis data and the probability density distribution function of the transformer substation.
Optionally, sharing the early warning information includes the following: importance coefficient, risk threshold, family defect, load current coefficient, and probability density distribution function.
Optionally, the method further comprises: and transmitting the shared early warning information to other substations except the current substation in a preset range.
In still another aspect, the present invention further provides a power equipment multistage cooperative early warning system based on state monitoring, including:
the information acquisition unit is used for carrying out state monitoring on the single electric equipment in the transformer substation to acquire state monitoring data, calculating the fault probability of the single electric equipment based on the state monitoring data and a probability density function of the faults of the historical single electric equipment, determining the risk value and the risk early warning level of the single electric equipment in the transformer substation based on the fault probability, the historical importance coefficient and the family defect analysis data of the transformer substation, and generating the reporting information of any single electric equipment in the transformer substation based on the state monitoring data, the risk value and the risk early warning level;
the computing unit is used for acquiring the whole network operation information in the transformer substation and the meteorological information in the transformer substation area, and determining the early warning information in the transformer substation based on the reported information, the whole network operation information and the meteorological information;
and the early warning unit is used for generating shared early warning information based on the early warning information and transmitting the shared early warning information to the power equipment in the transformer substation so as to finish multi-stage collaborative fault early warning of the power equipment in the transformer substation.
Optionally, the reporting information of the single power device includes the following steps: fault location, fault type, fault state quantity, ledger, load current and content information of dissolved gas in equipment oil at fault moment of single power equipment.
Optionally, the early warning information includes the following: fault device, fault location of the fault device, and fault state quantity of the fault device.
Optionally, generating shared early warning information based on the early warning information includes:
and determining a probability density distribution function of a single power device in the transformer substation under the fault condition based on the early warning information, calculating a load current coefficient of the single power device in the transformer substation based on the report information, and generating shared early warning information based on a calculation result of the load current coefficient, the family defect analysis data and the probability density distribution function of the transformer substation.
Optionally, sharing the early warning information includes the following: importance coefficient, risk threshold, family defect, load current coefficient, and probability density distribution function.
Optionally, the early warning unit is further configured to: and transmitting the shared early warning information to other substations except the current substation in a preset range.
In yet another aspect, the present invention also provides a computing device comprising: one or more processors;
a processor for executing one or more programs;
the method as described above is implemented when the one or more programs are executed by the one or more processors.
In yet another aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program which, when executed, implements a method as described above.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a state monitoring-based power equipment multistage cooperative early warning method, which comprises the following steps: performing state monitoring on single electric equipment in a transformer substation to obtain state monitoring data, calculating the fault probability of the single electric equipment based on the state monitoring data and a probability density function of a historical single electric equipment fault, determining a risk value and a risk early warning level of the single electric equipment in the transformer substation based on the fault probability, a historical importance coefficient and family defect analysis data of the transformer substation, and generating reporting information of any single electric equipment in the transformer substation based on the state monitoring data, the risk value and the risk early warning level; acquiring whole network operation information in the transformer substation and meteorological information in a transformer substation area, and determining early warning information in the transformer substation based on the reported information, the whole network operation information and the meteorological information; and generating shared early warning information based on the early warning information and transmitting the shared early warning information to the power equipment in the transformer substation so as to finish multistage collaborative fault early warning of the power equipment in the transformer substation. The invention overcomes the defect that only a single device is used for early warning in the past, realizes multi-level early warning and risk information whole network sharing, and improves the accuracy and macroscopicity of early warning of the transformer substation equipment.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic flow chart of an embodiment of the method of the present invention;
fig. 3 is a block diagram of the system of the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the examples described herein, which are provided to fully and completely disclose the present invention and fully convey the scope of the invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, like elements/components are referred to by like reference numerals.
Unless otherwise indicated, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. In addition, it will be understood that terms defined in commonly used dictionaries should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
Example 1:
at present, the source of early warning information of primary equipment of a power transformation is basically limited to the state information of a single equipment, so that risk information sharing among all equipment in a transformer substation is lacked, risk information sharing among multiple transformer substations in a regional power grid is lacked, and the requirements of different levels of reliability in intelligent operation of the power grid are difficult to meet.
The invention aims to establish a hierarchical collaborative early warning method system, define the collaborative early warning task division and collaborative mode of a station end-centralized control station, overcome the defect that only a single device is used for early warning in the past, realize multi-level early warning and risk information whole network sharing, improve the accuracy and macroscopicity of early warning, and provide a power device multi-level collaborative early warning method based on state monitoring, as shown in figure 1, and comprises the following steps:
step 1, performing state monitoring on single electric equipment in a transformer substation to obtain state monitoring data, calculating to obtain failure probability of the single electric equipment based on the state monitoring data and a probability density function of a historical single electric equipment failure, determining a risk value and a risk early warning level of the single electric equipment in the transformer substation based on the failure probability, a historical importance coefficient and family defect analysis data of the transformer substation, and generating reporting information of any single electric equipment in the transformer substation based on the state monitoring data, the risk value and the risk early warning level;
step 2, acquiring whole network operation information in the transformer substation and meteorological information in a transformer substation area, and determining early warning information in the transformer substation based on the reported information, the whole network operation information and the meteorological information;
and step 3, based on the early warning information, generating shared early warning information and transmitting the shared early warning information to the power equipment in the transformer substation so as to finish multi-stage collaborative fault early warning of the power equipment in the transformer substation.
The reporting information of the single power equipment comprises the following steps: fault location, fault type, fault state quantity, ledger, load current and content information of dissolved gas in equipment oil at fault moment of single power equipment.
The early warning information comprises the following steps: fault device, fault location of the fault device, and fault state quantity of the fault device.
Wherein generating shared pre-warning information based on the pre-warning information comprises:
and determining a probability density distribution function of a single power device in the transformer substation under the fault condition based on the early warning information, calculating a load current coefficient of the single power device in the transformer substation based on the report information, and generating shared early warning information based on a calculation result of the load current coefficient, the family defect analysis data and the probability density distribution function of the transformer substation.
Wherein, sharing early warning information includes as follows: importance coefficient, risk threshold, family defect, load current coefficient, and probability density distribution function.
Wherein the method further comprises: and transmitting the shared early warning information to other substations except the current substation in a preset range.
The present busy is further described below in connection with specific applications:
the invention can be specifically applied to a station end, a centralized control station and an area transformer substation, and the specific application flow is shown in figure 2 and specifically comprises the following steps:
based on a serial model in reliability and a common cause failure theory, factors such as common meteorological conditions, common power grid operation conditions, common manufacturing process and the like are respectively used as handles, and a station end-centralized control station collaborative early warning method system is established. The station end is responsible for early warning of a single device of the transformer substation and reports early warning information (including information of devices with fault risks, fault positions, fault types and the like) to the centralized control station; the centralized control station performs whole station equipment early warning and multi-station equipment early warning by comprehensively analyzing the reported information, the whole network operation information and the regional weather information from the station end, and sends necessary shared early warning information (such as equipment family defect early warning and the like) to each station end, and even can transmit the macro risk information of the power grid by sending alarm threshold adjustment information to the station end. Meanwhile, the centralized control station performs statistical analysis and equipment importance assessment of the whole network equipment, and transmits basic data such as statistical probability distribution, importance and the like of the equipment required by early warning to the station end.
The centralized control station and the regional transformer substation jointly guide the station end to calculate the risk value of the single equipment.
The synergistic mode mainly comprises the following steps:
station end:
performing fault probability calculation of a single device;
reporting station end equipment fault information, fault state quantity, fault position, equipment account and load current to a centralized control station;
centralized control station:
blending importance coefficients;
calculating a load current coefficient;
family defect analysis;
the information such as importance coefficient, risk threshold, family defect, load current coefficient and probability density distribution function is issued to the station end;
uploading fault equipment information to the regional transformer substation;
regional substation:
statistically analyzing probability density distribution functions under fault conditions;
and (5) issuing a probability density distribution function to the centralized control station.
The specific implementation steps of the specific application comprise the following steps:
the first step: station-side transformer C 1 ,C 2 ,…,C n-1 ,C n The station account information, the fault position and the fault time content x of the fault equipment 0 The basic information is uploaded to the centralized control station B 1 ,B 2 ,…,B k-1 ,B k (k<n), and uploading the information to the regional substation by each centralized control station.
And a second step of: collecting data of dissolved gas in oil under all transformer fault conditions in the region by using a regional transformer substation A, and calculating a transformer fault probability density distribution function f by statistics D (y) and issue to centralized control station B 1 ,B 2 ,…,B k-1 ,B k 。
And a third step of: centralized control station B 1 ,B 2 ,…,B k-1 ,B k Receiving probability density distribution function f from regional substation A statistics D (y) and issue to station side transformer C 1 ,C 2 ,…,C n-1 ,C n 。
Fourth step: station-side transformer C 1 ,C 2 ,…,C n-1 ,C n Calculating the fault probability F of each device according to the on-line monitored dissolved gas in the oil, and calculating the fault probability F of each device and the value x of the dissolved gas in the oil 0 Load current I is uploaded to centralized control station B 1 ,B 2 ,…,B k-1 ,B k . The method for calculating the fault probability refers to patent 202211466906.3, and specifically comprises the following steps:
assuming probability density distribution function f obtained by regional substation A statistics D (y) is in the form of a log-normal distribution, and the dissolved gas content x in the oil of the transformer is measured at a certain moment 0 The fault probability of the transformer is a probability density function f D (y) at x 0 The following constant value is expressed by the formula (1).
Where y is the dissolved gas content in the oil at fault and σ is the probability density distribution function f D (y) standard deviation, μ is a probability density distribution function f D Mean value of (y), x 0 The dissolved gas content in the oil is monitored online for a certain moment.
Fifth step: centralized control station B 1 ,B 2 ,…,B k-1 ,B k Based on the station side equipment asset P and the running load current eta i (t) deploying an importance factor Q of the plant, wherein the asset factor P comprises a plant value P 1 Y user class P 2 Status of equipment P 3 The three aspects are as follows:
Q(t)＝P×η i (t) (2)
wherein i=1-3, 1 is the device value, 2 is the user level, and 3 is the device status; w (W) Pi Is an asset factor; p (P) i Is a certain asset factor; p is the asset, and the asset class classification description is given in Table 1.
TABLE 1
The load current coefficient calculation formula is as follows:
wherein I (t) is the load current, I min Is rated for current I N Is 0.4 times that of I max Is rated for current I N Is 0.8 times as large as the above.
Sixth step: centralized control station B 1 ,B 2 ,…,B k-1 ,B k According to station end C 1 ,C 2 ,…,C n-1 ,C n If the uploaded fault information analysis equipment has family defects, if the transformers of the same manufacturer fail for a plurality of times in one overhaul period, the equipment of the manufacturer is judged to have family faults, at the moment, the family defect information and the adjustment coefficient T need to be issued to the corresponding station-end equipment, and the setting value of the family defect adjustment coefficient T is shown in the table 2 below.
TABLE 2
Seventh step: centralized control station B 1 ,B 2 ,…,B k-1 ,B k Setting early warning thresholds of all early warning levels, wherein the level I early warning equipment can continue to operate but monitoring in operation should be enhanced, and the risk threshold is 3%; the II-level early warning equipment should monitor operation in a key way and arrange power failure maintenance in good time, and the risk threshold value is 9%; the III-level early warning equipment should be scheduled for power failure maintenance as soon as possible, and the risk threshold value is 27%; IV, immediately arranging power failure maintenance for the early warning equipment, wherein the risk threshold is 81%.
Eighth step: station end C 1 ,C 2 ,…,C n-1 ,C n According to the centralized control station B 1 ,B 2 ,…,B k-1 ,B k The issued importance coefficient Q and the family defect adjustment coefficient T calculate the equipment risk value R (T), the calculation formula is as follows, the early warning grade of the equipment at the station end is output by comparing with the risk threshold value, and the risk value and the early warning grade are uploaded to a centralized control station for reference by an maintainer;
R(t)＝F(t)×Q(t)×T (5)
wherein R (t) is a risk value of the equipment at a certain moment, F (t) is a fault probability of the equipment at a certain moment, and Q (t) is a fault probability at a certain moment.
The invention overcomes the defect that only a single device is used for early warning in the past, realizes multi-level early warning and risk information whole network sharing, and improves the accuracy and macroscopicity of early warning of the transformer substation equipment.
Example 2:
the invention also provides a state monitoring-based power equipment multistage cooperative early warning system 200, as shown in fig. 3, comprising:
the information acquisition unit 201 is configured to perform state monitoring on a single electric device in a substation to obtain state monitoring data, calculate a fault probability of the single electric device based on the state monitoring data and a probability density function of a historical single electric device fault, determine a risk value and a risk early warning level of the single electric device in the substation based on the fault probability, a historical importance coefficient and a family defect analysis data of the substation, and generate reporting information of any single electric device in the substation based on the state monitoring data, the risk value and the risk early warning level;
the computing unit 202 is configured to obtain whole network operation information in the substation and weather information in a substation area, and determine early warning information in the substation based on the reported information, the whole network operation information and the weather information;
and the early warning unit 203 is configured to generate shared early warning information based on the early warning information and send the shared early warning information to the power equipment in the substation, so as to complete multi-stage collaborative fault early warning for the power equipment in the substation.
The reporting information of the single power equipment comprises the following steps: fault location, fault type, fault state quantity, ledger, load current and content information of dissolved gas in equipment oil at fault moment of single power equipment.
The early warning information comprises the following steps: fault device, fault location of the fault device, and fault state quantity of the fault device.
Wherein generating shared pre-warning information based on the pre-warning information comprises:
and determining a probability density distribution function of a single power device in the transformer substation under the fault condition based on the early warning information, calculating a load current coefficient of the single power device in the transformer substation based on the report information, and generating shared early warning information based on a calculation result of the load current coefficient, the family defect analysis data and the probability density distribution function of the transformer substation.
Wherein, sharing early warning information includes as follows: importance coefficient, risk threshold, family defect, load current coefficient, and probability density distribution function.
Wherein, the early warning unit 203 is further configured to: and transmitting the shared early warning information to other substations except the current substation in a preset range.
The invention overcomes the defect that only a single device is used for early warning in the past, realizes multi-level early warning and risk information whole network sharing, and improves the accuracy and macroscopicity of early warning of the transformer substation equipment.
Example 3:
based on the same inventive concept, the invention also provides a computer device comprising a processor and a memory for storing a computer program comprising program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application SpecificIntegrated Circuit, ASIC), off-the-shelf Programmable gate arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computational core and control core of the terminal adapted to implement one or more instructions, in particular adapted to load and execute one or more instructions within a computer storage medium to implement the corresponding method flow or corresponding functions to implement the steps of the method in the embodiments described above.
Example 4:
based on the same inventive concept, the present invention also provides a storage medium, in particular, a computer readable storage medium (Memory), which is a Memory device in a computer device, for storing programs and data. It is understood that the computer readable storage medium herein may include both built-in storage media in a computer device and extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium herein may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the steps of the methods in the above-described embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 scheme in the embodiment of the invention can be realized by adopting various computer languages, such as object-oriented programming language Java, an transliteration script language JavaScript and the like.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (14)

1. The utility model provides a multistage collaborative early warning method of power equipment based on state monitoring which characterized in that, the method includes:
performing state monitoring on single electric equipment in a transformer substation to obtain state monitoring data, calculating the fault probability of the single electric equipment based on the state monitoring data and a probability density function of a historical single electric equipment fault, determining a risk value and a risk early warning level of the single electric equipment in the transformer substation based on the fault probability, a historical importance coefficient and family defect analysis data of the transformer substation, and generating reporting information of any single electric equipment in the transformer substation based on the state monitoring data, the risk value and the risk early warning level;
acquiring whole network operation information in the transformer substation and meteorological information in a transformer substation area, and determining early warning information in the transformer substation based on the reported information, the whole network operation information and the meteorological information;
and generating shared early warning information based on the early warning information and transmitting the shared early warning information to the power equipment in the transformer substation so as to finish multistage collaborative fault early warning of the power equipment in the transformer substation.
2. The method of claim 1, wherein reporting information for the single power device comprises: fault location, fault type, fault state quantity, ledger, load current and content information of dissolved gas in equipment oil at fault moment of single power equipment.
3. The method of claim 1, wherein the pre-warning information comprises the following: fault device, fault location of the fault device, and fault state quantity of the fault device.
4. The method of claim 1, wherein generating shared alert information based on the alert information comprises:
and determining a probability density distribution function of a single power device in the transformer substation under the fault condition based on the early warning information, calculating a load current coefficient of the single power device in the transformer substation based on the report information, and generating shared early warning information based on a calculation result of the load current coefficient, the family defect analysis data and the probability density distribution function of the transformer substation.
5. The method of claim 1, wherein the sharing of the pre-warning information comprises: importance coefficient, risk threshold, family defect, load current coefficient, and probability density distribution function.
6. The method according to claim 1, wherein the method further comprises: and transmitting the shared early warning information to other substations except the current substation in a preset range.
7. A power equipment multistage cooperative early warning system based on state monitoring, characterized in that the system comprises:
the information acquisition unit is used for carrying out state monitoring on the single electric equipment in the transformer substation to acquire state monitoring data, calculating the fault probability of the single electric equipment based on the state monitoring data and a probability density function of the faults of the historical single electric equipment, determining the risk value and the risk early warning level of the single electric equipment in the transformer substation based on the fault probability, the historical importance coefficient and the family defect analysis data of the transformer substation, and generating the reporting information of any single electric equipment in the transformer substation based on the state monitoring data, the risk value and the risk early warning level;
the computing unit is used for acquiring the whole network operation information in the transformer substation and the meteorological information in the transformer substation area, and determining the early warning information in the transformer substation based on the reported information, the whole network operation information and the meteorological information;
and the early warning unit is used for generating shared early warning information based on the early warning information and transmitting the shared early warning information to the power equipment in the transformer substation so as to finish multi-stage collaborative fault early warning of the power equipment in the transformer substation.
8. The system of claim 7, wherein the reporting information of the single power device comprises the following: fault location, fault type, fault state quantity, ledger, load current and content information of dissolved gas in equipment oil at fault moment of single power equipment.
9. The system of claim 7, wherein the pre-warning information comprises the following: fault device, fault location of the fault device, and fault state quantity of the fault device.
10. The system of claim 7, wherein the generating shared pre-warning information based on the pre-warning information comprises:
and determining a probability density distribution function of a single power device in the transformer substation under the fault condition based on the early warning information, calculating a load current coefficient of the single power device in the transformer substation based on the report information, and generating shared early warning information based on a calculation result of the load current coefficient, the family defect analysis data and the probability density distribution function of the transformer substation.
11. The system of claim 7, wherein the shared pre-warning information comprises the following: importance coefficient, risk threshold, family defect, load current coefficient, and probability density distribution function.
12. The system of claim 7, wherein the pre-warning unit is further configured to: and transmitting the shared early warning information to other substations except the current substation in a preset range.
13. A computer device, comprising:
one or more processors;
a processor for executing one or more programs;
the method of any of claims 1-6 is implemented when the one or more programs are executed by the one or more processors.
14. A computer readable storage medium, characterized in that a computer program is stored thereon, which computer program, when executed, implements the method according to any of claims 1-6.
CN202310752506.7A 2023-06-25 2023-06-25 Power equipment multistage cooperative early warning method and system based on state monitoring Pending CN116961217A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310752506.7A CN116961217A (en) 2023-06-25 2023-06-25 Power equipment multistage cooperative early warning method and system based on state monitoring

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310752506.7A CN116961217A (en) 2023-06-25 2023-06-25 Power equipment multistage cooperative early warning method and system based on state monitoring

Publications (1)

Publication Number Publication Date
CN116961217A true CN116961217A (en) 2023-10-27

Family

ID=88457416

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310752506.7A Pending CN116961217A (en) 2023-06-25 2023-06-25 Power equipment multistage cooperative early warning method and system based on state monitoring

Country Status (1)

Country Link
CN (1) CN116961217A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117239938A (en) * 2023-11-13 2023-12-15 国网浙江省电力有限公司杭州供电公司 Inspection control method, inspection control device, inspection control system, inspection control equipment and inspection control medium for power distribution station

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117239938A (en) * 2023-11-13 2023-12-15 国网浙江省电力有限公司杭州供电公司 Inspection control method, inspection control device, inspection control system, inspection control equipment and inspection control medium for power distribution station
CN117239938B (en) * 2023-11-13 2024-02-23 国网浙江省电力有限公司杭州供电公司 Inspection control method, inspection control device, inspection control system, inspection control equipment and inspection control medium for power distribution station

Similar Documents

Publication Publication Date Title
WO2018086488A1 (en) Risk identification system and method for power distribution network, and computer storage medium
CN105116291B (en) A kind of fault diagnosis system and method based on operation of power networks stable state monitoring information
CN108537344A (en) Secondary device intelligence O＆M method based on closed loop information management
KR101683256B1 (en) Asset management system and method for electric power apparatus
CN111950800B (en) Method, system, device and readable storage medium for clearing electric power spot day-ahead market
CN206312210U (en) State evaluation system of power distribution network equipment
CN112467724A (en) Low-voltage distribution network fault studying and judging method
CN116961217A (en) Power equipment multistage cooperative early warning method and system based on state monitoring
CN104538957B (en) Power grid model self-adaptive processing method for counting low-frequency low-voltage load shedding capacity
CN104820884A (en) Power network dispatching real-time data inspection method combined with characteristics of power system
CN111929527B (en) Adaptability evaluation system and method for equipment fault characteristic regularization knowledge representation
CN104484837A (en) Power distribution network risk evaluation comprehensive quantizing method
CN117852773B (en) Fault positioning system for power distribution network
CN112766783B (en) Method and system for evaluating operation quality of power equipment, terminal equipment and medium
Zhang et al. Research on power grid fault diagnosis based on a quantitative representation of alarm information
Ibe et al. Adequacy analysis and security reliability evaluation of bulk power system
CN107767028B (en) Transmission section limit automatic identification and out-of-limit calculation method based on xml
CN110765986A (en) Method for automatically auditing monitoring signals of main station of power system
CN114418237B (en) Distribution network power supply safety capability evaluation standard quantification method, system, equipment and medium
CN116204839A (en) Transformer oil temperature anomaly analysis method
EP3352326A1 (en) System for generating real-time information and forecasting power rules due to network bottlenecks in a power supply network
CN111553810A (en) Power supply reliability fuzzy statistical method and device and computer readable storage medium
CN112241587A (en) Distribution line risk assessment model construction method, risk assessment method and system
CN112417363B (en) Load analysis method and system for transformer substation
Chen et al. A state assessment method of relay protection device based on pso optimizes svm parameters and cloud model

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication