CN114389231B - Load prediction diagnosis method based on power distribution network protection and equipment real-time data - Google Patents

Load prediction diagnosis method based on power distribution network protection and equipment real-time data Download PDF

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CN114389231B
CN114389231B CN202111660382.7A CN202111660382A CN114389231B CN 114389231 B CN114389231 B CN 114389231B CN 202111660382 A CN202111660382 A CN 202111660382A CN 114389231 B CN114389231 B CN 114389231B
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equipment
image
load prediction
time data
distribution network
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CN114389231A (en
Inventor
汪娇娇
胡铁斌
魏千钧
黄子千
贺臣
叶睆
刘银
胡子侯
丁健
王汉军
陆俊宇
许建远
吴茂育
张明刚
张东强
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Maoming Power Supply Bureau of Guangdong Power Grid Co Ltd
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Maoming Power Supply Bureau of Guangdong Power Grid Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02H—EMERGENCY PROTECTIVE CIRCUIT ARRANGEMENTS
    • H02H1/00—Details of emergency protective circuit arrangements
    • H02H1/0092—Details of emergency protective circuit arrangements concerning the data processing means, e.g. expert systems, neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25—Integrating or interfacing systems involving database management systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • 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
    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention provides a load prediction diagnosis method based on power distribution network protection and equipment real-time data, which is characterized by comprising the following steps: setting real-time data of the computing equipment based on a local power grid distribution network, and storing the real-time data in a database; based on a KMP algorithm, multistage equipment cooperation calculation is realized, the time-convenient cooperation relation matching of corresponding fixed values is analyzed, and the matching relation is displayed on a system through a graphic visualization technology; performing image-text recognition, then performing load prediction and providing a protection diagnosis strategy; the invention converts the image into the characters and outputs the structured data by means of the image-text recognition technology, and automatically records the structured data to the background, thereby greatly saving labor and improving efficiency; the image is processed, analyzed and understood by a computer through an integrated image-text recognition technology so as to recognize targets and objects in various different modes, realize that basic parameters and fixed value sheets of equipment are recognized into characters and are stored in a database; the speed and the effectiveness of the load prediction diagnosis method are greatly improved.

Description

Load prediction diagnosis method based on power distribution network protection and equipment real-time data
Technical Field
The invention relates to the field of power distribution network data protection, in particular to a load prediction diagnosis method based on power distribution network protection and equipment real-time data.
Background
Relay protection is an extremely important safety guarantee tool for the power system, and various software required for realizing relay protection operation and management automation is one of the key points of research and development of application software of the power system at home and abroad.
In recent decades, in order to improve the automation level of relay protection operation and management, related software of a main network such as relay protection setting calculation software, fixed value management software and the like is developed successively by a Guangdong power grid, but a distribution network intelligent realization platform is not yet constructed, power load prediction is a basis of power grid planning, and the traditional power grid planning is to predict total load by taking the whole planning area as an object. An efficient and rapid load predictive diagnostic method that combines power distribution network protection with real-time data of the equipment is lacking.
Disclosure of Invention
The invention aims to provide a load prediction diagnosis method based on power distribution network protection and equipment real-time data, so as to solve the problems in the background technology.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a load prediction diagnosis method based on power distribution network protection and equipment real-time data is characterized by comprising the following steps:
s1: setting real-time data of the computing equipment based on a local power grid distribution network, and storing the real-time data in a database;
s2: based on a KMP algorithm and combining with a breadth search and depth search principle, according to the topological relation of corresponding equipment, intelligently and automatically analyzing the corresponding coordination relation between each equipment, realizing the coordination calculation of multi-level equipment, and analyzing corresponding primary switches, secondary switches and tertiary switches; the time-convenient matching relation matching of corresponding fixed values is realized, and the optimal matching relation scheme is displayed on a system through a graphic visualization technology;
s3: and carrying out image-text recognition on the fixed value graph scheme after the matching relation is realized, then carrying out load prediction and providing a protection diagnosis strategy.
Because part of basic parameters and fixed value sheets of the equipment adopt formats such as pictures or PDF files, the traditional image-text data are recorded manually, and the time consumption is high; in the step S1, the image is converted into characters by means of the image-text recognition technology, the structured data is output, and the structured data is automatically recorded to the background, so that labor can be greatly saved, and efficiency is improved; the image is processed, analyzed and understood by a computer through integrating image-text recognition technology to recognize targets and objects in various different modes, so that basic parameters and fixed value sheets of equipment are recognized into characters, and the characters are stored in a database.
In the step S3, the running condition of the equipment is intelligently and automatically calculated and analyzed through information such as tripping at a fault point, and the condition automatic push notification reminding of key users and power-supply-protecting users is supported; and the method supports the back-pushing and re-calculating of upper and lower fixed values according to factors such as the switch position, the disconnection position, the fault type and the like, automatically analyzes the upper and lower protection coordination conditions of the fixed values, and intelligently discovers abnormal fixed values and pushes messages through intelligent association drawings and topology technology calculation to judge the fixed values of the problems of improper fixed value coordination and out-of-limit coordination, realizes accident reproduction and gives modification comments.
The load prediction diagnosis method comprises the following steps: in the specific load prediction process, the accuracy of the result is ensured by combining with an actual selection suitable method; comprising the following steps: regression analysis, load density and elastic modulus.
(1) Regression analysis: the method is applied based on a statistical principle, data information in a specific time period is selected and analyzed, a scientific linear regression model, a nonlinear regression model and a data model thereof are constructed, and the relation among variables is calculated and obtained through the correspondence of the data relation; (2) load Density method: the method refers to dividing prediction basis according to functions. For example, business areas, industrial areas and residential areas are divided according to different functions, load prediction is carried out on land areas and electric quantity densities of different areas, and accurate results are finally obtained; (3) coefficient of elasticity method: the method refers to the development of prediction work and is based on a comparison mode, and the electric power elasticity coefficient is determined: the average annual growth of the electricity consumption is compared with the average annual growth rate of the total production value, and the ratio is the final result.
Compared with the prior art, the invention has the beneficial effects that:
the invention converts the image into the characters and outputs the structured data by means of the image-text recognition technology, and automatically records the structured data to the background, thereby greatly saving labor and improving efficiency; the image is processed, analyzed and understood by a computer through an integrated image-text recognition technology so as to recognize targets and objects in various different modes, realize that basic parameters and fixed value sheets of equipment are recognized into characters and are stored in a database; the speed and the effectiveness of the load prediction diagnosis method are greatly improved.
Drawings
FIG. 1 is a schematic flow diagram of a load prediction diagnosis method based on power distribution network protection and equipment real-time data;
FIG. 2 is a diagram of an embodiment of an integrated architecture based on Web Services and OMS.
Detailed Description
The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by persons skilled in the art without making creative efforts based on the embodiments in the present invention are all within the protection scope of the present invention.
Examples
As shown in fig. 1, a load prediction diagnosis method based on power distribution network protection and equipment real-time data is characterized by comprising the following steps:
s1: setting real-time data of the computing equipment based on a local power grid distribution network, and storing the real-time data in a database;
s2: based on a KMP algorithm and combining with a breadth search and depth search principle, according to the topological relation of corresponding equipment, intelligently and automatically analyzing the corresponding coordination relation between each equipment, realizing the coordination calculation of multi-level equipment, and analyzing corresponding primary switches, secondary switches and tertiary switches; the time-convenient matching relation matching of corresponding fixed values is realized, and the optimal matching relation scheme is displayed on a system through a graphic visualization technology;
s3: and carrying out image-text recognition on the fixed value graph scheme after the matching relation is realized, then carrying out load prediction and providing a protection diagnosis strategy.
Because part of basic parameters and fixed value sheets of the equipment adopt formats such as pictures or PDF files, the traditional image-text data are recorded manually, and the time consumption is high; in the step S1, the image is converted into characters by means of the image-text recognition technology, the structured data is output, and the structured data is automatically recorded to the background, so that labor can be greatly saved, and efficiency is improved; the image is processed, analyzed and understood by a computer through integrating image-text recognition technology to recognize targets and objects in various different modes, so that basic parameters and fixed value sheets of equipment are recognized into characters, and the characters are stored in a database.
In the step S3, the running condition of the equipment is intelligently and automatically calculated and analyzed through information such as tripping at a fault point, and the condition automatic push notification reminding of key users and power-supply-protecting users is supported; and the method supports the back-pushing and re-calculating of upper and lower fixed values according to factors such as the switch position, the disconnection position, the fault type and the like, automatically analyzes the upper and lower protection coordination conditions of the fixed values, and intelligently discovers abnormal fixed values and pushes messages through intelligent association drawings and topology technology calculation to judge the fixed values of the problems of improper fixed value coordination and out-of-limit coordination, realizes accident reproduction and gives modification comments.
The load prediction diagnosis method comprises the following steps: in the specific load prediction process, the accuracy of the result is ensured by combining with an actual selection suitable method; comprising the following steps: regression analysis, load density and elastic modulus.
(1) Regression analysis: the method is applied based on a statistical principle, data information in a specific time period is selected and analyzed, a scientific linear regression model, a nonlinear regression model and a data model thereof are constructed, and the relation among variables is calculated and obtained through the correspondence of the data relation;
(2) Load density method: the method refers to dividing prediction basis according to functions. For example, business areas, industrial areas and residential areas are divided according to different functions, load prediction is carried out on land areas and electric quantity densities of different areas, and accurate results are finally obtained;
(3) Elastic coefficient method: the method refers to the development of prediction work and is based on a comparison mode, and the electric power elasticity coefficient is determined: the average annual growth of the electricity consumption is compared with the average annual growth rate of the total production value, and the ratio is the final result.
In this embodiment, each item of basic data required for construction is distributed in other systems, for example, information such as basic information of equipment, a single line diagram of a distribution network, etc. is in a graph model system or a GIS, and a fixed value single auditing flow is circulated in a name-lux OMS system. The system follows standard interface modes such as a Web Service mode, an OSB bus-based Service interface mode, a database-based interface mode, a file-based interface mode and the like. Integrating all the Services through a general integration platform based on an SOA architecture, adopting weblogic as middleware software of system configuration, packaging service functions into Web Services according to interface specifications, registering the Web Services to an ALSB, and calling the Services of the integration platform; the data center is a storage switching center of equipment account data of the OMS.
As shown in FIG. 2, a Web Services and OMS based integrated architecture diagram is shown. Integration is achieved through a general-purpose SOA-based integration platform.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the foregoing embodiments, and that the foregoing embodiments and description are merely preferred embodiments of the invention, and are not intended to limit the invention, but that various changes and modifications may be made therein without departing from the novel spirit and scope of the invention, which is defined by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (2)

1. A load prediction diagnosis method based on power distribution network protection and equipment real-time data is characterized by comprising the following steps:
s1: setting real-time data of the computing equipment based on a local power grid distribution network, and storing the real-time data in a database;
s2: based on a KMP algorithm and combining a breadth search principle and a depth search principle, according to the topological relation of corresponding equipment, intelligently and automatically analyzing the corresponding coordination relation between each equipment, realizing the coordination calculation of multi-level equipment, and analyzing corresponding primary switches, secondary switches and tertiary switches; the time-convenient matching relation matching of corresponding fixed values is realized, and the optimal matching relation scheme is displayed on a system through a graphic visualization technology;
s3: carrying out image-text recognition on the fixed value graph scheme after the matching relation is realized, then carrying out load prediction and providing a protection diagnosis strategy;
in the step S1, specifically, an image is converted into characters by means of an image-text recognition technology, structured data are output, and the characters are automatically recorded to a background; the image is processed, analyzed and understood by a computer through an integrated image-text recognition technology so as to recognize targets and objects in various different modes, realize that basic parameters and fixed value sheets of equipment are recognized into characters and are stored in a database;
in the step S3, specifically, the operation condition of the equipment is intelligently and automatically calculated and analyzed through the trip information of the fault point, and the condition automatic push notification reminding of key users and power-supply-protecting users is supported; and the method supports back-pushing and re-calculating upper and lower fixed values according to the switch position, the circuit breaking position and the fault type factors, automatically analyzes upper and lower protection coordination conditions of the fixed values, and intelligently discovers abnormal fixed values and pushes messages through intelligent association drawings and topology technology calculation to judge the fixed values where the problems of improper fixed value coordination and out-of-limit coordination occur, so that accident reproduction is realized, and modification comments are given.
2. The load prediction diagnosis method based on real-time data of power distribution network protection and equipment according to claim 1, wherein the load prediction diagnosis method comprises: in the specific load prediction process, the accuracy of the result is ensured by combining with an actual selection suitable method; comprising the following steps: regression analysis, load density and elastic modulus.
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