CN114389231A - 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 PDFInfo
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- H—ELECTRICITY
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Abstract
The invention provides a load prediction and diagnosis method based on power distribution network protection and equipment real-time data, which is characterized by comprising the following steps of: calculating real-time data of the equipment based on local power grid distribution network setting, and storing the real-time data in a database; based on a KMP algorithm, the cooperation calculation of multi-level equipment is realized, the matching relation matching of corresponding fixed values with convenient time is analyzed, and the matching relation matching is displayed on a system through a graphic visualization technology; carrying out image-text recognition, then carrying out load prediction and proposing a protection diagnosis strategy; the invention converts the image into the character by means of the image-text recognition technology and outputs the structured data, and the structured data is automatically recorded to the background, so that the labor can be greatly saved and the efficiency can be improved; through the integrated image-text recognition technology, the image is processed, analyzed and understood by a computer to recognize various targets and objects in different modes, so that basic parameters and fixed value sheets of equipment are recognized into characters and stored in a database; the speed and the effectiveness of the load forecasting and diagnosing method are greatly improved.
Description
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
The 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 also one of the key points of research and development of application software of the power system at home and abroad.
In recent decades, relevant software of major networks such as relay protection setting calculation software and setting value management software are developed successively in the Guangdong power grid to improve the automation level of relay protection operation and management, but a distribution network intelligent implementation platform is not built, power load prediction is the basis of power grid planning, and the traditional power grid planning takes the whole planning area as an object to predict total load. An effective and fast load prediction diagnosis method combining power distribution network protection and real-time equipment data is lacking.
Disclosure of Invention
The invention aims to provide a load prediction and 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 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: calculating real-time data of the equipment based on local power grid distribution network setting, and storing the real-time data in a database;
s2: based on a KMP algorithm, combining with the principles of breadth search and depth search, intelligently and automatically analyzing corresponding coordination relations among all devices according to corresponding device topological relations, realizing the coordination calculation of multi-level devices, and analyzing corresponding primary switches, secondary switches and tertiary switches; the matching relation matching of the corresponding fixed value and the time convenience is realized, and the optimal matching relation scheme is displayed on the system through a graphic visualization technology;
s3: and identifying the image and text of the fixed value graph scheme after the matching relation is realized, then predicting the load and proposing a protection diagnosis strategy.
Because some basic parameters and fixed values of the equipment adopt formats such as pictures or PDF files, the traditional picture and text data are manually recorded, and the time consumption is high; in the step S1, specifically, the image is converted into the text by means of the image-text recognition technology, the structured data is output, and the text is automatically recorded to the background, so that the labor can be greatly saved, and the efficiency is improved; by integrating image-text recognition technology, the image is processed, analyzed and understood by a computer to recognize various targets and objects in different modes, and basic parameters and fixed value sheets of the equipment are recognized into characters and stored in a database.
In the step S3, specifically, the operation condition of the device is intelligently and automatically calculated and analyzed according to information such as trip of a fault point, and the conditions of a heavy-point user and a power-supply-protecting user are supported to automatically push notification and prompt; the method supports back-stepping recalculation of the upper and lower level fixed values according to factors such as switch positions, circuit breaking positions and fault types, automatically analyzes the matching situation of the fixed value upper and lower level protection, calculates and judges the fixed value where the problems of improper fixed value matching and overlimit matching occur through an intelligent association drawing and a topology technology, intelligently finds abnormal fixed values and pushes messages, realizes accident recurrence and gives modification suggestions.
The load forecasting and diagnosing method comprises the following steps: in the specific load prediction process, the accuracy of the result is guaranteed by combining with an actually selected proper method; the method comprises the following steps: regression analysis, load density, and elastic coefficient.
(1) Regression analysis method: the method is applied by taking a statistical principle as a basis, selecting and analyzing data information in a specific time period, constructing a scientific linear regression model, a non-linear regression model and a data model thereof, and calculating and obtaining the relation between variables through the correspondence of data relations; (2) load density method: the method is to divide the prediction basis according to functions. For example, a business area, an industrial area and a residential area are divided according to different functions, and then load prediction is performed on land areas and electric quantity densities of different areas in combination, so that a more accurate result is finally obtained; (3) modulus of elasticity method: the method is characterized in that the development of prediction work is based on a comparison mode, and the electric power elasticity coefficient is determined as follows: the average annual increase of the electricity consumption is compared with the average annual increase 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 character by means of the image-text recognition technology and outputs the structured data, and the structured data is automatically recorded to the background, so that the labor can be greatly saved and the efficiency can be improved; through the integrated image-text recognition technology, the image is processed, analyzed and understood by a computer to recognize various targets and objects in different modes, so that basic parameters and fixed value sheets of equipment are recognized into characters and stored in a database; the speed and the effectiveness of the load forecasting and diagnosing method are greatly improved.
Drawings
FIG. 1 is a schematic view of a process framework of a load prediction diagnosis method based on power distribution network protection and equipment real-time data according to the present invention;
fig. 2 is a diagram of an architecture based on Web Services and OMS integration in the present embodiment.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by those skilled in the art without any inventive work based on the embodiments of the present invention belong to the 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: calculating real-time data of the equipment based on local power grid distribution network setting, and storing the real-time data in a database;
s2: based on a KMP algorithm, combining with the principles of breadth search and depth search, intelligently and automatically analyzing corresponding coordination relations among all devices according to corresponding device topological relations, realizing the coordination calculation of multi-level devices, and analyzing corresponding primary switches, secondary switches and tertiary switches; the matching relation matching of the corresponding fixed value and the time convenience is realized, and the optimal matching relation scheme is displayed on the system through a graphic visualization technology;
s3: and identifying the image and text of the fixed value graph scheme after the matching relation is realized, then predicting the load and proposing a protection diagnosis strategy.
Because some basic parameters and fixed values of the equipment adopt formats such as pictures or PDF files, the traditional picture and text data are manually recorded, and the time consumption is high; in the step S1, specifically, the image is converted into the text by means of the image-text recognition technology, the structured data is output, and the text is automatically recorded to the background, so that the labor can be greatly saved, and the efficiency is improved; by integrating image-text recognition technology, the image is processed, analyzed and understood by a computer to recognize various targets and objects in different modes, and basic parameters and fixed value sheets of the equipment are recognized into characters and stored in a database.
In the step S3, specifically, the operation condition of the device is intelligently and automatically calculated and analyzed according to information such as trip of a fault point, and the conditions of a heavy-point user and a power-supply-protecting user are supported to automatically push notification and prompt; the method supports back-stepping recalculation of the upper and lower level fixed values according to factors such as switch positions, circuit breaking positions and fault types, automatically analyzes the matching situation of the fixed value upper and lower level protection, calculates and judges the fixed value where the problems of improper fixed value matching and overlimit matching occur through an intelligent association drawing and a topology technology, intelligently finds abnormal fixed values and pushes messages, realizes accident recurrence and gives modification suggestions.
The load forecasting and diagnosing method comprises the following steps: in the specific load prediction process, the accuracy of the result is guaranteed by combining with an actually selected proper method; the method comprises the following steps: regression analysis, load density, and elastic coefficient.
(1) Regression analysis method: the method is applied by taking a statistical principle as a basis, selecting and analyzing data information in a specific time period, constructing a scientific linear regression model, a non-linear regression model and a data model thereof, and calculating and obtaining the relation between variables through the correspondence of data relations;
(2) load density method: the method is to divide the prediction basis according to functions. For example, a business area, an industrial area and a residential area are divided according to different functions, and then load prediction is performed on land areas and electric quantity densities of different areas in combination, so that a more accurate result is finally obtained;
(3) modulus of elasticity method: the method is characterized in that the development of prediction work is based on a comparison mode, and the electric power elasticity coefficient is determined as follows: the average annual increase of the electricity consumption is compared with the average annual increase rate of the total production value, and the ratio is the final result.
Various basic data required by construction of the embodiment are distributed in other systems, for example, information such as equipment basic information and a distribution network single line diagram is in a graph model system or a GIS, and a fixed value single auditing flow is circulated in a named OMS system. The system follows standard interface modes such as a Web Service mode, a Service interface mode based on an OSB bus, an interface mode based on a database, an interface mode based on a file and the like. The integration of all Services is realized through a universal integrated platform based on an SOA architecture, weblogic is adopted as middleware software of system configuration, service functions are packaged into Web Services according to interface specifications and are registered to ALSB, and meanwhile, the Services of the integrated platform can be called; the data center is a storage switching center for equipment account data of the OMS.
As shown in fig. 2, the architecture diagram is based on Web Services and OMS integration. The integration is realized through a universal integration platform based on SOA architecture.
The foregoing shows and describes the general principles, essential 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 embodiments described above, and that the preferred embodiments of the invention and described in the specification are only preferred embodiments of the invention and are not intended to limit the invention, and that various changes and modifications may be made without departing from the novel spirit and scope of the invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (4)
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: calculating real-time data of the equipment based on local power grid distribution network setting, and storing the real-time data in a database;
s2: based on a KMP algorithm, combining with the principles of breadth search and depth search, intelligently and automatically analyzing corresponding coordination relations among all devices according to corresponding device topological relations, realizing the coordination calculation of multi-level devices, and analyzing corresponding primary switches, secondary switches and tertiary switches; the matching relation matching of the corresponding fixed value and the time convenience is realized, and the optimal matching relation scheme is displayed on the system through a graphic visualization technology;
s3: and identifying the image and text of the fixed value graph scheme after the matching relation is realized, then predicting the load and proposing a protection diagnosis strategy.
2. The power distribution network protection and equipment real-time data-based load prediction diagnosis method according to claim 1, characterized in that: in step S1, specifically, the image is converted into text by means of an image-text recognition technology, structured data is output, and the text is automatically recorded to a background; by integrating image-text recognition technology, the image is processed, analyzed and understood by a computer to recognize various targets and objects in different modes, and basic parameters and fixed value sheets of the equipment are recognized into characters and stored in a database.
3. The power distribution network protection and equipment real-time data-based load prediction diagnosis method according to claim 1, characterized in that: in the step S3, specifically, the operation condition of the device is intelligently and automatically calculated and analyzed according to information such as trip of a fault point, and the conditions of a heavy-point user and a power-supply-protecting user are supported to automatically push notification and prompt; the method supports back-stepping recalculation of the upper and lower level fixed values according to factors such as switch positions, circuit breaking positions and fault types, automatically analyzes the matching situation of the fixed value upper and lower level protection, calculates and judges the fixed value where the problems of improper fixed value matching and overlimit matching occur through an intelligent association drawing and a topology technology, intelligently finds abnormal fixed values and pushes messages, realizes accident recurrence and gives modification suggestions.
4. The power distribution network protection and equipment real-time data-based load forecasting and diagnosis method according to claim 1, characterized in that the load forecasting and diagnosis method comprises the following steps: in the specific load prediction process, the accuracy of the result is guaranteed by combining with an actually selected proper method; the method comprises the following steps: regression analysis, load density, and elastic coefficient.
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