CN113269462A - Transformer area line loss calculation method based on edge calculation - Google Patents
Transformer area line loss calculation method based on edge calculation Download PDFInfo
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Abstract
The application discloses a distribution room line loss calculation method based on edge calculation, which comprises the following steps: installing edge computing terminal equipment in the transformer area, debugging the edge computing terminal equipment installed in the transformer area, and acquiring line loss rates of other transformer areas and the transformer area to be detected and operation parameters of a transformer area power distribution network by using an acquisition module in the edge computing terminal equipment; grouping edge computing terminal equipment in the transformer area, connecting the grouped transformer area edge computing terminal equipment with main control terminal equipment, and connecting the main control terminal equipment with a plurality of grouped transformer area edge computing terminal equipment. The invention can locally calculate the line loss rate of the transformer area and assist operation and maintenance personnel to maintain the transformer area, and can correspondingly search the reason of the abnormal line loss rate of the transformer area by combining the transformer area loss information base so as to be used as a decision reference for operation scheduling personnel or an intelligent power grid.
Description
Technical Field
The application relates to the technical field of prediction of line loss rate of a transformer area, in particular to a transformer area line loss calculation method based on edge calculation.
Background
Along with the development of social economy, the power consumption load increases, the line loss electric quantity of the low-voltage distribution network accounts for about 40% of the loss of the whole power network, and the problem of station area loss is more and more prominent. The real-time monitoring of the station area loss is an important task of a power supply enterprise, and a basis is provided for formulating reasonable loss reduction measures.
The traditional line loss calculation method depends on power grid parameters and operation data, the required data is large during calculation, the calculation amount is large, the precision is generally low, the power grid data of a low-voltage transformer area needs manual detection, the efficiency is low, the cost is high, and in a smart power grid, automatic monitoring can be achieved by applying an advanced deep learning algorithm on a powerful cloud calculation platform and combining internet of things equipment such as a smart camera. However, the performance of cloud monitoring is still unsatisfactory because large amounts of data transmitted over the internet can cause high latency and low frame rate problems. Therefore, a method for calculating the line loss of the transformer area based on the edge calculation is proposed to solve the problems.
Disclosure of Invention
The embodiment provides a platform area line loss calculation method based on edge calculation, which is used for solving the problems that cloud monitoring performance in a smart grid is poor, and high delay and low frame rate are caused by transmission of a large amount of data on the internet in the prior art.
According to an aspect of the present application, there is provided a method for calculating a line loss of a distribution room based on edge calculation, where the method for calculating the line loss of the distribution room based on edge calculation includes the following steps:
(1) installing edge computing terminal equipment in the transformer area, debugging the edge computing terminal equipment installed in the transformer area, and acquiring line loss rates of other transformer areas and the transformer area to be detected and operation parameters of a transformer area power distribution network by using an acquisition module in the edge computing terminal equipment;
(2) grouping edge computing terminal equipment in a transformer area, connecting the grouped transformer area edge computing terminal equipment with main control terminal equipment, and connecting the main control terminal equipment with a plurality of grouped transformer area edge computing terminal equipment;
(3) collecting and processing the electrical characteristic parameters of the distribution room in the historical data of all the edge computing terminal devices controlled by the main control terminal device, and storing the data through a cache module;
(4) acquiring electrical characteristic parameter data of different transformer areas at different historical time periods and different moments by utilizing edge computing terminal equipment, and computing running state parameter data of the transformer areas through an edge computing module;
(5) clustering the electrical characteristic parameter data and the operating state parameter data in the historical data of the transformer area through a clustering algorithm, making a transformer area line loss rate prediction model according to the historical data, and transplanting the transformer area line loss rate prediction model into a main control terminal;
(6) selecting historical data of electrical characteristic parameters of the to-be-tested transformer area as a training sample data set
(7) Predicting through a station area line loss rate prediction model to obtain a real-time predicted line loss rate;
(8) and comparing the predicted real-time line loss rate with the theoretical line loss rate of the distribution room, calculating an error rate and uploading the error rate to the main control terminal.
Further, in the step (1), the electrical characteristic parameters and the line loss rate at different times in a certain historical time period are used as historical data.
Further, in the step (1), the plurality of distribution areas are classified, and historical data closest to the distribution area to be measured is selected as a reference.
Further, the step (2) of obtaining the operating parameters of the distribution network of the station is divided station of the station;
further, in the step (3), all grouped edge computing terminal devices are located through a locating module, and all edge computing terminal devices are monitored in real time through the main control terminal device.
Further, in the step (4), the electrical characteristic parameter data and the operation state parameter data are uniformly classified and stored, and are uploaded to a database of the main control terminal.
Further, an AP clustering algorithm is adopted in the step (5) to cluster the electrical characteristic parameter data and the operation state parameter data.
Further, in the step (8), the prediction model of the edge device is periodically updated through the collected station area historical data and the line loss rate predicted in real time, so that the accuracy of the prediction model of the edge computing terminal device is ensured.
Further, in the step (8), when the error rate is greater than 3%, it is determined that the line loss rate of the station area is abnormal.
Further, in the step (8), the static anomaly is sent to the main control terminal device through the edge computing terminal device, and the alarm information is sent to the operation and maintenance personnel through the main control terminal device in time.
Through the embodiment of the application, the edge computing is introduced into the smart grid monitoring system, the edge monitoring has the great advantage of low delay, and compared with cloud monitoring, the monitoring quality can be greatly improved. On the basis, the platform area is designed to be used as an edge calculation node, a platform area technical framework based on an edge calculation mode is provided, and a line loss rate calculation method based on the edge calculation mode and applied to the platform area is provided. The method can locally calculate the line loss rate of the transformer area and assist operation and maintenance personnel to maintain the transformer area, and the reason of the abnormal line loss rate of the transformer area can be correspondingly searched by combining the transformer area loss information base so as to be used as a decision reference for operation scheduling personnel or the intelligent power grid.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a flow chart of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In this application, the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings. These terms are used primarily to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to a particular orientation or to be constructed and operated in a particular orientation.
Moreover, some of the above terms may be used to indicate other meanings besides the orientation or positional relationship, for example, the term "on" may also be used to indicate some kind of attachment or connection relationship in some cases. The specific meaning of these terms in this application will be understood by those of ordinary skill in the art as appropriate.
Furthermore, the terms "mounted," "disposed," "provided," "connected," and "sleeved" are to be construed broadly. For example, it may be a fixed connection, a removable connection, or a unitary construction; can be a mechanical connection, or an electrical connection; may be directly connected, or indirectly connected through intervening media, or may be in internal communication between two devices, elements or components. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
The method for calculating the line loss of the transformer area in this embodiment may be used to quickly monitor the line loss calculation and feedback in real time, for example, the following method for calculating the line loss of the transformer area based on edge calculation is provided in this embodiment, and the method for calculating the line loss of the transformer area in this embodiment may be used to improve the efficiency of calculating the line loss.
A line loss classifier-based distribution room line loss analysis method comprises the following steps:
step 1, selecting a typical distribution area by adopting a regional characteristic method.
And 2, inputting parameters such as characteristic parameters, power supply quantity, electricity selling quantity and the like of the typical distribution area into a theoretical line loss calculation module of the distribution area to obtain the line loss rate of the typical distribution area. The transformer area theoretical line loss calculation module integrates various transformer area line loss rate calculation methods such as an equivalent resistance method, a power flow method and an iteration method. This step forms typical cell line loss rates and other relevant information.
And 3, inputting the typical platform area into a sample platform area functional module to form a sample platform area set. This step forms a set of sample stations that provide samples for later data mining training. The user can continuously update and increase the typical platform area and the sample platform area according to the requirement.
And 4, inputting the sample platform area set into a platform area line loss trainer function module, and classifying the sample platform areas by the platform area line loss trainer to form a classification principle and obtain sample platform areas of various types. In the step, the transformer area line loss trainer carries out classification on the sample transformer area by utilizing various data mining technologies to form a classification principle.
And 5, inputting the classified sample transformer areas of each category into a transformer area line loss rate multiple linear regression calculation model function module to obtain a transformer area line loss rate multiple linear regression calculation model of each category. In the step, a platform area line loss rate multiple linear regression calculation model is formed by utilizing sample platform area data information and classification information, and a calculation method is provided for calculating all platform area line loss rates in the subsequent steps.
And 6, inputting the classification principle and the multivariate linear regression calculation model of the line loss rate of each class of distribution areas into a line loss classifier, and updating the data of the classification principle and the multivariate linear regression calculation model of the line loss rate of each class of distribution areas by the line loss classifier. The step continuously optimizes and updates the line loss classifier, so that the line loss classifier is continuously evolved, and the processing capacity of the line loss classifier in the transformer area is improved.
And 7, acquiring information of each distribution area from the metering automation system and the marketing system by the system interface module according to a fixed period, and inputting the information of all the distribution areas into the line loss classifier. The acquisition of the step is carried out for a fixed period, and data information acquisition is generally carried out according to the day and the month. This step obtains information for each station area.
And 8, classifying the acquired information of each distribution area by the line loss classifier according to the updated classification principle, attaching a distribution area class electronic tag, and obtaining the calculated line loss rate of each distribution area by using an updated multiple linear regression calculation model. The line loss classifier performs electronic tag pasting and line loss rate calculation on each distribution area.
And 9, calculating the line loss rate of each distribution area obtained by using the multiple linear regression calculation model by using the line loss classifier, comparing the calculated line loss rate with the statistical line loss rate acquired from the metering automation system and the marketing system, and giving line loss abnormal alarm information when the error exceeds a limited range. The step judges whether the line loss rate of each station area statistic is abnormal.
And step 10, the line loss classifier feeds back the class labels of the distribution areas, the normal distribution areas and the line loss abnormal alarm information to the metering automation system and the marketing system, and warns the abnormal distribution areas. The step is that the line loss classifier feeds back information such as the line loss rate calculated by the distribution room obtained by processing to the metering automation system and the marketing system.
And step 11, the line loss classifier conveys the transformer area with normal line loss rate to a line loss analysis module, corrects the line loss rate of the transformer area with abnormal line loss and conveys the transformer area with abnormal line loss to the line loss analysis module, and the line loss analysis module carries out monthly statistics and partition statistics on the transformer area and issues the line loss rate information of the transformer area. The step is to carry out overall analysis on the line loss rates of all the transformer areas and to distribute the line loss rate information of all the transformer areas to line loss management personnel.
Example one
Referring to fig. 1, a method for calculating line loss of a distribution room based on edge calculation includes the following steps:
(1) installing edge computing terminal equipment in the transformer area, debugging the edge computing terminal equipment installed in the transformer area, and acquiring line loss rates of other transformer areas and the transformer area to be detected and operation parameters of a transformer area power distribution network by using an acquisition module in the edge computing terminal equipment;
(2) grouping edge computing terminal equipment in a transformer area, connecting the grouped transformer area edge computing terminal equipment with main control terminal equipment, and connecting the main control terminal equipment with a plurality of grouped transformer area edge computing terminal equipment;
(3) collecting and processing the electrical characteristic parameters of the distribution room in the historical data of all the edge computing terminal devices controlled by the main control terminal device, and storing the data through a cache module;
(4) acquiring electrical characteristic parameter data of different transformer areas at different historical time periods and different moments by utilizing edge computing terminal equipment, and computing running state parameter data of the transformer areas through an edge computing module;
(5) clustering the electrical characteristic parameter data and the operating state parameter data in the historical data of the transformer area through a clustering algorithm, making a transformer area line loss rate prediction model according to the historical data, and transplanting the transformer area line loss rate prediction model into a main control terminal;
(6) selecting historical data of electrical characteristic parameters of the to-be-tested transformer area as a training sample data set
(7) Predicting through a station area line loss rate prediction model to obtain a real-time predicted line loss rate;
(8) and comparing the predicted real-time line loss rate with the theoretical line loss rate of the distribution room, calculating an error rate and uploading the error rate to the main control terminal.
Further, in the step (1), the electrical characteristic parameters and the line loss rate at different times in a certain historical time period are used as historical data.
Further, in the step (1), the plurality of distribution areas are classified, and historical data closest to the distribution area to be measured is selected as a reference.
Further, the step (2) of obtaining the operating parameters of the distribution network of the station is divided station of the station;
further, in the step (3), all grouped edge computing terminal devices are located through a locating module, and all edge computing terminal devices are monitored in real time through the main control terminal device.
Further, in the step (4), the electrical characteristic parameter data and the operation state parameter data are uniformly classified and stored, and are uploaded to a database of the main control terminal.
Further, an AP clustering algorithm is adopted in the step (5) to cluster the electrical characteristic parameter data and the operation state parameter data.
Further, in the step (8), the prediction model of the edge device is periodically updated through the collected station area historical data and the line loss rate predicted in real time, so that the accuracy of the prediction model of the edge computing terminal device is ensured.
Further, in the step (8), when the error rate is greater than 3%, it is determined that the line loss rate of the station area is abnormal.
Further, in the step (8), the static anomaly is sent to the main control terminal device through the edge computing terminal device, and the alarm information is sent to the operation and maintenance personnel through the main control terminal device in time.
The method is suitable for line loss calculation aiming at a plurality of similar station areas.
Example two
Referring to fig. 1, a method for calculating line loss of a distribution room based on edge calculation includes the following steps:
(1) installing edge computing terminal equipment in the transformer area, debugging the edge computing terminal equipment installed in the transformer area, and acquiring line loss rates of other transformer areas and the transformer area to be detected and operation parameters of a transformer area power distribution network by using an acquisition module in the edge computing terminal equipment;
(2) grouping edge computing terminal equipment in a transformer area, connecting the grouped transformer area edge computing terminal equipment with main control terminal equipment, and connecting the main control terminal equipment with a plurality of grouped transformer area edge computing terminal equipment;
(3) collecting and processing the electrical characteristic parameters of the distribution room in the historical data of all the edge computing terminal devices controlled by the main control terminal device, and storing the data through a cache module;
(4) acquiring electrical characteristic parameter data of different transformer areas at different historical time periods and different moments by utilizing edge computing terminal equipment, and computing running state parameter data of the transformer areas through an edge computing module;
(5) clustering the electrical characteristic parameter data and the operating state parameter data in the historical data of the transformer area through a clustering algorithm, making a transformer area line loss rate prediction model according to the historical data, and transplanting the transformer area line loss rate prediction model into a main control terminal;
(6) selecting historical data of electrical characteristic parameters of the to-be-tested transformer area as a training sample data set
(7) Predicting through a station area line loss rate prediction model to obtain a real-time predicted line loss rate;
(8) and comparing the predicted real-time line loss rate with the theoretical line loss rate of the distribution room, calculating an error rate and uploading the error rate to the main control terminal.
Further, in the step (1), the electrical characteristic parameters and the line loss rate at different times in a certain historical time period are used as historical data.
Further, in the step (1), the plurality of distribution areas are classified, and historical data closest to the distribution area to be measured is selected as a reference.
Further, the step (2) of obtaining the operating parameters of the distribution network of the station is divided station of the station;
further, in the step (3), all grouped edge computing terminal devices are located through a locating module, and all edge computing terminal devices are monitored in real time through the main control terminal device.
Further, in the step (4), the electrical characteristic parameter data and the operation state parameter data are uniformly classified and stored, and are uploaded to a database of the main control terminal.
Further, an AP clustering algorithm is adopted in the step (5) to cluster the electrical characteristic parameter data and the operation state parameter data.
Further, in the step (8), the prediction model of the edge device is periodically updated through the collected station area historical data and the line loss rate predicted in real time, so that the accuracy of the prediction model of the edge computing terminal device is ensured.
Further, in the step (8), when the error rate is greater than 3%, it is determined that the line loss rate of the station area is abnormal.
Further, in the step (8), the static anomaly is sent to the main control terminal device through the edge computing terminal device, and the alarm information is sent to the operation and maintenance personnel through the main control terminal device in time.
The method carries out line loss calculation aiming at a plurality of transformer areas.
The application has the advantages that:
the invention introduces edge calculation into an intelligent power grid monitoring system, the edge monitoring has the great advantage of low delay, compared with cloud monitoring, the monitoring quality can be greatly improved, on the basis, a platform area is designed to be used as an edge calculation node, a platform area technical framework based on an edge calculation mode is provided, and a line loss rate calculation method based on the edge calculation mode and applied to the platform area is provided.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. The line loss calculation method of the transformer area based on the edge calculation is characterized by comprising the following steps: the method for calculating the line loss of the transformer area based on the edge calculation comprises the following steps:
(1) installing edge computing terminal equipment in the transformer area, debugging the edge computing terminal equipment installed in the transformer area, and acquiring line loss rates of other transformer areas and the transformer area to be detected and operation parameters of a transformer area power distribution network by using an acquisition module in the edge computing terminal equipment;
(2) grouping edge computing terminal equipment in a transformer area, connecting the grouped transformer area edge computing terminal equipment with main control terminal equipment, and connecting the main control terminal equipment with a plurality of grouped transformer area edge computing terminal equipment;
(3) collecting and processing the electrical characteristic parameters of the distribution room in the historical data of all the edge computing terminal devices controlled by the main control terminal device, and storing the data through a cache module;
(4) acquiring electrical characteristic parameter data of different transformer areas at different historical time periods and different moments by utilizing edge computing terminal equipment, and computing running state parameter data of the transformer areas through an edge computing module;
(5) clustering the electrical characteristic parameter data and the operating state parameter data in the historical data of the transformer area through a clustering algorithm, making a transformer area line loss rate prediction model according to the historical data, and transplanting the transformer area line loss rate prediction model into a main control terminal;
(6) selecting historical data of electrical characteristic parameters of the to-be-tested transformer area as a training sample data set
(7) Predicting through a station area line loss rate prediction model to obtain a real-time predicted line loss rate;
(8) and comparing the predicted real-time line loss rate with the theoretical line loss rate of the distribution room, calculating an error rate and uploading the error rate to the main control terminal.
2. The method of claim 1, wherein the method comprises: in the step (1), the electrical characteristic parameters and the line loss rate at different moments in a certain historical time period are used as historical data.
3. The method of claim 1, wherein the method comprises: and (2) classifying the plurality of distribution areas in the step (1), and selecting the historical data closest to the distribution area to be measured as reference.
4. The method of claim 1, wherein the method comprises: in the step (2), the operation parameters of the distribution network of the distribution area are obtained, specifically, the distribution area users are divided according to the structure of the distribution area, the distribution area line is divided into a plurality of sections, the phase of the users is judged, and the operation parameters of the distribution network of the distribution area are obtained according to the actual operation state of the line;
5. the method of claim 1, wherein the method comprises: and (4) positioning all grouped edge computing terminal equipment through a positioning module in the step (3), and monitoring all edge computing terminal equipment in real time through the main control terminal equipment.
6. The method of claim 1, wherein the method comprises: and (4) uniformly classifying and storing the electrical characteristic parameter data and the operating state parameter data, and uploading the data to a database of the main control terminal.
7. The method of claim 1, wherein the method comprises: and (5) clustering the electrical characteristic parameter data and the operating state parameter data by adopting an AP clustering algorithm.
8. The method of claim 1, wherein the method comprises: and (5) updating the prediction model of the edge equipment periodically through the acquired station area historical data and the real-time predicted line loss rate in the step (8), so as to ensure the accuracy of the prediction model of the edge computing terminal equipment.
9. The method of claim 1, wherein the method comprises: and (4) judging that the line loss rate of the station area is abnormal when the error rate is more than 3% in the step (8).
10. The method of claim 1, wherein the method comprises: and (8) sending the static exception to the main control terminal equipment through the edge computing terminal equipment, and sending alarm information to operation and maintenance personnel in time through the main control terminal equipment.
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| CN117638881A (en) * | 2023-11-20 | 2024-03-01 | 国网江苏省电力有限公司南京供电分公司 | Method for analyzing line loss reason based on edge calculation in HPLC (high Performance liquid chromatography) platform area |
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