CN115800553A

CN115800553A - HPLC-based platform area topology identification method and device, electronic equipment and storage medium

Info

Publication number: CN115800553A
Application number: CN202310025140.3A
Authority: CN
Inventors: 王艳芹; 周凤华; 张海宁; 妙红英; 席海阔; 李蒙; 詹俊男; 苗宏佳; 李超
Original assignee: State Grid Corp of China SGCC; Chengde Power Supply Co of State Grid Jibei Electric Power Co Ltd
Current assignee: State Grid Corp of China SGCC; Chengde Power Supply Co of State Grid Jibei Electric Power Co Ltd
Priority date: 2023-01-09
Filing date: 2023-01-09
Publication date: 2023-03-14

Abstract

The application discloses a method, a device, electronic equipment and a storage medium for identifying a platform area topology based on HPLC (high performance liquid chromatography), wherein the scheme is specifically that preliminary identification is carried out based on characteristic information of the platform area to obtain a first platform area topology; acquiring a distribution voltage co-spectral spectrum of a distribution transformer low-voltage side of a transformer area and a user voltage harmonic spectral spectrum of a user intelligent electric meter in real time, and extracting a distribution voltage characteristic harmonic spectral spectrum and a user voltage characteristic harmonic spectral spectrum from the distribution transformer low-voltage side; performing secondary identification based on the similarity between the distribution transformer voltage characteristic harmonic spectrum and the user voltage characteristic harmonic spectrum to obtain a second station area topology; and comparing the first platform area topology with the second platform area topology, if the first platform area topology and the second platform area topology are the same, using the first platform area topology map as an identification result, and if the first platform area topology and the second platform area topology are different, recalculating the second platform area topology, and obtaining the second platform area topology again as the identification result. By the scheme, the station area house variable relation can be accurately identified, so that the line loss of the station area can be accurately calculated.

Description

HPLC-based platform area topology identification method and device, electronic equipment and storage medium

Technical Field

The present application relates to the field of power equipment technologies, and in particular, to a method and an apparatus for identifying a platform area topology based on HPLC, an electronic device, and a storage medium.

Background

The HPLC (High-speed Power Line Communication) technology is a Communication technology for data transmission using a Power Line as a Communication medium. Because the power line has the characteristics of wide popularization range, large coverage range and the like, the power line is utilized to transmit data, great convenience is realized, and all electric appliances connected with the power line can form a communication network without rewiring in some occasions to carry out information interaction and communication. The method is simple to implement and convenient to maintain, can effectively reduce the operation cost and reduce the expenditure for constructing a new communication network, and therefore, the method becomes a main communication means for application of smart grids, energy management, smart homes, photovoltaic power generation, electric vehicle charging and the like.

The inventor of the application finds that the identification accuracy of the current station area automatic identification scheme based on the HPLC technology is low, nodes belonging to the station area are easily registered to adjacent station areas, and accurate identification of station area indoor variation relation is the key point for ensuring accurate calculation of the line loss of the station area.

Disclosure of Invention

In view of this, the present application provides a method, an apparatus, an electronic device and a storage medium for identifying a distribution room topology based on HPLC, so as to improve the accuracy of identifying a distribution room-variant relationship, so as to ensure that the line loss of the distribution room can be accurately calculated.

In order to achieve the above object, the following solutions are proposed:

a platform area topology identification method based on HPLC is applied to a broadband carrier communication network of a platform area, the broadband carrier communication network comprises a main station, a concentrator, a main node and sub-nodes, and the platform area topology identification method comprises the following steps:

performing initial identification based on the characteristic information of the distribution area to obtain a first distribution area topology;

acquiring a distribution voltage co-spectral spectrum of a distribution low-voltage side of the distribution area and a user voltage co-spectral spectrum of a user intelligent ammeter in real time, and performing feature extraction on the distribution voltage co-spectral spectrum and the user voltage co-spectral spectrum by utilizing a pre-trained multilayer perceptron neural network to obtain a distribution voltage feature co-spectral spectrum of the distribution voltage co-spectral spectrum and a user voltage feature co-spectral spectrum of the user voltage co-spectral spectrum;

performing secondary identification based on the similarity between the distribution transformer voltage characteristic harmonic spectrum and the user voltage characteristic harmonic spectrum to obtain a second station area topology;

and comparing the first platform area topology with the second platform area topology, if the first platform area topology is the same as the second platform area topology, using the first platform area topology map as an identification result, if the first platform area topology is different from the second platform area topology, returning to the step of acquiring the distribution voltage co-spectrum of the distribution low-voltage side of the platform area and the user voltage co-spectrum of the user intelligent electric meter in real time, and using the second platform area topology obtained again as an identification result.

Optionally, the performing preliminary identification based on the feature information of the station area to obtain a first station area topology includes:

performing primary identification on the characteristic information in a centralized identification mode to obtain the first station area topology;

or, performing preliminary identification on the characteristic information in a distributed identification mode to obtain the first station area topology.

Optionally, the performing preliminary identification on the feature information in a centralized identification manner to obtain the first station area topology includes:

the main node starts the distribution area feature collection of the sub-nodes, after the sub-nodes are collected, the distribution area feature collection results of the sub-nodes are collected, the distribution area features of the main node and the distribution area features of the sub-nodes are compared, a preliminary distribution area membership relationship is formed through multiple iterations, and the first distribution area topology is obtained.

Optionally, the performing preliminary identification on the feature information in a distributed identification manner to obtain the first station area topology includes:

the main node starts the distribution area characteristic collection of the sub-nodes, after the sub-nodes are collected, the characteristic information of the main node is sent to each sub-node through a message, each sub-node compares the distribution area characteristic information sent by the main node with the characteristic information of the sub-node, and a preliminary distribution area membership relation is formed through multiple iterations to obtain the first distribution area topology.

Optionally, the obtaining, in real time, a coordination voltage co-spectrum on a coordination low-voltage side of the distribution area and a user voltage harmonic spectrum of a user smart meter, and performing feature extraction on the coordination voltage harmonic spectrum and the used voltage harmonic spectrum by using a pre-trained multilayer perceptron neural network to obtain a coordination voltage feature harmonic spectrum of the coordination voltage harmonic spectrum and a user voltage feature harmonic spectrum of the user voltage co-spectrum includes:

collecting a three-phase voltage waveform at the low-voltage side of the distribution transformer, and carrying out voltage harmonic spectrum analysis on the three-phase voltage waveform to obtain a distribution transformer voltage harmonic spectrum;

carrying out feature extraction on the distribution transformer voltage harmonic spectrum by utilizing the multilayer perceptron neural network to obtain distribution transformer spectrum features, and reconstructing the distribution transformer spectrum features to obtain the distribution transformer voltage feature harmonic spectrum;

collecting voltage waveforms of intelligent electric meters at the user side of the transformer area, and performing harmonic spectrum analysis on the voltage waveforms to obtain user voltage harmonic spectrums;

and performing feature extraction on the user voltage harmonic spectrum by adopting the multilayer perceptron neural network to obtain the user frequency spectrum feature, and reconstructing the user frequency spectrum feature to obtain the user voltage feature harmonic spectrum.

Optionally, the performing second identification based on the similarity between the distribution voltage characteristic harmonic spectrum and the user voltage characteristic harmonic spectrum to obtain a second distribution area topology includes:

calculating a similarity value between the distribution voltage characteristic harmonic spectrum and the user voltage characteristic harmonic spectrum by adopting a Pearson correlation coefficient calculation formula;

and comparing the similarity value with a preset threshold value, and if the similarity value is higher than the preset threshold value, judging that the user intelligent electric meter is positioned in the distribution area, so as to obtain the second distribution area topology.

A platform district topology recognition device based on HPLC is applied to a broadband carrier communication network of a platform district, wherein the broadband carrier communication network comprises a main station, a concentrator, a main node and sub-nodes, and the platform district topology recognition device comprises:

the first identification module is configured to perform preliminary identification based on the characteristic information of the distribution area to obtain a first distribution area topology;

the characteristic extraction module is configured to acquire a distribution voltage co-spectrum on a distribution low-voltage side of the transformer area and a user voltage harmonic spectrum of a user intelligent electric meter in real time, and perform characteristic extraction on the distribution voltage harmonic spectrum and the used voltage harmonic spectrum by utilizing a pre-trained multilayer perceptron neural network to obtain a distribution voltage characteristic harmonic spectrum of the distribution voltage harmonic spectrum and a user voltage characteristic harmonic spectrum of the user voltage co-spectrum;

a second identification module configured to perform a second identification based on a similarity between the distribution voltage characteristic harmonic spectrum and the user voltage characteristic harmonic spectrum, resulting in a second distribution area topology;

and the result output module is configured to compare the first zone topology with the second zone topology, if the first zone topology is the same as the second zone topology, the first zone topology map is used as an identification result, and if the first zone topology is different from the second zone topology, the feature extraction module and the second identification module are controlled to perform secondary identification, and the second zone topology obtained again is used as an identification result.

Optionally, the first identification module includes:

the centralized identification unit is configured to perform preliminary identification on the characteristic information in a centralized identification mode to obtain the first station area topology;

and the distribution identification unit is configured to perform preliminary identification on the characteristic information in a distributed identification mode to obtain the first station area topology.

Optionally, the centralized identification unit is configured to control the master node to start the area feature collection of the child nodes, after the collection of the child nodes is completed, start to collect the area feature collection results of each child node, compare the area features of the master node with the area features of each child node, and form a preliminary area membership relationship through multiple iterations to obtain the first area topology.

Optionally, the distribution identification unit is configured to control the master node to start the distribution area feature acquisition of the child nodes, after the collection of the child nodes is completed, the feature information of the child nodes is sent to each child node through a message, and each child node compares the distribution area feature information sent by the master node with the feature information of the child node, and forms a preliminary distribution area membership through multiple iterations to obtain the first distribution area topology.

Optionally, the feature extraction module includes:

the first acquisition unit is configured to acquire three-phase voltage waveforms on the low-voltage side of the distribution transformer and perform voltage harmonic spectrum analysis on the three-phase voltage waveforms to obtain harmonic spectra of the distribution transformer;

the first extraction unit is configured to perform feature extraction on the distribution voltage harmonic spectrum by using the multilayer perceptron neural network to obtain distribution spectrum features, and reconstruct the distribution spectrum features to obtain the distribution voltage harmonic spectrum;

the second acquisition unit is configured to acquire voltage waveforms of the user-side smart meters in the distribution area, and harmonic spectrum analysis is performed on the voltage waveforms to obtain user voltage harmonic spectra;

and the second extraction unit is configured to perform feature extraction on the user voltage harmonic spectrum by using the multilayer perceptron neural network to obtain the user frequency spectrum feature, and reconstruct the user frequency spectrum feature to obtain the user voltage feature harmonic spectrum.

Optionally, the second identification module includes:

a similarity value calculation module configured to calculate a similarity value between the distribution voltage characteristic harmonic spectrum and the user voltage characteristic harmonic spectrum using a pearson correlation coefficient calculation formula;

and the identification execution unit is configured to compare the similarity value with a preset threshold, and if the similarity value is higher than the preset threshold, the user intelligent electric meter is judged to be located in the distribution area, so that the second distribution area topology is obtained.

An electronic device comprising at least one processor and a memory coupled to the processor, wherein:

the memory is used for storing computer programs or instructions;

the processor is configured to execute the computer program or the instructions to enable the electronic device to implement the station area topology identification method as described above.

A storage medium applied to an electronic device, the storage medium carrying one or more computer programs which, when executed by the electronic device, can enable the electronic device to implement the station region topology identification method as described above.

From the technical scheme, the application discloses a method, a device, electronic equipment and a storage medium for identifying the platform area topology based on HPLC (high performance liquid chromatography), and the scheme is specifically that the primary identification is carried out based on the characteristic information of the platform area to obtain the first platform area topology; acquiring a distribution voltage co-spectral spectrum of a distribution low-voltage side of a distribution area and a user voltage harmonic spectral spectrum of a user intelligent ammeter in real time, and extracting a distribution voltage characteristic harmonic spectral spectrum and a user voltage characteristic harmonic spectral spectrum from the distribution voltage co-spectral spectrum and the user voltage harmonic spectral spectrum; performing secondary identification based on the similarity between the distribution transformer voltage characteristic harmonic spectrum and the user voltage characteristic harmonic spectrum to obtain a second station area topology; and comparing the first platform area topology with the second platform area topology, if the first platform area topology and the second platform area topology are the same, using the first platform area topology map as an identification result, and if the first platform area topology and the second platform area topology are different, recalculating the second platform area topology, and obtaining the second platform area topology again as the identification result. By the scheme, the station area house variable relation can be accurately identified, so that the line loss of the station area can be accurately calculated.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.

Fig. 1 is a flowchart of a method for identifying a distribution room topology based on HPLC according to an embodiment of the present disclosure;

fig. 2 is a schematic diagram of a wideband carrier communication network according to an embodiment of the present application;

fig. 3 is a flowchart of a method for identifying a platform topology based on HPLC according to an embodiment of the present application;

fig. 4 is a block diagram of an electronic device according to an embodiment of the present application.

Detailed Description

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 a part of the embodiments of the present application, and not all of the 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.

The accurate establishment of the station area house variable relationship, namely the determination of the station area topology is the key point for ensuring the accurate calculation of the line loss of the station area, and the station area identification technology is adopted, so that the working station areas of different HPLC networks can be identified, the accuracy of judging the house variable relationship is improved, the management of the line loss of the station area is facilitated, and the economic operation level of a power grid is improved.

Research shows that the existing scheme for identifying the topology of the distribution area through a centralized identification mode or a distributed identification mode has larger errors, and the situation that the nodes belonging to the distribution area are registered to the adjacent distribution area is very easy to occur, based on the problem, the following technical scheme is provided, and the technical concept of the application is as follows: based on the fact that harmonic voltage contents of phases of distribution transformers in different transformer areas are different, the total load amount on power supply lines of the distribution transformers in the same transformer area is different, and load access randomness, the harmonic voltage contents of the phases of the distribution transformers are different, accurate identification results of the intelligent electric meter area can be obtained by calculating the correlation degree of a distribution transformer voltage characteristic harmonic spectrum and a user voltage characteristic harmonic spectrum, on the basis of the existing identification mode, the identification results are corrected by using the correlation degree of the distribution transformer voltage characteristic harmonic spectrum and the user voltage characteristic harmonic spectrum, and the accuracy of topology identification of the area can be effectively improved. Based on this, this application especially proposes the following technical scheme:

example one

Fig. 1 is a flowchart of a method for identifying a distribution room topology based on HPLC according to an embodiment of the present disclosure.

The embodiment provides a hop-out topology identification method based on HPLC, which is implemented by a broadband carrier communication network based on a distribution area, where the broadband carrier communication network includes a master station, a concentrator, a master node COO and a plurality of sub-nodes STA, where the master node is connected to the concentrator, the concentrator is connected to the master node, and the master node is connected to each sub-node, as shown in fig. 2.

As shown in fig. 1, the method for identifying a cell topology according to this embodiment is applied to a corresponding electronic device, where the electronic device may be understood as a computer or a service with information processing capability and data computing capability, and the electronic device implements communication through the broadband carrier communication network. The method for identifying the topology of the transformer area comprises the following steps:

s1, performing primary identification based on the characteristic information of the transformer area to obtain a first transformer area topology.

Specifically, the primary identification of the cell topology is performed in a centralized identification manner or a distributed identification manner, so as to obtain a primary identification result, which is referred to as a first cell topology herein.

The centralized identification mode specifically comprises the following steps: the CCO is controlled to start the area feature collection of the STA, after the STA collection is finished, the area feature collection results of all the STAs are collected, the area features of the CCO and the area features of all the STAs are compared, a preliminary area membership relation is formed through multiple iterations, and the first area topology is obtained through a centralized recognition mode.

The distributed identification mode is specifically that CCO is controlled to start the station area feature collection of the STA, after the STA finishes collecting, the feature information of the STA is sent to each STA through a message, each STA compares the station area feature information sent by the CCO with the feature information of the STA, a preliminary station area membership relation is formed through multiple iterations, and the first station area topology is obtained through the distributed identification mode.

Specifically, the acquisition service related to the automatic identification of the distribution area in the conventional distribution area identification process includes:

screening abnormal areas of the household variable relationship: and the master station screens the disordered distribution areas managed by the file information aiming at the acquisition success rate and the line loss qualification rate of each distribution area, and initiates a distribution area identification service aiming at the distribution areas.

Starting a platform area identification task: for a cell needing to start a cell identification function, a cell identification task is remotely started, the simultaneous identification of adjacent cells is generally needed to be started, and the CCO and the STA in the cell are subjected to cell identification according to characteristic information of various cells.

And (3) reporting the identification result of the transformer area: the CCO and the STA in the station area are matched with each other to form a relatively correct station area identification result, the identification period is generally 1 day, the identification result is reported to the concentrator, and the concentrator continues to report to the master station.

And (3) processing station attribution error information: and the master station carries out response processing aiming at the zone attribution error information reported by the zone, deletes the wrong file relationship in the wrong concentrator and adds the correct file relationship into the correct concentrator.

And (3) closing the platform area identification task: and after the platform area identification task is completed, remotely closing the platform area identification task.

As shown in fig. 1, the parts are described in cooperation:

because the communication nodes in the transformer area are mixed, the following problems need to be solved in order to ensure the interconnection and intercommunication recognition effect:

1) CCO identification defaults to adopt a power frequency cycle (NTB) mode, at least transmits a power frequency cycle protocol message, and other identification modes are transmitted according to the self selection of each manufacturer;

2) Distributed flow is adopted by default in the whole network, and attribution is judged by the STA locally according to the characteristics of the transformer area;

3) The CCO needs a hardware double-edge mode, is compatible with STAs (different hardware schemes of different manufacturers) in different edge modes, has no problem in mixed loading, and simultaneously identifies the wiring problems such as zero-fire reverse connection and the like;

4) At the STA end, each manufacturer makes a protocol complete set, namely messages in various modes can normally respond, but the identification result is executed according to the scheme of each manufacturer.

And after CCO identification is finished, reporting the station area identification result to the concentrator, and reporting the station area identification result to the master station by the concentrator.

(1) STA-side task

The work related to station identification in the development process of STAs includes:

responding to a station area characteristic acquisition starting command sent by a CCO: and the STA locally acquires corresponding station area characteristics according to the acquisition characteristic type, the acquisition frequency, the acquisition period start and the acquisition point number in the command.

Executing a platform area characteristic acquisition task: and acquiring the characteristic information of the STA local station area according to an acquisition scheme specified by the CCO, and storing the information for subsequent CCO acquisition.

Responding to a station area characteristic query command sent by a CCO: after the local area feature collection is completed, after a collected data query command sent by the CCO is received, successfully collected data are returned to the CCO.

Responding the distribution area characteristic release information sent by the CCO: when the CCO issues the station area characteristic information of the STA to the STA, the STA compares the information with the station area characteristic information which is successfully acquired by the STA, and a correct station area membership relationship is formed through multiple iterations.

Reporting a platform area identification result: when the STA distributed zone identification mode is adopted, if the STA has identified the correct zone affiliation, it reports the zone identification result to the CCO and indicates the correct zone identification result.

Responding to the station area identification result query command: when receiving the station area identification result query command from the CCO, the STA needs to organize a response packet, which includes an identification completion status and identification result information.

STA distributed (default mode)

And the CCO issues a district characteristic acquisition starting message, acquires a characteristic default power frequency cycle characteristic, and acquires other characteristics according to selectable power frequency voltage characteristics, power frequency characteristics and the like. And simultaneously configuring parameters such as acquisition period, acquisition mode and the like.

And the STA node acquires and stores data according to the acquisition parameters issued by the CCO.

And the CCO performs data acquisition according to the configured acquisition parameters, sends the acquisition parameters to the STA through a station area characteristic information notification message, and the STA calculates after acquiring the CCO data to obtain the station area attribution result of the STA.

And the CCO issues a station area judgment result query command and polls and reads the station area identification result of the STA.

In order to ensure normal service communication (daily freezing reading, fee control issuing, high-frequency acquisition, event reporting and the like) during the station area identification period, the STA does not actively leave the network even if judging that the station area identification result is wrongly attributed, and reports the station area identification result information (a CCO informs a concentrator and an event reports to a main station) on the current network.

The STA only forwards the platform area characteristic information message of the CCO master node which has accessed the network, and any message of other networks, including all messages of a link layer, a network layer and an application layer, cannot be forwarded.

(2) CCO end task

In the development process of the CCO, the work content related to the station area identification includes:

responding to the station identification enabling control command sent by the concentrator: after receiving a station area identification starting command sent by a concentrator, a CCO needs to start a station area identification function, in order to ensure normal service communication, a white list filtering function of the CCO is in a starting state during the station area identification, and for a newly added electric meter, the newly added electric meter is added into a network and added into a file in a meter searching stage (see chapter 8 file automatic synchronization); when receiving the station area identification stop command, the station area identification function needs to be stopped. The CCO needs to perform the cell identification work according to the enable switch of the cell identification function, identify the process diversity type identification mode and the distributed identification mode, and default to the distributed identification process.

Centralized recognition function (standby mode): when the CCO works in a centralized identification mode, the CCO needs to start the station area feature acquisition activities of the STAs, starts to collect station area feature acquisition results of the STAs after the STAs finish acquisition, compares the station area features of the CCO with the characteristics of the STAs, and forms a correct station area membership relationship through multiple iterations.

Distributed recognition function (default mode): when the CCO works in a distributed identification mode, the CCO needs to start the station area feature acquisition activity of the STA, after the STA finishes acquisition, the self feature information is issued to each STA, each STA compares the station area feature information issued by the CCO with the self feature information, and a correct station area membership relationship is formed through multiple iterations. Because the characteristic information of the power frequency cycle is a necessary option, other characteristic information is a selectable option. For the power frequency periodic characteristic information, the CCO is required to support a double-edge acquisition mode of a rising edge and a falling edge, and the double-edge power frequency periodic characteristic information is required to be issued to the STA. Different edge modes of the STA of each manufacturer are compatible, and the interconnection and intercommunication identification effect of mixed assembly of field manufacturers is guaranteed.

And inquiring the identification result of the transformer area: when the distributed identification mode is adopted, when the CCO considers that the STA identification result is finished, a station area identification result query command may be initiated to the STA, and the STA may respond to the identification state and the identification result.

And (3) reporting the identification result of the response STA station area: when the distributed identification mode is adopted, if the STA identifies the correct subordination relationship of the station area, the identification result can be reported to the CCO, and the CCO needs to record and process the reported information.

Reporting and processing the file information of the wrong distribution area: when the CCO finds that the wrong distribution area file is set, the error information is reported to the concentrator, the concentrator further reports to the master station, and the master station needs to initiate file correction work.

CCO centralized type (standby mode)

And the CCO issues a distribution room characteristic acquisition starting message, wherein the acquisition content comprises a power frequency voltage characteristic, a power frequency characteristic, a power frequency period characteristic and a signal-to-noise ratio characteristic. And simultaneously configuring parameters such as acquisition period, acquisition mode and the like.

And the CCO collects messages through the station area characteristic information and polls and reads the collection result of the STA node. And the STA node informs the CCO of the acquisition result through the station area characteristic information informing message. And the CCO calculates after acquiring the data of the STA to obtain the area attribution of the STA.

(3) Concentrator design task

The relevant development work of the concentrator in the cell identification service is as follows:

responding to a station identification enabling and disabling command initiated by the master station: when the concentrator receives the station area identification enabling and disabling command sent by the master station, the concentrator firstly executes the confirmation work of the command and then controls the relevant operation of the CCO.

Controlling a CCO to start or end a station area identification function: and after receiving the district identification enabling and prohibiting command of the master station, the concentrator controls the CCO to start or end the district identification function.

Processing CCO reported error file information: in the process of enabling the CCO zone identification function, if the CCO reports an incorrect zone attribution event, the concentrator needs to report the information to the master station, and the master station is expected to perform relevant file correction work.

And the concentrator acquires and stores the station area identification list reported by the CCO and reports the station area identification list to the main station.

(5) Master station tasks

The station identification related development content in the master station system is as follows:

screening abnormal areas of the household variable relationship: and screening the problem areas in a database for identifying the areas by acquiring the characteristics of abnormal success rate and low line loss qualified rate, wherein the generally screened areas are a group of adjacent areas.

The station area identification work enables: and sending a remote command to start a station area identification function aiming at the adjacent problem station areas, executing station area identification service by the group of station areas, and reporting wrong station area files to the master station.

And (3) reporting an event by mistake to the platform zone file: the master station should respond to the erroneous file information reported by the concentrator and correct the file data in the relevant concentrator.

And (3) closing the platform area identification work: and after the identification of the group of the station area relationship is completed, remotely closing the station area identification function.

And S2, extracting a distribution voltage characteristic harmonic spectrum and a user voltage characteristic harmonic spectrum.

The method specifically comprises the steps of acquiring a distribution transformer low-voltage side of a transformer area and a user intelligent ammeter voltage harmonic spectrum in real time, and extracting features by utilizing a pre-trained multilayer perceptron neural network to obtain a distribution transformer voltage feature harmonic spectrum and a user voltage feature harmonic spectrum. The method specifically comprises the following steps:

firstly, collecting three-phase voltage waveforms on the low-voltage side of the distribution transformer, and respectively analyzing the three-phase voltage harmonic spectrum to obtain a distribution transformer voltage harmonic spectrum

And then, extracting the distribution-transformation spectrum characteristics from the distribution-transformation voltage harmonic spectrum by using the multi-layer perceptron neural network obtained by training in advance, and reconstructing the distribution-transformation spectrum characteristics to obtain the distribution-transformation voltage characteristic harmonic spectrum.

And then, collecting the voltage waveform of the intelligent ammeter at the user side, and carrying out harmonic spectrum analysis on the voltage to obtain a user voltage harmonic spectrum.

And finally, performing feature extraction on the voltage waveform by using the multilayer perceptron neural network to obtain user spectrum features, and reconstructing the user spectrum features to obtain user voltage feature harmonic spectrums.

The MLP Network model constructed in the embodiment comprises an input layer, four hidden layers and an output layer, each hidden layer in the four hidden layers has the same number of neurons, an activation function of each hidden layer adopts a Linear rectification function (ReLU), in order to prevent overfitting and improve the generalization capability of the model, a dropout layer is arranged behind each hidden layer, and the loss rate of the dropout layer is set to be 0.2.

And S3, carrying out secondary identification on the platform area topology to obtain a second platform area topology.

The distribution transformer voltage characteristic harmonic spectrum and the user voltage characteristic harmonic spectrum are subjected to similarity identification again, and the distribution transformer area topology obtained through identification is called a second distribution transformer area topology so as to be distinguished from the distribution transformer area topology obtained through initial identification. The specific process is as follows:

firstly, the similarity between the distribution voltage characteristic harmonic spectrum and the user voltage characteristic harmonic spectrum is calculated by adopting a Pearson correlation coefficient calculation formula to obtain a similarity value.

And then, comparing the similarity value with a preset threshold, and if the similarity value is higher than the preset threshold, judging that the user intelligent electric meter is located in the distribution transformer area, namely obtaining the second area topology.

Wherein, the Pearson correlation coefficient calculation formula is as follows:

wherein, X is the distribution voltage characteristic harmonic spectrum, and Y is the user voltage characteristic harmonic spectrum.

And S4, determining an identification result according to the first platform area topology and the second platform area topology.

Comparing the first platform area topology and the second platform area topology obtained by the operation, and if the first platform area topology and the second platform area topology are the same, outputting the first platform area topology or the second platform area topology as an identification result; and if the two are different, returning to the step S2, identifying again to obtain a new second platform area topology, and outputting the new second platform area topology as an identification result.

And finally, reporting the final identification result to a master station through the concentrator, deleting the original wrong file relationship in the wrong concentrator by the master station based on the identification result, and adding the correct file relationship into the correct concentrator.

Furthermore, in this embodiment, in consideration of dynamic changes in power load requirements in practical applications, the scheduling system may perform corresponding switching on the power supply relationship of the distribution room, including power supply splitting and combining; to this problem, this embodiment provides a scheme for switching to fast identification of the distribution area:

and after the switching of the transformer area occurs, generating a transformer area change application event, comparing newly added users applying for network access with the white list, generating a newly added user event (CCO refusing list reporting) when the users applying for network access are not in the white list, and actively reporting to the master station (when CCO networking and optimization are not completed, the newly added user event is not reported any more). And the quick identification of the newly added user is realized. This function, the slave STA, is not specially developed.

After networking is completed, the host node CCO organizes a rejection list event report message to inform a concentrator for a new networking request due to the fact that the information is not rejected in a white list. In order to avoid frequent reporting of reject list events by the CCO, the same slave node should only allow reporting once every six hours (filtering processing in the CCO), and the deduplication time is based on the clock interval of the CCO to integrally control the deduplication period of reporting activity, and is not calculated according to the event generation time of each table. The function CCO is turned off by default, requiring the remote master to issue a command to the CCO through the concentrator to enable the function. A CCO adds a cache mechanism, when the CCO generates rejection node information, if no new rejection node information is generated within 1 minute or the number of cache rejection nodes is equal to the maximum list (32), a rejection node information event message is formed and reported to a concentrator; and if the collector accesses the network by using the collector address, the information is defined according to the MAC address of the collector accessing the network.

The concentrator stores the information that the network access request of the node is rejected, and reports the information to the master station or receives the query command of the master station. The master station evaluates the information, studies and judges the switching scene of the platform area, and starts the subsequent file adjustment service.

As can be seen from the above technical solutions, the present embodiment provides a station area topology identification method based on HPLC, and specifically, performs preliminary identification based on the feature information of the station area to obtain a first station area topology; acquiring a distribution voltage co-spectral spectrum of a distribution transformer low-voltage side of a transformer area and a user voltage harmonic spectral spectrum of a user intelligent electric meter in real time, and extracting a distribution voltage characteristic harmonic spectral spectrum and a user voltage characteristic harmonic spectral spectrum from the distribution transformer low-voltage side; performing secondary identification based on the similarity between the distribution transformer voltage characteristic harmonic spectrum and the user voltage characteristic harmonic spectrum to obtain a second station area topology; and comparing the first platform area topology with the second platform area topology, if the first platform area topology and the second platform area topology are the same, using the first platform area topology map as an identification result, and if the first platform area topology and the second platform area topology are different, recalculating the second platform area topology, and obtaining the second platform area topology again as the identification result. By the scheme, the station area house variable relation can be accurately identified, so that the line loss of the station area can be accurately calculated.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Although the operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous.

It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, smalltalk, C + +, including conventional procedural programming languages, such as the C language, or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer.

Example two

Fig. 3 is a block diagram of a device for identifying a topology of a distribution room based on HPLC according to an embodiment of the present application.

As shown in fig. 3, the method for identifying a cell topology according to this embodiment is applied to a corresponding electronic device, where the electronic device may be understood as a computer or a service with information processing capability and data computing capability, and the electronic device implements communication through the broadband carrier communication network. The station area topology identification device comprises a first identification module 10, a feature extraction module 20, a second identification module 30 and a result output module 40.

The first identification module is used for carrying out preliminary identification based on the characteristic information of the transformer area to obtain a first transformer area topology.

Specifically, the primary identification of the platform area topology is performed in a centralized identification manner or a distributed identification manner, so as to obtain a primary identification result, where the primary identification result is referred to as a first platform area topology. The module comprises a centralized identification unit and a distributed identification unit.

The centralized identification unit is used for controlling the CCO to start the station area feature acquisition of the STA, after the STA is acquired, the station area feature acquisition results of all the STAs are collected, the station area features of the CCO and the station area features of all the STAs are compared, a preliminary station area membership relationship is formed through multiple iterations, and the first station area topology is obtained through a centralized identification mode.

The respective identification units are used for controlling the CCO to start the area feature collection of the STA, after the STA finishes collecting, the feature information of the STA is sent to each STA through a message, each STA compares the area feature information sent by the CCO with the feature information of the STA, a preliminary area membership relationship is formed through multiple iterations, and the first area topology is obtained through a distributed identification mode.

The characteristic extraction module is used for extracting a distribution voltage characteristic harmonic spectrum and a user voltage characteristic harmonic spectrum.

The method specifically comprises the steps of acquiring a distribution transformer low-voltage side of a transformer area and a user intelligent ammeter voltage harmonic spectrum in real time, and extracting features by utilizing a pre-trained multilayer perceptron neural network to obtain a distribution transformer voltage feature harmonic spectrum and a user voltage feature harmonic spectrum. The module comprises a first acquisition unit, a first extraction unit, a second acquisition unit and a second extraction unit.

The first acquisition unit is used for acquiring three-phase voltage waveforms on the low-voltage side of the distribution transformer and respectively analyzing the three-phase voltage harmonic spectrum to obtain the distribution transformer voltage harmonic spectrum

The first extraction unit is used for extracting the distribution transformer spectrum characteristics from the distribution transformer voltage harmonic spectrum by utilizing the multi-layer perceptron neural network obtained through pre-training, and reconstructing the distribution transformer spectrum characteristics to obtain the distribution transformer voltage characteristic harmonic spectrum.

The second acquisition unit is used for acquiring the voltage waveform of the intelligent ammeter at the user side and performing harmonic spectrum analysis on the voltage to obtain a user voltage harmonic spectrum.

The second extraction unit is used for extracting the characteristics of the voltage waveform by using the multilayer perceptron neural network to obtain user spectrum characteristics, and reconstructing the user spectrum characteristics to obtain a user voltage characteristic harmonic spectrum.

The second identification module is used for carrying out secondary identification on the platform area topology to obtain a second platform area topology.

The distribution transformer voltage characteristic harmonic spectrum and the user voltage characteristic harmonic spectrum are subjected to similarity identification again, and the distribution transformer area topology obtained through identification is called a second distribution transformer area topology so as to be distinguished from the distribution transformer area topology obtained through initial identification. The module includes a similarity value calculation unit and an identification execution unit.

And the similarity value calculation unit is used for calculating the similarity between the distribution voltage characteristic harmonic spectrum and the user voltage characteristic harmonic spectrum by adopting a Pearson correlation coefficient calculation formula to obtain a similarity value.

And the identification execution unit is used for comparing the similarity value with a preset threshold value, and if the similarity value is higher than the preset threshold value, judging that the user intelligent electric meter is located in the distribution transformer area, namely the second area topology is obtained.

The result output module is used for determining the identification result according to the first platform area topology and the second platform area topology.

Comparing the first platform area topology and the second platform area topology obtained by the operation, and if the first platform area topology and the second platform area topology are the same, outputting the first platform area topology or the second platform area topology as an identification result; and if the two are different, the control feature extraction module and the second identification module identify again to obtain a new second platform area topology, and the new second platform area topology is output as an identification result.

The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of a unit does not in some cases constitute a limitation of the unit itself, for example, the first retrieving unit may also be described as a "unit for retrieving at least two internet protocol addresses".

The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems on a chip (SOCs), complex Programmable Logic Devices (CPLDs), and the like.

EXAMPLE III

The present embodiment provides an electronic device, and referring to fig. 4, a schematic structural diagram of an electronic device suitable for implementing the embodiment of the present disclosure is shown. The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. This electronic device is merely an example and should not impose any limitations on the functionality or scope of use of embodiments of the present disclosure.

The electronic device may include a processing means (e.g., central processing unit, graphics processor, etc.) 401 that may perform various appropriate actions and processes in accordance with a program stored in a read only memory ROM or a program loaded from an input device 406 into a random access memory RAM 403. In the RAM, various programs and data necessary for the operation of the electronic apparatus are also stored. The processing device, ROM, and RAM are connected to each other by a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.

Generally, the following devices may be connected to the I/O interface: input devices including, for example, touch screens, touch pads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, and the like; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage devices including, for example, magnetic tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. While the figures illustrate an electronic device having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.

Example four

The embodiment provides a computer-readable storage medium, which carries one or more computer programs, and when the one or more computer programs are executed by the electronic device, the electronic device performs preliminary identification based on the feature information of the cell to obtain a first cell topology; acquiring a distribution voltage co-spectral spectrum of a distribution transformer low-voltage side of a transformer area and a user voltage harmonic spectral spectrum of a user intelligent electric meter in real time, and extracting a distribution voltage characteristic harmonic spectral spectrum and a user voltage characteristic harmonic spectral spectrum from the distribution transformer low-voltage side; performing secondary identification based on the similarity between the distribution transformer voltage characteristic harmonic spectrum and the user voltage characteristic harmonic spectrum to obtain a second station area topology; and comparing the first platform area topology with the second platform area topology, if the first platform area topology and the second platform area topology are the same, using the first platform area topology map as an identification result, and if the first platform area topology and the second platform area topology are different, recalculating the second platform area topology, and obtaining the second platform area topology again as the identification result. By the scheme, the station area house variable relation can be accurately identified, so that the line loss of the station area can be accurately calculated.

It should be noted that the computer readable medium of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.

While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.

Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrases "comprising one of \ 8230; \8230;" does not exclude the presence of additional like elements in a process, method, article, or terminal device that comprises the element.

The technical solutions provided by the present invention are described in detail above, and the principle and the implementation of the present invention are explained in this document by applying specific examples, and the descriptions of the above examples are only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims

1. A district topology identification method based on HPLC is applied to a broadband carrier communication network of a district, wherein the broadband carrier communication network comprises a main station, a concentrator, a main node and sub-nodes, and the district topology identification method comprises the following steps:

acquiring a distribution voltage co-spectrum of a distribution low-voltage side of the distribution area and a user voltage harmonic spectrum of a user intelligent electric meter in real time, and performing feature extraction on the distribution voltage harmonic spectrum and the user voltage harmonic spectrum by utilizing a pre-trained multilayer perceptron neural network to obtain a distribution voltage feature harmonic spectrum of the distribution voltage harmonic spectrum and a user voltage feature harmonic spectrum of the user voltage co-spectrum;

2. The method for identifying a topology of a cell according to claim 1, wherein the step of performing the preliminary identification based on the characteristic information of the cell to obtain the topology of the first cell comprises the steps of:

3. The method for identifying a topology of a distribution area according to claim 2, wherein the step of preliminarily identifying the feature information in a centralized identification manner to obtain the first topology of the distribution area comprises:

4. The method for identifying a topology of a distribution network according to claim 2, wherein the step of preliminarily identifying the characteristic information in a distributed identification manner to obtain the topology of the first distribution network comprises the steps of:

the main node starts the district characteristic collection of the sub-nodes, after the sub-nodes are completely collected, the characteristic information of the sub-nodes is sent to each sub-node through a message, each sub-node compares the district characteristic information sent by the main node with the characteristic information of the sub-node, and a preliminary district membership relationship is formed through multiple iterations to obtain the first district topology.

5. The method of identifying a distribution area topology of claim 1, wherein the step of obtaining in real time a distribution voltage co-spectrum of a distribution low voltage side of the distribution area and a user voltage harmonic spectrum of a user smart meter, and performing feature extraction on the distribution voltage co-spectrum and the user voltage harmonic spectrum using a pre-trained multi-layer perceptron neural network to obtain a distribution voltage feature harmonic spectrum of the distribution voltage co-spectrum and a user voltage feature harmonic spectrum of the user voltage co-spectrum comprises the steps of:

collecting three-phase voltage waveforms on the low-voltage side of the distribution transformer, and carrying out voltage harmonic spectrum analysis on the three-phase voltage waveforms to obtain a distribution transformer voltage harmonic spectrum;

6. The method of identifying a distribution area topology of claim 1, wherein said identifying a second distribution area topology based on a similarity between said distribution voltage characteristic harmonic spectrum and said user voltage characteristic harmonic spectrum to obtain a second distribution area topology comprises the steps of:

7. The utility model provides a district topology recognition device based on HPLC, is applied to the broadband carrier communication network in district, broadband carrier communication network includes main station, concentrator, master node and subnode, characterized in that, district topology recognition device includes:

a second identification module configured to perform secondary identification based on a similarity between the distribution voltage characteristic harmonic spectrum and the user voltage characteristic harmonic spectrum to obtain a second distribution area topology;

8. The station topology identifying apparatus of claim 7, wherein said first identifying module comprises:

9. The device according to claim 8, wherein the centralized identification unit is configured to control the master node to start the collection of the characteristics of the distribution area of the child nodes, and after the collection of the child nodes is completed, start to collect the results of the collection of the characteristics of the distribution area of each child node, compare the characteristics of the distribution area of the master node with the characteristics of the distribution area of each child node, and form a preliminary distribution area membership relationship through multiple iterations, thereby obtaining the first distribution area topology.

10. The device according to claim 8, wherein the distribution identification unit is configured to control the master node to start a distribution area feature collection of the child nodes, and after the collection of the child nodes is completed, send own feature information to each of the child nodes through a message, and each of the child nodes compares the distribution area feature information sent by the master node with its own feature information, and forms a preliminary distribution area membership relationship through multiple iterations to obtain the first distribution area topology.

11. The device of claim 7, wherein the feature extraction module comprises:

a first extraction unit, configured to perform feature extraction on the distribution voltage harmonic spectrum by using the multilayer perceptron neural network to obtain a distribution spectrum feature, and reconstruct the distribution spectrum feature to obtain the distribution voltage harmonic spectrum;

12. The station topology identifying apparatus of claim 7, wherein said second identifying module comprises:

13. An electronic device comprising at least one processor and a memory coupled to the processor, wherein:

the memory is for storing a computer program or instructions;

the processor is configured to execute the computer program or instructions to cause the electronic device to implement the method of identifying a topology of a platform according to any one of claims 1 to 6.

14. A storage medium applied to an electronic device, wherein the storage medium carries one or more computer programs, and when the one or more computer programs are executed by the electronic device, the electronic device is enabled to implement the station zone topology identification method according to any one of claims 1 to 6.