Data validity identification method and device of optical fiber current transformer based on smart power grid

Abstract

The invention discloses a data validity identification method and device for an optical fiber current transformer based on a smart power grid. The method comprises the following steps: fitting to obtain relation functions of different state quantities and transformation ratios; generating a reference data set; obtaining a power grid tail end variable data curve corresponding to each reference time range; taking the power grid tail end variable data curve and the reference vector in each reference time range as a group of sample data; obtaining data identification models corresponding to different power grid end variables; the method comprises the following steps: regularly monitoring terminal variable data of a power grid, generating a corresponding data curve for a fluctuation point of each terminal variable, importing the data curves into a corresponding data identification model, and outputting a reference vector; and comparing the to-be-identified vector with the reference vector, and judging the validity of the to-be-identified vector according to a comparison result. According to the method, the data validity of the all-fiber current transformer can be identified in combination with the tail end power utilization data of the intelligent power grid monitoring system.

Description

Technical Field

[0001] The present invention relates to the technical field of optical fiber current transformers, and in particular to a method and device for identifying data validity of an optical fiber current transformer based on a smart grid. Background Art

[0002] Smart grids feature a remarkable integration of power flow, information flow and business flow. Information technology, sensor technology, automatic control technology and grid infrastructure are organically integrated to obtain panoramic information of the grid and to promptly discover and foresee possible failures.

[0003] Compared with traditional electromagnetic current transformers, all-fiber current transformers (FOCT) have a series of advantages such as good insulation properties, wide bandwidth, large dynamic range, high detection accuracy, small size and light weight. They are more compatible with modern digital control and protection systems, and have become an important research direction for electronic current transformers. They are gradually replacing traditional electromagnetic current transformers in smart grids.

[0004] However, during field operation, the fiber-optic current transformer (FOCT) will be affected by various external factors (such as ambient temperature, vibration, impact, electromagnetic interference, etc.) and internal factors (aging of optoelectronic device performance, increased optical path loss, etc.), which will cause changes in the internal state of the fiber-optic current transformer, thereby reducing the accuracy and reliability of the product. It is difficult for researchers to judge the validity of data output through other aspects, so it is very important to find a means to judge the validity of FOCT data. Summary of the invention

[0005] The purpose of the present invention is to provide a data validity identification method and device for an optical fiber current transformer based on a smart grid, which is combined with the terminal power consumption data of a smart grid monitoring system to identify the data validity of the full optical fiber current transformer. The identification result is directly related to the terminal power consumption data, which is beneficial for staff to promptly discover faulty optical fiber current transformers, thereby ensuring terminal power safety.

[0006] In order to achieve the above technical objectives, the technical solution adopted by the present invention is:

[0007] In a first aspect, the present invention discloses a data validity identification method for an optical fiber current transformer based on a smart grid, the data validity identification method comprising the following steps:

[0008] S1, determining the state quantity of the all-optical current transformer according to the factory parameters, characteristic parameters and power grid environment parameters of the all-optical current transformer;

[0009] S2, obtaining power grid environment parameters and historical state quantity data and historical transformation ratio data of the all-optical current transformer under different power grid environment parameters;

[0010] S3, performing correlation analysis on historical state quantity data and historical transformation ratio data, and fitting to obtain relationship functions between different state quantities and transformation ratios; using the relationship function between the state quantity and the transformation ratio, screen out historical state quantity data and historical transformation ratio data corresponding to the all-optical current transformer whose deviation is greater than a preset deviation threshold, combine the historical state quantity data and the historical transformation ratio data into a reference vector, and generate a reference data set in combination with the acquisition time of each reference vector;

[0011] S4, generating a reference time range for each acquisition time, obtaining a power grid terminal variable data curve corresponding to each reference time range from the smart grid monitoring system; taking the power grid terminal variable data curve and the reference vector of each reference time range as a set of sample data;

[0012] S5, taking the power grid terminal variable data curve as input and the reference vector as output, training the neural network to obtain the data identification model corresponding to different power grid terminal variables;

[0013] S6, regularly monitoring the terminal variable data of the power grid, generating a corresponding data curve for the fluctuation point of each terminal variable, importing the data curve into the corresponding data identification model, and outputting a reference vector;

[0014] S7, combining the state quantity data and transformation ratio data of the all-optical current transformer at the same time into a vector to be identified;

[0015] S8, comparing the vector to be identified with the reference vector, judging the validity of the vector to be identified according to the comparison result, and then identifying the validity of the data of the all-optical current transformer.

[0016] Furthermore, the state quantities of the all-optical current transformer include temperature data, power data, current and voltage data, and compensation data; wherein the temperature data includes the temperature data of each component of the all-optical current transformer and the environmental data of the all-optical current transformer; the power data includes the transmission power, the return power, and the return optical power; the current and voltage data include the modulation voltage applied by the modulator, the modulation phase, and the driving current of the light source; the compensation data includes the sensor ring compensation and the optical module compensation.

[0017] Furthermore, in step S8, the process of comparing the vector to be identified with the reference vector includes:

[0018] Calculate the variance between each element of the vector to be identified and the reference vector;

[0019] Calculate the mean of the variance of all elements;

[0020] It is determined whether it exceeds the variance threshold. If so, it is considered that the vector to be identified is of poor validity.

[0021] Furthermore, in step S8, the process of identifying the data validity of the all-optical current transformer includes:

[0022] The total number of vectors to be identified that are judged to be of poor effectiveness within a period of time is counted, and the data effectiveness of the all-optical current transformer is scored based on the total number of vectors to be identified with poor effectiveness and the corresponding variance average value.

[0023] In a second aspect, the present invention discloses a data validity identification device for an optical fiber current transformer based on a smart grid, the data validity identification device comprising:

[0024] A state quantity setting module is used to determine the state quantity of the all-optical current transformer according to the factory parameters, characteristic parameters and power grid environment parameters of the all-optical current transformer;

[0025] A transformer data acquisition module is used to obtain power grid environment parameters and historical state quantity data and historical transformation ratio data of the all-optical current transformer under different power grid environment parameters;

[0026] A reference data set generation module is used to perform correlation analysis on historical state quantity data and historical transformation ratio data, and to obtain relationship functions between different state quantities and transformation ratios by fitting; the relationship function between the state quantity and the transformation ratio is used to screen out historical state quantity data and historical transformation ratio data corresponding to the all-optical current transformer whose deviation is greater than a preset deviation threshold, and the historical state quantity data and historical transformation ratio data are combined into a reference vector, and a reference data set is generated in combination with the acquisition time of each reference vector;

[0027] The sample data generation module is used to generate a reference time range for each acquisition time, and obtain the power grid terminal variable data curve corresponding to each reference time range from the smart grid monitoring system; the power grid terminal variable data curve and the reference vector of each reference time range are used as a set of sample data;

[0028] The model training module is used to train the neural network with the power grid terminal variable data curve as input and the reference vector as output to obtain the data identification model corresponding to different power grid terminal variables;

[0029] The power grid data monitoring module is used to regularly monitor the power grid terminal variable data, generate corresponding data curves for the fluctuation points of each terminal variable, import the data curves into the corresponding data identification model, and output reference vectors;

[0030] A vector generation module to be identified is used to combine the state quantity data and transformation ratio data of the all-optical current transformer at the same time into a vector to be identified;

[0031] The validity identification module is used to compare the vector to be identified with the reference vector, judge the validity of the vector to be identified according to the comparison result, and then identify the data validity of the all-optical current transformer.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The data validity identification method and device of the optical fiber current transformer based on the smart grid of the present invention, combined with the terminal power consumption data of the smart grid monitoring system, identifies the data validity of the full optical fiber current transformer. The identification result is directly related to the terminal power consumption data, which is beneficial for the staff to find the faulty optical fiber current transformer in time, thereby ensuring the safety of terminal power consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 The present invention is a flow chart of a method for identifying data validity of a fiber optic current transformer based on a smart grid. DETAILED DESCRIPTION

[0035] The embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0036] See also Figure 1 The present invention discloses a data validity identification method for a fiber optic current transformer based on a smart grid, and the data validity identification method comprises the following steps:

[0037] S1, determining the state quantity of the all-optical current transformer according to the factory parameters, characteristic parameters and power grid environment parameters of the all-optical current transformer;

[0038] In the present invention, the state quantity of the all-optical current transformer includes temperature data, power data, current and voltage data, and compensation data; wherein the temperature data includes the temperature data of each component of the all-optical current transformer and the environmental data of the all-optical current transformer; the power data includes the transmission power, the return power, and the return optical power; the current and voltage data include the modulation voltage applied by the modulator, the modulation phase, and the driving current of the light source; the compensation data includes the sensor ring compensation and the optical module compensation.

[0039] S2, obtaining power grid environment parameters and historical state quantity data and historical transformation ratio data of the all-optical current transformer under different power grid environment parameters;

[0040] S3, performing correlation analysis on historical state quantity data and historical transformation ratio data, and fitting to obtain relationship functions between different state quantities and transformation ratios; using the relationship function between the state quantity and the transformation ratio, screen out historical state quantity data and historical transformation ratio data corresponding to the all-optical current transformer whose deviation is greater than a preset deviation threshold, combine the historical state quantity data and the historical transformation ratio data into a reference vector, and generate a reference data set in combination with the acquisition time of each reference vector;

[0041] S4, generating a reference time range for each acquisition time, obtaining a power grid terminal variable data curve corresponding to each reference time range from the smart grid monitoring system; taking the power grid terminal variable data curve and the reference vector of each reference time range as a set of sample data;

[0042] S5, taking the power grid terminal variable data curve as input and the reference vector as output, training the neural network to obtain the data identification model corresponding to different power grid terminal variables;

[0043] S6, regularly monitoring the terminal variable data of the power grid, generating a corresponding data curve for the fluctuation point of each terminal variable, importing the data curve into the corresponding data identification model, and outputting a reference vector;

[0044] S7, combining the state quantity data and transformation ratio data of the all-optical current transformer at the same time into a vector to be identified;

[0045] S8, comparing the vector to be identified with the reference vector, judging the validity of the vector to be identified according to the comparison result, and then identifying the validity of the data of the all-optical current transformer.

[0046] The process of comparing the vector to be identified with the reference vector includes:

[0047] Calculate the variance between each element of the vector to be identified and the reference vector; calculate the average variance of all elements; determine whether it exceeds the variance threshold. If so, the vector to be identified is considered to have poor validity.

[0048] The process of identifying the data validity of the all-optical current transformer includes:

[0049] The total number of vectors to be identified that are judged to be of poor effectiveness within a period of time is counted, and the data effectiveness of the all-optical current transformer is scored based on the total number of vectors to be identified with poor effectiveness and the corresponding variance average value.

[0050] The present invention discloses a data validity identification device for an optical fiber current transformer based on a smart grid, the data validity identification device comprising:

[0051] A state quantity setting module is used to determine the state quantity of the all-optical current transformer according to the factory parameters, characteristic parameters and power grid environment parameters of the all-optical current transformer;

[0052] A transformer data acquisition module is used to obtain power grid environment parameters and historical state quantity data and historical transformation ratio data of the all-optical current transformer under different power grid environment parameters;

[0053] A reference data set generation module is used to perform correlation analysis on historical state quantity data and historical transformation ratio data, and to obtain relationship functions between different state quantities and transformation ratios by fitting; the relationship function between the state quantity and the transformation ratio is used to screen out historical state quantity data and historical transformation ratio data corresponding to the all-optical current transformer whose deviation is greater than a preset deviation threshold, and the historical state quantity data and historical transformation ratio data are combined into a reference vector, and a reference data set is generated in combination with the acquisition time of each reference vector;

[0054] The sample data generation module is used to generate a reference time range for each acquisition time, and obtain the power grid terminal variable data curve corresponding to each reference time range from the smart grid monitoring system; the power grid terminal variable data curve and the reference vector of each reference time range are used as a set of sample data;

[0055] The model training module is used to train the neural network with the power grid terminal variable data curve as input and the reference vector as output to obtain the data identification model corresponding to different power grid terminal variables;

[0056] The power grid data monitoring module is used to regularly monitor the power grid terminal variable data, generate corresponding data curves for the fluctuation points of each terminal variable, import the data curves into the corresponding data identification model, and output reference vectors;

[0057] A vector generation module to be identified is used to combine the state quantity data and transformation ratio data of the all-optical current transformer at the same time into a vector to be identified;

[0058] The validity identification module is used to compare the vector to be identified with the reference vector, judge the validity of the vector to be identified according to the comparison result, and then identify the data validity of the all-optical current transformer.

[0059] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0060] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0061] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0062] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0063] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0064] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A data validity identification method for an optical fiber current transformer based on a smart grid, characterized in that: The data validity identification method comprises the following steps: S1, determining the state quantity of the all-optical current transformer according to the factory parameters, characteristic parameters and power grid environment parameters of the all-optical current transformer; S2, obtaining power grid environment parameters and historical state quantity data and historical transformation ratio data of the all-optical current transformer under different power grid environment parameters; S3, performing correlation analysis on historical state quantity data and historical transformation ratio data, and fitting to obtain relationship functions between different state quantities and transformation ratios; using the relationship function between the state quantity and the transformation ratio, screen out historical state quantity data and historical transformation ratio data corresponding to the all-optical current transformer whose deviation is greater than a preset deviation threshold, combine the historical state quantity data and the historical transformation ratio data into a reference vector, and generate a reference data set in combination with the acquisition time of each reference vector; S4, generating a reference time range for each acquisition time, obtaining a power grid terminal variable data curve corresponding to each reference time range from the smart grid monitoring system; taking the power grid terminal variable data curve and the reference vector of each reference time range as a set of sample data; S5, taking the power grid terminal variable data curve as input and the reference vector as output, training the neural network to obtain the data identification model corresponding to different power grid terminal variables; S6, regularly monitoring the terminal variable data of the power grid, generating a corresponding data curve for the fluctuation point of each terminal variable, importing the data curve into the corresponding data identification model, and outputting a reference vector; S7, combining the state quantity data and transformation ratio data of the all-optical current transformer at the same time into a vector to be identified; S8, comparing the vector to be identified with the reference vector, judging the validity of the vector to be identified according to the comparison result, and then identifying the validity of the data of the all-optical current transformer.

2. The data validity identification method of the optical fiber current transformer based on the smart grid according to claim 1 is characterized in that: The state quantity of the all-optical current transformer includes temperature data, power data, current and voltage data and compensation data; wherein the temperature data includes the temperature data of each component of the all-optical current transformer and the environmental data of the all-optical current transformer; the power data includes the transmission power, the return power and the return optical power; the current and voltage data includes the modulation voltage applied by the modulator, the modulation phase and the driving current of the light source; The compensation data includes sensor ring compensation and optical module compensation.

3. The data validity identification method of the optical fiber current transformer based on the smart grid according to claim 1 is characterized in that: In step S8, the process of comparing the vector to be identified with the reference vector includes: Calculate the variance between each element of the vector to be identified and the reference vector; Calculate the mean of the variance of all elements; It is determined whether it exceeds the variance threshold. If so, it is considered that the vector to be identified is of poor validity.

4. The data validity identification method of the optical fiber current transformer based on the smart grid according to claim 1 is characterized in that: In step S8, the process of identifying the data validity of the all-optical current transformer includes: The total number of vectors to be identified that are judged to be of poor effectiveness within a period of time is counted, and the data effectiveness of the all-optical current transformer is scored based on the total number of vectors to be identified with poor effectiveness and the corresponding variance average value.

5. A data validity identification device for an optical fiber current transformer based on a smart grid, characterized in that: The data validity identification device comprises: A state quantity setting module is used to determine the state quantity of the all-optical current transformer according to the factory parameters, characteristic parameters and power grid environment parameters of the all-optical current transformer; A transformer data acquisition module is used to obtain power grid environment parameters and historical state quantity data and historical transformation ratio data of the all-optical current transformer under different power grid environment parameters; A reference data set generation module is used to perform correlation analysis on historical state quantity data and historical transformation ratio data, and to obtain relationship functions between different state quantities and transformation ratios by fitting; the relationship function between the state quantity and the transformation ratio is used to screen out historical state quantity data and historical transformation ratio data corresponding to the all-optical current transformer whose deviation is greater than a preset deviation threshold, and the historical state quantity data and historical transformation ratio data are combined into a reference vector, and a reference data set is generated in combination with the acquisition time of each reference vector; The sample data generation module is used to generate a reference time range for each acquisition time, and obtain the power grid terminal variable data curve corresponding to each reference time range from the smart grid monitoring system; the power grid terminal variable data curve and the reference vector of each reference time range are used as a set of sample data; The model training module is used to train the neural network with the power grid terminal variable data curve as input and the reference vector as output to obtain the data identification model corresponding to different power grid terminal variables; The power grid data monitoring module is used to regularly monitor the power grid terminal variable data, generate corresponding data curves for the fluctuation points of each terminal variable, import the data curves into the corresponding data identification model, and output reference vectors; A vector generation module to be identified is used to combine the state quantity data and transformation ratio data of the all-optical current transformer at the same time into a vector to be identified; The validity identification module is used to compare the vector to be identified with the reference vector, judge the validity of the vector to be identified according to the comparison result, and then identify the data validity of the all-optical current transformer.

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