Typical environment secondary fusion sensor performance evaluation method and device

Abstract

The application relates to the technical field of sensor evaluation, and is a typical environment-primary-secondary fusion sensor performance evaluation method and device. The former comprises the following steps: performing correlation analysis on each environment variable and error characteristics, screening environment variable data, inputting the error characteristics and the screened environment variable data into a regression model to obtain regression model coefficients of each environment variable data and the error characteristics, and jointly analyzing original data scatter diagrams corresponding to the environment variable data and regression model results to determine the relationship between the environment variable and the error of the measured primary-secondary fusion sensor. The application utilizes correlation analysis and a regression model to establish the relationship between the environment variable and the error of the measured primary-secondary fusion sensor, obtains the environment variable which has an influence on the sensor error under a typical environment and the influence relationship, provides data support for stable operation of a power distribution network, and meanwhile, the application can be flexibly applied to offline analysis or real-time online analysis, thereby expanding the use range of the application.

Description

Technical Field

[0001] This invention relates to the field of sensor evaluation technology, specifically a method and apparatus for evaluating the performance of a typical environmental primary and secondary fusion sensor. Background Technology

[0002] The integration of primary and secondary distribution networks deeply integrates some functions of secondary equipment into primary equipment, which can improve the intelligence and standardization of equipment, realize the standardization of overall equipment, the independence of functional modules, and the flexibility of equipment interchangeability. This reduces the pressure of purchasing, assembling, and commissioning primary and secondary equipment, improves the quality and efficiency of power distribution equipment operation and maintenance, effectively improves the reliability of power supply in the distribution network, and promotes the intelligent, safe, and economical operation of the distribution network.

[0003] Currently, the systematic, standardized, and integrated development of primary and secondary fusion sensors for smart distribution networks is still in its early stages, and there is a lack of systematic research on the difficulties and problems brought about by these sensors. Furthermore, with the continuous expansion of power system transmission capacity and the widening of power grid distribution areas, the number of primary and secondary fusion sensors is constantly increasing, and the installation environment is becoming increasingly complex. Simultaneously, stable operation of these sensors is required. Therefore, it is necessary to assess the impact of typical environments on these sensors, identify the environmental variables that affect their errors under typical conditions, and understand their relationships, to contribute to the stable operation of the distribution network. However, existing technologies lack methods for evaluating primary and secondary fusion sensors under typical environments. Summary of the Invention

[0004] This invention provides a method and apparatus for evaluating the performance of primary and secondary fusion sensors in typical environments, overcoming the shortcomings of the prior art. It can effectively solve the problem that existing power distribution network evaluation methods cannot evaluate primary and secondary fusion sensors in typical environments.

[0005] One of the technical solutions of this invention is achieved through the following measures: a method for evaluating the performance of a typical environment-based primary and secondary fusion sensor, comprising:

[0006] Obtain environmental variable data and error characteristics of the tested primary and secondary fusion sensor, wherein the error characteristics of the tested primary and secondary fusion sensor include the ratio difference and angle difference between the data sampled by the standard primary and secondary fusion sensor and the data sampled by the test primary and secondary fusion sensor at a high sampling rate.

[0007] Correlation analysis was performed on each environmental variable and error characteristic to obtain the corresponding correlation coefficients. The correlation coefficients were then subjected to a significance test, and the environmental variable data corresponding to the correlation coefficients that passed the significance test were selected.

[0008] The error characteristics and the selected environmental variable data are input into the regression model to obtain the regression model coefficients of each environmental variable data and the error characteristics, and the weight of each environmental variable data is calculated.

[0009] By combining the scatter plot of the original data corresponding to the environmental variable data with the regression model results, the relationship between the environmental variables and the error of the primary and secondary fusion sensor under test can be determined.

[0010] The following are further optimizations and / or improvements to the above-mentioned technical solution:

[0011] The error characteristics of the primary and secondary fusion sensor obtained above include:

[0012] At a high sampling rate, data were acquired from both the standard primary and secondary fusion sensor and the test primary and secondary fusion sensor to obtain the corresponding sequences. Where {i=1,2,3,…,N}, and N is the number of data points;

[0013] Obtain the valid values ​​(rms) of the two sets of data. ref and RMS test And use the effectiveness value to obtain the corresponding ratio difference R;

[0014]

[0015]

[0016]

[0017] Where coief is the ratio coefficient;

[0018] Perform a Fast Fourier Transform on the two sets of data to obtain the corresponding phase sequences. And the corresponding angular difference is obtained using the phase sequence;

[0019]

[0020] Where df is the spectral resolution.

[0021] In obtaining the angular difference, the data length for the Fast Fourier Transform of the two sets of data is greater than 1 second, and the data sampling frequency is greater than 2560. Furthermore, T represents the data time length and is a multiple of 50.

[0022] The above-mentioned regression model coefficients of various environmental variables and error characteristics are used to calculate the weight of each environmental variable data, including:

[0023] Obtain the regression model coefficients for each environmental variable data and error characteristics, where the regression model coefficients include the weight coefficients α and bias β for each environmental variable data and error characteristics;

[0024] The weight coefficient α is used to obtain the weight ω of the corresponding environmental variable data using the following formula;

[0025]

[0026] Where L is the number of environmental variables that are correlated with the sensor's ratio difference and angle difference.

[0027] The environmental variables mentioned above include temperature, magnetic field, humidity, light intensity, air pressure, and PM2.5; and / or the regression model is a least mean square multiple regression model.

[0028] The second technical solution of the present invention is achieved through the following measures: a typical environment primary and secondary fusion sensor performance evaluation device, comprising:

[0029] The first processing unit obtains environmental variable data and error characteristics of the tested primary and secondary fusion sensor, wherein the error characteristics of the tested primary and secondary fusion sensor include the ratio difference and angle difference between the data sampled by the standard primary and secondary fusion sensor and the data sampled by the test primary and secondary fusion sensor at a high sampling rate.

[0030] The second processing unit performs correlation analysis on each environmental variable and error characteristic to obtain the corresponding correlation coefficient, performs a significance test on the correlation coefficient, and filters the environmental variable data corresponding to the correlation coefficients that pass the significance test.

[0031] The first evaluation unit inputs the error characteristics and the selected environmental variable data into the regression model to obtain the regression model coefficients of each environmental variable data and the error characteristics, and calculates the weight of each environmental variable data.

[0032] The second evaluation unit combines the scatter plots of the raw data corresponding to the environmental variables with the regression model results to determine the relationship between the environmental variables and the error characteristics of the primary and secondary fusion sensors being measured.

[0033] The third technical solution of the present invention is achieved through the following measures: a storage medium storing a computer program that can be read by a computer, the computer program being configured to execute instructions for the steps in the typical environment primary and secondary fusion sensor performance evaluation method when running.

[0034] The fourth technical solution of the present invention is achieved by the following measures: an electronic device, including a processor and a memory, wherein one or more programs are stored in the memory and configured to be executed by the processor, the programs including instructions for performing steps in a typical environment primary and secondary fusion sensor performance evaluation method.

[0035] This invention features a simple and low-complexity processing procedure. It uses correlation analysis to select environmental variable data and the error characteristics of the primary and secondary fusion sensors, identifying those with correlation and passing significance tests. A regression model is then used to determine the weights of each environmental variable. The relationship between environmental variables and the error characteristics of the primary and secondary fusion sensors is adaptively analyzed by combining scatter plots of the original environmental variable data with line graphs of the regression model results. This establishes the relationship between environmental variables and the errors of the primary and secondary fusion sensors, revealing the environmental variables that influence sensor errors under typical conditions and their impact relationships. This provides data support for the stable operation of power distribution networks. Furthermore, this invention is flexibly applicable to offline or real-time online analysis, expanding its scope of application. Attached Figure Description

[0036] Appendix Figure 1 This is a schematic diagram of the method flow of the present invention.

[0037] Appendix Figure 2 This is a schematic diagram of the method for obtaining error characteristics in this invention.

[0038] Appendix Figure 3 This is a schematic diagram of the device structure of the present invention. Detailed Implementation

[0039] The present invention is not limited to the following embodiments, and the specific implementation can be determined according to the technical solution of the present invention and the actual situation.

[0040] The present invention will be further described below with reference to embodiments and accompanying drawings:

[0041] Example 1: As shown in the attached document Figure 1 As shown in the figure, this invention discloses a method for evaluating the performance of a primary and secondary fusion sensor in a typical environment, including:

[0042] Step S101: Obtain environmental variable data and error characteristics of the tested primary and secondary fusion sensor, wherein the error characteristics of the tested primary and secondary fusion sensor include the ratio difference and angle difference between the data sampled by the standard primary and secondary fusion sensor and the data sampled by the test primary and secondary fusion sensor at a high sampling rate.

[0043] Step S102: Perform correlation analysis on each environmental variable and error characteristic to obtain the corresponding correlation coefficient, perform significance test on the correlation coefficient, and screen the environmental variable data corresponding to the correlation coefficient that passes the significance test.

[0044] Step S103: Input the error characteristics and the selected environmental variable data into the regression model to obtain the regression model coefficients of each environmental variable data and the error characteristics, and calculate the weight of each environmental variable data.

[0045] Step S104: Combine the scatter plot of the original data corresponding to the environmental variable data with the regression model results for analysis to determine the relationship between the environmental variables and the error of the primary and secondary fusion sensor being measured.

[0046] This invention discloses a method for evaluating the performance of primary and secondary fusion sensors under typical environmental conditions. The process is simple and low-complexity. Environmental variable data and the error characteristics of the tested primary and secondary fusion sensors are analyzed for correlation, and environmental variable data that are correlated and pass significance tests are selected. A regression model is used to obtain the weights of the environmental variable data. The relationship between environmental variables and the error characteristics of the tested primary and secondary fusion sensors is adaptively analyzed by jointly using scatter plots of the original environmental variable data and line plots of the regression model results. This establishes the relationship between environmental variables and the errors of the tested primary and secondary fusion sensors, identifying the environmental variables that influence sensor errors under typical conditions and their influence relationships. This provides data support for the stable operation of power distribution networks. Furthermore, this invention can be flexibly applied to offline analysis or real-time online analysis, expanding its scope of application.

[0047] Example 2: As shown in the attached document Figure 1 , 2 As shown in the figure, this invention discloses a method for evaluating the performance of a primary and secondary fusion sensor in a typical environment, including:

[0048] Step S201: Obtain environmental variable data and error characteristics of the tested primary and secondary fusion sensor. The error characteristics of the tested primary and secondary fusion sensor include the ratio difference and angle difference between the data sampled by the standard primary and secondary fusion sensor at high sampling rates and the data sampled by the test primary and secondary fusion sensor, as shown in the attached figure. Figure 2 As shown, it specifically includes:

[0049] Step S2011: Obtain environmental variable data; here, environmental variable data can be obtained through various environmental sensors, including temperature, magnetic field, humidity, light intensity, air pressure, and PM2.5; the number of data samples should reach more than 500, and should cover the range of changes in the monitored environmental variables in the sensor test area.

[0050] Step S2012: At a high sampling rate, data is acquired from both the standard primary and secondary fusion sensor and the test primary and secondary fusion sensor to obtain the corresponding sequences. Where {i=1,2,3,…,N}, and N is the number of data points. To ensure the acquisition of error characteristics, the data sampling frequency can be set to be greater than 2560.

[0051] Step S2013: Obtain the valid values ​​(rms) of the two sets of data. ref and RMS test And use the effectiveness value to obtain the corresponding ratio difference R;

[0052]

[0053]

[0054]

[0055] Where coief is the ratio coefficient;

[0056] Step S2014: Perform a Fast Fourier Transform on the two sets of data to obtain the corresponding phase sequences. And the corresponding angular difference is obtained using the phase sequence;

[0057]

[0058] Where df is the spectral resolution.

[0059] The data length for the Fast Fourier Transform of these two sets of data is greater than 1 second, and the data sampling frequency is greater than 2560. Furthermore, T represents the data time length and is a multiple of 50.

[0060] Step S202: Perform correlation analysis on each environmental variable and error characteristics to obtain the corresponding correlation coefficients, perform significance test on the correlation coefficients, and screen the environmental variable data corresponding to the correlation coefficients that pass the significance test.

[0061] Correlation analysis is used here to observe the degree of association between two variables, that is, to obtain the correlation coefficient between the two variables. The correlation coefficient indicates whether a correlation exists between the two variables and the strength of the correlation. The significance test of the correlation coefficient further illustrates whether there is a relationship between the two variables. Therefore, this invention filters the environmental variable data corresponding to the correlation coefficients that pass the significance test, that is, it filters out the environmental variable data that are determined to be related to the error of the primary and secondary fusion sensors. Here, the correlation interval can also be given, and further, based on the correlation interval, the environmental variable data that pass the significance test are used to filter out the environmental variable data with a strong correlation.

[0062] The following example, using the relationship between temperature and specific gravity, illustrates the process of obtaining the correlation coefficient:

[0063] The correlation coefficient between temperature and specific gravity is obtained by the following formula:

[0064]

[0065] Where M is the number of samples in different time periods. These are the mean values ​​of temperature and specific gravity data, respectively.

[0066] Step S203: Input the error characteristics and the selected environmental variable data into the regression model to obtain the regression model coefficients of each environmental variable data and the error characteristics, and calculate the weight of each environmental variable data.

[0067] The regression model described above can be a least mean square multiple regression model.

[0068] The above-mentioned regression model coefficients of various environmental variables and error characteristics are used to calculate the weight of each environmental variable data, including:

[0069] (1) Obtain the regression model coefficients of each environmental variable data and error characteristics, where the regression model coefficients include the weight coefficients α and bias β of each environmental variable data and error characteristics;

[0070] (2) The weight ω of the corresponding environmental variable data is obtained by using the following formula: the weight coefficient α is used to obtain the weight ω of the corresponding environmental variable data;

[0071]

[0072] Where L is the number of environmental variables that are correlated with the sensor's ratio difference and angle difference.

[0073] Step S204: Combine the scatter plot of the original data corresponding to the environmental variable data with the regression model results for analysis to determine the relationship between the environmental variables and the error of the primary and secondary fusion sensor being measured.

[0074] Here, the regression model results can be converted into a line graph. By comparing and analyzing the original data scatter plot corresponding to the environmental variable data with the regression model result line graph, the relationship between the environmental variables and the error of the primary and secondary fusion sensors can be found.

[0075] Example 3: As shown in the attached document Figure 3 As shown in the figure, an embodiment of the present invention discloses a typical environment primary and secondary fusion sensor performance evaluation device, comprising:

[0076] The first processing unit obtains environmental variable data and error characteristics of the tested primary and secondary fusion sensor, wherein the error characteristics of the tested primary and secondary fusion sensor include the ratio difference and angle difference between the data sampled by the standard primary and secondary fusion sensor and the data sampled by the test primary and secondary fusion sensor at a high sampling rate.

[0077] The second processing unit performs correlation analysis on each environmental variable and error characteristic to obtain the corresponding correlation coefficient, performs a significance test on the correlation coefficient, and filters the environmental variable data corresponding to the correlation coefficients that pass the significance test.

[0078] The first evaluation unit inputs the error characteristics and the selected environmental variable data into the regression model to obtain the regression model coefficients of each environmental variable data and the error characteristics, and calculates the weight of each environmental variable data.

[0079] The second evaluation unit combines the scatter plots of the raw data corresponding to the environmental variables with the regression model results to determine the relationship between the environmental variables and the error characteristics of the primary and secondary fusion sensors being measured.

[0080] Example 4: This embodiment of the invention discloses a storage medium storing a computer program that can be read by a computer. The computer program is configured to execute a method for identifying weak links in the power grid based on extreme ice storms when it runs.

[0081] The aforementioned storage media may include, but are not limited to, USB flash drives, read-only memory, portable hard drives, magnetic disks, optical disks, and other media capable of storing computer programs.

[0082] Example 5: This embodiment of the invention discloses an electronic device, including a processor and a memory, wherein one or more programs are stored in the memory and configured to be executed by the processor, the programs including instructions for performing steps in a typical environment primary and secondary fusion sensor performance evaluation method.

[0083] The aforementioned electronic device also includes transmission devices and input / output devices, wherein both the transmission devices and the input / output devices are connected to the processor.

[0084] The aforementioned processor can be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an ASIC, an FPGA, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. It can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0085] The aforementioned storage device can be a storage device, including but not limited to: USB flash drive, read-only memory, portable hard drive, magnetic disk or optical disk, and other media that can store computer programs.

[0086] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may include an electronic device.

[0087] The above technical features constitute the preferred embodiment of the present invention, which has strong adaptability and optimal implementation effect. Unnecessary technical features can be added or removed according to actual needs to meet the requirements of different situations.

Claims

1. A method for evaluating the performance of a typical environmental primary and secondary fusion sensor, characterized in that, include: Obtain environmental variable data and error characteristics of the tested primary and secondary fusion sensor, wherein the error characteristics of the tested primary and secondary fusion sensor include the ratio difference and angle difference between the data sampled by the standard primary and secondary fusion sensor and the data sampled by the test primary and secondary fusion sensor at a high sampling rate. Correlation analysis was performed on each environmental variable and error characteristic to obtain the corresponding correlation coefficients. The correlation coefficients were then tested for significance, and the environmental variable data corresponding to the correlation coefficients that passed the significance test were selected. The error characteristics and the selected environmental variable data are input into the regression model to obtain the regression model coefficients of each environmental variable data and the error characteristics. The weights of each environmental variable data are then calculated, including: The error characteristics and the selected environmental variable data are input into the regression model to obtain the regression model coefficients of each environmental variable data and error characteristics. The regression model coefficients include the weight coefficients α and bias β of each environmental variable data and error characteristics. The weight coefficient α is used to obtain the weight ω of the corresponding environmental variable data using the following formula; Where L is the number of environmental variables that are correlated with the sensor's ratio difference and angle difference; By combining the scatter plot of the original data corresponding to the environmental variable data with the regression model results, the relationship between the environmental variables and the error of the primary and secondary fusion sensor under test can be determined.

2. The method for evaluating the performance of a primary and secondary fusion sensor in a typical environment according to claim 1, characterized in that, The method of obtaining the error characteristics of the primary and secondary fusion sensor under test includes: At a high sampling rate, data were acquired from both the standard primary and secondary fusion sensor and the test primary and secondary fusion sensor to obtain the corresponding sequences. Where {i = 1, 2, 3, ..., N}, and N is the number of data points; Obtain the valid values ​​(rms) of the two sets of data. ref and RMS test And use the effectiveness value to obtain the corresponding ratio difference R; Where coief is the ratio coefficient; Perform a Fast Fourier Transform on the two sets of data to obtain the corresponding phase sequences. And the corresponding angular difference is obtained using the phase sequence; Where df is the spectral resolution.

3. The method for evaluating the performance of a primary and secondary fusion sensor in a typical environment according to claim 2, characterized in that, The two sets of data are then subjected to a Fast Fourier Transform to obtain the corresponding phase sequence. The corresponding angular difference is obtained using the phase sequence. The data length of the two sets of data subjected to fast Fourier transform is greater than 1 second, and the data sampling frequency is greater than 2560. It is also a multiple of 50, where T is the data time length.

4. The typical environment primary and secondary fusion sensor performance evaluation method according to claim 1, 2, or 3, characterized in that, The environmental variables include temperature, magnetic field, humidity, light intensity, air pressure, PM2.5; and / or the regression model is a least mean square multiple regression model.

5. A typical environment primary and secondary fusion sensor performance evaluation device, wherein the typical environment primary and secondary fusion sensor performance evaluation device uses the typical environment primary and secondary fusion sensor performance evaluation method as described in any one of claims 1 to 4, characterized in that, include: The first processing unit obtains environmental variable data and error characteristics of the tested primary and secondary fusion sensor, wherein the error characteristics of the tested primary and secondary fusion sensor include the ratio difference and angle difference between the data sampled by the standard primary and secondary fusion sensor and the data sampled by the test primary and secondary fusion sensor at a high sampling rate. The second processing unit performs correlation analysis on each environmental variable and error characteristic to obtain the corresponding correlation coefficient, performs a significance test on the correlation coefficient, and filters the environmental variable data corresponding to the correlation coefficients that pass the significance test. The first evaluation unit inputs the error characteristics and the selected environmental variable data into the regression model to obtain the regression model coefficients of each environmental variable data and the error characteristics, and calculates the weight of each environmental variable data, including: The error characteristics and the selected environmental variable data are input into the regression model to obtain the regression model coefficients of each environmental variable data and error characteristics. The regression model coefficients include the weight coefficients α and bias β of each environmental variable data and error characteristics. The weight coefficient α is used to obtain the weight ω of the corresponding environmental variable data using the following formula; Where L is the number of environmental variables that are correlated with the sensor's ratio difference and angle difference; The second evaluation unit combines the scatter plots of the raw data corresponding to the environmental variables with the regression model results to determine the relationship between the environmental variables and the error characteristics of the primary and secondary fusion sensors being measured.

6. A storage medium, characterized in that, The storage medium stores a computer program that can be read by a computer, and the computer program is configured to execute instructions for the steps in the typical environment primary and secondary fusion sensor performance evaluation method as described in any one of claims 1 to 4 when it is run.

7. An electronic device, characterized in that, It includes a processor and a memory, in which one or more programs are stored and configured to be executed by the processor, the programs including instructions for performing steps in the typical environment-secondary fusion sensor performance evaluation method as described in any one of claims 1 to 4.

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