A substation secondary device adapted to climate and environmental changes and a method of using the same

Abstract

The present invention discloses a substation secondary device that adapts to climate and environmental changes and a method for using the same, including calculating the accuracy deviation values ​​between the current sampled voltage data and the standard voltage input value, and the sampled current data and the standard current input value according to the set standard voltage input value, standard current input value, sampling plug-in number, climate type and climate change threshold in the self-learning stage, obtaining multiple accuracy deviation curves through cyclic testing, and generating accuracy deviation files for each sampling plug-in under different climate environments according to the fitted accuracy deviation curves, sampling plug-in number and climate type. In the self-correction stage, the sampled voltage and current data are compensated and corrected by querying the accuracy deviation file according to the set sampling plug-in number and climate type. The present invention can make up for the problem of insufficient consistency of sampling deviation accuracy of current substation secondary equipment in harsh and changeable sampling environments at a relatively low cost, thereby improving equipment reliability.

Description

Technical Field

[0001] The invention relates to a transformer substation secondary device adapting to climate and environmental changes and a use method thereof, belonging to the technical field of power system secondary devices. Background Art

[0002] As the national economy develops, the demand for stable power supply is getting higher and higher, and the reliability requirements for secondary equipment in substations are getting higher and higher. The secondary equipment involved in the operation status collection of primary equipment in current substations mainly includes merging units and measurement and control devices, which mainly collect the voltage and current signals transmitted by the primary transformer. my country has a wide territory, and substations may operate in various climates such as high temperature, low temperature, high humidity, and high altitude. In particular, merging units are operated in outdoor cabinets in some substations, and the operating environment is relatively harsh. Climate changes, the selection of secondary transformers and sampling chips often have an impact on sampling accuracy, especially the gap between the currently advocated self-sampling chips and imported chips, and the sampling consistency needs to be improved. Summary of the invention

[0003] The present invention aims to provide a substation secondary device and a method of use that can adapt to climate changes, so as to solve the problem of insufficient sampling consistency of the secondary transformer and sampling chip in the substation secondary device when the climate changes. The present invention adopts the following technical solutions.

[0004] A substation secondary device adapted to climate and environmental changes, comprising: a human-machine interface module, a sampling plug-in and a function plug-in;

[0005] The human-machine interface module is used to obtain the set standard voltage input value, standard current input value, sampling plug-in number, climate type and climate change threshold;

[0006] The sampling plug-in is used to collect voltage and current data, as well as environmental climate data of a specific climate type;

[0007] The functional plug-in includes a CPU, in which a deviation self-learning module and a deviation self-correction module are arranged;

[0008] The deviation self-learning module is used to calculate the accuracy deviation values ​​between the sampled voltage data and the standard voltage input value, and the sampled current data and the standard current input value respectively according to the environmental climate data of the specific climate type, the set climate change threshold, the standard voltage input value, and the standard current input value, and finally obtain the accuracy deviation curve, and generate the accuracy deviation file corresponding to the sampling plug-in according to the accuracy deviation curve, the sampling plug-in number and the climate type;

[0009] The deviation self-correction module is used to query the corresponding accuracy deviation file according to the sampling plug-in number set by the human-machine interface module and the climate type involved in the sampling correction, and compensate and correct the sampled voltage and current data.

[0010] Furthermore, the sampling plug-in is provided with a climate sensor, a voltage transformer, a current transformer and a sampling chip;

[0011] The climate sensor is used to collect environmental climate data of a specific climate type and transmit the data to the functional plug-in;

[0012] The voltage transformer and the current transformer are respectively connected to the voltage and current analog quantities outside the device, and transmit the converted analog quantity small signals to the sampling chip;

[0013] The sampling chip is used to perform analog-to-digital conversion on the analog small signals transformed by the voltage transformer and the current transformer, and send the discrete values ​​of the voltage and current data obtained after the conversion to the functional plug-in so that the functional plug-in calculates the voltage and current values ​​of the external access.

[0014] Furthermore, the human-machine interface module is also used to start the deviation self-learning module and trigger the deviation self-learning module to fit multiple precision deviation curves, and generate a final precision deviation file according to the fitted precision deviation curves.

[0015] Furthermore, the climate sensor includes at least one of a temperature sensor, a humidity sensor and an air pressure sensor.

[0016] Furthermore, the climate sensor adopts a surface-mount climate sensor chip, and the climate sensor chip communicates with the CPU of the functional plug-in through an I2C bus.

[0017] Furthermore, the calculation method of the accuracy deviation is as follows:

[0018]

[0019] Where Δf is the deviation value, val1 is the set standard value, and val2 is the sample value calculated by the current device.

[0020] Furthermore, the expression for sampling compensation correction using the precision deviation value is as follows:

[0021] val=val2*Δf

[0022] Where val is the sampled value after compensation correction, val2 is the sampled value calculated by the current device, and Δf is the deviation value (%).

[0023] Furthermore, the accuracy deviation file also includes a climate change threshold. When the deviation self-learning module senses that the changes in the collected environmental climate data exceed the set climate change threshold, the deviation self-correction module queries the accuracy curve file to compensate and correct the sampled voltage and current data.

[0024] In a second aspect, the present invention provides a method for using a substation secondary device that is adaptable to climate and environmental changes. The substation secondary device that is adaptable to climate and environmental changes adopts a substation secondary device that is adaptable to climate and environmental changes provided by any possible implementation of the above technical solution, and the method for using the substation secondary device comprises: setting a plurality of sampling plug-ins and numbering them respectively;

[0025] The selected sampling plug-in is built into the secondary equipment of the substation;

[0026] Use the climate test box to provide different external climate environments for the deviation self-learning module in the secondary equipment of the substation. Each time, select a test climate type and set the change of the climate to a linear value that changes with time; put the equipment into the climate test box and start it with power on;

[0027] Starting the deviation self-learning module of the substation secondary equipment through the human-machine interface module;

[0028] Set the standard voltage input value, standard current input value, sampling plug-in number, climate type and climate change threshold through the human-machine interface module;

[0029] During the test cycle, when the deviation self-learning module senses that the changes in the collected environmental climate data exceed the set climate change threshold, it calculates the accuracy deviation between the sampled voltage data and the standard voltage input value, and between the sampled current data and the standard current input value;

[0030] When a climate test cycle is completed, a test completion instruction is issued through the human-machine interface module, and the accuracy deviation curve in the entire test cycle is obtained, so that the deviation self-learning module obtains a temporary accuracy deviation file corresponding to the sampling plug-in and the climate type according to the accuracy deviation curve, the sampling plug-in number and the climate type;

[0031] By repeating the above-mentioned periodic test for multiple times, multiple temporary accuracy deviation files are obtained, and the accuracy deviation curves in the multiple temporary accuracy deviation files are fitted by triggering the deviation self-learning module through the human-machine interface, and a final accuracy deviation file for the current sampling module and climate type is generated;

[0032] Replace the selected sampling plug-in and climate environment in the substation secondary equipment and repeat all the above processes to obtain various accuracy deviation files for different sampling plug-ins and different climate types.

[0033] Download various types of accuracy deviation files in the substation secondary equipment; after the equipment is powered on, set the sampling plug-in number and climate type used by the device through the human-machine interface, so that the deviation self-correction module selects the sampling plug-in and the accuracy deviation file corresponding to the climate type involved to correct the sampled voltage and current data.

[0034] The beneficial technical effects achieved by the present invention are as follows: the present invention can compensate for the problem of insufficient consistency of sampling deviation accuracy of current substation secondary equipment under harsh and changeable sampling environments at a relatively low cost, thereby improving equipment reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Schematic diagram of the system architecture solution of deviation self-learning in an embodiment of the present invention;

[0036] Figure 2 Schematic diagram of a system architecture solution for self-correction of deviations in an embodiment of the present invention;

[0037] Figure 3 is a flow chart of sampling deviation self-learning in an embodiment of the present invention;

[0038] Figure 4 is a flow chart of sampling deviation self-correction in an embodiment of the present invention;

[0039] Figure 5 It is a schematic diagram of the “precision deviation” curve in an embodiment of the present invention.

[0040] Figure 6 This is an example diagram of the content of the "precision deviation" file in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. In addition, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0042] Example: Figure 1 , 2 As shown, a substation secondary device adapting to climate and environmental changes includes a human-machine interface module, a sampling plug-in, and a function plug-in;

[0043] The human-machine interface module is used to set the standard voltage input value, the standard current input value, the sampling plug-in number, the climate type and the climate change threshold;

[0044] The sampling plug-in is used to collect voltage and current data, as well as environmental climate data of a specific climate type;

[0045] The functional plug-in includes a CPU, in which a deviation self-learning module and a deviation self-correction module are arranged;

[0046] The deviation self-learning module is used to calculate the accuracy deviation values ​​between the sampled voltage data and the standard voltage input value, and the sampled current data and the standard current input value respectively according to the environmental climate data of the specific climate type, the set climate change threshold, the standard voltage input value, and the standard current input value, and finally obtain the accuracy deviation curve, and generate the accuracy deviation file corresponding to each sampling plug-in according to the fitting of each accuracy deviation curve, the sampling plug-in number and the climate type;

[0047] The deviation self-correction module is used to query the corresponding accuracy deviation file according to the sampling plug-in number set by the human-machine interface module and the climate type required to be involved in the sampling correction, and correct the sampled voltage and current data.

[0048] In the specific embodiment, the human-machine interface module is usually a device LCD and interface, which also supports remote PC connection and can be operated through the simulated human-machine interface on the PC. It can trigger the start and stop of the deviation self-learning module and has the functions of uploading and downloading the "accuracy deviation" curve file.

[0049] Optionally, a climate sensor, a voltage transformer, a current transformer and a sampling chip are provided on the sampling plug-in. The high-precision climate sensor is used to sense the climate in the current device, such as temperature, humidity, air pressure, etc., and is arranged near the sampling chip and the transformer, which can more truly reflect the environmental climate of the sampling plug-in.

[0050] The voltage / current transformer is used to convert the voltage / current analog quantity input from the outside of the device into a small voltage signal that can be collected by the sampling chip. The sampling chip is used to perform analog-to-digital conversion on the small voltage signal and send it to the functional plug-in.

[0051] Various climate sensors communicate with the functional plug-in through the bus and transmit various climate values ​​to the functional plug-in. The sampling chip is responsible for converting the analog voltage small signal transmitted by the voltage / current transformer into digital form and sending the converted discrete value to the functional plug-in through the inter-board bus.

[0052] The deviation self-learning module and the deviation self-correction module run in the CPU of the functional plug-in in the form of a program. The deviation self-learning module obtains the effective value of the externally applied voltage / current by performing a series of calculations on the discrete sampling points transmitted by the sampling plug-in. At the same time, it can also obtain the standard voltage / current input value set by the human-machine interface module, the sampling plug-in number, the climate change threshold, the climate type, and the climate value input by the high-precision climate sensor.

[0053] During the entire deviation self-learning process, first select the sampling plug-in selected for this device, and input a standard voltage / current analog quantity into the acquisition plug-in through the analog quantity tester. Then input the standard voltage / current input value, sampling plug-in number, climate change threshold, and climate type into the deviation self-learning module through the human-machine interface module. Finally, start the climate experiment box for climate testing. When the self-learning module senses that the change in the external climate exceeds the climate change threshold, it performs a "precision deviation" calculation and generates a "precision deviation" coordinate point together with the current climate value. When the external environment climate completes a cycle test, the test completion instruction is issued through the human-machine interface module, and the deviation self-learning module draws the above series of "precision deviation" coordinate points into a "precision deviation" curve. The next step is to store the curve, the climate change threshold set by the human-machine interface module, the sampling plug-in number, and the climate type in the form of a file, that is, to complete a deviation self-learning and obtain a "precision deviation" temporary file. Repeat the above steps to obtain multiple "precision deviation" temporary files. The deviation self-learning module can be triggered through the human-machine interface to fit the precision deviation curves in these files again to obtain the final "precision deviation" curve and store it in the memory as a file. The file name includes the sampling plug-in number and climate type. The file also includes the climate change threshold, sampling plug-in number, and climate type information. Finally, delete other temporary files.

[0054] Replace the sampling plug-in in the test equipment and repeat all the above steps to obtain the "accuracy deviation" file of various sampling plug-ins with different hardware selections under this climate type.

[0055] Change the climate type and repeat all the above steps again to complete the self-learning of the "accuracy deviation" curve for different climates and different sampling plug-ins. Finally, a series of "accuracy deviation" files for different hardware and climate types will be obtained. These files can then be downloaded to the entire series of secondary devices as the basis for sampling deviation correction.

[0056] The deviation self-correction module reads the "accuracy deviation" file pre-downloaded by the human-machine interface module after the device is powered on. If the file does not exist, the module will not run. At the same time, the human-machine interface also sets the currently configured sampling plug-in number and climate sensor type for the deviation self-correction module. The deviation self-correction module can read the deviation curve in the corresponding "accuracy deviation" file according to the sampling plug-in number and climate type as the basis for sampling accuracy correction. The current climate is obtained through a high-precision climate sensor, and the sampling value deviation corresponding to the current climate type is obtained from the "accuracy deviation" curve by query. The deviation value is compensated to the current sampling value to complete the sampling compensation. Only when the change in climate value exceeds the climate change threshold will an "accuracy deviation" query be triggered. The query method uses the climate value approximation query method to find a climate value in the accuracy deviation compensation curve that is closest to the current device climate, and then take the corresponding "accuracy deviation" curve. The accuracy deviation value is used as the compensation value.

[0057] The climate sensor arranged in the sampling plug-in is customizable. It can be just one temperature sensor, or multiple temperature, humidity, pressure sensors, etc. The type data of these sensors can be added in the human-machine interface and set as environmental parameters in the precision self-correction module. The precision self-correction module can also parse one or more "precision deviation" files at the same time according to the environmental parameter settings, and make multiple corrections to the sampling values ​​according to the "precision deviation" curves therein. For example, the precision deviation values ​​can be determined separately based on the precision deviation files under different climate types, and the total precision deviation value can be determined based on the various precision deviation values, and the sampling values ​​can be corrected.

[0058] In the specific embodiment, the high-precision climate sensor uses a type of surface mount climate sensor chip, which is directly welded on the PCB and arranged close to the sampling chip and the voltage / current transformer. The chip communicates with the deviation self-learning module and the deviation self-correction module of the functional plug-in through the I2C bus to transmit the climate value.

[0059] The substation secondary equipment adapted to climate and environmental changes provided in this embodiment calculates the accuracy deviation values ​​between the current sampled voltage data and the standard voltage input value, the sampled current data and the standard current input value according to the set standard voltage input value, standard current input value, sampling plug-in number, climate type and climate change threshold in the self-learning stage, and finally obtains the accuracy deviation curve, and generates the accuracy deviation file of each sampling plug-in under different climate environments according to the accuracy deviation curve, sampling plug-in number and climate type. In the self-correction stage, the sampled voltage and current data are compensated and corrected by querying the accuracy deviation file according to the set sampling plug-in number and climate type. The self-learning and self-correction modules in the present invention are both implemented in the embedded system of the secondary equipment in the form of software, which can make up for the problem of insufficient consistency of sampling deviation accuracy of the current substation secondary equipment in the case of harsh and changeable sampling environment at a relatively low cost, thereby improving the reliability of the equipment.

[0060] like Figure 3 As shown, the above embodiment provides a method for using a substation secondary device that adapts to climate changes (including specific implementation steps of a deviation self-learning module), which includes the following:

[0061] 1) Prepare multiple sampling plug-ins with different mutual inductors and sampling chips, and number different types of plug-ins to provide different external hardware environments for the deviation self-learning module.

[0062] 2) Prepare a climate test box to provide different external climate environments for the deviation self-learning module.

[0063] 3) The deviation self-learning module runs in the functional plug-in as a program. The deviation self-learning module is started by receiving instructions from the human-machine interface module. When the human-machine interface module sends the start-up instruction, it also sets the standard value of the current external input voltage / current, climate change threshold, sampling plug-in number, and climate type to the deviation self-learning module. After that, the deviation self-learning module starts to obtain the climate value provided by the high-precision climate sensor and the sampled discrete value provided by the sampling plug-in in real time.

[0064] 4) Each time a test climate type is selected and the change of the climate is set as a linear value that changes over time;

[0065] Insert the sampling plug-in with the selected number n into the substation secondary equipment to be tested in advance, put the equipment into the climate test box and power it on, use an external voltage / current tester to input the standard value into the sampling plug-in, set the sampling environment parameters through the human-machine interface, start the deviation self-learning module, and then start the climate test box to start working.

[0066] 5) As the external climate changes, when the deviation self-learning module senses that the climate change exceeds the set climate change threshold, it calculates the "precision deviation" by comparing the current sampling calculation value with the standard value set by the human-machine interface module to obtain the sampling precision error value, and then records the current climate and precision deviation value accordingly to obtain a "precision deviation" coordinate point, with the horizontal axis being the precision deviation value and the vertical axis being the climate value. As the external climate changes, a series of "precision deviation" coordinate points can be obtained.

[0067] 6) When the external environment climate completes a cycle test, the human-machine interface module sends a test completion instruction, and the deviation self-learning module draws the above series of "precision deviation" coordinate points into a "precision deviation" curve. The next step is to store the curve, the climate change threshold set by the human-machine interface module, the sampling plug-in number, and the climate type in the form of a file, that is, to complete a deviation self-learning and obtain a "precision deviation" temporary file. Figure 5 As shown, this is a "precision deviation" curve with temperature as the climate type, the ordinate is temperature, and the abscissa is the precision deviation value. In the present invention, the calculation formula of the precision deviation value is:

[0068]

[0069] Δf: Deviation value (%)

[0070] val1: External analog input standard value set by the human-machine interface

[0071] val2: the sampling value calculated by the current device

[0072] 7) Repeat steps (4-6) to obtain multiple "precision deviation" temporary files. The deviation self-learning module can be triggered through the human-machine interface to fit the precision deviation curves in these files again, thereby obtaining the final "precision deviation" curve and storing it in the memory as a file. Finally, delete other temporary files. Figure 6 As shown, the file name includes the climate type and sampling plug-in number. The file also includes climate change threshold, sampling plug-in number, and climate type information. The file name example is as follows:

[0073] Precision deviation file #x#y

[0074] x: represents the climate type number, for example, 1 represents temperature.

[0075] y: represents the sampling plug-in number. For example, 1 means that the sampling plug-in is equipped with a transformer from manufacturer A and a sampling chip from manufacturer B.

[0076] 8) Replace the sampling plug-in and repeat steps 4-7 to obtain the "accuracy deviation" file for sampling plug-ins with different hardware selections under this climate type.

[0077] Furthermore, the deviation self-learning process described in the above steps 4-8 is for one climate factor. By setting up multiple climate sensors, repeating steps 4-8 to complete the self-learning of the "accuracy deviation" curve for different climates and various sampling plug-ins, and finally obtaining a series of "accuracy deviation" files for different sampling environments, these files can then be downloaded to the full range of secondary devices as the basis for sampling deviation correction.

[0078] like Figure 4 As shown, the sampling accuracy self-correction method performed by the deviation self-correction module includes the following steps:

[0079] 1) Download the "accuracy deviation" files under various sampling environments generated by deviation self-learning to various types of secondary devices. After the equipment is powered on, the sampling plug-in number and climate type and other sampling environment parameters used by the device are set through the human-machine interface. The deviation self-correction module can read the corresponding "accuracy deviation" file as the basis for sampling accuracy correction according to the sampling plug-in number and climate type.

[0080] 2) Bias self-correction module analysis step 1) Select the "precision deviation" file suitable for itself, obtain the "precision deviation" curve and climate change threshold, obtain the current climate through the high-precision climate sensor, obtain the sampling value deviation corresponding to the current climate from the "precision deviation" curve by query, and compensate the deviation value to the current sampling value to complete the sampling compensation.

[0081] 3) Optionally, only when the change of climate value exceeds the climate change threshold, a "precision deviation" query is triggered. The query method adopts the climate value approximation query method to find the climate value closest to the current equipment climate in the precision deviation compensation curve, and then take the corresponding precision deviation value on the "precision deviation" curve as the compensation value. The sampling compensation formula is:

[0082] val=val2*Δf

[0083] Δf: Sampling deviation value under the influence of current climate (%)

[0084] val2: the sampling value calculated by the current device

[0085] val: sample value after compensation

[0086] Furthermore, the climate sensor arranged in the sampling plug-in is customizable and can be just one temperature sensor or multiple temperature, humidity, air pressure sensors, and so on. Figure 3The steps described in the above can obtain the "precision deviation" files for different sampling plug-ins under all climate types. The precision self-correction module can parse one or more "precision deviation" files at the same time according to the environmental parameters set by the human-machine interface, and perform multiple climate impact corrections on the sampling values ​​according to the "precision deviation" curves therein.

[0087] For example, the sampling plug-in (numbered 1) of the current device is designed with a temperature sensor and a humidity sensor, and the climate types of the two are 1 and 2 respectively. Then the accuracy self-correction module will parse two files: "Accuracy Deviation File #1#1" and "Accuracy Deviation File #2#1" to obtain the temperature "Accuracy Deviation" curve and the humidity "Accuracy Deviation" curve. The compensation formula is:

[0088] val=val2*Δf1*Δf2

[0089] Δf1: Sampling deviation value under the influence of current temperature (%)

[0090] Δf2: Sampling deviation value under the influence of current humidity (%)

[0091] val2: the sampling value calculated by the current device

[0092] val: The sample value after compensation.

[0093] The external environmental factors that may affect the analog sampling accuracy of secondary equipment in the substation include mutual inductors, sampling chips, climate, etc. Different types of secondary equipment will use different mutual inductors and sampling chips, and the operating environment on site is also different. The sampling accuracy of secondary equipment will show different deviation characteristics with different sampling environments. Therefore, it is necessary to allow the equipment to self-learn the "accuracy deviation" curve under various sampling environments through certain environmental change cycle tests. The present invention can obtain a series of "accuracy deviation" files for different sampling environments, and then these files can be downloaded to the entire series of secondary equipment as the basis for sampling deviation correction to compensate for the collected data. The secondary equipment provided by the present invention can adapt to different climatic environments.

[0094] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0095] 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.

[0096] 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.

[0097] 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. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0098] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A substation secondary equipment adapted to climate and environmental changes, characterized in that: include: Human-machine interface modules, sampling plug-ins, and functional plug-ins; The human-machine interface module is used to set the current standard voltage input value, standard current input value, sampling plug-in number, climate type and climate change threshold; The sampling plug-in is used to collect voltage and current data, as well as environmental climate data of a specific climate type; The functional plug-in includes a CPU, in which a deviation self-learning module and a deviation self-correction module are arranged; The deviation self-learning module is used to calculate the accuracy deviation values ​​between the sampled voltage data and the standard voltage input value, and the sampled current data and the standard current input value respectively according to the environmental climate data of the specific climate type, the set climate change threshold, the standard voltage input value, and the standard current input value, and finally obtain multiple accuracy deviation curves, and generate the accuracy deviation file corresponding to each sampling plug-in according to the fitting of each accuracy deviation curve, the sampling plug-in number and the climate type; Including: during the test cycle, when the deviation self-learning module senses that the changes in the collected environmental climate data exceed the set climate change threshold, it calculates the accuracy deviation between the sampled voltage data and the standard voltage input value, and the sampled current data and the standard current input value; When a climate test cycle is completed, a test completion instruction is issued through the human-machine interface module, and the accuracy deviation curve in the entire test cycle is obtained, so that the deviation self-learning module obtains a temporary accuracy deviation file corresponding to the sampling plug-in and the climate type according to the accuracy deviation curve, the sampling plug-in number and the climate type; By repeating the above-mentioned periodic test for multiple times, multiple temporary accuracy deviation files are obtained, and the accuracy deviation curves in the multiple temporary accuracy deviation files are fitted by triggering the deviation self-learning module through the human-machine interface, and a final accuracy deviation file for the current sampling module and climate type is generated; The deviation self-correction module is used to query the corresponding accuracy deviation file according to the sampling plug-in number set by the human-machine interface module and the climate type involved in the sampling correction, and compensate and correct the sampled voltage and current data.

2. A substation secondary equipment adapted to climate change according to claim 1, characterized in that: The sampling plug-in is provided with a climate sensor, a voltage transformer, a current transformer and a sampling chip; The climate sensor is used to collect environmental climate data of a specific climate type and transmit the data to the functional plug-in; The voltage transformer and the current transformer are respectively connected to the voltage and current analog quantities outside the device, and transmit the converted analog quantity small signals to the sampling chip; The sampling chip is used to perform analog-to-digital conversion on the analog small signals transformed by the voltage transformer and the current transformer, and send the discrete values ​​of the voltage and current data obtained after the conversion to the functional plug-in so that the functional plug-in calculates the voltage and current values ​​of the external access.

3. The substation secondary equipment adapted to climate change according to claim 1 is characterized in that: The human-machine interface module is also used to start the deviation self-learning module and trigger the deviation self-learning module to fit multiple precision deviation curves, and generate a final precision deviation file according to the fitted precision deviation curves.

4. The substation secondary equipment adapted to climate change according to claim 2 is characterized in that: The climate sensor includes at least one of a temperature sensor, a humidity sensor, and an air pressure sensor.

5. The substation secondary equipment adapted to climate change according to claim 4 is characterized in that: The climate sensor adopts a patch type climate sensor chip. The climate sensor chip communicates with the CPU via the I2C bus.

6. The substation secondary equipment adapted to climate change according to claim 1, characterized in that: The calculation method of the accuracy deviation value is as follows: ， in is the deviation value, val 1 is the set standard value, val2 The sample value calculated for the current device.

7. The substation secondary equipment adapted to climate change according to claim 1, characterized in that: The expression for sampling compensation correction using the precision deviation value is as follows: , in val To compensate for the corrected sampling value, val2 The sample value calculated for the current device, is the deviation value (%).

8. A method for using secondary equipment of a substation adapted to climate and environmental changes, characterized in that: The substation secondary equipment adapting to climate and environmental changes adopts the substation secondary equipment adapting to climate and environmental changes as claimed in any one of claims 1 to 7, and the method of using the substation secondary equipment adapting to climate and environmental changes comprises: setting a plurality of sampling plug-ins and numbering them respectively; The selected sampling plug-in is built into the secondary equipment of the substation; Use the climate test box to provide different external climate environments for the deviation self-learning module in the secondary equipment of the substation. Each time, select a test climate type and set the change of the climate to a linear value that changes with time; put the equipment into the climate test box and start it with power on; Set the standard voltage input value, standard current input value, sampling plug-in number, climate type and climate change threshold through the human-machine interface module; Starting the deviation self-learning module of the substation secondary equipment through the human-machine interface module, so that the deviation self-learning module calculates the accuracy deviation between the sampled voltage data and the standard voltage input value, and the sampled current data and the standard current input value during the test cycle; When a climate test cycle is completed, a test completion instruction is issued through the human-machine interface module, and an accuracy deviation curve is obtained, so that the deviation self-learning module generates a corresponding temporary accuracy deviation file according to the accuracy deviation curve, the sampling plug-in number and the climate type; By repeating the above-mentioned periodic test for multiple times, multiple temporary accuracy deviation files are obtained, and the accuracy deviation curves in the multiple temporary accuracy deviation files are fitted by triggering the deviation self-learning module through the human-machine interface, and a final accuracy deviation file for the current sampling module and climate type is generated; Replace the selected sampling plug-in and climate environment in the substation secondary equipment, repeat all the above processes, and you will get various accuracy deviation files for different sampling plug-ins and different climate types; Download various types of accuracy deviation files in the substation secondary equipment; after the equipment is powered on, set the sampling plug-in number and climate type used by the device through the human-machine interface, so that the deviation self-correction module selects the sampling plug-in and the accuracy deviation file corresponding to the climate type involved to correct the sampled voltage and current data.

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