Method and device for software to eliminate the influence of electromagnetic interference on the acquisition accuracy of sensors

Abstract

The present invention discloses a method for software to eliminate the influence of electromagnetic interference on the acquisition accuracy of sensors: real-time collect the voltage of a voltage sensor and the current of a current sensor, and store the sampling data of N cycles; locate the 3N sampling data with the largest values and the 3N sampling data with the smallest values; calculate the derivative of the updated sampling data of N cycles, identify the data across quadrants I to IV in the sampling data of N cycles, and replace the data across quadrants with the average value of the data on both sides adjacent to the positioning point or the quadratic fitting value; identify whether the sampling data is abnormal data within quadrants I to IV, and replace the abnormal data within the quadrants with the average value of the data on both sides adjacent to the positioning point or the quadratic fitting value; use the Fourier algorithm or the root mean square algorithm to update the effective value by calculating the sampling data of N cycles after replacement, and calculate the average value of the effective value using the optimized average value algorithm. The present invention also provides a corresponding device for software to eliminate the influence of electromagnetic interference on the acquisition accuracy of sensors.

Description

Technical Field

[0001] The present invention belongs to the technical field of relay protection in power systems, and more specifically, relates to a method and device for software to eliminate the influence of electromagnetic interference on the acquisition accuracy of sensors. Background Art

[0002] In 2021, the State Grid Corporation of China issued the technical specifications for the solidification of standardized distribution network materials, namely "Secondary Integrated Standardized Pole-mounted Circuit Breaker, AC10kV, 630A, 20kA", "Primary-secondary Integrated Standardized Ring Main Unit, AC10kV, 630A, Standardized, Centralized DTU, Incoming and Outgoing Circuit Breakers", and "Primary-secondary Integrated Standardized Ring Main Unit, AC10kV, 630A, Standardized, Decentralized DTU, Incoming and Outgoing Circuit Breakers".

[0003] The specification requires that the nominal value of the phase voltage sensor of the feeder terminal (Feeder Terminal Unit) is The nominal value of the zero-sequence voltage sensor is 6.5 / 3V, the nominal value of the phase current sensor is 1V, the nominal value of the zero-sequence voltage sensor is 0.2V, the phase voltage measurement accuracy: ≤0.5% (0.5 level), the zero-sequence voltage measurement accuracy: ≤0.5% (0.5 level), the phase current measurement accuracy: 0.5 level (≤1.2In), and the zero-sequence current measurement accuracy is 0.5 level.

[0004] The specification requires that the nominal value of the phase current sensor of the centralized DTU (Distribution Terminal Unit) and the decentralized DTU station terminal is 1V, the nominal value of the zero-sequence voltage sensor is 0.2V, the phase current measurement accuracy: 0.5 level (≤1.2In), and the zero-sequence current measurement accuracy is 0.5 level.

[0005] The specification requires that when the equipment is in the working state under normal working atmospheric conditions, the following specified high-frequency interference is applied to the signal input circuit and the AC power supply circuit, and the circuit composed of electronic logic circuits and the software program should be able to work normally, and all function and performance indicators should meet the relevant requirements. The change amount of the measurement error of the AC voltage and current input circuits should not be greater than 200% of the class index.

[0006] FTU feeder terminals, centralized DTUs, and decentralized DTU substation terminals operate in harsh environments and are extremely vulnerable to high-frequency electromagnetic interference from 10 kV lines and complex on-site operating environments. In an electromagnetic interference environment, interference signals are transmitted through pole-mounted circuit breakers / ring network cabinets to the voltage sensors and current sensors of FTU feeder terminals / DTU substation terminals. Hardware methods for eliminating high-frequency electromagnetic interference have limited measures, especially it is difficult to eliminate oscillatory wave interference. According to the national standard GB / T 17626.18, the oscillatory wave method in the laboratory environment is used as the test parameters: (1) Test voltage: Current port: Common mode ±2.5 kV, differential mode ±1.25 kV; (2) Test polarity: Positive and negative polarities, 6 pulse groups for each polarity. Test frequency: 1 MHz and 100 kHz; Pulse repetition rate: 400 times / s at 1 MHz, 40 times / s at 100 kHz; Test duration: 10 s; Test time interval: 10 s. Hardware methods cannot filter out interference at 400 times / s and 40 times / s, which easily leads to the sensor acquisition not meeting the measurement accuracy requirements. Existing software technologies use the effective value average of multiple calculations or the optimized effective value average method, which cannot reliably solve the problem of sensor acquisition accuracy during electromagnetic interference. Especially for zero-sequence current sensors with a nominal value of 0.2 V, the waveform is severely distorted in an electromagnetic interference environment, and the effective value average method fails. Therefore, it is necessary to study a software method to eliminate the influence of the electromagnetic interference environment on sensor sampling accuracy. Summary of the Invention

[0007] The accuracy of AC sensors in power system relay protection devices exceeds the accuracy requirements in the specification in an electromagnetic interference environment. Aiming at the above defects or improvement requirements of the existing technology, the present invention provides a software method for eliminating the influence of the electromagnetic interference environment on the accuracy of AC sensors, so that in an electromagnetic interference environment with a severity level of 4 according to national standards GB / T 17626.18, GB / T 17626.4, and GB / T 15153.1, the AC sensor acquisition still meets the accuracy requirements.

[0008] To achieve the above object, according to one aspect of the present invention, a software method for eliminating the influence of electromagnetic interference on sensor acquisition accuracy is provided. The method includes:

[0009] Real-time collect the voltage of the voltage sensor and the current of the current sensor, and store the sampling data of N cycles, where N is a preset value;

[0010] Locate the 3N sampling data with the largest values and the 3N sampling data with the smallest values, that is, locate the 3 largest sampling data and the 3 smallest sampling data of each cycle, and replace the located 3N sampling data with the largest values and the 3N sampling data with the smallest values with the mean value of the data on both sides adjacent to the positioning point or the quadratic fitting value;

[0011] Calculate the derivative of the updated N-cycle sampled data, identify the data that crosses the I-IV quadrants in the N-cycle sampled data, and replace the data that crosses the quadrants with the average value of the data on both sides adjacent to the positioning point or the quadratic fitting value;

[0012] Identify whether the sampled data is abnormal data within the I-IV quadrants, and replace the abnormal data within the quadrants with the average value of the data on both sides adjacent to the positioning point or the quadratic fitting value;

[0013] Use the Fourier algorithm or the root mean square algorithm to calculate the effective value of the replaced N-cycle sampled data, and use the optimized average value algorithm to calculate the average value of the effective values.

[0014] In one embodiment of the present invention, the calculation of the derivative of the updated N-cycle sampled data is specifically as follows:

[0015] u ki ' = u ki -u ki-1 (1)

[0016] i ki ' = i ki -i ki-1 (2)

[0017] Where u ki ' is the derivative of the phase voltage and zero-sequence voltage sampled data, u ki is the phase voltage and zero-sequence voltage sampled data, k is for the three phases A, B, C and zero-sequence, i = 1, 2, 3, 4......n, and n is the maximum number of samples in a cycle;

[0018] Where i ki ' is the derivative of the phase current and zero-sequence current sampled data, i ki is the phase current and zero-sequence current sampled data, k is for the three phases A, B, C and zero-sequence, i = 1, 2, 3, 4......n, and n is the maximum number of samples in a cycle.

[0019] In one embodiment of the present invention, the method for judging the data that crosses the I-IV quadrants in the N-cycle sampled data is as follows: the derivative directions of two adjacent sampled data are opposite, and the direction of one data among three adjacent sampled data is opposite to the directions of the other two data.

[0020] In one embodiment of the present invention, the method for judging the data that crosses the I-IV quadrants in the N-cycle sampled data is as follows:

[0021] u ki ' * u k(i-1) ' < 0

[0022] u k(i-1) * u ki * u k(i+1) < 0 (3)

[0023] i ki '*i k(i-1) '<0

[0024] i k(i-1) *i ki *i k(i+1) <0 (4)

[0025] When Equation (3) holds, the voltage crosses quadrants; when Equation (4) holds, the current crosses quadrants.

[0026] In one embodiment of the present invention, replacing the abnormal data within the quadrant with the quadratic fitting value specifically includes:

[0027] u (i-1) = at (i-1) 2 + bt (i-1) + c

[0028] u (i+1) = at (i+1) 2 + bt (i+1) + c

[0029] u (i+2) = at (i+2) 2 + bt (i+2) + c (5)

[0030] i (i-1) = at (i-1) 2 + bt (i-1) + c

[0031] i (i+1) = at (i+1) 2 + bt (i+1) + c

[0032] i (i+2) = at (i+2) 2 + bt (i+2) + c (6)

[0033] Equations (5) and (6) are quadratic fitting formulas, where t (i-1) is the previous sampling moment relative to the current sampling moment, t (i+1) is the next sampling moment relative to the current sampling moment, t (i+2) is the second next sampling moment relative to the current sampling moment, u (i-1) is the voltage at the previous sampling moment relative to the current sampling moment, u (i+1) is the voltage at the next sampling moment relative to the current sampling moment, u (i+2)is the voltage at the two sampling instants after the current sampling instant, i (i-1) is the current at the sampling instant immediately before the current sampling instant, i (i+1) is the current at the sampling instant immediately after the current sampling instant, i (i+2) is the current at the two sampling instants after the current sampling instant; given u (i-1) , u (i+1) , u (i+2) , i (i-1) , i (i+1) , i (i+2) , t (i-1) , t (i+1) , t (i+2) , calculate the coefficients a, b, c of the quadratic fitting formula, and use a, b, c and the known t i calculate u i and ii; u i and ii are the voltage and current at the current moment obtained by calculation.

[0034] In one embodiment of the present invention, the method for identifying whether the sampled data is abnormal data in Quadrants I to IV is as follows:

[0035] For the sampled data in the first quadrant, the derivative of the current sampling point is greater than the derivative of the adjacent previous sampling point, and all sampling points that meet the conditions are located;

[0036] For the sampled data in the second quadrant, the derivative of the current sampling point is greater than the derivative of the adjacent previous sampling point, and all sampling points that meet the conditions are located;

[0037] For the sampled data in the third quadrant, the derivative of the current sampling point is less than the derivative of the adjacent previous sampling point, and all sampling points that meet the conditions are located;

[0038] For the sampled data in the fourth quadrant, the derivative of the current sampling point is less than the derivative of the adjacent previous sampling point, and all sampling points that meet the conditions are located.

[0039] In one embodiment of the present invention, the method for identifying whether the sampled data is abnormal data in Quadrants I to IV is specifically:

[0040] Quadrant I: u ki '> u k(i-1) '

[0041] Quadrant II: u ki '> u k(i-1) '

[0042] Quadrant III: u ki '< u k(i-1) '

[0043] Quadrant IV: u ki '< uk(i-1) ' (7)

[0044] Quadrant I: i ki '> i k(i-1) '

[0045] Quadrant II: i ki '> i k(i-1) '

[0046] Quadrant III: i ki '< i k(i-1) '

[0047] Quadrant IV: i ki '< i k(i-1) ' (8)

[0048] Equation (7) is used to determine whether the voltage sampling data is abnormal data within a quadrant, where u ki ' is the derivative of the current voltage sampling point, and u k(i-1) ' is the derivative of the adjacent previous voltage sampling point;

[0049] Equation (8) is used to determine whether the current sampling data is abnormal data within a quadrant, where i ki ' is the derivative of the current current sampling point, and i k(i-1) ' is the derivative of the adjacent previous current sampling point.

[0050] In an embodiment of the present invention, the effective value is calculated by using the Fourier algorithm to update and replace the N-cycle sampling data, specifically as follows:

[0051]

[0052] Equation (9) is the Fourier calculation formula for calculating the voltage effective value, where u m is the voltage sampling value, U m is the voltage effective value, N is the number of samples in one cycle, pi is π, m is the harmonic number, m ≤ (N - 1) / 2, and n = 0, 1, 2, 3......N;

[0053] Equation (10) is the Fourier calculation formula for calculating the current effective value, where i m is the current sampling value, I m is the current effective value, N is the number of samples in one cycle, pi is π, m is the harmonic number, m ≤ (N - 1) / 2, and n = 0, 1, 2, 3......N.

[0054] In an embodiment of the present invention, N is taken as 1.

[0055] According to another aspect of the present invention, there is also provided a device for software to eliminate the influence of electromagnetic interference on the acquisition accuracy of sensors, including at least one processor and a memory. The at least one processor and the memory are connected through a data bus. The memory stores instructions executable by the at least one processor. After being executed by the processor, the instructions are used to complete the above-mentioned method for software to eliminate the influence of electromagnetic interference on the acquisition accuracy of sensors.

[0056] Generally speaking, compared with the prior art by the above technical solution conceived by the present invention, the following beneficial effects are obtained:

[0057] To sum up, the present invention provides a method for software to eliminate the influence of electromagnetic interference on the accuracy of sensors. Aiming at the electromagnetic interference problems of FTU feeder terminals and DTU substation terminals specified in the "Secondary Integration Standardized Pole-mounted Circuit Breaker, AC10kV, 630A, 20kA", "Primary and Secondary Integration Standardized Ring Main Unit, AC10kV, 630A, Standardized, Centralized DTU, Incoming and Outgoing Line Circuit Breaker" and "Primary and Secondary Integration Standardized Ring Main Unit, AC10kV, 630A, Standardized, Distributed DTU, Incoming and Outgoing Line Circuit Breaker" distribution network standardized material solidification technical specifications issued by the State Grid Corporation in 2021, the present invention collects analog data of voltage sensors and current sensors, adopts a software method, real-time identifies and processes the sampled data affected by electromagnetic interference, and calculates the average value of the effective value data. Therefore, the principle of this method solves the problem that it is difficult to eliminate high-frequency electromagnetic interference by hardware methods, is simple and reliable, is not affected by factors such as the neutral grounding method of the distribution network, the grid asymmetry degree, and the electromagnetic environment, and has reliability, accuracy and practicability. Description of the Drawings

[0058] Figure 1 is a schematic flowchart of the method for software to eliminate the influence of electromagnetic interference on the acquisition accuracy of sensors in an embodiment of the present invention;

[0059] Figure 2 is an implementation flowchart of a method for software to prevent the accuracy of analog quantities from being affected by electromagnetic interference in an embodiment of the present invention;

[0060] Figure 3 is an experimental circuit diagram of the common-mode oscillatory wave interference of the measured terminal in an embodiment of the present invention;

[0061] Figure 4 is an experimental circuit diagram of the differential-mode oscillatory wave interference of the measured terminal in an embodiment of the present invention. Detailed Embodiments

[0062] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0063] To achieve the above object, the present invention provides a method for software to eliminate the influence of electromagnetic interference on the acquisition accuracy of sensors, which can identify and process the abnormal sampling points of line voltage sensors and current sensors in real time, that is, identify the abnormal data in the sampling data in real time and replace the abnormal data with the mean value or quadratic fitting method. As Figure 1 shown, the method for software to eliminate the influence of electromagnetic interference on the acquisition accuracy of sensors provided by the present invention includes the following steps:

[0064] (1) Collect the voltage of the voltage sensor and the current of the current sensor in real time, and store the sampling data of N cycles, where N is a preset value;

[0065] (2) Locate the 3N sampling data with the largest values and the 3N sampling data with the smallest values, that is, locate the 3 largest sampling data and the 3 smallest sampling data in each cycle, and replace the located 3N sampling data with the largest values and the 3N sampling data with the smallest values with the mean value or quadratic fitting value of the data on both sides adjacent to the positioning point;

[0066] (3) Calculate the derivative of the updated N-cycle sampling data, identify the data crossing the I-IV quadrants in the N-cycle sampling data, and replace the data crossing the quadrants with the mean value or quadratic fitting value of the data on both sides adjacent to the positioning point;

[0067] (4) Identify whether the sampling data is abnormal data within the I-IV quadrants, and replace the abnormal data within the quadrants with the mean value or quadratic fitting value of the data on both sides adjacent to the positioning point;

[0068] (5) Use the Fourier algorithm or the root mean square algorithm to update the effective value of the replaced N-cycle sampling data, and use the optimized average value algorithm to calculate the average value of the effective value.

[0069] Further, the method of the present invention is described in detail, including the following steps:

[0070] 1. Collect the three-phase voltage and current of the line in real time;

[0071] 2. Store the voltage of the voltage sensor and the current of the current sensor data of 1 to 10 cycles in real time;

[0072] 3. Process the abnormal data generated by electromagnetic interference through software methods, specifically as follows:

[0073] ① Sort the sampled data of 1 to 10 cycles, either in ascending order or in descending order.

[0074] ② Locate the 3 to 30 sampled data with the largest values and the 3 to 30 sampled data with the smallest values, that is, locate the 3 largest sampled data and the 3 smallest sampled data for each cycle.

[0075] ③ Replace the 3 to 30 located sampled data with the largest values with the mean value of the data on both sides adjacent to the location point or the quadratic fitting value.

[0076] ④ Replace the 3 to 30 located sampled data with the smallest values with the mean value of the data on both sides adjacent to the location point or the quadratic fitting value.

[0077] ⑤ Calculate the derivative of the updated sampled data of 1 to 10 cycles.

[0078] ⑥ Identify the data that crosses the I - IV quadrants in the sampled data of 1 to 10 cycles. The judgment method is as follows: The derivative directions of two adjacent sampled data are opposite, and the direction of one data among three adjacent sampled data is opposite to the directions of the other two data.

[0079] ⑦ After identifying the data that crosses the quadrants, replace the data that crosses the quadrants with the mean value of the data on both sides adjacent to the location point or the quadratic fitting value.

[0080] ⑧ Judge the quadrant where each data in the sampled data of 1 to 10 cycles is located.

[0081] ⑨ Identify whether the sampled data is abnormal data within the I - IV quadrants. The identification method is as follows:

[0082] For the sampled data in the first quadrant, the derivative of the current sampling point is greater than the derivative of the previous adjacent sampling point, and locate all the sampling points that meet the conditions;

[0083] For the sampled data in the second quadrant, the derivative of the current sampling point is greater than the derivative of the previous adjacent sampling point, and locate all the sampling points that meet the conditions;

[0084] For the sampled data in the third quadrant, the derivative of the current sampling point is less than the derivative of the previous adjacent sampling point, and locate all the sampling points that meet the conditions;

[0085] For the sampled data in the fourth quadrant, the derivative of the current sampling point is less than the derivative of the previous adjacent sampling point, and locate all the sampling points that meet the conditions.

[0086] ⑩ Replace the abnormal data with the mean value of the data on both sides adjacent to the location point or the quadratic fitting value.

[0087] 11 Calculate the effective value by using the Fourier algorithm or the root mean square algorithm to update and replace the sampling data of the 1st to 10th cycles.

[0088] 12 Calculate the average value of the effective value by using the optimized average value algorithm.

[0089] Furthermore, as Figure 2 shown, the present invention provides a method for software to eliminate the interference of oscillating waves and the inaccuracy of analog quantity not meeting the specification requirements, including the following steps: Step 1, collect the three-phase voltage and current of the line in real time; Step 2, store the voltage of the voltage sensor and the current of the current sensor of 5 cycles in real time; Step 3, process the abnormal data generated by electromagnetic interference through software methods. Specifically,

[0090] 1. Sort the sampling data of 5 cycles from small to large or from large to small, and locate the 15 largest sampling data and the 15 smallest sampling data. Replace the located 15 largest sampling data with the average value of the data on both sides adjacent to the positioning point or the quadratic fitting value, and replace the located 15 smallest sampling data with the average value of the data on both sides adjacent to the positioning point or the quadratic fitting value.

[0091] 2. Calculate the derivative of each sampling data in the updated 5 cycles.

[0092] u ki ' = u ki - u ki-1 (1)

[0093] i ki ' = i ki - i ki-1 (2)

[0094] where u ki ' is the derivative of the phase voltage and zero-sequence voltage sampling data, u ki is the phase voltage and zero-sequence voltage sampling data, k is for three phases A, B, C and zero-sequence, i = 1, 2, 3, 4......n, and n is the maximum number of samplings in 5 cycles.

[0095] where i ki ' is the derivative of the phase current and zero-sequence current sampling data, i ki is the phase current and zero-sequence current sampling data, k is for three phases A, B, C and zero-sequence, i = 1, 2, 3, 4......n, and n is the maximum number of samplings in 5 cycles.

[0096] 3. Identify whether the sampling data of 5 cycles has data crossing the I - IV quadrants, and the judgment method is as follows: The directions of the derivatives of two adjacent sampling data are opposite, and the direction of one of the three adjacent sampling data is opposite to that of the other two.

[0097] u ki ' * uk(i-1) '<0

[0098] u k(i-1) *u ki *u k(i+1) <0 (3)

[0099] i ki '*i k(i-1) '<0

[0100] i k(i-1) *i ki *i k(i+1) <0 (4)

[0101] When formula (3) holds, the voltage crosses the quadrant. When formula (4) holds, the current crosses the quadrant. When the voltage or current crosses the quadrant, the mean value or quadratic fitting value of the normal sampling data on both sides of the abnormal point is used for updating.

[0102] u (i-1) =at (i-1) 2 +bt (i-1) +c

[0103] u (i+1) =at (i+1) 2 +bt (i+1) +c

[0104] u (i+2) =at (i+2) 2 +bt (i+2) +c (5)

[0105] i (i-1) =at (i-1) 2 +bt (i-1) +c

[0106] i (i+1) =at (i+1) 2 +bt (i+1) +c

[0107] i (i+2) =at (i+2 ) 2 +bt (i+2) +c (6)

[0108] Formulas (5) and (6) are quadratic fitting formulas, where t (i-1) is the previous sampling moment relative to the current sampling moment, t (i+1) is the next sampling moment relative to the current sampling moment, t (i+2) is the second next sampling moment relative to the current sampling moment, u (i-1)is the voltage at the previous sampling moment relative to the current sampling moment, u (i+1) is the voltage at the next sampling moment relative to the current sampling moment, u (i+2) is the voltage at the second next sampling moment relative to the current sampling moment, i (i-1) is the current at the previous sampling moment relative to the current sampling moment, i (i+1) is the current at the next sampling moment relative to the current sampling moment, i (i+2) is the current at the second next sampling moment relative to the current sampling moment. Given u (i-1) , u (i+1) , u (i+2) , i (i-1) , i (i+1) , i (i+2) , t (i-1) , t (i+1) , t (i+2) , calculate the coefficients a, b, c of the quadratic fitting formula, and use a, b, c and the known t i to calculate u i and i i . u i and i i are the voltage and current at the current moment obtained by calculation.

[0109] 4. Determine the quadrant where each sampling data is located in 5 cycles.

[0110] Determine whether the sampling data is abnormal data within the quadrant. The determination method is as follows:

[0111] For the sampling data in the first quadrant, the derivative of the current sampling point is greater than the derivative of the adjacent previous sampling point, and locate all sampling points that meet the conditions;

[0112] For the sampling data in the second quadrant, the derivative of the current sampling point is greater than the derivative of the adjacent previous sampling point, and locate all sampling points that meet the conditions;

[0113] For the sampling data in the third quadrant, the derivative of the current sampling point is less than the derivative of the adjacent previous sampling point, and locate all sampling points that meet the conditions;

[0114] For the sampling data in the fourth quadrant, the derivative of the current sampling point is less than the derivative of the adjacent previous sampling point, and locate all sampling points that meet the conditions.

[0115] Quadrant I: u ki '> u k(i-1) '

[0116] Quadrant II: u ki '> u k(i-1) '

[0117] Quadrant III: u ki '<u k(i-1) '

[0118] Quadrant IV: u ki '<u k(i-1) ' (7)

[0119] Quadrant I: i ki '>i k(i-1) '

[0120] Quadrant II: i ki '>i k(i-1) '

[0121] Quadrant III: i ki '<i k(i-1) '

[0122] Quadrant IV: i ki '<i k(i-1) ' (8)

[0123] Formula (7) is used to determine whether the voltage sampling data is abnormal data within a quadrant, where u ki ' is the derivative of the current voltage sampling point, and u k(i-1) ' is the derivative of the adjacent previous voltage sampling point;

[0124] Formula (8) is used to determine whether the current sampling data is abnormal data within a quadrant, where i ki ' is the derivative of the current current sampling point, and i k(i-1) ' is the derivative of the adjacent previous current sampling point.

[0125] 5. Replace the abnormal data with the average value or quadratic fitting value of the data on both sides adjacent to the abnormal data.

[0126] 6. Calculate the effective value of the 5-cycle sampling data after the update and replacement.

[0127] Perform Fourier calculation on the five-cycle sampling data

[0128]

[0129] Formula (9) is the Fourier calculation formula for calculating the voltage effective value, where u m is the voltage sampling value, U m is the voltage effective value, N is the number of samples in one cycle, pi is π, m is the harmonic number, m ≤ (N - 1) / 2, n = 0, 1, 2, 3......N;

[0130] Formula (10) is the Fourier calculation formula for calculating the current effective value, where i m is the current sampling value, Im where \(I\) is the effective current value, \(N\) is the number of samples in one cycle, \(\pi\) is pi, \(m\) is the number of harmonics, \(m\leqslant(N - 1) / 2\), and \(n = 0, 1, 2, 3,\cdots,N\).

[0131] 7. Calculate the average value of the effective value using an optimized average value algorithm.

[0132] 8. Further, to verify the correctness of the above steps, a differential-mode oscillation wave and Figure 3 common-mode oscillation wave interference experimental circuit as shown in Figure 4 was built in the laboratory. Then, the oscillation wave errors at different frequencies, different ports, and different polarities were detected. The specific results are shown in Table 1: The oscillation wave test results of the analog channel of the zero-sequence voltage sensor with a nominal value of 0.2V. The specified allowable error does not exceed 1%, and the measured error after using the method of this patent does not exceed 0.3%. The distribution terminal using the method of this patent can operate stably and reliably in an electromagnetic interference environment. Specifically:

[0133] 1. Different frequencies refer to two frequencies of 100 kHz and 1 MHz;

[0134] 2. L-PE and N-PE are common-mode ports, and L-N is a differential-mode port;

[0135] 3. The polarity is +−;

[0136] 4. Among them, the frequency and polarity are set and output on the oscillation wave interference test instrument;

[0137] 5. L-PE and N-PE are Figure 4 data of environmental tests; L-N is Figure 3 data of environmental tests.

[0138] Table 1 Oscillation wave test results of the analog channel of the zero-sequence voltage sensor with a nominal value of 0.2V

[0139]

[0140] In summary, the present invention provides a method for software to eliminate the influence of electromagnetic interference on the sampling accuracy of sensors. Aiming at the electromagnetic interference problems of FTU feeder terminals and DTU substation terminals specified in the technical specifications for solidified distribution network standard materials of "Secondary Integration Standardized Pole-mounted Circuit Breaker, AC10kV, 630A, 20kA", "Primary and Secondary Integration Standardized Ring Main Unit, AC10kV, 630A, Standardized, Centralized DTU, Incoming and Outgoing Line Circuit Breaker", and "Primary and Secondary Integration Standardized Ring Main Unit, AC10kV, 630A, Standardized, Decentralized DTU, Incoming and Outgoing Line Circuit Breaker" issued by the State Grid Corporation in 2021, the present invention collects analog data of voltage sensors and current sensors, and uses a software method to first identify and locate abnormal data in the four quadrants in the sampling data. The identification method is a discriminant method combining sampling data and sampling derivative data, and three different types of abnormal data are identified: abnormal data exceeding the peak value of the sampling data, abnormal data in each quadrant of the four quadrants, and abnormal data outside each quadrant of the four quadrants. Then, the above three types of abnormal data are processed by the mean value or quadratic fitting method, and finally the effective value of the sampling data is calculated by the Fourier and optimized average value method. This method has been iteratively upgraded and verified more than 50 times, and is stable and reliable. Therefore, the principle of this method solves the problem that it is difficult to eliminate high-frequency electromagnetic interference by hardware methods, is simple and reliable, is not affected by factors such as the neutral grounding method of the distribution network, the grid asymmetry degree, and the electromagnetic environment, and has reliability, accuracy, and practicality.

[0141] Furthermore, the present invention also provides a device for software to eliminate the influence of electromagnetic interference on the acquisition accuracy of sensors, including at least one processor and a memory. The at least one processor and the memory are connected through a data bus. The memory stores instructions executable by the at least one processor, and after the instructions are executed by the processor, they are used to complete the above method for software to eliminate the influence of electromagnetic interference on the acquisition accuracy of sensors.

[0142] Those skilled in the art can easily understand that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for software to eliminate the influence of electromagnetic interference on the acquisition accuracy of sensors, characterized in that, the method includes: Real-time collect the voltage of the voltage sensor and the current of the current sensor, and store the sampling data of N cycles, where N is a preset value; Locate the 3N sampling data with the largest values and the 3N sampling data with the smallest values, that is, locate the 3 largest sampling data and the 3 smallest sampling data for each cycle, and use the average value of the data on both sides adjacent to the positioning point or the quadratic fitting value to replace the located 3N sampling data with the largest values and the 3N sampling data with the smallest values; Calculate the derivative of the updated N-cycle sampling data, identify the data crossing the I-IV quadrants in the N-cycle sampling data, and use the average value of the data on both sides adjacent to the positioning point or the quadratic fitting value to replace the data crossing the quadrants; The method for judging the data crossing the I-IV quadrants in the N-cycle sampling data is as follows: the derivative directions of two adjacent sampling data are opposite, and the direction of one data among three adjacent sampling data is opposite to the directions of the other two data; Identify whether the sampling data is abnormal data within the I-IV quadrants, and use the average value of the data on both sides adjacent to the positioning point or the quadratic fitting value to replace the abnormal data within the quadrants; The method for identifying whether the sampling data is abnormal data within the I-IV quadrants is as follows: for the sampling data in the first quadrant, the derivative of the current sampling point is greater than the derivative of the adjacent previous sampling point, and locate all sampling points that meet the conditions; for the sampling data in the second quadrant, the derivative of the current sampling point is greater than the derivative of the adjacent previous sampling point, and locate all sampling points that meet the conditions; for the sampling data in the third quadrant, the derivative of the current sampling point is less than the derivative of the adjacent previous sampling point, and locate all sampling points that meet the conditions; for the sampling data in the fourth quadrant, the derivative of the current sampling point is less than the derivative of the adjacent previous sampling point, and locate all sampling points that meet the conditions; Use the Fourier algorithm or the root mean square algorithm to update the effective value calculated from the replaced N-cycle sampling data, and use the optimized average value algorithm to calculate the average value of the effective value.

2. The method for software to eliminate the influence of electromagnetic interference on the acquisition accuracy of sensors according to claim 1, characterized in that, the calculation of the derivative of the updated N-cycle sampling data is specifically: u ki ' = u ki -u ki-1 (1) i ki ' = i ki -i ki -1 (2) where u ki ' is the derivative of the sampled data of the phase voltage and the zero-sequence voltage, u ki is the sampled data of the phase voltage and the zero-sequence voltage, k is for the three phases A, B, C and the zero sequence, i = 1, 2, 3, 4......n, and n is the maximum number of samples per cycle; where i ki ' is the derivative of the sampled data of the phase current and zero-sequence current, and i ki is the sampled data of the phase current and zero-sequence current, k is for the three phases A, B, C and the zero-sequence, i = 1, 2, 3, 4......n, and n is the maximum number of samples per cycle.

3. The method for software to eliminate the influence of electromagnetic interference on the acquisition accuracy of sensors according to claim 1, characterized in that, the method for judging the data crossing the I-IV quadrants in the N-cycle sampling data is as follows: u ki '*u k(i-1) '<0 u k(i-1) *u ki *u k(i+1) <0 (3) i ki '*i k(i-1) '<0 i k(i-1) *i ki *i k(i+1) <0 (4) When formula (3) holds, the voltage crosses the quadrant, and when formula (4) holds, the current crosses the quadrant.

4. The method for software to eliminate the influence of electromagnetic interference on the acquisition accuracy of sensors according to claim 1, characterized in that, the use of the quadratic fitting value to replace the abnormal data within the quadrant is specifically: u (i-1) = at (i-1) 2 + bt (i-1) + c u (i+1) = at (i+1) 2 + bt (i+1) + c u (i+2) = at (i+2) 2 + bt (i+2) + c(5) i (i-1) = at (i-1) 2 + bt (i-1) + c i (i+1) = at (i+1) 2 + bt (i+1) + c i (i+2) = at (i+2) 2 + bt (i+2) + c(6) Equations (5) and (6) are quadratic fitting equations, where t (i-1) is the previous sampling moment relative to the current sampling moment, t (i+1) is the next sampling moment relative to the current sampling moment, t (i+2) is the second next sampling moment relative to the current sampling moment, u (i-1) is the voltage at the previous sampling moment relative to the current sampling moment, u (i+1) is the voltage at the next sampling moment relative to the current sampling moment, u (i+2) is the voltage at the second next sampling moment relative to the current sampling moment, i (i-1) is the current at the previous sampling moment relative to the current sampling moment, i (i+1) is the current at the next sampling moment relative to the current sampling moment, i (i+2) is the current at the second next sampling moment relative to the current sampling moment; given u (i-1) , u (i+1) , u (i+2) , i (i-1) , i (i+1) , i (i+2) , t (i-1) , t (i+1) , t (i+2) , the coefficients a, b, c of the quadratic fitting equation are calculated. Using a, b, c and the known t i , u i and i i are calculated; u i and i i are the voltage and current at the calculated current moment.

5. The method for software to eliminate the influence of electromagnetic interference on the acquisition accuracy of sensors according to claim 1, characterized in that, the identification of whether the sampling data is abnormal data within the I-IV quadrants is specifically: Quadrant I: u ki '> u k(i-1) ' Quadrant II: u ki '> u k(i-1) ' Quadrant III: u ki '<u k(i-1) ' Quadrant IV: u ki '<u k(i-1) ' (7) Quadrant I: i ki '> i k(i-1) ' Quadrant II: i ki '> i k(i-1) ' Quadrant III: i ki '<i k(i-1) ' Quadrant IV: i ki '<i k(i-1) ' (8) Formula (7) is used to determine whether the voltage sampling data is abnormal data within a quadrant, where u ki ' is the derivative of the current voltage sampling point, and u k(i-1) ' is the derivative of the previous adjacent voltage sampling point; Formula (8) is used to determine whether the current sampling data is abnormal data within a quadrant, where i ki ' is the derivative of the current current sampling point, and i k(i-1) ' is the derivative of the previous adjacent current sampling point.

6. The method for software to eliminate the influence of electromagnetic interference on the acquisition accuracy of sensors according to claim 1 or 2, characterized in that, the use of the Fourier algorithm to update the effective value calculated from the replaced N-cycle sampling data is specifically: Formula (9) is the Fourier calculation formula for calculating the effective voltage, where u m is the voltage sampling value, U m is the effective voltage, N is the number of samples in one cycle, pi is π, m is the number of harmonics, m ≤ (N - 1) / 2, n = 0, 1, 2, 3......N; Formula (10) is the Fourier calculation formula for calculating the effective current value, where i m is the current sampling value, I m is the effective current value, N is the number of samples in one cycle, pi is π, m is the number of harmonics, m ≤ (N - 1) / 2, and n = 0, 1, 2, 3......N.

7. The method for software to eliminate the influence of electromagnetic interference on the acquisition accuracy of sensors as claimed in claim 1 or 2, characterized in that, N takes the value of 1.

8. An apparatus for software to eliminate the influence of electromagnetic interference on the acquisition accuracy of sensors, characterized in that: It includes at least one processor and a memory. The at least one processor and the memory are connected through a data bus. The memory stores instructions executable by the at least one processor. After being executed by the processor, the instructions are used to complete the method for software to eliminate the influence of electromagnetic interference on the acquisition accuracy of sensors according to any one of claims 1-7.

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