CN110619175B - Dynamic attenuation integral reduction method for acquisition unit of electronic transformer - Google Patents
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Abstract
The invention discloses a dynamically attenuated integral reduction method for an acquisition unit of an electronic transformer, which comprises the following steps: firstly, an input sampling value at the current moment is obtained, sampling value integral data is iteratively calculated through a discrete fixed integral algorithm, and then whether the calculated integral value needs to be attenuated or not is judged by combining sampling information cached in a preorder. If the current input is identified as normal sampling, the integral value of the previous time is attenuated; if the current input is identified as a faulty sample, the previous integral value is not attenuated. And finally, filtering the cycle null shift of the output data to obtain the integral data of the original sampling. The invention effectively ensures the precision and stability of the integral transmission characteristic of the sampling value output by the acquisition unit, provides a later performance optimization space, considers the reliability and flexibility of the integral method, iteratively attenuates integral repeatedly, reduces the influence of sampling direct current offset and sampling abnormity on an integral algorithm, and ensures the convergence of an integral system and the steady-state transmission precision of the acquisition unit.
Description
Technical Field
The invention belongs to the technical field of power system digital substations, and particularly relates to a dynamic attenuation integral reduction method for an acquisition unit of an electronic transformer.
Background
The electronic transformer has the characteristics of small volume, light weight, no magnetic saturation, no secondary open circuit, convenience in combination installation with primary high-voltage switch equipment and the like, effectively improves the equipment integration level, reduces the land occupation, is convenient to install and transport, and has wide market application prospect. The electronic transformer in the broad sense comprises a sensing coil, a local acquisition unit and a sampling merging unit. Current and voltage signals of the primary system are converted into secondary small analog quantity signals after being transmitted and converted by the mutual inductor body, then are collected by the local collecting unit and converted into digital quantity sampling signals, and finally are output to the merging unit through optical fibers to complete synchronization and merging of sampling values. The acquisition unit is a core electrical component of the electronic transformer, the quality of the sampling transmission characteristic of the acquisition unit directly determines the overall sampling precision of the electronic transformer, and particularly for the electronic current transformer based on the Rogowski coil principle or the electronic voltage transformer based on the differential capacitance voltage division principle, a differential proportion relation exists between a secondary analog quantity signal output by a body and a primary electrical signal, the secondary signal needs to be subjected to integral reduction through the acquisition unit, and the integral transmission characteristic of the acquisition unit has important influence on the operation reliability of the electronic transformer.
The current mainstream acquisition unit integral reduction technology comprises hardware integral reduction and software integral reduction.
The hardware integral reduction technology is based on an analog integrator, and a hardware integral circuit is built through components such as capacitors, resistors, operational amplifiers and the like, so that integral reduction of analog input signals is realized. The hardware integration technology is simple in related circuit and easy to realize, and influences of direct current components can be restrained through RC loop resistance. The hardware integral reduction technology is realized by constructing an analog circuit by components, the sampling transmission characteristic of the hardware integral reduction technology is completely determined by the design of an integral circuit and the characteristics of the components, and the optimization and the adjustment are difficult in the later period. Due to the non-ideality of the physical components, the overall output characteristics of the integral loop can change along with the change of the external operating environment or the increase of the service time of the components, so that the transmission error of the acquisition unit is generated, and the output precision of the electronic transformer is influenced.
The software integral reduction technology is based on the discrete digital integral principle, and integral output is obtained by discretizing fixed integral and performing iterative operation with the previous integral result. The software integration algorithm is easily affected by sampling null shift or sampling abnormal interference, and digital saturation of sampling output by the acquisition unit can be caused in severe cases, so that the actual frequency spectrum of an input signal cannot be restored. The adoption of the software integration algorithm with attenuation can effectively inhibit the interference of sampling null shift or sampling abnormity on the integration algorithm, but cannot distinguish the direct current component contained in the original sampling frequency spectrum from the invalid direct current component generated by the sampling null shift, when the fault current with the attenuation direct current component occurs in a primary system, the direct current component can be inhibited by the algorithm, the phenomenon of over attenuation of the direct current component is generated, and the transient output characteristic of the acquisition unit cannot meet the requirement.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method solves the problems that the existing sampling integration technology of the electronic transformer acquisition unit in the digital transformer substation is insufficient, the software integration is easily interfered by sampling null shift or sampling abnormity, and the transient state transmission and transformation characteristics of fault current attenuation direct current components in a primary system cannot be met.
The technical scheme adopted by the invention is as follows: a dynamically attenuated integral reduction method for an acquisition unit of an electronic transformer is characterized by comprising the following steps: the method comprises the following steps: firstly, acquiring an input sampling value at the current moment, and iteratively calculating sampling value integral data through a discrete integration-fixed algorithm; secondly, judging whether the calculated integral value needs to be attenuated or not by combining the sampling information cached in the preorder, if the current input is identified to be normal sampling, attenuating the integral value of the previous time, and inhibiting interference and null shift, and if the current input is identified to be fault sampling, not attenuating the integral value of the previous time; and finally, filtering the cycle null shift of the output data to obtain the integral data of the original sampling.
A dynamically attenuated integral reduction method for an acquisition unit of an electronic transformer comprises the following specific steps:
step 1: high frequency sampling
Acquiring input discrete samples of the current acquisition unit through digital-to-analog conversion, and adopting high-speed AD analog-to-digital conversion to avoid abnormal conditions such as loss of high-frequency components of original signals, large sampling precision errors or unstable sampling data caused by insufficient sampling rate of an analog loop, and simultaneously effectively improving the precision of a software algorithm at the rear end, wherein the discrete sampling values are sequentially stored in a sampling cache according to sampling moments and are used when the integral value in the step 2 is calculated and the integral attenuation in the step 3 is judged;
step 1, after obtaining discretization sampling data, entering step 2, and iteratively calculating integral data of sampling values;
step 2: integral value calculation
Through the sampling value obtained in the step 1, the current sampling integral data is calculated in an iterative manner by combining the sampling integral value calculated at the previous sampling moment;
according to the differential output principle of the electronic transformer body, the primary electric quantity of the transformer is converted into the integral proportional relation of small signals input by the acquisition unit, the discrete approximation algorithm of the fixed integral is obtained according to the area equivalent principle, the formula is modified into iterative operation related to the previous integral result, and the operation quantity of single integral calculation is reduced;
and step 3: integral attenuation discrimination
After the integral data of the current sampling is obtained in the step 2, judging whether the integral data of the current sampling needs attenuation processing or not through a break variable by combining the sampling cache sequence calculated in the step 1;
attenuation discrimination is based on the sudden change characteristics of fault sampling, for steady-state power frequency sampling, hardware performance change and algorithm errors are considered, the periodicity change of sampling points is small, and for transient fault sampling, the change of a fault sampling value relative to a steady-state sampling value is large. By analyzing the periodic variation trend of the sampling sequence, the real-time sampling state can be obtained, and whether the integral value needs to be attenuated through the step 4 is determined;
and 4, step 4: integral attenuation processing
In step 3, the mutation quantity of the fault sampling is utilized to judge whether the current sampling belongs to normal sampling or fault sampling, and whether the integral result calculated in step 2 needs attenuation processing is determined;
the attenuation processing is beneficial to improving the anti-interference performance of the integral algorithm, has a good filtering effect on the incidental interference and has a certain inhibiting effect on the continuous interference, and the attenuation processing influences the response of the integral algorithm to the direct current component of the original signal, so that the targeted starting is needed. When detecting that the current integral data does not need to be attenuated, skipping the integral attenuation processing process; if the integral data needs to be attenuated, reducing the integral calculation result of the sampling point according to the actual requirement;
and 5: filtering of zero drift
After the integral attenuation processing is completed in the steps 3 and 4, the periodic null shift of the integral data is filtered in the step 5;
because of the influence of a sampling loop or the precision of an integral algorithm, an additional inherent null shift value exists in sampling integral output data, and the null shift can change along with the change of time and the external environment, an integral output of an acquisition unit based on the differential input principle of an electronic transformer does not theoretically contain an inherent direct current component, and the inherent null shift needs to be filtered in order to improve the transmission accuracy of the acquisition unit;
and calculating a null shift value of the integral data by periodically superposing integral output data, compensating before final output, finishing integral calculation of the current sampling point after filtering the null shift, putting an integral result into a buffer space, and directly iterating and using the next sampling point during calculation.
The conversion formula of the input samples in step 1 is as follows:
wherein: s is the conversion output digital quantity, and V is the input voltage analog quantity.
The integral value is calculated in step 2 as follows:
the reverse voltage output by the rogowski coil at any moment is as follows:
in the formula: v (t) is the reverse voltage of the secondary output of the coil at the time t; i (t) is the primary current value of the coil at the time t; m is a constant;
from the formula of definite integral:
in the formula: i (0) is an initial value of i (t).
Discretizing the formula (2) by adopting trapezoidal integration to obtain:
in the formula: i isnThe current sampling value of the nth point is taken as the current sampling value of the nth point; i is0The current sampling value is the initial point; vnIs the output reverse voltage of the nth point; t issIs a sampling period;
the integral output formula obtained by modifying the formula (3) into iterative operation is as follows:
limiting the calculated result to InLimited within a set range when InAnd (4) replacing the cache according to an extremum when the time is out of limit.
The formula for calculating the mutation in step 3 is as follows:
SΔ＝Sn+Sn-2T-2×Sn-T (5)
in the formula: sΔIs a sampling burstA variable; snIs the current sampling value; sn-2TIs a sampling value before 2 cycles; sn-TIs a sampling value before 1 cycle; when the mutation amount of the continuous sampling points exceeds a threshold value and the sampling absolute value is in an increasing change trend, judging that the sampling is primary fault current, entering a fault starting mode, and reducing or stopping the successive attenuation of the integral value in the mode; after the fault is started, judging whether the fault returns in real time, and if the fault does not return after the start is overtime, forcibly restoring the original integral attenuation processing process; and when the absolute value of the continuous whole-period sampling point is smaller than the fault return threshold value, judging that the fault current disappears once, and immediately recovering the original integral attenuation process.
When attenuation processing is required in step 4, the inhibition formula is as follows:
in the formula: k is a disturbance attenuation coefficient; the value of the disturbance attenuation coefficient k is not more than 1, so that the integral result of the previous iteration is ensured to be smaller than the actual value each time, the continuous influence of the interference on the integral algorithm is released, in order to avoid the floating point operation process, k is converted into a fraction expression mode of power operation with the numerator smaller than the denominator and the denominator being 2, and the floating point operation is replaced by shaping multiplication and shaping displacement, as follows;
in the formula: NUM is a molecule of the attenuation coefficient; n is the denominator exponent (right shift number) of the decay coefficient.
The integral zero drift value calculation formula in the step 5 is as follows:
in the formula: zIA periodic null shift of the integral calculation value; n is the number of periodic sampling points; INTnCalculated as an integral.
The invention has the beneficial effects that: compared with the prior art, the invention has the following effects:
(1) the calculation mode realizes the integral output of the acquisition unit, and the high-fidelity discrete integral-fixed processing technology based on high-frequency digital-to-analog conversion can effectively ensure the precision and stability of the integral transmission characteristic of the sampling value output by the acquisition unit, simultaneously provides the later performance optimization space, and gives consideration to the reliability and flexibility of the integral method;
(2) the sampling interference successive attenuation method is used for improving the anti-interference capability of an integral algorithm and reducing the influence of a site adverse environment on the integral transmission characteristic of an acquisition unit, the algorithm carries out point-by-point amplitude limiting processing on an integral result, and meanwhile, the integral operation result is repeatedly attenuated in an iterative manner, so that the influence of sampling direct current offset and sampling abnormity on the integral algorithm is reduced to the maximum extent, and the convergence of an integral system and the steady-state transmission precision of the acquisition unit are ensured;
(3) and dynamic integral attenuation adjustment, namely monitoring the change trend of a sampling data window, distinguishing normal sampling input and fault current sampling input in real time, and dynamically adjusting the attenuation processing of integral data. On the premise of keeping the suppression capability of the integral algorithm on sampling interference, the phenomenon of over-attenuation of the attenuation processing on the direct-current component of the fault current is effectively reduced, and the transient transmission and transformation characteristic of the acquisition unit is improved;
(4) the algorithm is realized by adopting shaping operation, all functions of the software integral of the acquisition unit are completed through the shaping operation, the logic of the algorithm is clear and easy to write, the floating-point operation capability support is not needed, the algorithm can be realized in a bottom processor without an operating system, the overall power consumption and the development cost of the acquisition unit are reduced, and the requirements of low power consumption and low cost in a project site are met.
Drawings
FIG. 1 is a flow chart of the present invention;
fig. 2 is a schematic diagram of a dynamic integral attenuation algorithm.
Detailed Description
The invention is further described with reference to the accompanying drawings and specific embodiments.
Example (b): as shown in fig. 1-2, a method for integrally reducing a dynamically attenuated acquisition unit of an electronic transformer is characterized in that: the method comprises the following steps: firstly, acquiring an input sampling value at the current moment, and iteratively calculating sampling value integral data through a discrete integration-fixed algorithm; secondly, judging whether the calculated integral value needs to be attenuated or not by combining the sampling information cached in the preorder, if the current input is identified to be normal sampling, attenuating the integral value of the previous time, and inhibiting interference and null shift, and if the current input is identified to be fault sampling, not attenuating the integral value of the previous time; finally, filtering the cycle null shift of the output data to obtain the integral data of the original sampling; the method comprises the following specific steps:
step 1: high frequency sampling
Acquiring input discrete samples of the current acquisition unit through digital-to-analog conversion, and adopting high-speed AD analog-to-digital conversion to avoid abnormal conditions such as loss of high-frequency components of original signals, large sampling precision errors or unstable sampling data caused by insufficient sampling rate of an analog loop, and simultaneously effectively improving the precision of a software algorithm at the rear end, wherein the discrete sampling values are sequentially stored in a sampling cache according to sampling moments and are used when the integral value in the step 2 is calculated and the integral attenuation in the step 3 is judged;
synchronous analog-digital conversion of a plurality of paths of AD channels is realized through a plurality of groups of concurrent controls, and meanwhile, a hundred-megabyte frequency division clock is adopted to interact data with an AD module, so that the sampling frequency of the AD module of the acquisition unit is improved to the greatest extent, and the loss of high-frequency components in sampling signals is reduced;
after sampling period begins each time, software starts AD conversion signals, then monitors AD busy indication signals, after AD conversion is finished (AD busy indication is invalid), AD sampling data of this time are obtained bit by bit through an SPI bus, and the conversion formula of input sampling is as follows:
wherein: s is a conversion output digital quantity, and V is an input voltage analog quantity;
step 1, after obtaining discretization sampling data, entering step 2, and iteratively calculating integral data of sampling values;
step 2: integral value calculation
Through the sampling value obtained in the step 1, the current sampling integral data is calculated in an iterative manner by combining the sampling integral value calculated at the previous sampling moment;
according to the differential output principle of the electronic transformer body, the primary electric quantity of the transformer is converted into the integral proportional relation of small signals input by the acquisition unit, the discrete approximation algorithm of the fixed integral is obtained according to the area equivalent principle, the formula is modified into iterative operation related to the previous integral result, and the operation quantity of single integral calculation is reduced;
the integral value is calculated as follows:
according to the discrete digital integration principle, taking an electronic current transformer of the rogowski coil principle as an example, the reverse voltage output by the rogowski coil at any moment is as follows:
in the formula: v (t) is the reverse voltage of the secondary output of the coil at the time t; i (t) is the primary current value of the coil at the time t; m is a constant;
from the formula of definite integral:
in the formula: i (0) is an initial value of i (t).
Discretizing the formula (2) by adopting trapezoidal integration to obtain:
in the formula: i isnThe current sampling value of the nth point is taken as the current sampling value of the nth point; i is0The current sampling value is the initial point; vnIs the output reverse voltage of the nth point; t issIs a sampling period;
the integral output formula obtained by modifying the formula (3) into iterative operation is as follows:
limiting the calculated result to InLimited within a set range when InReplacing the cache according to an extreme value when exceeding the limit;
and step 3: integral attenuation discrimination
After the integral data of the current sampling is obtained in the step 2, judging whether the integral data of the current sampling needs attenuation processing or not through a break variable by combining the sampling cache sequence calculated in the step 1;
attenuation discrimination is based on the sudden change characteristics of fault sampling, for steady-state power frequency sampling, hardware performance change and algorithm errors are considered, the periodicity change of sampling points is small, and for transient fault sampling, the change of a fault sampling value relative to a steady-state sampling value is large. By analyzing the periodic variation trend of the sampling sequence, the real-time sampling state can be obtained, and whether the integral value needs to be attenuated through the step 4 is determined;
the integral attenuation regulating mechanism is shown in fig. 2, after an input sampling value is obtained each time, the sampling buffer sequence is combined to calculate the mutation quantity of the current sampling point, the current change trend of the sampling value is judged, and the mutation quantity calculation formula is as follows:
SΔ＝Sn+Sn-2T-2×Sn-T(5)
in the formula: sΔSampling break variable; snIs the current sampling value; sn-2TIs a sampling value before 2 cycles; sn-TIs a sampling value before 1 cycle; as shown in fig. 2, when the abrupt change amount of the continuous sampling points exceeds the threshold value and the sampling absolute value is an increasing change trend, it is determined that the sampling is a primary fault current, and a fault starting mode is entered, in which the gradual attenuation of the integral value is reduced or stopped; after the fault is started, judging whether the fault returns in real time, and if the fault does not return after the start is overtime, forcibly restoring the original integral attenuation processing process; when the absolute value of the continuous whole period sampling point is less than the fault return threshold value,judging that the primary fault current disappears, and immediately recovering the original integral attenuation process;
the transient process of the primary electric quantity is judged through the sampling mutation quantity, and the integral attenuation is reduced or stopped in the transient process, so that the direct current component of the original sampling signal in the transient process is not influenced by the attenuation. The setting of the start threshold value of the break variable is related to the sensitivity of the integral attenuation adjustment, the smaller the setting of the threshold value is, the faster the start is when the sampling is broken, and the higher the sensitivity of the integral attenuation adjustment is; if the threshold value of the break variable is set too low, the integral attenuation adjusting algorithm may be started by mistake when the frequency of the original sampling signal changes or the harmonic component is contained in the original sampling signal is large, and the steady-state accuracy of integral output of the acquisition unit is influenced;
and 4, step 4: integral attenuation processing
In step 3, the mutation quantity of the fault sampling is utilized to judge whether the current sampling belongs to normal sampling or fault sampling, and whether the integral result calculated in step 2 needs attenuation processing is determined;
the attenuation processing is beneficial to improving the anti-interference performance of the integral algorithm, has a good filtering effect on the incidental interference and has a certain inhibiting effect on the continuous interference, and the attenuation processing influences the response of the integral algorithm to the direct current component of the original signal, so that the targeted starting is needed. When detecting that the current integral data does not need to be attenuated, skipping the integral attenuation processing process; if the integral data needs to be attenuated, reducing the integral calculation result of the sampling point according to the actual requirement;
for the ideal integral discrete algorithm described in equation (4), when it is determined that the current integral value needs to be attenuated, equation (3) may be modified to suppress infinite amplification of the interference or direct current signal by the ideal integral algorithm of equation (3):
in the formula: k is a disturbance attenuation coefficient; the value of the disturbance attenuation coefficient k is not more than 1, so that the integral result of the previous iteration is ensured to be smaller than the actual value each time, the continuous influence of the interference on the integral algorithm is released, in order to avoid the floating point operation process, k is converted into a fraction expression mode of power operation with the numerator smaller than the denominator and the denominator being 2, and the floating point operation is replaced by shaping multiplication and shaping displacement, as follows;
in the formula: NUM is a molecule of the attenuation coefficient; n is the denominator index (right shift number) of the attenuation coefficient;
and 5: filtering of zero drift
After the integral attenuation processing is completed in the steps 3 and 4, the periodic null shift of the integral data is filtered in the step 5;
because of the influence of a sampling loop or the precision of an integral algorithm, an additional inherent null shift value exists in sampling integral output data, and the null shift can change along with the change of time and the external environment, an integral output of an acquisition unit based on the differential input principle of an electronic transformer does not theoretically contain an inherent direct current component, and the inherent null shift needs to be filtered in order to improve the transmission accuracy of the acquisition unit;
and calculating a null shift value of the integral data by periodically superposing integral output data, compensating before final output, finishing integral calculation of the current sampling point after filtering the null shift, putting an integral result into a buffer space, and directly iterating and using the next sampling point during calculation.
Periodically accumulating the integral calculation value of each point, calculating the inherent direct current component contained in the integral calculation value, and subtracting the null shift value before final output to realize inherent null shift filtering of integral output;
the integral zero drift value calculation formula is as follows:
in the formula: zIA periodic null shift of the integral calculation value; n is the number of periodic sampling points; INTnCalculated as an integral.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present invention, and therefore, the scope of the present invention should be determined by the scope of the claims.
Claims (6)
1. A dynamically attenuated integral reduction method for an acquisition unit of an electronic transformer is characterized by comprising the following steps: the method comprises the following steps: firstly, acquiring an input sampling value at the current moment, and iteratively calculating sampling value integral data through a discrete integration-fixed algorithm; secondly, judging whether the calculated integral value needs to be attenuated or not by combining the sampling information cached in the preorder, if the current input is identified to be normal sampling, attenuating the integral value of the previous time, and if the current input is identified to be fault sampling, not attenuating the integral value of the previous time; finally, filtering the cycle null shift of the output data to obtain the integral data of the original sampling; the method comprises the following specific steps:
step 1: high frequency sampling
Acquiring input discrete samples of the current acquisition unit through digital-to-analog conversion, adopting high-speed AD (analog-to-digital) conversion, sequentially storing the discrete sample values in a sample cache according to sampling moments, and using the discrete sample values when the integral value in the step 2 is calculated and the integral attenuation in the step 3 is judged;
step 1, after obtaining discretization sampling data, entering step 2, and iteratively calculating integral data of sampling values;
step 2: integral value calculation
Through the sampling value obtained in the step 1, the current sampling integral data is calculated in an iterative manner by combining the sampling integral value calculated at the previous sampling moment;
converting primary electric quantity of the transformer into an integral proportional relation of small signals input by a collecting unit according to a differential output principle of an electronic transformer body, and acquiring a discrete approximation algorithm of fixed integral according to an area equivalent principle;
and step 3: integral attenuation discrimination
After the integral data of the current sampling is obtained in the step 2, judging whether the integral data of the current sampling needs attenuation processing or not through a break variable by combining the sampling cache sequence calculated in the step 1;
by analyzing the periodic variation trend of the sampling sequence, the real-time sampling state can be obtained, and whether the integral value needs to be attenuated through the step 4 is determined;
and 4, step 4: integral attenuation processing
In step 3, the mutation quantity of the fault sampling is utilized to judge whether the current sampling belongs to normal sampling or fault sampling, and whether the integral result calculated in step 2 needs attenuation processing is determined;
when detecting that the current integral data does not need to be attenuated, skipping the integral attenuation processing process; if the integral data needs to be attenuated, reducing the integral calculation result of the sampling point according to the actual requirement;
and 5: filtering of zero drift
After the integral attenuation processing is completed in the steps 3 and 4, the periodic null shift of the integral data is filtered in the step 5;
and calculating a null shift value of the integral data by periodically superposing integral output data, compensating before final output, finishing integral calculation of the current sampling point after filtering the null shift, putting an integral result into a buffer space, and directly iterating and using the next sampling point during calculation.
2. The method according to claim 1, wherein the method comprises the steps of: the conversion formula of the input samples in step 1 is as follows:
wherein: s is the conversion output digital quantity, and V is the input voltage analog quantity.
3. The method according to claim 1, wherein the method comprises the steps of: the integral value is calculated in step 2 as follows:
the reverse voltage output by the rogowski coil at any moment is as follows:
in the formula: v (t) is the reverse voltage of the secondary output of the coil at the time t; i (t) is the primary current value of the coil at the time t; m is a constant;
from the formula of definite integral:
in the formula: i (0) is the initial value of i (t);
discretizing the formula (2) by adopting trapezoidal integration to obtain:
in the formula: i isnThe current sampling value of the nth point is taken as the current sampling value of the nth point; i is0The current sampling value is the initial point; vnIs the output reverse voltage of the nth point; t issIs a sampling period;
the integral output formula obtained by modifying the formula (3) into iterative operation is as follows:
limiting the calculated result to InLimited within a set range when InAnd (4) replacing the cache according to an extremum when the time is out of limit.
4. The method according to claim 1, wherein the method comprises the steps of: the formula for calculating the mutation in step 3 is as follows:
SΔ＝Sn+Sn-2T-2×Sn-T (5)
in the formula: sΔSampling break variable; snIs the current sampling value; sn-2TIs a sampling value before 2 cycles; sn-TIs a sampling value before 1 cycle; when the mutation amount of the continuous sampling points exceeds a threshold value and the sampling absolute value is in an increasing change trend, judging that the sampling is primary fault current, entering a fault starting mode, and reducing or stopping the successive attenuation of the integral value in the mode; after the fault is started, judging whether the fault returns in real time, and if the fault does not return after the start is overtime, forcibly restoring the original integral attenuation processing process; and when the absolute value of the continuous whole-period sampling point is smaller than the fault return threshold value, judging that the fault current disappears once, and immediately recovering the original integral attenuation process.
5. The method according to claim 1, wherein the method comprises the steps of: when attenuation processing is required in step 4, the inhibition formula is as follows:
in the formula: k is a disturbance attenuation coefficient; the value of the disturbance attenuation coefficient k is not more than 1, k is converted into a fractional expression mode of power operation with the numerator smaller than the denominator and the denominator being 2, floating point operation is replaced by shaping multiplication and shaping shift, and the following steps are performed:
in the formula: NUM is a molecule of the attenuation coefficient; n is the denominator index of the attenuation coefficient.
6. The method according to claim 1, wherein the method comprises the steps of: the integral zero drift value calculation formula in the step 5 is as follows:
in the formula: zIA periodic null shift of the integral calculation value; n is the number of periodic sampling points; INTnCalculated as an integral.
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