CN113406551A - Early diagnosis method for faults of Rogowski coil electronic current transformer - Google Patents
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Abstract
An early diagnosis method for faults of an electronic current transformer of a Rogowski coil comprises the steps of collecting the phase and the frequency of the electronic current transformer of the Rogowski coil, and constructing a phase difference sequence data model; obtaining a sample autocorrelation function; preliminarily judging the stable characteristics of the phase difference sequence by means of the change trend of the sample autocorrelation function curve, quantitatively analyzing the preliminarily judged stable phase difference sequence by a statistical method, and solving a statistical critical characteristic table of ADF unit root test; and finally, integrating the zeroing speed, the oscillation amplitude and the statistical critical characteristic table of the ADF unit root test of the phase difference time sequence of the autocorrelation function curve to finish the abnormal diagnosis of the early measurement data of the fault of the current transformer. The invention can timely find the abnormal condition of the measured data which is shown in the early stage of the fault of the current transformer under the condition of no power failure and no need of a standard transformer, thereby early warning the field fault in advance and reducing the loss caused by the abnormal measurement of the current transformer.
Description
Technical Field
The invention relates to the technical field of online monitoring of an electronic current transformer of a Rogowski coil, in particular to an early diagnosis method for faults of the electronic current transformer of the Rogowski coil based on a sample autocorrelation function and ADF unit root inspection.
Background
The Rogowski coil electronic current transformer gradually becomes mainstream current metering equipment of an intelligent substation due to the advantages of the Rogowski coil electronic current transformer in the aspects of dynamic range, insulation performance and the like, and is an important guarantee for digital relay protection, rapid state estimation of a power grid, rapid state estimation of the power grid and the like. However, in actual operation, the Rogowski coil electronic current transformer is comprehensively influenced by factors such as vibration, temperature, frequency, magnetic field, humidity and the like in different degrees, and measurement data abnormality may occur after operation for a period of time, so how to realize long-term stable operation becomes a problem which needs to be solved urgently. The measurement fault generally has a gradual change process, and the early expression form of the fault is measurement error abnormity, so that early diagnosis for evaluating whether the Rogowski coil electronic current transformer is abnormal becomes possible, namely, a reference basis is provided for early fault diagnosis through error analysis of measurement data.
In the prior art, documents 'Wangzao, Zhang Ning, Liulin, and the like', research and application of an active electronic transformer fault diagnosis technology [ J ]. power system protection and control, 2015,43(18):74-79. ',' bear Xiao Fu, He Ning, Yun Jun, and the like.
The method comprises the following steps of document 'Liqinxian, Chenghui, Gezhuijia, and the like', an information entropy method [ J ] of monitoring the operation failure risk of an electronic voltage transformer, an electric power system and an automatic chemical report thereof, 2019,31(12):44-48. ', document' Wanghipin, down Kunming, Xurelin, and the like.
Disclosure of Invention
In order to solve the technical problems, the invention provides an early diagnosis method for faults of the Rogowski coil electronic current transformer, which can timely find the abnormal condition of measurement data, which is shown in the early stage of the faults of the Rogowski coil electronic current transformer, under the conditions of no power outage and no need of a standard transformer, early warn the field faults in advance, and reduce the loss caused by the abnormal measurement of the Rogowski coil electronic current transformer. The method can be used for long-term online evaluation and has the advantages of low cost and simplicity in operation.
The technical scheme adopted by the invention is as follows:
the early diagnosis method for the faults of the Rogowski coil electronic current transformer comprises the following steps of firstly, collecting the phase and the frequency of the Rogowski coil electronic current transformer, and constructing a phase difference sequence data model; then, calculating the influence of frequency fluctuation on the phase difference, compensating, and solving the compensated phase difference sequence by using a numerical solving method to obtain a sample autocorrelation function; secondly, preliminarily judging the stable characteristics of the phase difference sequence by means of the change trend of the sample autocorrelation function curve, quantitatively analyzing the preliminarily judged stable phase difference sequence by a statistical method, and solving a statistical critical characteristic table of ADF unit root test; and finally, integrating the zeroing speed, the oscillation amplitude and the statistical critical characteristic table of the ADF unit root test of the phase difference time sequence of the autocorrelation function curve to finish the abnormal diagnosis of the early measurement data of the fault of the current transformer.
A fault early diagnosis method for an electronic current transformer of a Rogowski coil comprises the following steps:
step 1: acquiring a frequency and a Rogowski coil electronic current transformer phase sequence, analyzing and processing the acquired data, intercepting a phase difference sequence, and establishing a comparison standard;
step 2: calculating the influence of frequency fluctuation on the phase difference, compensating, solving the compensated phase difference sequence by using a numerical solving method to obtain a sample autocorrelation function, and then preliminarily diagnosing the fault early-stage measurement error of the Rogowski coil electronic current transformer according to an autocorrelation function curve graph;
and step 3: judging whether the ADF unit root test is carried out or not according to the preliminary diagnosis result of the autocorrelation function curve graph in the step 2, and obtaining a statistical critical characteristic table of the ADF unit root test;
and 4, step 4: and (4) integrating the zero-setting speed, the oscillation amplitude and the statistical critical characteristic table of the ADF unit root test of the phase difference time sequence of the autocorrelation function curve to finish the abnormal diagnosis of the measurement data expressed in the early stage of the fault of the Rogowski coil electronic current transformer.
In the step 1, the intercepted phase difference sequence is a current signal initial phase difference sequence with the same time interval, and the initial phase of the intercepted signal is irrelevant to the initial intercepting time, so that a comparison standard is established by taking the difference between the initial phases of the intercepted signal which is equal in theory (phase difference invariant principle) as a reference.
Establishing a mutual inductor comparison standard by utilizing the phase difference invariant characteristic, namely taking the stationarity of a phase difference sequence as the criterion of early measurement and diagnosis of the fault of the mutual inductor: and if the phase difference sequence is preliminarily judged to be a non-stationary sequence according to the sample autocorrelation function curve graph, directly judging that the measured data of the Rogowski coil current transformer is abnormal, otherwise, further judging according to p _ value, tau statistic, variance, tau statistic and a critical value of an ADF unit root test statistic critical characteristic table.
The step 2 comprises the following steps:
step 2.1: calculating and compensating the influence of the frequency fluctuation on the phase difference:
the amplitude normalization analytic formula under the ideal condition of the grid current waveform signal is set as follows:
f(t)＝cos(ω0t+φ0)；
in the formula, ω0、φ0The frequency and the initial phase of the current signal are respectively, and the current normalization analytic expression at the time t + Δ t is:
f(t+△t)＝cos[ω0(t+△t)+φ0]；
therefore, the current phase difference at time interval Δ t is ω0Δ t, is only related to the system frequency and time interval, so the following equation can be used to correct for the effect of frequency variation on phase:
△ψ＝△ω*△t；
in the formula, Δ ω is an offset of the system frequency; Δ t is the time interval for intercepting the current signal; Δ ψ is a phase difference change caused by frequency fluctuation.
Step 2.2: calculating the self-correlation function of the sample for the compensated phase difference sequence by using a numerical solving method, recording equal time intervals, intercepting the sampled data and calculating the phase difference sequence delta phi after differential operation1，Δφ2，…，ΔφnIs { Δ φiWhere 1, 2 …, n, the sample autocorrelation function of the phase difference time series is:
in the formula, i and j represent sample phasesThe serial number of the difference sequence, n represents the number of elements of the sample phase difference sequence,
represents the average of the sample phase difference sequences.
When j is continuously increased, if the sample autocorrelation function curve is rapidly reduced and tends to 0, the phase difference sequence can be judged to be a stable sequence for the first time; if the autocorrelation curve is descending slowly or has obvious oscillation, it is a non-stationary sequence.
In step 3, it is determined whether to continue to use the ADF root test to obtain the statistical critical feature table for further phase difference time series stationarity determination: if the phase difference sequence is preliminarily judged to be a stable sequence according to the sample autocorrelation function curve graph, calling a statmodels tool in python to carry out ADF quantitative inspection; and if the phase difference sequence is preliminarily judged to be a non-stable sequence according to the sample autocorrelation function curve diagram, directly judging that the measured data of the Rogowski coil current transformer is abnormal, and early warning in time.
In the steps 3 and 4, the quantitative evaluation parameters of the stability of the phase difference time sequence are judged by ADF unit root test and are p _ value, tau statistic, variance and the like in the statistical critical characteristic table. If the p _ value is obviously smaller than 0.05 and the tau statistic value is smaller than the critical value of 10%, the sequence is a stable sequence, otherwise, the early measurement data abnormality of the Rogowski coil electronic current transformer can be warned.
The invention discloses an early diagnosis method for faults of an electronic current transformer of a Rogowski coil, which has the following technical effects:
1) the method establishes a comparison standard by taking the phase difference invariant principle as a reference, and diagnoses the early measurement state of the fault of the Rogowski coil electronic current transformer by detecting the stability of a phase difference sequence. The abnormal measurement condition which is shown in the early stage of the fault is found in time, and reference is provided for on-site fault early warning and scheduling decision; the method can evaluate the measuring error state of the mutual inductor for a long time, and is simple in actual operation and low in cost.
2) The method utilizes the frequency correction formula to perform characteristic compensation on the phase difference sequence, a group of stable sequences can be obtained when the equipment is in a normal measurement state, otherwise, the equipment is abnormal in measurement, and reference is provided for field fault early warning and scheduling decision.
3) According to the invention, the phase difference sequence is subjected to preliminary stability inspection according to the autocorrelation function curve diagram, so that obvious unstable sequences can be effectively and visually discriminated, and time is saved for field fault early warning and equipment maintenance.
4) According to the method, the statmodels in python are used for ADF unit root inspection to obtain a statistical distribution critical table containing p _ value, tau statistic, variance and critical value, the stability of the phase difference sequence is quantitatively analyzed from a statistical angle, the reliability of the phase difference time sequence stability inspection result is guaranteed, and time is provided for making field scheduling decision and maintaining in advance.
Drawings
Fig. 1 is a measured frequency distribution histogram.
Fig. 2 is a probability distribution diagram of an actually measured phase difference sequence.
Fig. 3 is a graph of the autocorrelation function of a sequence of phase differences under normal random fluctuations measured by the apparatus.
FIG. 4 is a graph of the autocorrelation function of a sequence of phase differences under abnormal random fluctuations measured by the apparatus.
Fig. 5 is a graph of the autocorrelation function of a sequence of phase differences with a positive direction gradient for an abnormal measurement of the apparatus.
FIG. 6 is a graph of the autocorrelation function of a sequence of phase differences under abnormal and negative direction ramp of the device measurement.
Fig. 7 is a flow chart of measurement abnormality diagnosis.
Detailed Description
A fault early diagnosis method for an electronic current transformer of a Rogowski coil diagnoses a fault which shows measurement data abnormality in the early stage of the electronic current transformer of the Rogowski coil through a sample autocorrelation function graph and ADF unit root inspection. The method comprises the steps of carrying out differential operation according to captured and collected current signals to obtain a phase difference sequence, establishing a comparison standard by taking a phase difference invariant principle as a reference, diagnosing the early measurement state of the fault of the Rogowski coil electronic current transformer by detecting the stability of the phase difference sequence, finding out the abnormal measurement condition expressed in the early stage of the fault in advance, and providing reference for field fault early warning and scheduling decision.
A fault early diagnosis method for an electronic current transformer of a Rogowski coil comprises the following steps:
step 1, obtaining analysis data:
the experimental data are derived from the measured data of a laboratory Rogowski coil electronic current transformer, wherein the measured frequency distribution randomly fluctuates around ± 0.03Hz as shown in fig. 1, and thus the phase difference distribution shown in fig. 2 also randomly fluctuates around 7'; and intercepting the current signals at equal time intervals to obtain an initial phase difference sequence.
Step 2, solving an autocorrelation function:
parameters such as the waveform of a power grid signal in actual operation are continuously fluctuated, and particularly when the frequency of a power grid is continuously changed, the phase difference of the same time interval is fluctuated within a certain range. In order to use the phase difference invariant characteristic to study the mutual inductor comparison standard, the frequency distribution characteristic of the power grid needs to be analyzed to compensate the influence of frequency fluctuation on the phase difference. In addition, how to effectively judge the stability of the phase difference time sequence is the key of early measurement and diagnosis of faults of the Rogowski coil electronic current transformer. Firstly, the stability characteristics are judged by means of the change trend of a graph curve, an intuitive and simple approach is provided for phase difference time sequence stability inspection, and a typical representation is an autocorrelation function graph. The method comprises the following specific steps:
step 2.1: calculating and compensating the influence of the frequency fluctuation on the phase difference: assuming that the amplitude normalization analytic expression under the ideal condition of the grid current waveform signal is as follows:
f(t)＝cos(ω0t+φ0)
in the formula, ω0、φ0Respectively, the frequency and the initial phase of the current signal. Then the current normalization analytic equation at time t + Δ t is:
f(t+△t)＝cos[ω0(t+△t)+φ0]
thus, it is possible to provideThe phase difference of the current at a time interval Δ t is ω0Δ t, which is only related to the system frequency and time interval, the following equation is used to correct for the effect of frequency variation on phase:
△ψ＝△ω*△t
in the formula, Δ ω is an offset of the system frequency; Δ t is the time interval for intercepting the current signal; Δ ψ is a phase difference change caused by frequency fluctuation.
Step 2.2: phase difference sequence delta phi obtained by recording equal time intervals, intercepting sampling data and carrying out differential operation1，Δφ2，…，Δφn-1Is { Δ φi(i ═ 1, 2 …, n-1), then the sample autocorrelation function for the phase difference time series is:
in the formula, i and j both represent the serial number of the sample phase difference sequence, n represents the number of the elements of the sample phase difference sequence,
represents the average of the sample phase difference sequences.
The sample autocorrelation function is obtained for the compensated phase difference sequence by using a numerical solving method, and autocorrelation function graphs plotted using origin are shown in fig. 3 to 6.
Step 3, preliminary diagnosis and critical characteristic table solving:
as can be seen from the autocorrelation function graphs shown in fig. 3 and 6, when j is increased continuously, the sample autocorrelation function curve decreases rapidly and approaches 0, and the phase difference sequence can be determined as a stationary sequence; the slow descending of the autocorrelation function curves of fig. 4 and 5 or obvious oscillation is a non-stationary sequence, so that the abnormal measurement of the transformer can be directly diagnosed and early-warning can be realized.
Obviously, the stability judgment through the convergence speed of the autocorrelation function curve and the oscillation state has certain subjectivity, and the non-stationary sequence can be misjudged as the stationary sequence, so the method can only be used as an initial judgment basis for the stability test of the phase difference sequence. In order to further judge the stability of the phase difference sequence, the invention adopts ADF unit root inspection which is widely applied at present and has better performance to improve the accuracy of phase difference stability inspection. The statistical critical features obtained by invoking statmodels in python for ADF quantification testing are shown in table 1, where the first through fourth sets correspond to the difference sequence data of fig. 3-6, respectively.
TABLE 1 statistical Critical characteristics Table for ADF root test of sample sequences
how to effectively judge the stability of the phase difference time sequence is the key of early measurement and diagnosis of faults of the Rogowski coil electronic current transformer. As shown in fig. 7, the hybrid stationarity testing method based on the sample autocorrelation function graph and the ADF unit root test of the present invention tests the phase difference sequence, and integrates the zeroing speed, the oscillation amplitude of the autocorrelation function curve and the statistical critical characteristic table of the phase difference time sequence ADF unit root test, thereby completing the abnormality diagnosis of the measurement data that is shown in the early stage of the fault of the Rogowski coil electronic current transformer.
According to the data in table 1, the p _ value of the second group of sample data and the third group of sample data is obviously greater than 0.05, the two groups of sequences have no unit root, that is, the sequences are non-stationary, and the accuracy of the autocorrelation function curve on the judgment of the non-stationary sequences is also verified. Although the p _ value of the first and the fourth groups is less than 0.05 and the tau statistic is less than the critical value of 10%, the variance of the fourth group is obviously larger than that of the other four groups, so that the fault gradual change abnormal fluctuation measurement fault of the Rogowski coil electronic current transformer is identified. Stability inspection is carried out by means of the change trend of the sample sequence autocorrelation function curve and the statistical characteristics of the sample sequence, so that the measurement abnormity of the Rogowski coil electronic current transformer can be identified in time, and the field maintenance and scheduling early warning are carried out in advance; the method overcomes the defect of subjectivity in judging the stability of the autocorrelation function curve through ADF unit root quantitative inspection phase difference sequence stability, further improves the reliability of the inspection result, and strives for time for making field scheduling decision and maintenance in advance.
Claims (6)
1. A fault early diagnosis method for an electronic current transformer of a Rogowski coil is characterized by comprising the following steps: firstly, acquiring the phase and frequency of an electronic current transformer of a Rogowski coil, and constructing a phase difference sequence building data model; then, calculating the influence of frequency fluctuation on the phase difference, compensating, and solving the compensated phase difference sequence by using a numerical solving method to obtain a sample autocorrelation function; secondly, preliminarily judging the stable characteristics of the phase difference sequence by means of the change trend of the sample autocorrelation function curve, quantitatively analyzing the preliminarily judged stable phase difference sequence by a statistical method, and solving a statistical critical characteristic table of ADF unit root test; and finally, integrating the zeroing speed, the oscillation amplitude and the statistical critical characteristic table of the ADF unit root test of the phase difference time sequence of the autocorrelation function curve to finish the abnormal diagnosis of the early measurement data of the fault of the current transformer.
2. A fault early diagnosis method for an electronic current transformer of a Rogowski coil is characterized by comprising the following steps:
step 1: acquiring a frequency and a Rogowski coil electronic current transformer phase sequence, analyzing and processing the acquired data, intercepting a phase difference sequence, and establishing a comparison standard;
step 2: calculating the influence of frequency fluctuation on the phase difference, compensating, solving the compensated phase difference sequence by using a numerical solving method to obtain a sample autocorrelation function, and then preliminarily diagnosing the fault early-stage measurement error of the Rogowski coil electronic current transformer according to an autocorrelation function curve graph;
and step 3: judging whether the ADF unit root test is carried out or not according to the preliminary diagnosis result of the autocorrelation function curve graph in the step 2, and obtaining a statistical critical characteristic table of the ADF unit root test;
and 4, step 4: and (4) integrating the zero-setting speed, the oscillation amplitude and the statistical critical characteristic table of the ADF unit root test of the phase difference time sequence of the autocorrelation function curve to finish the abnormal diagnosis of the measurement data expressed in the early stage of the fault of the Rogowski coil electronic current transformer.
3. A method for early diagnosis of faults in a Rogowski coil electronic current transformer according to claim 2, wherein: in the step 1, the intercepted phase difference sequence is a current signal initial phase difference sequence with the same time interval, the initial phase of the intercepted signal is irrelevant to the initial intercepting time, and a comparison standard is established by taking the difference between the initial phases of the intercepted signal as a reference.
4. A method for early diagnosis of faults in a Rogowski coil electronic current transformer according to claim 2, wherein: the step 2 comprises the following steps:
step 2.1: calculating and compensating the influence of the frequency fluctuation on the phase difference:
the amplitude normalization analytic formula under the ideal condition of the grid current waveform signal is set as follows:
f(t)＝cos(ω0t+φ0)；
in the formula, ω0、φ0The frequency and the initial phase of the current signal are respectively, and the current normalization analytic expression at the time t + Δ t is:
f(t+△t)＝cos[ω0(t+△t)+φ0]；
therefore, the current phase difference at time interval Δ t is ω0Δ t, is only related to the system frequency and time interval, so the following equation can be used to correct for the effect of frequency variation on phase:
△ψ＝△ω*△t；
in the formula, Δ ω is an offset of the system frequency; Δ t is the time interval for intercepting the current signal; delta psi is the phase difference change caused by frequency fluctuation;
step 2.2: calculating the autocorrelation function of the sample of the compensated phase difference sequence by using a numerical solving method, and recording the time intervalPhase difference sequence delta phi after intercepting sampling data and differential operation1，Δφ2，…，Δφn-1Is { Δ φi(i ═ 1, 2 …, n-1), then the sample autocorrelation function for the phase difference time series is:
in the formula, i and j both represent the serial number of the sample phase difference sequence, n represents the number of the elements of the sample phase difference sequence,
an average value representing a sequence of sample phase differences;
when j is continuously increased, if the sample autocorrelation function curve is rapidly reduced and tends to 0, the phase difference sequence can be judged to be a stable sequence for the first time; if the curve of the autocorrelation function is descending slowly or has obvious oscillation, the autocorrelation function is a non-stable sequence;
5. a method for early diagnosis of faults in a Rogowski coil electronic current transformer according to claim 2, wherein: in step 3, it is determined whether to continue to use the ADF root test to obtain the statistical critical feature table for further phase difference time series stationarity determination: if the phase difference sequence is preliminarily judged to be a stable sequence according to the sample autocorrelation function curve graph, calling a statmodels tool in python to carry out ADF quantitative inspection; and if the phase difference sequence is preliminarily judged to be a non-stable sequence according to the sample autocorrelation function curve diagram, directly judging that the measured data of the Rogowski coil current transformer is abnormal, and early warning in time.
6. A method for early diagnosis of faults in a Rogowski coil electronic current transformer according to claim 2, wherein: the ADF unit root test judges quantitative evaluation parameters of phase difference time sequence stationarity, and the quantitative evaluation parameters are p _ value, tau statistic and variance in a statistical critical feature table; if the p _ value is obviously smaller than 0.05 and the tau statistic value is smaller than the critical value of 10%, the sequence is a stable sequence, otherwise, the early measurement data abnormality of the Rogowski coil electronic current transformer can be warned.
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Application publication date: 20210917 |