CN106771520A - A kind of power distribution network temporary overvoltage classifying identification method and device - Google Patents
A kind of power distribution network temporary overvoltage classifying identification method and device Download PDFInfo
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Abstract
The present invention relates to a kind of power distribution network temporary overvoltage classifying identification method and device, the method main contents include obtaining waveform sampling data, calculate the energy contribution rate and singular spectrum entropy of sampled data, extract the time domain energy distribution characteristics of sampled data, carry out local feature Scale Decomposition and bandpass filtering to sampled data to obtain center of gravity frequency band, realize power distribution network temporary overvoltage type identification with reference to threshold value diagnostic method.The present invention does not need grader, algorithm is simple, the calculating time is few, single phase metal ground connection, intermittent arc grounding, high-frequency resonant, fundamental resonance, the class power distribution network over-voltage type of Subharmonic Resonance five can accurately be recognized, power distribution network over-voltage kind identification method of the invention has stronger adaptability, still has overvoltage type identification accuracy higher under the operating mode of noise jamming.
Description
Technical Field
The invention relates to the field of power distribution networks, in particular to a method and a device for identifying a temporary overvoltage type of a power distribution network.
Background
Operation experience shows that overvoltage is one of important factors influencing the safe operation of the power distribution network. The temporary overvoltage lasts for a long time and is easy to cause equipment insulation damage, so that various short-circuit faults are caused, and the power supply reliability of the power distribution network is endangered. Therefore, the overvoltage of the power distribution network is detected in time, the overvoltage types are accurately distinguished, and the method is very necessary for power distribution network disaster prevention and fault analysis. However, the existing overvoltage monitoring device does not have the capability of identifying the type of the overvoltage, more is judged by manual experience, and the efficiency is low and the accuracy is not high. Therefore, the characteristic quantity of the temporary overvoltage signal is quickly extracted, the overvoltage type is automatically identified, and the method has great significance for improving the self-healing capacity of the power distribution network and constructing the active power distribution network.
Disclosure of Invention
In view of this, the present invention provides a method and an apparatus for identifying a temporary overvoltage type of a power distribution network, which can identify the temporary overvoltage type timely and accurately.
The invention is realized by adopting the following scheme: a classification and identification method for temporary overvoltage of a power distribution network comprises the following steps:
step S1: after the overvoltage occurs to the power distribution network, acquiring waveform sampling data of three-phase voltage and zero-sequence voltage of a bus in a period of time before and after the overvoltage occurs;
step S2: calculating the energy contribution rate E of two power frequency cycles after the fault on the zero sequence voltage sampling data obtained in the step S1, judging whether the energy contribution rate E is greater than a threshold value, if so, judging the energy contribution rate E to be an operation overvoltage, and ending the identification process;
step S3: if E is smaller than the threshold, judging the transient overvoltage, calculating the singular spectrum entropy S of the corresponding three-phase voltage sampling data, judging whether S is larger than the threshold, if so, judging the single-phase metallic grounding overvoltage, and ending the identification process;
step S4: if S is smaller than the threshold value, local characteristic scale decomposition, Hilbert transform and band-pass filtering are carried out on corresponding zero-sequence voltage sampling data, and gravity center frequency band N is calculatedg；
Step S5: three ferromagnetic resonance overvoltages and intermittent arc grounding overvoltages are distinguished by adopting a threshold discrimination method: if N is presentgEqual to 6, judging as high-frequency resonance overvoltage; if N is presentgIf the value is equal to 5, judging the value to be a fundamental frequency resonance overvoltage; if N is presentgLess than 5 and more than or equal to 2, and judging the frequency division resonance overvoltage; if N is presentgAnd if the voltage is equal to 1, judging the voltage is intermittent arc grounding overvoltage, and finishing the identification process.
Further, the specific process of intercepting the waveform sample data in step S1 is as follows: in order to obtain a complete frequency division resonance waveform, at least 5 voltage sampling data of power frequency cycles are intercepted.
Further, the specific process of calculating the energy contribution rate E in step S2 is as follows:
energy contribution rate according to the formulaCalculation of where N1The number of sampling points is 2 cycles; n is a radical of2The number of sampling points of the intercepted total time period; v. of0(k) Is a zero sequence voltage signal sequence; and selecting the energy contribution rate of 60% as a classification criterion of temporary overvoltage and operation overvoltage.
Further, the specific process of calculating the three-phase voltage sample data singular spectrum entropy in step S3 is as follows:
setting the number of the intercepted signal sampling points as n and the three-phase voltage signal matrix as U ═ Ua，ub，uc]The matrix U is subjected to singular value decomposition to obtain a singular spectrum Λ ═ diag { mu }1，μ2，μ3Entropy of singular spectrum of the signal
Wherein,the threshold value for judging the single-phase metallic grounding overvoltage is 1.15.
5. The method for identifying the temporary overvoltage type of the power distribution network according to claim 1, wherein the method comprises the following steps: step S4 includes the following steps:
step S41: performing local characteristic scale decomposition on the zero-sequence voltage sampling data to obtain a plurality of intrinsic scale components, namely ISC components;
step S42: performing Hilbert transform on each ISC component to obtain an instantaneous frequency matrix;
step S43: the total frequency band is divided into 6 bands: 0-10 Hz, 10-20 Hz, 20-30 Hz, 30-40 Hz, 40-80 Hz and 80-600 Hz, wherein the frequency bands are numbered in sequence, the frequency band 1 is 0-10 Hz, the frequency band 2 is 10-20 Hz, and the rest is done in the same way until the 6 th frequency band; reconstructing each intrinsic scale component obtained by decomposing the LCD by adopting a band-pass filtering algorithm according to multiple sub-bands of a Hilbert instantaneous frequency matrix to obtain component data of zero-sequence voltage sampling data in each frequency band;
step S44: and calculating the energy of the component data in each frequency band, and selecting the frequency band number with the highest energy as a gravity center frequency band.
The invention is also realized by adopting the following scheme: a device for recognizing the type of temporary overvoltage in power distribution network is composed of
The data acquisition module is used for acquiring waveform sampling data of bus three-phase voltage and zero sequence voltage in 0.5 power frequency periods before overvoltage occurs and 4.5 power frequency periods after overvoltage occurs after the overvoltage occurs;
and the energy contribution rate construction module is used for calculating the energy contribution rate of the obtained zero-sequence voltage sampling data and judging whether the obtained overvoltage sampling data belong to the category of temporary overvoltage.
The singular spectrum entropy construction module is used for calculating the singular spectrum entropy of the obtained three-phase voltage sampling data after judging that the obtained overvoltage sampling data belong to the category of the temporary overvoltage, and judging whether the obtained temporary overvoltage sampling data belong to the category of the single-phase metallic overvoltage or not;
the waveform sub-band reconstruction module is used for performing local characteristic scale decomposition, Hilbert transform and band-pass filtered sub-band reconstruction on the acquired zero-sequence voltage waveform sampling data;
the gravity center frequency band construction module is used for calculating the gravity center frequency band of the reconstructed signal and judging whether the obtained temporary overvoltage sampling data is high-frequency resonance, fundamental frequency resonance, frequency division resonance or intermittent arc grounding overvoltage;
and the overvoltage type identification module is used for judging which temporary overvoltage the obtained overvoltage sampling data is by combining a threshold identification method.
Further, the waveform sub-band reconstruction module includes:
the local characteristic scale decomposition module is used for carrying out local characteristic scale decomposition on the zero-sequence voltage waveform sampling data to obtain a plurality of ISC components;
a Hilbert transform module for performing Hilbert transform on each ISC component to obtain an instantaneous frequency matrix;
and the sub-band reconstruction module is used for carrying out band-pass filtering according to the instantaneous frequency matrix and decomposing each ISC component into 6 frequency bands.
Further, the sub-band reconstruction module decomposes each ISC component into 6 bands: 0-10 Hz, 10-20 Hz, 20-30 Hz, 30-40 Hz, 40-80 Hz and 80-600 Hz, and all ISC components in each frequency band are superposed to obtain a reconstructed waveform of the zero-sequence voltage waveform sampling data in each frequency band; and meanwhile, calculating the energy value of the component data of the zero-sequence voltage waveform sampling data in each frequency band as a data source of the gravity center frequency band building module.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention can completely describe the time-frequency characteristics of the overvoltage signal waveform in each sub-frequency band by utilizing the signals reconstructed by LCD, Hilbert transform and band-pass filtering algorithm sub-band, and the time-frequency localized information comprises the time-frequency localized information representing the essential characteristics of the signals.
2. The method combines singular value decomposition and a method for carrying out mathematical calculation by applying a statistical principle, can effectively extract main characteristic quantities reflecting the amplitude distribution characteristics of the overvoltage signals, can represent the energy ratio of two cycles of the overvoltage signals after the fault, and presents larger difference between single-phase grounding overvoltage with large difference of three-phase voltage amplitudes and other temporary overvoltage.
3. The threshold discrimination method does not need a classifier, has simple algorithm and less calculation time, and can accurately identify the temporary overvoltage types of five types of power distribution networks, namely single-phase metallic grounding, intermittent arc grounding, high-frequency resonance, fundamental frequency resonance and frequency division resonance.
4. The method for identifying the temporary overvoltage type of the power distribution network still has higher overvoltage type identification accuracy under the working condition of noise interference and has stronger adaptability.
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FIG. 1 is a flow chart of the present invention.
Fig. 2 shows a 10kV distribution network model applied in the embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
The embodiment provides a method for classifying and identifying temporary overvoltage of a power distribution network, as shown in fig. 1, which includes the following steps:
step S1: after the overvoltage occurs to the power distribution network, acquiring waveform sampling data of three-phase voltage and zero-sequence voltage of a bus in a period of time before and after the overvoltage occurs;
step S2: calculating the energy contribution rate E of two power frequency cycles after the fault on the zero sequence voltage sampling data obtained in the step S1, judging whether the energy contribution rate E is greater than a threshold value, if so, judging the energy contribution rate E to be an operation overvoltage, and ending the identification process; the method specifically comprises the following steps:
step S21: calculating the ratio E of the energy of 2 cycles of the signal after the fault to the total energy of the intercepted period:
wherein N is1The number of sampling points is 2 cycles; n is a radical of2The number of sampling points of the intercepted total time period; v. of0(k) Is a zero sequence voltage signal sequence.
Step S22: selecting 60% of energy contribution rate as a classification criterion of temporary overvoltage and operation overvoltage;
step S3: if E is smaller than the threshold, judging the transient overvoltage, calculating the singular spectrum entropy S of the corresponding three-phase voltage sampling data, judging whether S is larger than the threshold, if so, judging the single-phase metallic grounding overvoltage, and ending the identification process; the method specifically comprises the following steps:
setting the number of the intercepted signal sampling points as n and the three-phase voltage signal matrix as U ═ Ua，ub，uc]The matrix U is subjected to singular value decomposition to obtain a singular spectrum Λ ═ diag { μ }1，μ2，μ3Entropy of singular spectrum of the signal
Wherein,s can effectively represent the unevenness of the singular value distribution of the three-phase voltage signals, further reflect the difference of the amplitude distribution among the three-phase voltages and judge that the threshold value of the single-phase metallic grounding overvoltage is 1.15;
step S4: if S is smaller than the threshold value, carrying out local characteristic scale decomposition on corresponding zero sequence voltage sampling dataHilbert transform and band-pass filtering, calculating the center of gravity band Ng(ii) a The method specifically comprises the following steps:
step S41: carrying out local characteristic scale decomposition on the zero-sequence voltage waveform sampling data to obtain a plurality of ISC components, specifically: first, a single-component signal that satisfies the following two conditions may be referred to as an intrinsic scale component:
the method comprises the following steps that (I) signs of any two adjacent extreme points of signal data are different;
(II) the ratio of the function value of a straight line determined by any two adjacent maximum (or small) value points of the signal data at the abscissa corresponding to the minimum (or large) value point between the two points to the minimum (or large) value point is kept unchanged;
according to the two conditions, the overvoltage signal is subjected to local characteristic scale decomposition to be decomposed into the sum of a plurality of intrinsic scale components and a residual term, namelyWherein, x (t) is the original signal, n is the number of the intrinsic scale components; isci(t) is the ith intrinsic scale component; r (t) is the residual component.
Step S42: hilbert transform is performed on each ISC component, and the ith intrinsic scale component transform formula is
Constructing a corresponding analytic signal Yi(t) is
Yi(t)＝isci(t)+jH[isci(t)]
Then the ith intrinsic scale component instantaneous frequency function fi(t) is
Step S43: band pass filtering is performed according to the instantaneous frequency matrix, decomposing each ISC component into 6 frequency bands: 0-10 Hz, 10-20 Hz, 20-30 Hz, 30-40 Hz, 40-80 Hz and 80-600 Hz, and all ISC components in each frequency band are superposed to obtain components of the zero-sequence voltage waveform sampling data in each frequency band.
Step S44: calculating the energy value of the component data of the zero sequence voltage waveform sampling data in each frequency band, wherein the energy of the ith frequency band is
Wherein e isiSignal energy for the ith frequency band; m is the data length of the reconstructed signal; c. Ci(k) Is a reconstructed signal of the ith frequency band.
Step S45: number of frequency band with maximum energy:
Ng＝imax
wherein N isgIs the center of gravity frequency band; i.e. imaxThe frequency band with the largest energy is numbered. Center of gravity frequency band NgThe characteristic quantity of frequency division resonance, fundamental frequency resonance, high frequency resonance and intermittent arc grounding overvoltage can be identified;
step S5: three ferromagnetic resonance overvoltages and intermittent arc grounding overvoltages are distinguished by adopting a threshold discrimination method: if N is presentgEqual to 6, judging as high-frequency resonance overvoltage; if N is presentgIf the value is equal to 5, judging the value to be a fundamental frequency resonance overvoltage; if N is presentgLess than 5 and more than or equal to 2, and judging the frequency division resonance overvoltage; if N is presentgAnd if the voltage is equal to 1, judging the voltage is intermittent arc grounding overvoltage, and finishing the identification process.
The embodiment also provides a device for identifying the type of the temporary overvoltage of the power distribution network, which comprises
The data acquisition module is used for acquiring waveform sampling data of bus three-phase voltage and zero sequence voltage in 0.5 power frequency periods before overvoltage occurs and 4.5 power frequency periods after overvoltage occurs after the overvoltage occurs;
and the energy contribution rate construction module is used for calculating the energy contribution rate of the obtained zero-sequence voltage sampling data and judging whether the obtained overvoltage sampling data belong to the category of temporary overvoltage.
The singular spectrum entropy construction module is used for calculating the singular spectrum entropy of the obtained three-phase voltage sampling data after judging that the obtained overvoltage sampling data belong to the category of the temporary overvoltage, and judging whether the obtained temporary overvoltage sampling data belong to the category of the single-phase metallic overvoltage or not;
the waveform sub-band reconstruction module is used for performing local characteristic scale decomposition, Hilbert transform and band-pass filtered sub-band reconstruction on the acquired zero-sequence voltage waveform sampling data;
the gravity center frequency band construction module is used for calculating the gravity center frequency band of the reconstructed signal and judging whether the obtained temporary overvoltage sampling data is high-frequency resonance, fundamental frequency resonance, frequency division resonance or intermittent arc grounding overvoltage;
and the overvoltage type identification module is used for judging which temporary overvoltage the obtained overvoltage sampling data is by combining a threshold identification method.
In this embodiment, the waveform sub-band reconstruction module includes:
the local characteristic scale decomposition module is used for carrying out local characteristic scale decomposition on the zero-sequence voltage waveform sampling data to obtain a plurality of ISC components;
a Hilbert transform module for performing Hilbert transform on each ISC component to obtain an instantaneous frequency matrix;
and the sub-band reconstruction module is used for carrying out band-pass filtering according to the instantaneous frequency matrix and decomposing each ISC component into 6 frequency bands.
In this embodiment, the sub-band reconstruction module decomposes each ISC component into 6 bands: 0-10 Hz, 10-20 Hz, 20-30 Hz, 30-40 Hz, 40-80 Hz and 80-600 Hz, and all ISC components in each frequency band are superposed to obtain a reconstructed waveform of the zero-sequence voltage waveform sampling data in each frequency band; and meanwhile, calculating the energy value of the component data of the zero-sequence voltage waveform sampling data in each frequency band as a data source of the gravity center frequency band building module.
In the embodiment, as shown in fig. 2, a 10kV distribution network model is built by using PSCAD/EMTDC software to acquire overvoltage data, and test results show that the method can quickly and accurately identify temporary overvoltage of the distribution network occurring at different fault points, fault transition resistances and fault initial phase angles, and has good adaptability under noise interference, and on the basis, simulation experiments of five temporary overvoltage types are performed, and three-phase voltage and zero-sequence voltage of a bus are collected, so that 4 overvoltage waveforms are obtained. In the power distribution network line model, S1 is a 110kV infinite system power supply; s2 is a 110kV/10.5kV main transformer, S3 is a 10kV/0.4kV distribution transformer; s4 is an equivalent model of the electromagnetic voltage transformer, an S41 resistor and an S42 nonlinear inductor are connected in series for equivalence, and a switch Q is used for switching the electromagnetic voltage transformer model; k is a fault grounding resistance switching switch; the feeder lines are all 3 types of overhead long lines, all cable lines and line-cable mixed lines, wherein the total number of the feeder lines is 6: l1 to L6, the first feeder L1 comprises 4km of overhead line L11, 3km of overhead line L12 and 2km of overhead line L13, the second feeder L2 comprises 1.5km of cable line L21, 2km of overhead line L22, 6km of overhead line L23 and 2km of overhead line L24, the third feeder L3 comprises 0.5km of cable line L31, 7km of overhead line L32 and 1.5km of cable line L33, the fourth feeder L4 comprises 0.7km of cable line L41, 2km of cable line L42 and 2km of overhead line L43, the fifth feeder L43 comprises 1km of cable line L43, 2km of cable line L43 and 1km of cable line L43, the sixth feeder L43 comprises 1.5km of cable line L43, 1km of cable line L43 and 1km of cable line L43, the first feeder L43 to 2km of cable line L43 is the same parameter as the first feeder L43: r1 is 0.27 omega/km, C1 is 0.339 mu F/km, L1 is 0.255mH/km, and zero sequence parameters of the cable line are as follows: r0 ═ 2.7 Ω/km, C0 ═ 0.28 μ F/km, L0 ═ 1.019mH/km, and the overhead line positive sequence parameters were: r1 is 0.125 omega/km, C1 is 0.0096 mu F/km, L1 is 1.3mH/km, and the zero sequence parameters of the overhead line are as follows: r0 ═ 0.275 Ω/km, C0 ═ 0.0054 μ F/km, and L0 ═ 4.6 mH/km.
The factors such as a fault point, a fault initial phase angle and a fault transition resistance are comprehensively considered, and the temporary overvoltage generated by the neutral point ungrounded system shown in the figure 2 is simulated to verify the effectiveness of the identification method. The results of partial identification of the single-phase metallic grounding and intermittent arc grounding overvoltage are shown in tables 1 to 3. In table 1, the fault closing angle is 60 °, the transition resistance is 20 Ω, and the identification effect is obtained when different fault points have faults. The change of the fault point position can realize the change of the line type and the length, including a pure overhead line, a pure cable line and an overhead cable mixed line. Table 2 shows the effect of recognition when the fault closing angle is 60 °, the fault point is F24, and the transition resistance varies among 1 Ω, 2 Ω, 5 Ω, 10 Ω, and 20 Ω. Table 3 shows the recognition effect when the transition resistance is 20 Ω, the fault point is F24, and the fault closing angle is varied between-90 °, -45 °, 0 °, 45 °, and 90 °.
TABLE 1 recognition effect at different points of failure
TABLE 2 recognition effect at different transition resistances
TABLE 3 recognition effect under different fault closing angles
In order to meet the parameter matching requirement of the ferromagnetic resonance, only the single-circuit feeder line L1 in FIG. 2 is used for simulating the ferromagnetic resonance, the line length between the fault point F11 and the bus is changed to realize the change of the type of the resonance overvoltage, and the fault closing angle and the transition resistance value are used as two groups of variables to verify the accuracy of identifying the ferromagnetic resonance overvoltage. Table 4 shows the effect of identifying when the transition resistance is 5 Ω and the fault closing angle changes between-90 °, 30 °, and 150 °. Table 5 shows the effect of identifying when the fault closing angle is-90 °, and the transition resistance changes between 0 Ω, 5 Ω, and 20 Ω.
TABLE 4 recognition effect under different fault closing angles
TABLE 5 recognition effect at different transition resistances
The suitability of the proposed identification method is checked by the following test results:
a plurality of groups of wave recording data are extracted from each class of successfully identified overvoltage data, 20dB of white Gaussian noise is added, overvoltage classification identification is carried out, the influence of noise on the identification method is verified, the identification result is shown in table 6, the identification accuracy is 98%, the influence of the noise on the method is very weak due to the fact that a band-pass filtering algorithm has a good filtering effect, and the method has good anti-noise performance.
TABLE 6 identification results under noise interference
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.
Claims (8)
1. A classification and identification method for temporary overvoltage of a power distribution network is characterized by comprising the following steps: the method comprises the following steps:
step S1: after the overvoltage occurs to the power distribution network, acquiring waveform sampling data of three-phase voltage and zero-sequence voltage of a bus in a period of time before and after the overvoltage occurs;
step S2: calculating the energy contribution rate E of two power frequency cycles after the fault on the zero sequence voltage sampling data obtained in the step S1, judging whether the energy contribution rate E is greater than a threshold value, if so, judging the energy contribution rate E to be an operation overvoltage, and ending the identification process;
step S3: if E is smaller than the threshold, judging the transient overvoltage, calculating the singular spectrum entropy S of the corresponding three-phase voltage sampling data, judging whether S is larger than the threshold, if so, judging the single-phase metallic grounding overvoltage, and ending the identification process;
step S4: if S is smaller than the threshold value, local characteristic scale decomposition, Hilbert transform and band-pass filtering are carried out on corresponding zero-sequence voltage sampling data, and gravity center frequency band N is calculatedg；
Step S5: three ferromagnetic resonance overvoltages and intermittent arc grounding overvoltages are distinguished by adopting a threshold discrimination method: if N is presentgEqual to 6, judging as high-frequency resonance overvoltage; if N is presentgIf the value is equal to 5, judging the value to be a fundamental frequency resonance overvoltage; if N is presentgLess than 5 and more than or equal to 2, and judging the frequency division resonance overvoltage; if N is presentgAnd if the voltage is equal to 1, judging the voltage is intermittent arc grounding overvoltage, and finishing the identification process.
2. The method for classifying and identifying the temporary overvoltage of the power distribution network according to claim 1, wherein the method comprises the following steps: the specific process of intercepting the waveform sample data in step S1 is as follows: in order to obtain a complete frequency division resonance waveform, at least 5 voltage sampling data of power frequency cycles are intercepted.
3. The method for classifying and identifying the temporary overvoltage of the power distribution network according to claim 1, wherein the method comprises the following steps: the specific process of calculating the energy contribution rate E in step S2 is as follows:
energy contribution rate according to the formulaCalculation of where N1The number of sampling points is 2 cycles; n is a radical of2The number of sampling points of the intercepted total time period; v. of0(k) Is a zero sequence voltage signal sequence; and selecting the energy contribution rate of 60% as a classification criterion of temporary overvoltage and operation overvoltage.
4. The method for classifying and identifying the temporary overvoltage of the power distribution network according to claim 1, wherein the method comprises the following steps: the specific process of calculating the three-phase voltage sampling data singular spectrum entropy in step S3 is as follows:
setting the number of the intercepted signal sampling points as n and the three-phase voltage signal matrix as U ═ Ua，ub，uc]The matrix U is subjected to singular value decomposition to obtain a singular spectrum Λ ═ diag { mu }1，μ2，μ3Entropy of singular spectrum of the signal
Wherein,the threshold value for judging the single-phase metallic grounding overvoltage is 1.15.
5. The method for identifying the temporary overvoltage type of the power distribution network according to claim 1, wherein the method comprises the following steps: step S4 includes the following steps:
step S41: performing local characteristic scale decomposition on the zero-sequence voltage sampling data to obtain a plurality of intrinsic scale components, namely ISC components;
step S42: performing Hilbert transform on each ISC component to obtain an instantaneous frequency matrix;
step S43: the total frequency band is divided into 6 bands: 0-10 Hz, 10-20 Hz, 20-30 Hz, 30-40 Hz, 40-80 Hz and 80-600 Hz, wherein the frequency bands are numbered in sequence, the frequency band 1 is 0-10 Hz, the frequency band 2 is 10-20 Hz, and the rest is done in the same way until the 6 th frequency band; reconstructing each intrinsic scale component obtained by decomposing the LCD by adopting a band-pass filtering algorithm according to multiple sub-bands of a Hilbert instantaneous frequency matrix to obtain component data of zero-sequence voltage sampling data in each frequency band;
step S44: and calculating the energy of the component data in each frequency band, and selecting the frequency band number with the highest energy as a gravity center frequency band.
6. A distribution network temporary overvoltage type recognition device which is characterized in that: comprises that
The data acquisition module is used for acquiring waveform sampling data of bus three-phase voltage and zero sequence voltage in 0.5 power frequency periods before overvoltage occurs and 4.5 power frequency periods after overvoltage occurs after the overvoltage occurs;
and the energy contribution rate construction module is used for calculating the energy contribution rate of the obtained zero-sequence voltage sampling data and judging whether the obtained overvoltage sampling data belong to the category of temporary overvoltage.
The singular spectrum entropy construction module is used for calculating the singular spectrum entropy of the obtained three-phase voltage sampling data after judging that the obtained overvoltage sampling data belong to the category of the temporary overvoltage, and judging whether the obtained temporary overvoltage sampling data belong to the category of the single-phase metallic overvoltage or not;
the waveform sub-band reconstruction module is used for performing local characteristic scale decomposition, Hilbert transform and band-pass filtered sub-band reconstruction on the acquired zero-sequence voltage waveform sampling data;
the gravity center frequency band construction module is used for calculating the gravity center frequency band of the reconstructed signal and judging whether the obtained temporary overvoltage sampling data is high-frequency resonance, fundamental frequency resonance, frequency division resonance or intermittent arc grounding overvoltage;
and the overvoltage type identification module is used for judging which temporary overvoltage the obtained overvoltage sampling data is by combining a threshold identification method.
7. The device for identifying the type of temporary overvoltage on the power distribution network according to claim 6, wherein: the waveform sub-band reconstruction module includes:
the local characteristic scale decomposition module is used for carrying out local characteristic scale decomposition on the zero-sequence voltage waveform sampling data to obtain a plurality of ISC components;
a Hilbert transform module for performing Hilbert transform on each ISC component to obtain an instantaneous frequency matrix;
and the sub-band reconstruction module is used for carrying out band-pass filtering according to the instantaneous frequency matrix and decomposing each ISC component into 6 frequency bands.
8. A device for identifying the type of temporary overvoltage on a distribution network according to claim 7, wherein: the sub-band reconstruction module decomposes each ISC component into 6 bands: 0-10 Hz, 10-20 Hz, 20-30 Hz, 30-40 Hz, 40-80 Hz and 80-600 Hz, and all ISC components in each frequency band are superposed to obtain a reconstructed waveform of the zero-sequence voltage waveform sampling data in each frequency band; and meanwhile, calculating the energy value of the component data of the zero-sequence voltage waveform sampling data in each frequency band as a data source of the gravity center frequency band building module.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110161370A (en) * | 2019-04-25 | 2019-08-23 | 国网辽宁省电力有限公司 | A kind of electric network fault detection method based on deep learning |
| CN110988597A (en) * | 2019-12-15 | 2020-04-10 | 云南电网有限责任公司文山供电局 | Resonance type detection method based on neural network |
| CN111723684A (en) * | 2020-05-29 | 2020-09-29 | 华南理工大学 | Method for identifying transient overvoltage type in offshore wind farm |
| CN113311219A (en) * | 2021-03-11 | 2021-08-27 | 国网福建省电力有限公司 | Power distribution network temporary overvoltage identification method |
| CN113671239A (en) * | 2021-08-10 | 2021-11-19 | 国网湖南省电力有限公司 | Intelligent overvoltage identification method, device and system for high-voltage switch PT cabinet |
| CN113702760A (en) * | 2021-08-26 | 2021-11-26 | 济南大学 | Method and system for identifying transverse fault and ferromagnetic resonance state of distribution line |
| CN114325072A (en) * | 2022-03-14 | 2022-04-12 | 南昌航空大学 | Ferromagnetic resonance overvoltage identification method and device based on gram angular field coding |
| CN118449102A (en) * | 2024-07-08 | 2024-08-06 | 国网浙江省电力有限公司丽水供电公司 | Directional overcurrent protection method and system based on transient energy |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102023262A (en) * | 2010-11-05 | 2011-04-20 | 重庆市电力公司綦南供电局 | Method for recognizing arc grounding overvoltage of 35 kV power grid |
| CN102135558A (en) * | 2010-11-05 | 2011-07-27 | 重庆市电力公司綦南供电局 | Acquisition and hierarchical identification system of overvoltage data and hierarchical pattern identification method of overvoltage types |
| CN103197124A (en) * | 2013-03-14 | 2013-07-10 | 重庆市电力公司电力科学研究院 | Overvoltage identification method based on time-frequency matrix singular value |
-
2016
- 2016-12-15 CN CN201611159505.8A patent/CN106771520B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102023262A (en) * | 2010-11-05 | 2011-04-20 | 重庆市电力公司綦南供电局 | Method for recognizing arc grounding overvoltage of 35 kV power grid |
| CN102135558A (en) * | 2010-11-05 | 2011-07-27 | 重庆市电力公司綦南供电局 | Acquisition and hierarchical identification system of overvoltage data and hierarchical pattern identification method of overvoltage types |
| CN103197124A (en) * | 2013-03-14 | 2013-07-10 | 重庆市电力公司电力科学研究院 | Overvoltage identification method based on time-frequency matrix singular value |
Non-Patent Citations (3)
| Title |
|---|
| DU LIN ET AL.: "A Smart On-line Over-voltage Layered Indentification System", 《2012 IEEE INTERNATIONAL CONFERENCE ON CONDITION MONITORING AND DIAGNOSIS》 * |
| 司马文霞等: "采用数学形态学的弧光接地过电压识别方法", 《高电压技术》 * |
| 李欣等: "110kV变电站过电压智能识别系统应用研究", 《电磁避雷器》 * |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110161370A (en) * | 2019-04-25 | 2019-08-23 | 国网辽宁省电力有限公司 | A kind of electric network fault detection method based on deep learning |
| CN110988597A (en) * | 2019-12-15 | 2020-04-10 | 云南电网有限责任公司文山供电局 | Resonance type detection method based on neural network |
| CN111723684B (en) * | 2020-05-29 | 2023-07-21 | 华南理工大学 | Identification method for transient overvoltage type in offshore wind farm |
| CN111723684A (en) * | 2020-05-29 | 2020-09-29 | 华南理工大学 | Method for identifying transient overvoltage type in offshore wind farm |
| CN113311219A (en) * | 2021-03-11 | 2021-08-27 | 国网福建省电力有限公司 | Power distribution network temporary overvoltage identification method |
| CN113311219B (en) * | 2021-03-11 | 2022-11-08 | 国网福建省电力有限公司 | Power distribution network temporary overvoltage identification method |
| CN113671239A (en) * | 2021-08-10 | 2021-11-19 | 国网湖南省电力有限公司 | Intelligent overvoltage identification method, device and system for high-voltage switch PT cabinet |
| CN113671239B (en) * | 2021-08-10 | 2023-08-15 | 国网湖南省电力有限公司 | Intelligent overvoltage identification method, device and system for high-voltage switch PT cabinet |
| CN113702760A (en) * | 2021-08-26 | 2021-11-26 | 济南大学 | Method and system for identifying transverse fault and ferromagnetic resonance state of distribution line |
| CN113702760B (en) * | 2021-08-26 | 2023-08-25 | 济南大学 | Method and system for identifying transverse faults and ferromagnetic resonance states of distribution line |
| CN114325072A (en) * | 2022-03-14 | 2022-04-12 | 南昌航空大学 | Ferromagnetic resonance overvoltage identification method and device based on gram angular field coding |
| CN114325072B (en) * | 2022-03-14 | 2022-06-21 | 南昌航空大学 | Ferromagnetic resonance overvoltage identification method and device based on gram angular field coding |
| CN118449102A (en) * | 2024-07-08 | 2024-08-06 | 国网浙江省电力有限公司丽水供电公司 | Directional overcurrent protection method and system based on transient energy |
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