CN111896890B - Micro-grid line fault diagnosis method and system based on Hilbert-Huang transform - Google Patents
Micro-grid line fault diagnosis method and system based on Hilbert-Huang transform Download PDFInfo
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Abstract
The invention provides a micro-grid line fault diagnosis method and system based on Hilbert-Huang transform, wherein the method comprises the following steps of: acquiring operation state data of the microgrid; voltage signals of fault detection points are subjected to Hilbert-yellow conversion to obtain a two-dimensional time-frequency spectrogram and a Hilbert spectrogram; fault judgment is carried out according to the two-dimensional time-frequency spectrogram and the Hilbert spectrogram; identifying the type of the short-circuit fault according to the characteristics of the Hilbert spectrogram and correlation coefficients among phases; identifying the energy mutation of the point voltage and/or current signal according to the direction on the fault line to obtain a fault line; the method improves the accuracy of fault diagnosis, the diagnosis scheme is not influenced by the variable characteristic of the micro-grid tide, and the adaptability of the novel diagnosis method is improved.
Description
Technical Field
The disclosure relates to the technical field of microgrid fault diagnosis, in particular to a microgrid circuit fault diagnosis method and system based on Hilbert-Huang transform.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The energy internet represents the development direction of the future energy industry, and the micro-grid is an important composition foundation of the energy internet and an effective means for improving the utilization rate of renewable energy. When a short-circuit fault occurs to a microgrid circuit, the influences of voltage drop, current increase, power quality deterioration and the like can be generated, if the detection protection is not timely performed, equipment can be damaged, and even disconnection breakdown of the whole large power grid system can be caused. A fast and efficient fault diagnosis scheme is a prerequisite for system recovery after a fault. The micro-grid fault diagnosis based on the electrical quantity information has obvious advantages in the aspects of data completeness, accuracy, reflection of fault characteristics of a power grid and the like. The fault of the power grid line is usually accompanied by sudden change of current and voltage, and the current and voltage detection signals show time-varying or non-stationarity characteristics. The common signal decomposition methods include wavelet transform and Fourier transform, which have the defects of needing to determine a basis function in advance, being not suitable for nonlinear signals, being incapable of performing characteristic extraction and the like, and the Fourier transform can only perform two-dimensional representation of the signals. Hilbert-Huang Transform (HHT) is a time-frequency joint domain analysis method, and when a fault occurs, HHT time-frequency spectrogram changes remarkably, is distributed in three dimensions and high in resolution, and can comprehensively reflect essential characteristics of signals. HHT has the advantages of being adaptive to the basis function, being applicable to nonlinear non-stationary signals, being capable of feature extraction, and the like.
Researchers provide a differential energy scheme based on HHT, HHT is carried out on current signals at two ends of a line, differential energy at two ends of a fault line is calculated, and a threshold value is set for fault detection and line selection. The method based on the threshold value has a simple principle, and the scheme is easy to realize, but the threshold value is determined improperly, so that the problems of false alarm of fault information and the like can be caused. Researchers can obtain amplitude and frequency distortion degree by HHT of a suspicious fault line, and the amplitude and frequency distortion degree is used as element fault probability representation to carry out fault line selection. The source of the suspicious fault line depends on the switching value information, and the accuracy of fault detection is reduced due to the fact that the circuit breaker is prone to generating factors such as misoperation. Researchers judge the fault classification of the power transmission line by a fuzzy support vector machine-based method by taking characteristic energy values S and S obtained by solving square integral of marginal spectrum functions of HHT conversion results in a specific frequency section as fault characteristic vectors. The classification precision is related to the power grid scale, and the method is more suitable for fault detection of large-scale micro-power grids or micro-power grid groups. Researchers use HHT to complete the extraction of the transient signal characteristic quantity, and use a probabilistic neural network as a diagnosis fault classifier to perform fault phase selection. Because the micro-grid has uncertain factors such as variable tide and the like, the scheme is used in the micro-grid, and the difficulty in training a reliable neural network model by experimental data is high. Researchers compare and analyze time-frequency spectrograms before and after a fault based on HHT decomposition results to obtain fault characteristics and obtain fault detection criteria, but the fault type and the line cannot be determined.
Disclosure of Invention
In order to overcome the defects of the prior art, the method and the system for diagnosing the faults of the micro-grid circuit based on Hilbert-Huang transformation are provided, the accuracy of fault diagnosis is improved, the diagnosis scheme is not influenced by the variable characteristic of the micro-grid power flow, and the adaptability of the novel diagnosis method is improved.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
the first aspect of the disclosure provides a micro-grid line fault diagnosis method based on Hilbert-Huang transform.
A micro-grid line fault diagnosis method based on Hilbert-Huang transform comprises the following steps:
acquiring operation state data of the microgrid;
voltage signals of fault detection points are subjected to Hilbert-yellow conversion to obtain a two-dimensional time-frequency spectrogram and a Hilbert spectrogram;
fault judgment is carried out according to the two-dimensional time-frequency spectrogram and the Hilbert spectrogram;
identifying the type of the short-circuit fault according to the characteristics of the Hilbert spectrogram and correlation coefficients among phases;
and identifying the energy sudden change of the point voltage and/or current signal according to the direction on the line during the fault to obtain the fault line.
A second aspect of the present disclosure provides a system for diagnosing a fault of a microgrid circuit based on hilbert-yellow transform.
A micro-grid line fault diagnosis system based on Hilbert-Huang transform, comprising:
a data acquisition module configured to: acquiring operation state data of the microgrid;
a Hilbert-Huang transform module configured to: voltage signals of fault detection points are subjected to Hilbert-yellow conversion to obtain a two-dimensional time-frequency spectrogram and a Hilbert spectrogram;
a fault determination module configured to: fault judgment is carried out according to the two-dimensional time-frequency spectrogram and the Hilbert spectrogram;
a fault type identification module configured to: identifying the type of the short-circuit fault according to the characteristics of the Hilbert spectrogram and correlation coefficients among phases;
a faulty line judgment module configured to: and identifying the energy sudden change of the point voltage and/or current signal according to the direction on the line during the fault to obtain the fault line.
A third aspect of the present disclosure provides a medium on which a program is stored, the program implementing, when executed by a processor, the steps in the method for diagnosing faults of a microgrid line based on a hilbert-yellow transform according to the first aspect of the present disclosure.
A fourth aspect of the present disclosure provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, where the processor executes the program to implement the steps in the method for diagnosing faults of a microgrid circuit based on hilbert-yellow transform according to the first aspect of the present disclosure.
Compared with the prior art, the beneficial effect of this disclosure is:
1. the method, the system, the medium and the electronic equipment disclosed by the disclosure are used for carrying out sudden change frequency deviation and energy increase and decrease condition coefficient calculation based on HHT images and data of voltage signals of fault detection points to obtain fault detection criteria, so that whether faults occur in the microgrid can be accurately determined.
2. According to the method, the system, the medium and the electronic equipment, the fault type can be determined more accurately by analyzing the Hilbert spectrum characteristic of the HHT result and calculating the correlation coefficient.
3. According to the method, the system, the medium and the electronic equipment, when the fault is detected, fault direction identification calculation based on the mutation energy function is triggered, and the fault line selection function is realized
4. According to the method, the system, the medium and the electronic equipment, filtering is not needed for fault direction identification based on the mutation energy function, the complexity of micro-grid fault direction identification is reduced, and the accuracy is improved; compared with the traditional fault diagnosis scheme based on threshold values, the method has the advantage that the accuracy of fault diagnosis is improved based on the combination of image features and a mathematical representation method.
5. The method, the system, the medium and the electronic equipment are not influenced by the variable characteristic of the micro-grid power flow, so that the adaptability of the novel diagnosis method is improved; and when no fault exists in the microgrid, the calculation of the correlation coefficient and the mutation function is not required to be carried out in real time, and the workload of a diagnosis system is reduced.
6. According to the method, the system, the medium and the electronic equipment, the phase selection classification and the line selection positioning of various short-circuit faults inside the microgrid can be effectively completed through the scheme of performing example analysis and comparison verification by simulating the short-circuit faults, the diagnosis precision is high, and the scheme can be popularized and applied to line fault diagnosis in the power distribution network.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a schematic diagram of a simulation model of a microgrid provided in embodiment 1 of the present disclosure.
Fig. 2 is a HHT time-frequency spectrogram provided in embodiment 1 of the present disclosure.
Fig. 3 is a schematic diagram of a fault detection criterion provided in embodiment 1 of the present disclosure.
Fig. 4 is a schematic diagram of a fault line selection scheme provided in embodiment 1 of the present disclosure.
Fig. 5 is a HHT time-frequency spectrogram of case 1 provided in embodiment 1 of the present disclosure.
Fig. 6 is a HHT time-frequency spectrogram of case 2 provided in embodiment 1 of the present disclosure.
Fig. 7 is a schematic diagram of a threshold-based scheme provided in embodiment 1 of the present disclosure.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
Example 1:
the embodiment 1 of the disclosure provides a micro-grid line fault diagnosis method based on Hilbert-Huang transform, which includes the following steps:
acquiring operation state data of the microgrid;
voltage signals of fault detection points are subjected to Hilbert-yellow conversion to obtain a two-dimensional time-frequency spectrogram and a Hilbert spectrogram;
fault judgment is carried out according to the two-dimensional time-frequency spectrogram and the Hilbert spectrogram;
identifying the type of the short-circuit fault according to the characteristics of the Hilbert spectrogram and correlation coefficients among phases;
and identifying the energy sudden change of the point voltage and/or current signal according to the direction on the line during the fault to obtain the fault line.
In detail, the following contents are included:
s1: theoretical analysis
S1.1：HHT
The HHT mainly comprises two parts, the first part is Empirical Mode Decomposition (EMD); the second part is the hilbert transform.
S1.1.1 empirical mode decomposition
Doctor Huang et al think that signals are composed of Intrinsic Mode Functions (IMFs), and EMD adaptively decomposes a complex signal into IMFs, and then performs Hilbert transform on the IMFs. The IMF satisfies two conditions: (1) the number of the signal poles is equal to or different from the number of the zeros by one. (2) At any time, the average value of the upper and lower envelopes of the signal formed by the local maximum point and minimum point is 0.
The EMD decomposition process is as follows:
1) calculating the upper envelope line m and the lower envelope line m of the maximum value point and the minimum value point of the signal+(t)、m-Average value m of (t)1(t)：
2) Calculating the mean m of the signal x (t) and the upper and lower envelope1(t) difference: h is1(t)＝x(t)-m1(t) of (d). If h1(t) if the IMF condition is satisfied, the process proceeds directly to the next step 3). Otherwise, use h1(t) replacing the initial signal x (t) and repeating the above steps until the k step h1(t) satisfying IMF condition, obtaining the first IMF component, and recording as h1(t)＝c1(t)。
3) Subtracting c from the original signal x (t)1(t) obtaining r1(t) adding r1(t) repeating the above steps (1) to (2) as an input signal to sequentially obtain IMF components c of respective ordersn(t) of (d). Up to a residual component rn(t) is a monotonous signal or until there is only one pole.
The original signal expression is then:
s1.1.2: hilbert transform
And performing Hilbert transform on the IMF to obtain the instantaneous frequency and amplitude of each IMF. Based on the spectrum analysis result of the Hilbert transform, a three-dimensional distribution diagram of time-frequency-energy (amplitude), namely a Hilbert spectrogram, is drawn.
For IMF component cn(t) performing a Hilbert transform to construct an analytic signal:
amplitude function:
phase function:
instantaneous frequency:
the original signal can be represented as:
the Hilbert spectrum of the signal x (t) is recorded as H (omega, t), and the Hilbert spectrum is a function of constructing instantaneous frequency and time to express amplitude, can reflect the change rule of the signal amplitude and shows the distribution condition of time-frequency spectrum energy.
S1.2: correlation coefficient
The Pearson correlation coefficient performs data centralization processing on the two groups of data, and then calculates cosine similarity, so that the consistency of the two groups of waveforms can be measured. The statistical method utilizes the product of the standard deviations of x and y to normalize the covariance and represents the variation trend of the two groups of data. And (3) obtaining a Pearson correlation coefficient P (x, y), wherein the value range is (-1,1) as shown in (9), and the closer the P (x, y) is to 1, the higher the positive correlation of x and y is, the higher the similarity is.
In order to realize the fault phase selection function by matching with the Pearson correlation coefficient, single group data of the phase A, the phase B and the phase C are subjected to centralization and normalization processing to obtain a coefficient P (x), as shown in (10). Judging the condition of energy value increase or loss at the fault moment by a coefficient P (x) calculated value, wherein P (x) is less than zero and energy is lost; p (x) is greater than zero, the energy increases.
In the formula, m and N are respectively the starting time and the ending time of the fault, and N is the number of the complete data set data.
S1.3: mutation function
According to the superposition property, a power system with a fault can be decomposed into a fault component system and a fault-free system. In the fault component system, the sudden change frequency deviation is used as the basis of frequency sudden change, and the sudden change energy function is used as the basis of judging the fault direction of the line.
Calculating sudden change frequency deviation, and allowing deviation of power supply frequency: the power grid capacity is less than 300 ten thousand kilowatts and is +/-0.5 Hz.
And (3) performing mutation frequency deviation calculation on the frequency signal sequence:
Δfi＝|fi-50| (11)
in the formula fiFor the frequency corresponding to the ith signal sequence, Δ fiCorresponding abrupt change frequency deviations. When a frequency jump is detected, the time point corresponding to the frequency signal can be located according to the value of i.
The frequency mutation criterion is as follows:
when a line fault occurs inside a microgrid, sudden changes of voltage and current are generated at the fault position. The voltage and current sudden change values are as follows:
in the formula, T is a power frequency period.
The single-phase abrupt power function is:
ΔPx＝ΔU·ΔI (13)
in the formula, the subscript x is a three-phase symbol A, B, C.
Sum of three-phase abrupt power functions:
ΔP＝ΔPA+ΔPB+ΔPC (14)
in the fault component system, the sudden change energy function is:
in the event of a short-circuit fault, a superimposed component of the electrical quantity is generated by the excitation of an additional power supply at the short-circuit point. Defining the bus pointing to the line as the positive direction, and when the short circuit point is positioned in the positive direction of the fault direction identification point, the vector expression of the fault component is
The energy value at the direction identification point is negative at this time. Similarly, when the short circuit point is positioned in the negative direction of the direction identification point, the energy value at the direction identification point is positive.
Therefore, the fault direction identification criterion is as follows:
s2: micro-grid fault line selection and phase selection scheme
S2.1: HHT time-frequency analysis-based fault detection criterion
A typical structure model of a microgrid is built in Matlab, the left side part of a B1 bus is a large power grid area, the right side part of a B2 bus is a microgrid area, and fault detection points are arranged on connecting lines between the two areas. The transformers of the large power grid part are connected in a star mode, and 2 DGs in the micro power grid adopt a droop control strategy. The grounding mode of the low-voltage system comprises a TN (twisted nematic) system, a TT (TT) system and an IT (information technology) system, and the system adopts an IT wiring mode in consideration of protecting the micro-grid equipment after the fault. The microgrid structure is shown in fig. 1, and the parameters of the microgrid part are shown in table 1.
Table 1: micro-grid simulation partial parameters
When the microgrid operates normally, voltage signals of fault detection points pass through HHT to obtain a two-dimensional time-frequency spectrogram and a Hilbert spectrogram, which are respectively shown as a in fig. 2 and b in fig. 2. The interior of the microgrid is respectively set to work conditions of single-phase grounding short-circuit fault, two-phase grounding short-circuit fault and three-phase short-circuit fault, HHT time-frequency analysis of voltage signals at fault detection points is carried out, the HHT time-frequency analysis is compared with HHT results of normal fault-free conditions, fault features are extracted, and feature comparison analysis is shown in table 2.
Table 2: comparative analysis of HHT results
The fault detection criterion is obtained from the HHT result comparison analysis in combination with the correlation calculation, as shown in fig. 3. In a two-dimensional time-frequency spectrogram, frequency mutation points occur, and the condition I: the deviation of the mutation frequency calculated the signal point where | Δ f | >0.5 appears. In the Hilbert spectrogram, in a time period when energy is increased or lost at a power frequency of 50Hz, the condition II: coefficient value calculation | p (x) | 1
S2.2: phase selection criterion based on HHT and correlation coefficient
The change characteristics of the Hilbert spectrum of the three-phase voltage signal under the simulated short-circuit fault are extracted and summarized as follows: the energy change characteristic at the power frequency of 50Hz at the fault moment, single-phase grounding short circuit fault: one phase (failed phase) energy is missing and two phases energy is added. Two-phase ground short circuit fault: two phases (failed phase) are missing and one phase is increasing. Two-wire short circuit fault: three phases are missing of energy and two phases (failed phase) have consistency. Three-phase short-circuit fault: three phases are energy-lost and three phases (fault phases) have consistency.
Various fault types can be distinguished according to the characteristics of Hilbert spectrums at various short-circuit fault moments, and the energy increase and loss conditions and the waveform consistency are described in a mathematical mode by using P (x) coefficients and Pearson correlation coefficients respectively. And (3) carrying out P (x) coefficient calculation and Pearson correlation coefficient calculation on the three-phase energy value signal sequence to obtain three P (x) coefficient values and three Pearson correlation coefficient calculation values. Considering the operation error factor, the Pearson correlation coefficient is greater than or equal to 0.9, and the two waveforms are determined to have consistency. A correspondence table of the failure type and the coefficient calculation can be obtained as shown in table 3.
Table 3: fault type and coefficient corresponding table
S2.3: line selection criterion based on HHT and mutation energy
The microgrid short-circuit fault line selection function is based on communication between the detection point D and the direction recognition points A1 and A2, binary coding output is carried out on the detection result and the direction recognition result, and finally fault line selection is carried out through output results of O1 and O2, as shown in FIG. 4.
When a fault is detected at the point D, the system establishes communication with direction identification points A1 and A2, and performs sudden change energy calculation on voltage and current signal values at the time of the fault at the points A1 and A2 to identify the fault direction. When a fault is detected at the point D, outputting a binary code of 1; no fault is detected and the output binary is 0. A forward fault is detected at points A1 and A2, and the output binary code is 1; and the reverse fault is realized, the output binary code is 0, and therefore fault line selection can be realized through the output results of O1 and O2. When no fault is detected at the point D, the calculation of the mutation energy function is not needed at the points A1 and A2, and the workload of the system is reduced. Table 4 shows the correspondence between the output results of O1 and O2 and the faulty line.
Table 4: faulty line determination
S3: results and analysis of the experiments
Example 1: phase a is shorted to ground at F1.
At F1, setting the A phase grounding short-circuit fault at 0.3s, restoring the normal operation at 0.7s, and verifying the validity of the proposed scheme according to the proposed short-circuit fault diagnosis scheme.
According to a fault detection criterion, condition I: the calculated value of the mutation frequency deviation at 0.3s, | Δ f |, 3.2> 0.5. The two-dimensional time-frequency spectrogram meets the condition that the fault starting moment has frequency mutation, as shown in a in figure 5. Condition II: coefficient value calculation: p (a) ═ 1, p (b) ═ 1, and p (c) ═ 1. The Hilbert spectrum satisfies the phenomenon of energy addition or loss at 50Hz at the fault time, as shown by b, c and d in FIG. 5. And according to the image characteristics and the data calculation result, the occurrence of the short-circuit fault inside the microgrid is known.
According to the fault phase selection criterion, P (x) coefficient calculation and Pearson correlation coefficient calculation are carried out on the energy value signal sequence of the Hilbert spectrum of the three-phase voltage signal, and the calculation result is shown in Table 5. According to table 3, the fault type and coefficient correspondence table, the calculation result is a single-phase ground short circuit fault. Three-phase Hilbert spectrogram is shown as b, c and d in FIG. 5.
Table 5: coefficient value calculation result
According to the fault line selection criterion, D, A1 and A4 are combined, and the output result of binary coding is as follows: o1 outputs 1 and O2 outputs 0. The line selection result is as follows: the L1 line failed short. The mutation energy value calculation results are associated with the corresponding binary code, as shown in table 6.
Table 6: abrupt energy value calculation
Example 2: AB two-phase short circuit at F2
And at F2, setting AB two-phase short-circuit fault at 0.3s, restoring normal operation at 0.7s, and verifying the effectiveness of the proposed scheme according to the proposed short-circuit fault diagnosis scheme.
According to a fault detection criterion, condition I: the calculated value of the mutation frequency deviation at 0.3s | Δ f | -1.8 > 0.5. The two-dimensional time-frequency spectrogram meets the condition that the fault starting moment has frequency mutation, as shown in a in figure 6. Condition II: coefficient value calculation: p (a) ═ 1, p (b) ═ 1, and p (c) ═ 1. The Hilbert spectrum satisfies the phenomenon of energy addition or loss at 50Hz at the fault time, as shown by b, c and d in FIG. 6. And according to the image characteristics and the data calculation result, the occurrence of the short-circuit fault inside the microgrid is known.
According to the fault phase selection criterion, P (x) coefficient calculation and Pearson correlation coefficient calculation are carried out on the energy value signal sequence of the Hilbert spectrum of the three-phase voltage signal, and the calculation results are shown in Table 7. According to table 3, the fault type and coefficient correspondence table, the calculation result is a two-phase short circuit fault. And the three-phase Hilbert spectrogram is shown as b, c and d in figure 6.
Table 7: coefficient value calculation result
According to the fault line selection criterion, D, A2 and A7 are combined, and the output result of binary coding is as follows: o1 output 1 and O2 output 1. The line selection result is as follows: the L6 line failed short. The mutation energy value calculation results are associated with the corresponding binary code, as shown in table 8.
Table 8: abrupt energy value calculation
Example 3: protocol comparison analysis
The accuracy of the fault diagnosis scheme and the threshold-based scheme is calculated, compared and analyzed, and the effectiveness and the reliability of the proposed scheme are proved.
The fault detection scheme based on the threshold value is characterized in that HHT is respectively carried out on three-phase current signals at two ends of a line to obtain Hilbert spectrums, spectrum energy is calculated, and differential energy at two ends of the line is solved. The fault phase selection and line selection are performed according to whether the differential energy exceeds the threshold value, and a basic diagnosis scheme diagram based on the threshold value scheme is shown in fig. 7.
The Hilbert spectrum solving process is shown in formulas (1) to (7), and the differential energy calculation process is as follows:
the spectral energy of each natural modal function is:
EH＝[A(t)]2 (14)
the differential energy across the line is:
Ed＝EH1-EH2 (15)
in the formula, EH1And EH2Spectral energy, E, across the line respectivelydIs the differential energy.
And selecting 30 different fault points at different positions inside the microgrid, respectively setting different short-circuit fault types, and respectively calculating and verifying the diagnosis scheme and the threshold-based scheme. Under different schemes, the correct times (accuracy rate) of line selection and phase selection of different fault types corresponding to 30 fault points are calculated, and two different threshold value schemes of 0.2 and 0.4 are selected for comparison with the scheme in the text by the comparison and verification. The calculation results of the correct times (accuracy) of the fault line selection and the fault phase selection under different fault types are shown in table 9 and table 10, respectively.
Table 9: fault line selection result accuracy
Table 10: fault phase selection result accuracy
Through comparative analysis, the fault diagnosis scheme based on the threshold value has certain influence on the accuracy of the fault diagnosis result if the threshold value is not properly selected, and the scheme is actually determined by combining a large amount of experimental data with manual experience, so that the reliable threshold value is difficult to select. The scheme provided by the embodiment can adapt to phase selection and line selection of various short-circuit faults of the internal circuit of the microgrid, does not need to determine a threshold value, and is high in fault diagnosis precision.
The scheme of the embodiment also has the following characteristics:
the embodiment provides a microgrid circuit fault diagnosis scheme based on a HHT method, and achieves the functions of detecting, selecting phases and selecting lines of microgrid circuit short-circuit faults.
Selecting a fault detection point, performing HHT on the three-phase voltage signals of the fault detection point, performing time-frequency analysis on a two-dimensional time-frequency spectrum and a Hilbert spectrum of an HHT result, and obtaining a fault detection criterion by combining the sudden change frequency deviation and the coefficient calculation of the energy increase and decrease condition.
And extracting fault characteristics of the Hilbert spectrum of the three-phase voltage signal to obtain fault classification criteria, and performing correlation analysis by combining correlation coefficient calculation to complete a fault phase selection function.
And when a fault is detected at the fault detection point, the calculation of the mutation energy function of the short-circuit fault direction identification point is triggered, the fault direction is identified, and the fault line selection function is realized.
The scheme is based on a mode of combining image characteristics and mathematical expression, so that the extraction of fault characteristics is facilitated, and a fault detection criterion is obtained. By simulating short-circuit faults to perform example analysis and comparison verification, the provided scheme can effectively complete phase selection classification and line selection positioning of various short-circuit faults inside the microgrid, has high diagnosis precision, and can be popularized and applied to line fault diagnosis in the power distribution network.
Example 2:
the embodiment 2 of the disclosure provides a micro-grid line fault diagnosis system based on Hilbert-Huang transform.
A micro-grid line fault diagnosis system based on Hilbert-Huang transform, comprising:
a data acquisition module configured to: acquiring operation state data of the microgrid;
a Hilbert-Huang transform module configured to: voltage signals of fault detection points are subjected to Hilbert-yellow conversion to obtain a two-dimensional time-frequency spectrogram and a Hilbert spectrogram;
a fault determination module configured to: fault judgment is carried out according to the two-dimensional time-frequency spectrogram and the Hilbert spectrogram;
a fault type identification module configured to: identifying the type of the short-circuit fault according to the characteristics of the Hilbert spectrogram and correlation coefficients among phases;
a faulty line judgment module configured to: and identifying the energy sudden change of the point voltage and/or current signal according to the direction on the line during the fault to obtain the fault line.
The working method of the system is the same as the fault diagnosis method of the microgrid circuit based on Hilbert-Huang transform provided in embodiment 1, and details are not repeated here.
Example 3:
the embodiment 3 of the present disclosure provides a medium, on which a program is stored, and when the program is executed by a processor, the method for diagnosing the fault of the microgrid circuit based on the hilbert-yellow transform according to embodiment 1 of the present disclosure includes the following steps:
acquiring operation state data of the microgrid;
voltage signals of fault detection points are subjected to Hilbert-yellow conversion to obtain a two-dimensional time-frequency spectrogram and a Hilbert spectrogram;
fault judgment is carried out according to the two-dimensional time-frequency spectrogram and the Hilbert spectrogram;
identifying the type of the short-circuit fault according to the characteristics of the Hilbert spectrogram and correlation coefficients among phases;
and identifying the energy sudden change of the point voltage and/or current signal according to the direction on the line during the fault to obtain the fault line.
The detailed steps are the same as the method for diagnosing the fault of the microgrid circuit based on the Hilbert-Huang transform provided in the embodiment 1, and are not described again here.
Example 4:
the embodiment 4 of the present disclosure provides an electronic device, which includes a memory, a processor, and a program stored in the memory and capable of running on the processor, where the processor executes the program to implement the steps in the method for diagnosing a fault of a microgrid circuit based on hilbert-yellow transform according to embodiment 1 of the present disclosure, where the steps are as follows:
acquiring operation state data of the microgrid;
voltage signals of fault detection points are subjected to Hilbert-yellow conversion to obtain a two-dimensional time-frequency spectrogram and a Hilbert spectrogram;
fault judgment is carried out according to the two-dimensional time-frequency spectrogram and the Hilbert spectrogram;
identifying the type of the short-circuit fault according to the characteristics of the Hilbert spectrogram and correlation coefficients among phases;
and identifying the energy sudden change of the point voltage and/or current signal according to the direction on the line during the fault to obtain the fault line.
The detailed steps are the same as the method for diagnosing the fault of the microgrid circuit based on the Hilbert-Huang transform provided in the embodiment 1, and are not described again here.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (10)
1. A micro-grid line fault diagnosis method based on Hilbert-Huang transform is characterized by comprising the following steps of:
acquiring operation state data of the microgrid;
voltage signals of fault detection points are subjected to Hilbert-yellow conversion to obtain a two-dimensional time-frequency spectrogram and a Hilbert spectrogram;
fault judgment is carried out according to the two-dimensional time-frequency spectrogram and the Hilbert spectrogram;
identifying the type of the short-circuit fault according to the characteristics of the Hilbert spectrogram and correlation coefficients among phases;
identifying the energy mutation of the point voltage and/or current signal according to the direction on the fault line to obtain a fault line; the method specifically comprises the following steps: defining the bus pointing to the line as the positive direction, and when the energy value at the direction identification point is negative, the short-circuit point is positioned in the positive direction of the fault direction identification point; when the energy value at the direction identification point is positive, the short-circuit point is positioned in the negative direction of the direction identification point;
the fault judgment is carried out according to the two-dimensional time-frequency spectrogram and the Hilbert spectrogram, and the fault judgment specifically comprises the following steps:
a time period when frequency mutation points occur in the two-dimensional time-frequency spectrogram and energy increase or loss occurs at the power frequency of 50Hz in the Hilbert spectrogram;
identifying the type of the short-circuit fault according to the characteristics of the Hilbert spectrogram and the correlation coefficient among phases, which specifically comprises the following steps:
and carrying out phase selection coefficient calculation and Pearson correlation coefficient calculation on the three-phase energy value signal sequence to obtain three phase selection coefficients and three Pearson correlation coefficient calculation values in total, and identifying the fault type according to the obtained phase selection coefficient values and the Pearson correlation coefficients.
2. The method for diagnosing faults of the microgrid circuit based on Hilbert-Huang transform as claimed in claim 1, wherein the frequency mutation points appearing in the two-dimensional time-frequency spectrogram specifically are as follows: signal points with the absolute value of the calculated value of the frequency deviation of the occurrence of the mutation being greater than 0.5; the time period of the energy increase or loss at the power frequency of 50Hz in the Hilbert spectrogram is specifically as follows: the absolute value of the phase selection coefficient is equal to zero.
3. The method for diagnosing faults of the microgrid circuit based on Hilbert-Huang transform as claimed in claim 2, characterized in that single-group data of the A phase, the B phase and the C phase are subjected to centralization and normalization processing to obtain phase selection coefficients.
4. The hubert-yellow transform-based microgrid line fault diagnosis method of claim 1, characterized by a phase-a grounded short-circuit fault when the following conditions are simultaneously satisfied:
the phase selection coefficient of the phase A is negative, the phase selection coefficients of the phase B and the phase C are positive, the Pearson correlation coefficient between the phase A and the phase B is smaller than a preset value, the Pearson correlation coefficient between the phase A and the phase C is smaller than a preset value, and the Pearson correlation coefficient between the phase B and the phase C is larger than or equal to a preset value.
5. The hubert-yellow transform-based microgrid line fault diagnosis method of claim 1, characterized by being an AB two-phase ground short-circuit fault when the following conditions are simultaneously satisfied:
the phase selection coefficients of the A phase and the B phase are negative, the phase selection coefficient of the C phase is positive, the Pearson correlation coefficient between the A phase and the B phase is greater than or equal to a preset value, the Pearson correlation coefficient between the A phase and the C phase is smaller than the preset value, and the Pearson correlation coefficient between the B phase and the C phase is smaller than the preset value.
6. The hubert-yellow transform-based microgrid line fault diagnosis method of claim 1, characterized by being an AB two-phase short-circuit fault when the following conditions are simultaneously satisfied:
the phase selection coefficients of the A phase, the B phase and the C phase are all negative, the Pearson correlation coefficient between the A phase and the B phase is greater than or equal to a preset value, the Pearson correlation coefficient between the A phase and the C phase is smaller than the preset value, and the Pearson correlation coefficient between the B phase and the C phase is smaller than the preset value.
7. The hubert-yellow transform-based microgrid line fault diagnosis method of claim 1, characterized by being an ABC three-phase metallic short-circuit fault when the following conditions are simultaneously satisfied:
the phase selection coefficients of the A phase, the B phase and the C phase are all negative, the Pearson correlation coefficient between the A phase and the B phase is greater than or equal to a preset value, the Pearson correlation coefficient between the A phase and the C phase is greater than or equal to a preset value, and the Pearson correlation coefficient between the B phase and the C phase is greater than or equal to a preset value.
8. A micro-grid line fault diagnosis system based on Hilbert-Huang transform, comprising:
a data acquisition module configured to: acquiring operation state data of the microgrid;
a Hilbert-Huang transform module configured to: voltage signals of fault detection points are subjected to Hilbert-yellow conversion to obtain a two-dimensional time-frequency spectrogram and a Hilbert spectrogram;
a fault determination module configured to: fault judgment is carried out according to the two-dimensional time-frequency spectrogram and the Hilbert spectrogram;
a fault type identification module configured to: identifying the type of the short-circuit fault according to the characteristics of the Hilbert spectrogram and correlation coefficients among phases;
a faulty line judgment module configured to: identifying the energy mutation of the point voltage and/or current signal according to the direction on the fault line to obtain a fault line; the method specifically comprises the following steps: defining the bus pointing to the line as the positive direction, and when the energy value at the direction identification point is negative, the short-circuit point is positioned in the positive direction of the fault direction identification point; when the energy value at the direction identification point is positive, the short-circuit point is positioned in the negative direction of the direction identification point;
the fault judgment is carried out according to the two-dimensional time-frequency spectrogram and the Hilbert spectrogram, and the fault judgment specifically comprises the following steps:
a time period when frequency mutation points occur in the two-dimensional time-frequency spectrogram and energy increase or loss occurs at the power frequency of 50Hz in the Hilbert spectrogram;
identifying the type of the short-circuit fault according to the characteristics of the Hilbert spectrogram and the correlation coefficient among phases, which specifically comprises the following steps:
and carrying out phase selection coefficient calculation and Pearson correlation coefficient calculation on the three-phase energy value signal sequence to obtain three phase selection coefficients and three Pearson correlation coefficient calculation values in total, and identifying the fault type according to the obtained phase selection coefficient values and the Pearson correlation coefficients.
9. A computer-readable storage medium, on which a program is stored, which program, when being executed by a processor, carries out the steps of the method for hubert-yellow transform-based microgrid line fault diagnosis according to any one of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor implements the steps of the hubert-yellow transform-based microgrid circuit fault diagnosis method according to any one of claims 1 to 7 when executing the program.
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