CN108710861B - Power grid disturbance signal detection and identification method - Google Patents

Power grid disturbance signal detection and identification method Download PDF

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CN108710861B
CN108710861B CN201810503766.XA CN201810503766A CN108710861B CN 108710861 B CN108710861 B CN 108710861B CN 201810503766 A CN201810503766 A CN 201810503766A CN 108710861 B CN108710861 B CN 108710861B
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纪萍
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Abstract

The invention discloses a power grid disturbance signal detection and identification method which comprises the steps of collecting power grid signals, carrying out multi-resolution decomposition on the power grid signals by utilizing wavelet transformation, carrying out feature extraction by calculating energy coefficients of each layer to obtain feature signals, and carrying out identification and classification on the power grid signals according to extracted signal feature information. The invention has the advantages that: the method can comprehensively realize the preparation classification and identification under the condition that various disturbance signals disturb the power grid, and can clearly detect the real disturbance on the power grid, thereby realizing the management of the power grid according to the received disturbance signals.

Description

Power grid disturbance signal detection and identification method
Technical Field
The invention relates to the technical field of power grids, in particular to a method for detecting and identifying a power grid disturbance signal.
Background
With the continuous development of national economy, the power demand of China is also rapidly increased. Some electrical devices, such as solid state switching devices, non-linear and power electronic switching loads, unbalanced electrical systems, lighting control systems, computer and data processing equipment, and plant rectifiers and inverters, are increasingly being used in electrical systems, causing an increasing pollution to the grid, causing more interference signals to enter the grid, resulting in a reduction in the quality of the grid. Meanwhile, with the rapid development of electronic technology and the introduction of high-precision equipment, the requirement of power consumers on the quality of electric energy is higher and higher. Electrical devices are difficult to tolerate interfering signals in the electrical network. Therefore, the interference signals in the power grid are effectively analyzed, the type of the interference signals is determined, and the method is very necessary and positive for treating the power grid pollution and improving the power quality.
Disturbance signals affecting the quality of electric energy in the power grid include voltage sag, voltage interruption, transient pulse, harmonic wave, flicker and the like. In an actual power grid, the power quality disturbance is often formed by overlapping various single disturbances and partial noises in a staggered manner to form composite disturbance, and the disturbance signals easily cause serious consequences such as equipment overheating, motor stalling, protection failure, inaccurate metering and the like, so that serious economic loss and social influence are caused. Noise is often mixed in the using process of the electric equipment, so that the difficulty is increased in the process of identifying the disturbance signal. The existing identification method only stops identifying part of single signals, and rarely considers the noisy condition, so that part of interference signals are not identified and detected, and the interference source cannot be reasonably determined.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a power grid disturbance signal detection and identification method.
In order to achieve the purpose, the invention adopts the technical scheme that: a power grid disturbance signal detection and identification method comprises the steps of,
collecting power grid signals, carrying out multi-resolution decomposition on the power grid signals by utilizing wavelet transformation, carrying out feature extraction by calculating energy coefficients of each layer to obtain feature signals, and identifying and classifying the power grid signals according to extracted signal feature information.
When signal feature extraction is carried out, multi-resolution decomposition based on wavelet transformation is carried out, and db wavelet series are selected from mother wavelets.
The method comprises the steps of carrying out multi-resolution decomposition by utilizing wavelet transformation DWT, carrying out N-layer decomposition on the acquired power grid signals, and calculating the energy of each layer by utilizing the Pasteval theorem, wherein the formula is as follows:
Figure BDA0001670813860000021
wherein the first term on the right represents the approximate energy value of the decomposed signal, and the second term represents the energy value of the decomposed signal of the detail feature, which respectively correspond to:
Figure BDA0001670813860000022
A i,j representative is signal MSDCoefficient of decomposition, D i,j Detail feature coefficients representing the decomposed signal, from layer 1 to layer l, respectively. i denotes the number of signal decomposition levels, j denotes the scaling factor, N denotes the number of detailed signal coefficients of each level decomposition, WD i Representative is the energy decomposition coefficient, WA, of the i-th layer l Is the approximate energy coefficient of the l layer for MSD decomposition; the feature vector of the l-th layer containing the perturbation signal can be represented as follows: FV (WD) DS )＝[WD 1 WD 2 ... WD l WA l ](ii) a The feature vector of the sinusoidal signal can be expressed as: FV (WD) PS )＝[WD 1 WD 2 ... WD l WA l ]Then a vector representing the characteristics of the signal may be represented as Δ W = WD DS -WD PS ；
Calculating the percentage of characteristic signal energy entropy to sinusoidal signal:
Figure BDA0001670813860000031
with W AE And classifying various disturbance signals.
The method also comprises
Calculate the absolute percentage of signal entropy after MSD decomposition:
Figure BDA0001670813860000032
W DSabs stands for WD DS Absolute value of (1), W PSabs Stands for WD PS Absolute value of (a), W obtained by calculation AEabs Effectively distinguish high frequency disturbance signal and low frequency disturbance signal.
W AE Greater than zero:
(1) When W AE Is positive and has a value range of 18-28%, which indicates that the voltage-rising disturbance is contained, when W is AE Is equal to W AEvswell For voltage rise, when W AE Is equal to W AEvswell+hr For voltage rise and harmonic combined disturbance, when W AE Is equal to W AEvswell+n A composite disturbance of voltage rise and noise.
(2) When W is AE When the number is positive and the value range is 8% -12%, the disturbance containing voltage flicker is shown, when W is AE Is equal to W AEvf Is a voltage flicker when W AE Is equal to W AEvf+n Is a composite disturbance of voltage flicker and noise.
(3) When W is AE When the number is positive and the value range is 5% -8%, the disturbance containing harmonic waves is shown, when W is AE Is equal to W AEhr Is a harmonic of voltage, when W AE Is equal to W AEabs-hrn Is a composite disturbance of harmonics and noise;
(4) When W is AE When the number is positive and the value range is 0% -1%, the transient disturbance, W, is contained AE Less than zero:
when W is AE When the number is negative and the value range is negative 8% -17%, the voltage drop disturbance is contained, and when W is AE Is equal to W AEvsag Is a voltage drop when W AE Is equal to W AEvsag+hr Is a composite disturbance of voltage droop and harmonics. When W is AE Is equal to W AEvsag+n Is a composite disturbance of voltage drop and noise. When W is AE Is equal to W AEvi Is a voltage interruption when W AE Is equal to W AEcs Is capacitor switching disturbance when W AE Is equal to W AEls Is a load switch disturbance;
when W is AE When the signal is equal to zero, no interference signal is considered in the power grid, and the signal is a high-quality power grid signal.
When W is AE When the signal is positive and the value range is 0% -1%, the transient signal is contained, and W is further calculated AEabs When W is AEabs Is equal to W AEabs-hft Is a high frequency transient disturbance when W AE Is equal to W AEabs-lft Is a low frequency transient disturbance.
The invention has the advantages that: the method can comprehensively realize the preparation of classification and identification under the condition that various disturbance signals interfere the power grid, can clearly detect the real interference on the power grid, and can further realize the management of the power grid according to the received disturbance signals.
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The contents of the expressions in the various figures of the present specification and the labels in the figures are briefly described as follows:
FIG. 1 is a schematic diagram of a DWT-based MSD three-layer decomposition of the present invention;
FIG. 2 is a schematic diagram of a power grid disturbance signal feature extraction method of the invention.
Detailed Description
The following description of preferred embodiments of the invention will be made in further detail with reference to the accompanying drawings.
The invention provides a novel method for detecting and classifying power grid disturbance signals. The method comprises the steps of carrying out effective characteristic extraction and classification on single disturbance signals, composite disturbance and the condition containing noise. The single perturbation signal model is referred to table 1.
Figure BDA0001670813860000041
Figure BDA0001670813860000051
TABLE 1
In order to realize the invention, the specific implementation scheme is as follows:
collecting power grid signals as input signals of a system to carry out power quality analysis.
And 2, performing multi-resolution analysis based on wavelet transformation on the acquired signals so as to extract signal features.
Wavelet transform formula of signal f (t):
Figure BDA0001670813860000052
wherein i, j are integers, # i,j As a function of wavelet expansion, a i,j The coefficients of the wavelet transform, which are the signal f (t), can be expressed as
Figure BDA0001670813860000053
By aligning psi i,j The mother wavelet function psi can be obtained by operation i,j ，ψ i,j ＝2 i/2 ψ(2 i t-j), j is the transformation parameter, i is the scale parameter. The mother wavelet is not unique, a series of conditions need to be met, and the requirement for multi-resolution decomposition needs to be met
Figure BDA0001670813860000054
Where phi (t) is a scale function
Figure BDA0001670813860000055
h (k) is to satisfy uniqueness, orthogonality, and a certain degree of regularity.
MSD scale analysis using wavelets can result:
Figure BDA0001670813860000056
wherein
Figure BDA0001670813860000057
The scale function is shown shifted, the wavelet expansion function is shown by. Wherein the coefficient c in formula j Expressed is the scale shift coefficient of the j-th layer, d j The wavelet decomposition coefficients of the j-th layer are expressed as follows
Figure BDA0001670813860000061
Wherein: g (n) and h (n) are in the relation: g (n) = (-1) n h (L-1-n). The first step is to divide the spectrum of the signal into a low pass band h (n) and a high pass band g (n); the second step divides the low-pass signal into another lower low-pass band and another high-pass band, and so on. Where L represents the length of the filter, MSD decomposition is performed based on wavelet transform. The fundamental frequency of the signal is f, L represents the length of the filter, and the frequency of the collected voltage signal is f sp . Three-layer MSD based on DWT as shown in fig. 1, according to the MSD principle, high and low frequency division is performed according to the frequency of the signal. The low-frequency signal range of the first layer is 0-f/2 and highThe frequency signals are f/2-f. By analogy, after n layers of MSD decomposition, the low-frequency signal of each layer is 0-f/(2) n ) The high frequency signal is [ f/(2) ] n )]-f/2 n-1 。，
When signal feature extraction is carried out, MSD decomposition based on wavelet transformation is carried out, mother wavelets are very important to select, the mother wavelets for signal processing and analysis comprise db, harr, daubechies, symlets wavelets and the like, and db wavelet series are the most easily subjected to electric energy feature extraction through comparative analysis. Compared with other wavelets, the db wavelet has shorter filtering width and fast calculation time, and is very suitable for a real-time monitoring system of a power grid. The signal feature extraction is carried out, namely key features of different types of signals are selected, so that the aim of effectively distinguishing various types of signals is fulfilled. The MSD technology is carried out by DWT, N layers of decomposition is carried out on the collected signals, and the energy of each layer is calculated by the Pasteval theorem. The formula is as follows:
Figure BDA0001670813860000062
wherein the first term on the right represents the approximate energy value of the decomposed signal, and the second term represents the energy value of the decomposed signal of the detail feature, which respectively correspond to:
Figure BDA0001670813860000063
A i,j representative of the coefficients of MSD decomposition of the signal, D i,j Detail feature coefficients representing the decomposed signal, respectively from layer 1 to layer l. i denotes the number of signal decomposition levels, j denotes the scaling factor, N denotes the number of detailed signal coefficients of each level decomposition, WD i Representative is the energy decomposition coefficient, WA, of the i-th layer l Is the approximate energy coefficient of the l-th layer for MSD decomposition; the feature vector of the l-th layer containing the perturbation signal can be represented as follows: FV (WD) DS )＝[WD 1 WD 2 ... WD l WA l ](ii) a The feature vector of the sinusoidal signal can be expressed as: FV (WD) PS )＝[WD 1 WD 2 ... WD l WA l ]Then the vector representing the characteristics of the signal may be represented asΔW＝WD DS -WD PS ；
The percentage of the characteristic signal energy entropy relative to the sinusoidal signal is further calculated:
Figure BDA0001670813860000071
calculating W by equation (1) AE Firstly, presetting a power grid signal containing multiple single and composite disturbance components such as harmonic waves, voltage flicker and the like, and simulating various disturbance states of the power grid in advance to calculate the corresponding W of the power grid AE Value of (W of harmonic disturbance) AE Is W AEhr W of voltage flicker disturbance AE Is W AEvf Etc.) and then calculates the actual W of the grid from the collected signals AE The value of (A) and the pre-calculated W under various disturbances AE And comparing to judge what type of interference signal is.
By calculating W of various disturbance signals AE Obtaining the power grids W corresponding to different disturbances in advance AE Value, then according to the actual grid W calculated AE Value and W under various disturbances AE W AE Comparing values, and judging which disturbance the disturbance in the actual power grid belongs to; various disturbances are classified as: sinusoidal signal W AEps Voltage drop W AEvsag Voltage rise W AEvswell Harmonic wave W AEhr Voltage flicker W AEvf Voltage interruption W AEvi Capacitor switch W AEcs And a load switch W AEls The generated disturbance, and the harmonic and voltage-reduced composite disturbance W AEvsag+hr Harmonic and voltage rise composite disturbance W AEvswell+hr Composite disturbance W of voltage rise and noise AEvswell+n Composite disturbance W of voltage flicker and noise AEvf+n Composite disturbance W of voltage drop noise AEvsag+n . W due to HFT and LFT AE The values are relatively close and therefore difficult to distinguish. The absolute percentage of signal entropy after MSD decomposition can be calculated.
Figure BDA0001670813860000072
W DSabs Represents W DS Absolute value of (2), W PSabs Represents W PS Absolute value of (1), W of HFT AEabs Is W AEabs-hft W of LFT AEabs Is W AEabs-lft W of HFT or LFT signal AEabs The difference between the two is large, so that W can be calculated AEabs Effectively distinguishing these two types of signals.
3, classifying the disturbance signals
By calculating the absolute percentage of the characteristic signal energy entropy and the signal energy entropy, the single disturbance signal, the composite disturbance signal and the characteristic vector of the disturbance signal under the noise condition can be effectively extracted, so that the disturbance signal can be rapidly classified. By adopting the method for calculating the energy entropy, 16 types of disturbance signals can be classified, namely pure sine signals, voltage rise, voltage drop, composite disturbance of harmonic wave and voltage rise, composite disturbance of voltage rise and noise, composite disturbance of voltage drop and noise, composite disturbance of harmonic wave and noise, voltage flicker, composite disturbance of voltage flicker and noise, high-frequency transient signal disturbance, low-frequency transient signal disturbance, voltage interruption, capacitor switch disturbance and load switch disturbance.
1) Computing a feature vector W using MSD decomposition AE A value of (d);
2) When W is AE When the voltage is more than zero, 11 types of disturbance signal voltage rise, composite disturbance of harmonic wave and voltage rise, composite disturbance of voltage rise and noise, composite disturbance of harmonic wave, harmonic wave and noise, voltage flicker, composite disturbance of voltage flicker and noise, high-frequency transient signal disturbance and low-frequency transient signal disturbance can be determined,
(1) When W is AE Is positive and has a value range of 18% -28%, indicating that the voltage-rising disturbance is contained, when W AE Is equal to W AEvswell For voltage rise, when W AE Is equal to W AEvswell+hr For voltage rise and harmonic combined disturbance, when W AE Is equal to W AEvswell+n A composite disturbance of voltage rise and noise.
(2) When W is AE When the number is positive and the value range is 8% -12%, the disturbance containing voltage flicker is shown, when W is AE Is equal to W AEvf Is a voltage flicker when W AE Is equal to W AEvf+n Is a composite disturbance of voltage flicker and noise.
(3) When W is AE When the number is positive and the value range is 5% -8%, the disturbance containing harmonic waves is shown, and when W is AE Is equal to W AEhr Is a harmonic of voltage, when W AE Is equal to W AEabs-hrn Is a complex perturbation of harmonics and noise.
(4) When W is AE When the signal is positive and the value range is 0% -1%, the transient disturbance is contained, but the high-frequency disturbance signal and the low-frequency disturbance signal cannot be effectively distinguished.
3) When W AE When the voltage is less than zero, the voltage drop and the composite disturbance of noise, the voltage interruption, the capacitor switch disturbance and the load switch disturbance are in 5 conditions, namely W AE When the number is negative and the value range is negative 8% -17%, the voltage drop disturbance is contained, and when W is AE Is equal to W AEvsag Is a voltage drop when W AE Is equal to W AEvsag+hr Is a composite disturbance of voltage droop and harmonics. When W is AE Is equal to W AEvsag+n Is a composite disturbance of voltage drop and noise. When W is AE Is equal to W AEvi Is a voltage interruption when W AE Is equal to W AEcs Is capacitor switching disturbance when W AE Is equal to W AEls Is a load switch disturbance.
4) When W is AE When the signal is equal to zero, no interference signal is considered to exist in the power grid, and the signal is a high-quality power grid signal.
5) When W is AE When the value is positive and the value range is 0% -1%, the transient signal is contained, and further calculation of W is needed AEabs When W is AEabs Is equal to W AEabs-hft Is a high frequency transient disturbance when W AE Is equal to W AEabs-lft Is a low frequency transient disturbance.
The invention provides a method for detecting and identifying multiple disturbance signals under noise. The method can comprehensively realize the preparation of classification and identification under the condition that various disturbance signals interfere the power grid, and can clearly detect the real interference on the power grid.
It is clear that the specific implementation of the invention is not restricted to the above-described embodiments, but that various insubstantial modifications of the inventive process concept and technical solutions are within the scope of protection of the invention.

Claims (5)

1. A power grid disturbance signal detection and identification method is characterized by comprising the following steps: the detection and identification method comprises the steps of collecting power grid signals, carrying out MSD decomposition on the power grid signals by using wavelet transformation, carrying out feature extraction by calculating energy coefficients of each layer to obtain feature signals, and identifying and classifying the signals of the power grid according to extracted signal feature information;
the method comprises the steps of carrying out MSD decomposition by utilizing wavelet transformation, carrying out N-layer decomposition on collected power grid signals, and calculating the energy of each layer by utilizing the Pasteval theorem, wherein the formula is as follows:
Figure FDA0003671892850000011
wherein the first term on the right represents the approximate energy value of the decomposed signal, and the second term represents the energy value of the decomposed signal of the detail feature, which respectively correspond to:
Figure FDA0003671892850000012
A i,j representing the coefficients of MSD decomposition of the signal, D i,j Detail characteristic coefficients representing the decomposed signal, from layer 1 to layer l, respectively; i denotes the number of levels of signal decomposition, j denotes the scaling factor, N denotes the number of detailed signal coefficients of each level decomposition, WD i Representative is the energy decomposition coefficient, WA, of the i-th layer l Is the approximate energy coefficient of the l layer for MSD decomposition; the feature vector of the l-th layer containing the perturbation signal can be represented as follows: FV (WD) DS )＝[WD 1DS WD 2DS ...WD lDS WA lDS ]；
The feature vector of the sinusoidal signal can be expressed as: FV (WD) PS )＝[WD 1PS WD 2PS ...WD lPS WA lPS ]Then the vector representing the characteristics of the signal may be represented as aw = WD DS -WD PS (ii) a Wherein:
WD lDS refers to the energy decomposition coefficient, WA, of the l-th layer containing the perturbation signal lDS The approximate energy coefficient of the l layer containing the disturbance signal is referred to; WD lPS Refers to the energy resolution factor, WA, of the l-th layer of a clean, undisturbed sinusoidal signal lPS The approximate energy coefficient of the l layer of a pure undisturbed sinusoidal signal is referred to;
calculating the percentage of the characteristic signal energy entropy relative to the sinusoidal signal:
Figure FDA0003671892850000013
with W AE And classifying various disturbance signals.
2. The method for detecting and identifying the power grid disturbance signal as claimed in claim 1, wherein: and when signal characteristic extraction is carried out, MSD decomposition based on wavelet transformation is carried out, and db wavelet series are selected from mother wavelets.
3. The method for detecting and identifying the power grid disturbance signal as claimed in claim 1, wherein: the method further comprises the following steps:
calculating the absolute percentage of signal entropy after MSD decomposition:
Figure FDA0003671892850000021
W DSabs stands for WD DS Absolute value of (WD) PSabs Stands for WD PS Absolute value of (a), W obtained by calculation AEabs The high frequency disturbance signal and the low frequency disturbance signal are distinguished.
4. A method as claimed in claim 1 or 3, wherein the method comprises: the method comprises the following steps of,
W AE when the value is more than zero:
(1) When W is AE Is positive number and has a value range of 18% -28%, and when W contains voltage rise disturbance AE Is equal to W AEvswell For voltage rise, when W AE Is equal to W AEvswell+hr For voltage rise and harmonic combined disturbance, when W AE Is equal to W AEvswell+n A composite disturbance which is a voltage rise and noise;
(2) When W AE When the number is positive and the value range is 8% -12%, the disturbance containing voltage flicker is shown, when W is AE Is equal to W AEvf Is a voltage flicker when W AE Is equal to W AEvf+n Is a composite disturbance of voltage flicker and noise;
(3) When W is AE When the number is positive and the value range is 5% -8%, the disturbance containing harmonic waves is shown, and when W is AE Is equal to W AEhr Is a harmonic of voltage, when W AE Is equal to W AEabs-hrn Is a composite disturbance of harmonics and noise;
(4) When W is AE When the number is positive and the value range is 0% -1%, the transient disturbance is contained,
W AE less than zero:
when W is AE When the voltage is negative and the value range is negative 8% -17%, the voltage drop disturbance is contained, when W is AE Is equal to W AEvsag Is a voltage drop when W AE Is equal to W AEvsag+hr Is a composite disturbance of voltage droop and harmonics;
when W is AE Is equal to W AEvsag+n Is a composite disturbance of voltage droop and noise; when W is AE Is equal to W AEvi Is a voltage interruption when W AE Is equal to W AEcs Is capacitor switching disturbance when W AE Is equal to W AEls Is a load switch disturbance;
when W AE When the signal is equal to zero, the power grid is considered to have no interference signal, and the signal is a high-quality power grid signal;
wherein:
W AE : the percentage of characteristic signal energy entropy relative to sinusoidal signal;
W AEps : pre-calculating to obtain power grid W corresponding to sine signal disturbance AE A value;
W AEvsag : pre-calculating to obtain the power grid W corresponding to the voltage drop disturbance AE A value;
W AEvswell : pre-calculating to obtain the power grid W corresponding to the voltage rise disturbance AE A value;
W AEhr : pre-calculating to obtain the power grid W corresponding to the harmonic disturbance AE A value;
W AEvf : pre-calculating to obtain the power grid W corresponding to the voltage flicker disturbance AE A value;
W AEvi : pre-calculating to obtain the power grid W corresponding to the voltage interruption disturbance AE A value;
W AEcs : pre-calculating to obtain the power grid W corresponding to the capacitor switch disturbance AE A value;
W AEls : pre-calculating to obtain the power grid W corresponding to the load switch disturbance AE A value;
W AEvsag+hr : pre-calculating to obtain power grid W corresponding to harmonic and voltage-reduced composite disturbance AE A value;
W AEvswell+hr : pre-calculating to obtain power grid W corresponding to harmonic wave and voltage rising composite disturbance AE A value;
W AEvswell+n : pre-calculating to obtain the power grid W corresponding to the composite disturbance of the voltage rise and the noise AE A value;
W AEvf+n : pre-calculating to obtain the power grid W corresponding to the composite disturbance of voltage flicker and noise AE A value;
the WAEvsag + n is a power grid W corresponding to the composite disturbance of the voltage drop noise obtained by pre-calculation AE The value is obtained.
5. The method for detecting and identifying the power grid disturbance signal according to claim 4, wherein: when W AE When the signal is positive and the value range is 0% -1%, the signal contains transient signal, further calculate W AEabs When W is AEabs Is equal to W AEabs-hft Is a high frequency transient disturbance when W AE Is equal to W AEabs-lft Is a low frequency transient disturbance; wherein:
W Aeabs is the absolute percentage of the calculated signal entropy after MSD decomposition;
W AEabs-lft obtaining the power grid W corresponding to the low-frequency disturbance by pre-calculation AEabs A value;
W AEabs-HFT for pre-calculating to obtain the power grid W corresponding to the high-frequency disturbance AEabs The value is obtained.
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