CN111639583A - Method and system for identifying power quality disturbance of power grid - Google Patents

Method and system for identifying power quality disturbance of power grid Download PDF

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Publication number
CN111639583A
CN111639583A CN202010457500.3A CN202010457500A CN111639583A CN 111639583 A CN111639583 A CN 111639583A CN 202010457500 A CN202010457500 A CN 202010457500A CN 111639583 A CN111639583 A CN 111639583A
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disturbance
sample
power grid
photovoltaic
characteristic
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杨茂涛
胡军华
刘谋海
陈向群
刘小平
柳青
余敏琪
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State Grid Corp of China SGCC
State Grid Hunan Electric Power Co Ltd
Metering Center of State Grid Hunan Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Hunan Electric Power Co Ltd
Metering Center of State Grid Hunan Electric Power Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G06F2218/04—Denoising
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24133—Distances to prototypes
    • G06F18/24137—Distances to cluster centroïds
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching

Abstract

The invention discloses a method and a system for identifying power quality disturbance of a power grid, wherein the method comprises the following steps: 1) monitoring the voltage value of each node of the power distribution network in real time, and immediately starting disturbance mode identification if the sudden change of the voltage value exceeds a preset threshold value; 2) acquiring real-time data from a monitored line, and extracting a real-time feature sample x 'consisting of S disturbance feature quantities by utilizing S transformation'g(ii) a 3) To real-time feature sample x'gCarrying out standardization processing to obtain a sample x to be detectedgCalculating a sample x to be measuredgThe angle similarity between the photovoltaic disturbance cluster center and the public power grid disturbance cluster centerg1、g2(ii) a 4) Judgment ofg1Andg2the size of (2): if it isg1＞g2If so, photovoltaic disturbance occurs on the monitored line; if it isg1＜g2And the monitored line generates public power grid disturbance. The method has the advantages of high pattern recognition accuracy, high classification precision, strong noise resistance, improved classification effect and efficiency and the like.

Description

Method and system for identifying power quality disturbance of power grid
Technical Field
The invention relates to the technical field of power quality identification, in particular to a method and a system for identifying power quality disturbance of a power grid.
Background
The power quality disturbance can cause serious consequences such as equipment overheating, motor stalling, protection failure, inaccurate metering and the like, and serious economic loss and social influence are caused. However, with the development of society and the improvement of living standard, more and more power electronic devices sensitive to the quality of electric energy are put into use, and higher requirements are put forward on the quality of electric energy. Accurate identification of power quality disturbance can provide an auxiliary decision for management and treatment of power quality, and power supply quality can be improved.
Most of the power quality disturbances are non-stationary signals, and in recent years, time-frequency analysis methods of the non-stationary signals are well researched and commonly used include short-time Fourier transform, wavelet transform, time-frequency atom transform, S-transform and the like. The short-time Fourier transform has a fixed window, so that the frequency resolution is relatively fixed, and if the resolution is changed, the window needs to be selected again; wavelet transform has varying resolution and wavelet basis selection is difficult; in contrast, the S transform is insensitive to noise due to its good time-frequency local performance, and is often used to analyze the power quality disturbance signal, but when there are many disturbance types, it is difficult to extract features to distinguish all the disturbance types. The features extracted by the methods are input into a pattern recognition classifier, so that the automatic recognition of the power quality disturbance can be realized.
The pattern recognition method includes a clustering method, a neural network, a Support Vector Machine (SVM), an expert system, a decision tree method, and the like. The SVM is often used for disturbance classification, but when the disturbance types are more, the recognition error rate is increased due to the characteristic aliasing phenomenon; if the proper classification features are selected when the classes are small, the SVM can show excellent generalization capability. The decision tree method simulates the logic thinking of human beings to realize classification by establishing rules, but the characteristic threshold of the classification of certain classes is influenced by noise and is difficult to determine.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the technical problems in the prior art, the invention provides a method and a system for identifying power quality disturbance of a power grid, which have high feature extraction accuracy and high pattern identification accuracy.
In order to solve the technical problems, the invention adopts the technical scheme that:
a method for identifying power quality disturbance of a power grid comprises the following steps:
1) monitoring voltage values of nodes of the power distribution network in real time, wherein if sudden change delta U of the voltage values exceeds preset threshold delta UsetImmediately starting disturbance mode identification;
2) acquiring real-time data from a monitored line, and extracting a real-time feature sample x 'consisting of S disturbance feature quantities by utilizing S transformation'g；
3) To sample x 'to be tested'gCarrying out standardization processing to obtain a sample x to be detectedgCalculating a sample x to be measuredgRespectively with photovoltaic disturbance cluster center and public power grid disturbance cluster centerDegree of angular similarityg1、g2；
4) Judgment ofg1Andg2the size of (2): if it isg1＞g2If so, photovoltaic disturbance occurs on the monitored line; if it isg1＜g2And the monitored line generates public power grid disturbance.
Preferably, in step 3), the obtaining process of the photovoltaic disturbance classification center and the public power grid disturbance classification center is as follows:
3.1) extracting historical characteristic sample x 'under various running states'kStandardized to obtain xk；
3.2) historical feature sample x by fuzzy clustering algorithmkClassifying to obtain a clustering center c of the photovoltaic disturbance clustering center and the public power grid disturbance clustering centeri。
Preferably, the specific process of step 3.1) is:
3.1.1) gather s kinds of disturbance characteristic vector of monitored line under n kinds of operating condition through current distribution lines measuring equipment, define as n historical feature samples, wherein the kth sample is:
x′k＝(x′k1,x′k2,…,x′ks)T(6)
in the formula: k is an integer and the value interval is [1, n]，x′k1、…、x′ksThe specific values of the s disturbance characteristic quantities extracted under the kth running state are respectively;
3.1.2) carrying out standardization treatment on various voltage change samples to obtain the following results:
Figure BDA0002509849250000021
in the formula, the value interval of j is [1, s ];
after normalization, the kth sample xk＝(xk1,xk2,…,xks)T；
3.1.3) calculating real-time characteristic sample x of monitored line in real-time running state of power distribution networkg＝(xg1,…,xgj,…,xgs)T，xg1、...、xgsAnd the specific values of the s full-band voltage sudden change characteristic quantities extracted in real time for the monitored line are respectively.
Preferably, the specific process of step 3.2) is:
3.2.1) normalization of the processed samples x1，...，xnCarrying out fuzzy clustering analysis, dividing the sample into a photovoltaic disturbance class and a public power grid disturbance class, and solving a clustering center of the photovoltaic disturbance and the public power grid disturbance;
the objective function is set as:
Figure BDA0002509849250000022
in the formula: i has a value of 1 or 2, c1As a photovoltaic disturbance cluster center, c2Disturbing a clustering center for the public power grid; mu.sikRepresenting a cluster sample xkMembership degree belonging to the i-th cluster type, satisfying the condition
Figure BDA0002509849250000031
The clustering center and the membership degree can be solved through the extreme value:
Figure BDA0002509849250000032
Figure BDA0002509849250000033
3.2.2) assumed clustering center ciBy μikAnd ciAnd (4) performing mutual iteration to obtain the photovoltaic disturbance clustering center and the public power grid disturbance clustering center when the membership degree is smaller than an iteration stop threshold value.
Preferably, in step 3), the sample x to be measured is calculatedgThe angle similarity between the photovoltaic disturbance cluster center and the public power grid disturbance cluster centerg1、g2The process comprises the following steps:
sample x to be testedgWith class i center ciThe full-band voltage amplitude variation between the two is as follows:
Figure BDA0002509849250000034
in the formula: delta Ug1Representing the sample x to be measuredgA variation from a photovoltaic perturbation class center; delta Ug2Representing the sample x to be measuredgVariation from public grid disturbance class center;
defining the photovoltaic disturbance measure as:
Figure BDA0002509849250000035
the public power grid disturbance measure is as follows:
Figure BDA0002509849250000036
preferably, in step 4), the utility grid disturbance type includes one or more of voltage sag, voltage interruption, harmonic, electromagnetic pulse, oscillation transient, voltage flicker, voltage sag, voltage spike; the photovoltaic disturbance comprises two composite disturbance types of harmonic wave + sag and harmonic wave + sag.
Preferably, in step 2), the s disturbance characteristic quantities include one or more of a fundamental frequency amplitude characteristic, a middle frequency range amplitude characteristic, a fundamental frequency standard deviation characteristic, and a signal minimum value characteristic near a moment corresponding to a fundamental frequency amplitude minimum value;
the fundamental frequency amplitude feature extraction process comprises the following steps:
defining a fundamental frequency curve of amplitude variation with time at fundamental frequency corresponding to S transform mode time-frequency matrix as Vfb(l) The expression is as follows:
Vfb(l)＝Sa(l,fb) (1)
in the formula: l represents the sampling instant: f. ofbRepresents the fundamental frequency;
the fundamental frequency amplitude mean value characteristic of the S transformation is as follows:
Figure BDA0002509849250000041
in the formula: l is the total sampling point number; favThe change situation of the fundamental frequency amplitude is reflected;
preferably, the extraction process of the mid-frequency range amplitude features is as follows:
the time-varying function of the square sum of the amplitudes corresponding to the intermediate-frequency band frequencies in the S-transform mode time-frequency matrix and the variation with time is defined as Vfh(l)：
Figure BDA0002509849250000042
In the formula (3), the values in the mode time-frequency matrix are squared to reduce the influence of noise on signal processing, wherein m and n are the lower bound value and the upper bound value of the line range of the corresponding middle frequency band in the S transformation mode time-frequency matrix respectively, and the lower bound value and the upper bound value are used for Vfh(l) Obtaining a medium frequency range amplitude characteristic Fz by taking an average value, wherein the characteristic value is used for distinguishing electromagnetic pulses and other types of disturbance; by Fz and FavThe two eigenvalues distinguish between impulses and interruptions.
Preferably, the fundamental frequency amplitude minimum corresponds to the signal minimum characteristic around the moment, and is used for distinguishing the dip and the interruption of two types of disturbance, and the two types of disturbance can be calculated according to the following two steps:
① finding the fundamental frequency curve VfbThe minimum value point of (D) is denoted as Kmin；
Sa(Kmin,fb)＝Sa(l,fb) (4)
② calculating fundamental frequency minimum value point KminObtaining the minimum value characteristic of the signal near the moment corresponding to the minimum value of the fundamental frequency amplitude value by the root mean square value of the sampling points of the front and the back half cycles, and recording the minimum value characteristic as Fav；
Figure BDA0002509849250000043
Where x [ kT ] represents the perturbed sample signal, which is squared to reduce the effect of noise on the signal processing results.
The invention also discloses a system for identifying the power quality disturbance of the power grid, which comprises the following steps:
the monitoring module is used for monitoring the voltage value of each node of the power distribution network in real time, if the sudden change delta U of the voltage value exceeds a preset threshold delta UsetImmediately starting disturbance mode identification;
the feature extraction module is used for acquiring real-time data from the monitored line and extracting a real-time feature sample x 'consisting of S disturbance feature quantities by utilizing S transformation'g；
A calculation module for calculating a sample x 'to be measured'gCarrying out standardization processing to obtain a sample x to be detectedgCalculating a sample x to be measuredgThe angle similarity between the photovoltaic disturbance cluster center and the public power grid disturbance cluster centerg1、g2；
A judging module for judgingg1Andg2the size of (2): if it isg1＞g2If so, photovoltaic disturbance occurs on the monitored line; if it isg1＜g2And the monitored line generates public power grid disturbance.
Compared with the prior art, the invention has the advantages that:
aiming at the characteristic of time sequence of disturbance signals, the invention adopts a power quality disturbance mode identification method based on fuzzy clustering analysis, thereby improving the accuracy of mode identification under power quality disturbance; the method has high accuracy of extracting the characteristics of the power quality disturbance signals, the fuzzy clustering analysis method is superior to the classification method of single characteristic quantity, the classification effect and the classification efficiency are improved, and the method has high classification accuracy and good noise resistance.
According to the identification method for power quality disturbance of the power grid, the height and the width of the Gaussian window of the S transform of the disturbance characteristic quantity are extracted to change along with the frequency, the succession and the development of wavelet transform and short-time Fourier transform are adopted, the characteristic of wavelet transform multi-resolution analysis is achieved, the capacity of single-frequency independent analysis of short-time Fourier transform is achieved, and the problem of window function selection of the wavelet transform multi-resolution analysis and the short-time Fourier transform single-frequency independent analysis is solved; in addition, the disturbance characteristic quantity extracted based on the S transformation can effectively describe the photovoltaic disturbance class and the public power grid disturbance class.
When the power quality of the power distribution system changes under disturbance, the method calculates the sample x to be measuredgJudging a disturbance mode of a monitored line according to the similarity of the monitored line and each clustering center, and specifically providing a similarity measurement criterion of full-band voltage mutation values, namely judging the power quality disturbance state according to two sample power grid voltage mutation values; when the power quality of the distribution network lines is abnormal, the similarity between the sample to be detected extracted from each line and the voltage mutation quantity of each clustering center can be calculated, the accurate identification of line disturbance can be realized by integrating the power quality index information of each line, a coping strategy is formulated for the subsequent abnormal lines, and the effective management of the power quality is realized.
According to the method for identifying the power quality disturbance pattern, the similarity between the characteristic sample to be detected and the historical characteristic sample is analyzed by utilizing the similarity of the voltage variation, the sample to be detected is classified into a photovoltaic disturbance class or a public power grid disturbance class, the disturbance sample can be accurately identified by the identification method through matlab simulation verification, the disturbance pattern is identified, and meanwhile, S disturbance characteristic quantities are extracted based on S transformation, so that the problem that the identification characteristic quantity of the power quality disturbance is difficult to obtain is solved; in addition, a power quality disturbance mode identification method based on fuzzy clustering is established, and the problem that a single variable is identified in the traditional power quality disturbance identification method is solved; simulation analysis shows that the method for recognizing the power quality disturbance mode can effectively extract disturbance characteristic quantity, has high accuracy of cluster analysis, and can be combined with power quality management to carry out engineering popularization and application.
Drawings
FIG. 1 is a flow chart of an embodiment of the method of the present invention.
FIG. 2 is a block diagram illustrating the identification of different disturbance modes under the condition of power quality.
Detailed Description
The invention is further described below with reference to the figures and the specific embodiments of the description.
As shown in fig. 1, the present embodiment discloses a method for identifying power quality disturbance of a power grid, including the steps of:
1) monitoring voltage values of nodes of the power distribution network in real time, wherein if sudden change delta U of the voltage values exceeds preset threshold delta UsetImmediately starting disturbance mode identification;
2) acquiring real-time data from a monitored line, and extracting a real-time feature sample x 'consisting of S disturbance feature quantities by utilizing S transformation'g；
3) To sample x 'to be tested'gCarrying out standardization processing to obtain a sample x to be detectedgCalculating a sample x to be measuredgThe angle similarity between the photovoltaic disturbance cluster center and the public power grid disturbance cluster centerg1、g2；
4) Judgment ofg1Andg2the size of (2): if it isg1＞g2If so, photovoltaic disturbance occurs on the monitored line; if it isg1＜g2And the monitored line generates public power grid disturbance.
According to the identification method for power quality disturbance of the power grid, the height and the width of the Gaussian window of the S transform of the disturbance characteristic quantity are extracted to change along with the frequency, the succession and the development of wavelet transform and short-time Fourier transform are adopted, the characteristic of wavelet transform multi-resolution analysis is achieved, the capacity of single-frequency independent analysis of short-time Fourier transform is achieved, and the problem of window function selection of the wavelet transform multi-resolution analysis and the short-time Fourier transform single-frequency independent analysis is solved; in addition, the disturbance characteristic quantity extracted based on the S transformation can effectively describe the photovoltaic disturbance class and the public power grid disturbance class.
In this embodiment, in step 4), the types of the disturbance of the utility grid include: voltage sag (swell), voltage sag (sag), voltage interruption (outage), harmonics (harmonics), electromagnetic pulses (impulse), oscillatory transients (oscillatory), voltage flicker (flicker), voltage sag (notch), voltage spike (spike), and a composite disturbance type of two photovoltaic disturbances: harmonic + dip (harmonics and sag), harmonic + dip (harmonics and swell).
In this embodiment, in step 2), the s disturbance feature quantities include one or more of a fundamental frequency amplitude feature, a middle frequency range amplitude feature, a fundamental frequency standard deviation feature, and a signal minimum feature near a time corresponding to a fundamental frequency amplitude minimum. Specifically, the aboveS disturbance characteristic quantities are obtained by S transformation, the result after the S transformation is dispersed is a complex matrix, and an S transformation mode time-frequency matrix is obtained after elements in the complex matrix are subjected to modulus calculation and is represented as Sa[l,f](ii) a Wherein l represents a sampling time, and f represents a sampling frequency point; the S transformation is commonly used for extracting the characteristics of the power quality disturbance signal, and the following characteristics are extracted by adopting the S transformation:
(1) extracting the fundamental frequency amplitude features: defining a fundamental frequency curve of amplitude variation with time at fundamental frequency corresponding to S transform mode time-frequency matrix as Vfb(l) The expression is as follows:
Vfb(l)＝Sa(l,fb) (1)
in the formula: l represents the sampling instant: f. ofbRepresents the fundamental frequency;
the fundamental frequency amplitude mean value characteristic of the S transformation is as follows:
Figure BDA0002509849250000071
in the formula: l is the total sampling point number; favThe change situation of the fundamental frequency amplitude is reflected;
for example, analyzing 7 power quality disturbance characteristic values F of voltage rising and voltage falling, voltage interruption, transient oscillation, shear mark, spike and pulseavThe value range of (a). When the SNR is 30dB, each perturbation randomly generates 200 samples, namely the 7 perturbation signals FavThe value ranges of (A) are shown in Table 1. As can be seen from table 1, 2 or more groups of disturbances are distinguished by setting different thresholds. Cuts and spikes, pauses and dips/interruptions, oscillations and dips/interruptions can be distinguished, for example, by setting suitable thresholds. Meanwhile, simulation finds that for 3 types of disturbances including harmonic waves, harmonic waves + transient rise and harmonic waves + transient fall, the harmonic components contained in the disturbances do not affect the change condition of the fundamental frequency when the harmonics are not contained, so that the characteristic value F can be usedavTo distinguish between these 3 perturbations.
TABLE 17 eigenvalues F of the disturbancesavRange of
Figure BDA0002509849250000072
(2) Extracting the middle frequency range amplitude characteristic: the time-varying function of the square sum of the amplitudes corresponding to the intermediate-frequency band frequencies in the S-transform mode time-frequency matrix and the variation with time is defined as Vfh(l)：
Figure BDA0002509849250000073
In the formula (3), the values in the mode-time-frequency matrix are squared, in order to reduce the influence of noise on signal processing, m and n are respectively a lower bound value and an upper bound value of a row range of a corresponding middle frequency band in the S transform mode-time-frequency matrix, in this embodiment, m is 31, and n is 71, that is, the analyzed frequency range is 150-350 Hz; to Vfh(l) The mean value is taken to obtain the middle-frequency amplitude characteristic Fz, the characteristic value of the electromagnetic pulse is large, and the characteristic value is used for distinguishing the electromagnetic pulse from other types of disturbance. Harmonic waves/harmonic waves plus temporary drop/harmonic waves plus temporary rise, shear marks/spikes and flicker can be distinguished by combining an FFT (fast Fourier transform) with a dynamic measurement method, and 6 types of power quality disturbance of temporary rise, temporary drop, interruption, flicker, oscillation and pulse are remained; when the SNR is 30dB, 200 samples are randomly taken for each disturbance, and the value range of the eigenvalue Fz of the 6 disturbances is shown in table 2:
table 26 ranges of the characteristic value Fz of the disturbances
Figure BDA0002509849250000074
It can be seen that the characteristic value Fz of the pulse, apart from crossing somewhat with the interruption, can be well distinguished from the other 4 disturbances; and through Fz and FavThese 2 eigenvalues can distinguish between pulses and 2 disturbances that interrupt.
(3) Fundamental frequency standard deviation characteristics: the fundamental frequency amplitude of the several types of fundamental frequency disturbance such as pause, pause and interruption is greatly changed, and the fundamental frequency amplitude of the other several types such as cut mark and peak is very little changed, so that the fundamental frequency curve V is obtainedfb(l) The standard deviation of the optical fiber can help other characteristics to distinguish the shear mark, the peak and other disturbance; the standard deviation of fundamental frequency is characterized by Fm。
(4) The minimum value of the fundamental frequency amplitude corresponds to the characteristics of the minimum value of the signal near the moment: this feature is used to distinguish dip and interrupt class 2 disturbances and can be calculated in 2 steps as follows:
① finding the fundamental frequency curve VfbThe minimum value point of (D) is denoted as Kmin；
Sa(Kmin,fb)＝Sa(l,fb) (4)
② calculating fundamental frequency minimum value point KminObtaining the minimum value characteristic of the signal near the moment corresponding to the minimum value of the fundamental frequency amplitude value by the root mean square value of the sampling points of the front and the back half cycles, and recording the minimum value characteristic as Fav；
Figure BDA0002509849250000081
Where x [ kT ] represents the perturbed sample signal, which is squared to reduce the effect of noise on the signal processing results. Therefore, the disturbance characteristic quantity extracted based on the S transformation can effectively describe the photovoltaic disturbance class and the public power grid disturbance class.
In this embodiment, in step 3), the obtaining process of the photovoltaic disturbance classification center and the public power grid disturbance classification center is as follows:
3.1) extracting historical characteristic sample x 'under various running states'kStandardized to obtain xkThe method specifically comprises the following steps:
3.1.1) need carry out data acquisition and processing at first, gather s kinds of disturbance characteristic vector of monitored line under n kinds of running state through current distribution lines measuring equipment, define as n historical feature samples, wherein the kth sample is:
x′k＝(x′k1,x′k2,…,x′ks)T(6)
in the formula: k is an integer and the value interval is [1, n]，x′k1、…、x′ksThe specific values of the s disturbance characteristic quantities extracted under the kth running state are respectively;
3.1.2) carrying out standardization treatment on various voltage change samples to obtain the following results:
Figure BDA0002509849250000082
in the formula, the value interval of j is [1, s ];
after normalization, the kth sample xk＝(xk1,xk2,…,xks)T；
3.1.3) calculating the real-time characteristic sample x of the monitored line under the real-time running state of the power distribution network in the same wayg＝(xg1,…,xgj,…,xgs)T，xg1、...、xgsSpecific values of the s full-band voltage sudden change characteristic quantities extracted in real time for the monitored line are respectively extracted;
3.2) historical feature sample x by fuzzy clustering algorithmkClassifying to obtain a clustering center c of the photovoltaic disturbance clustering center and the public power grid disturbance clustering centeriThe method specifically comprises the following steps:
3.2.1) normalization of the processed samples x1，...，xnCarrying out fuzzy clustering analysis, dividing the sample into a photovoltaic disturbance class and a public power grid disturbance class, and solving a clustering center of the photovoltaic disturbance and the public power grid disturbance;
the objective function is set as:
Figure BDA0002509849250000091
in the formula: i has a value of 1 or 2, c1As a photovoltaic disturbance cluster center, c2Disturbing a clustering center for the public power grid; mu.sikRepresenting a cluster sample xkMembership degree belonging to the i-th cluster type, satisfying the condition
Figure BDA0002509849250000092
The clustering center and the membership degree can be solved through the extreme value:
Figure BDA0002509849250000093
Figure BDA0002509849250000094
3.2.2) assumed clustering center ciBy μikAnd ciAnd (4) performing mutual iteration to obtain the photovoltaic disturbance clustering center and the public power grid disturbance clustering center when the membership degree is smaller than an iteration stop threshold value.
Aiming at the characteristic of time sequence of disturbance signals, the invention adopts a power quality disturbance mode identification method based on fuzzy clustering analysis, thereby improving the accuracy of mode identification under power quality disturbance; the method has high accuracy of extracting the characteristics of the power quality disturbance signals, the fuzzy clustering analysis method is superior to the classification method of single characteristic quantity, the classification effect and the classification efficiency are improved, and the method has high classification accuracy and good noise resistance.
In this embodiment, in step 3), the sample x to be measured is calculatedgThe angle similarity between the photovoltaic disturbance cluster center and the public power grid disturbance cluster centerg1、g2The process comprises the following steps:
sample x to be testedgWith class i center ciThe full-band voltage amplitude variation between the two is as follows:
Figure BDA0002509849250000101
in the formula: delta Ug1Representing the sample x to be measuredgA variation from a photovoltaic perturbation class center; delta Ug2Representing the sample x to be measuredgVariation from public grid disturbance class center;
defining the photovoltaic disturbance measure as:
Figure BDA0002509849250000102
the public power grid disturbance measure is as follows:
Figure BDA0002509849250000103
if it isg1＜g2If so, indicating that the real-time characteristic sample data belongs to the public power grid disturbance class, and the quality of the electric energy generated by the monitored line under the disturbance action does not reach the standard; if it isg1＞g2And (3) monitoring the photovoltaic disturbance of the monitored line, as shown in FIG. 2, wherein the point o represents the public power grid disturbance, and × represents the photovoltaic disturbance, and when the power quality of the power distribution system changes under the disturbance of the corresponding parameter, calculating a sample x to be measuredgJudging a disturbance mode of a monitored line according to the similarity of the monitored line and each clustering center, and specifically providing a similarity measurement criterion of full-band voltage mutation values, namely judging the power quality disturbance state according to two sample power grid voltage mutation values; when the power quality of the distribution network lines is abnormal, the similarity between the sample to be detected extracted from each line and the voltage mutation quantity of each clustering center can be calculated, the accurate identification of line disturbance can be realized by integrating the power quality index information of each line, a coping strategy is formulated for the subsequent abnormal lines, and the effective management of the power quality is realized.
In this embodiment, in step 1), Δ UsetPlus or minus 0.5 percent of U, wherein U is a standard voltage value when the system power quality is normal; in the step 4), if photovoltaic disturbance occurs to the monitored line, classifying the real-time characteristic sample into a fault historical characteristic sample set, and returning to the step 1); if the monitored line generates public power grid disturbance, classifying the real-time characteristic sample into a public power grid disturbance type historical characteristic sample set, and returning to the step 1); and judging each line, identifying a specific disturbance mode and finishing a line power quality disturbance identification scheme.
In order to verify the effectiveness of the electric energy quality disturbance identification method, various electric energy quality disturbance signal models are established to test the identification effect of the method. Wherein the parameter ranges for each power quality disturbance signal are randomly generated.
Specifically, a power distribution network model is built in a matlab simulation environment, the system comprises three overhead lines and a cable line, and specific parameters of the line are listed in a table 3. Below, a disturbance of the motionless type is implemented at each line end. At the beginning of the line, a measuring element is arranged, from the quality of the collected electric energyExtracting characteristic quantity from the quantity disturbance information, wherein the disturbance characteristic quantity is the phase difference x measured by the load side end relay in sequencek1Harmonic voltage x of each frequency bandk2Load node voltage xk3And a system resistance xk4。
TABLE 3 distribution network System parameters
Figure BDA0002509849250000111
Two system fault states are simulated on the basis of the distribution network parameter setting, wherein 8 groups are data of lines in photovoltaic disturbance, the rest 8 groups are data of other lines in public power grid disturbance, disturbance characteristic quantities are respectively extracted, the data are listed in a table 4, and a photovoltaic disturbance clustering center and a public power grid disturbance clustering center can be calculated according to a formula (4).
TABLE 4 System historical characteristics sample set
Figure BDA0002509849250000112
TABLE 5 clustering center coordinates of historical data
Figure BDA0002509849250000113
TABLE 6 real-time feature sample identification data
Figure BDA0002509849250000121
TABLE 7 monitored line disturbance identification
Figure BDA0002509849250000122
Two power quality disturbance states are simulated, and disturbance characteristic quantities are respectively collected and listed in table 4. And calculating the disturbance measure according to a voltage mutation quantity similarity formula, and obtaining a monitored identification result listed in table 7. According to the method, when the photovoltaic disturbance occurs to the measured line, the photovoltaic disturbance measure is larger than the public power grid disturbance measure, and the disturbance types can be accurately distinguished; and when other measured lines generate public power grid disturbance, the photovoltaic disturbance measure is smaller than the public power grid disturbance measure. The results in table 7 are consistent with the perturbation assumptions in table 6, verifying the feasibility of the well-described pattern recognition method.
According to the method for identifying the power quality disturbance mode, the similarity between the characteristic sample to be detected and the historical characteristic sample is analyzed by utilizing the similarity of the voltage variation, the sample to be detected is classified into a photovoltaic disturbance class or a public power grid disturbance class, the disturbance sample can be accurately identified by the identification method through matlab simulation verification, the disturbance mode is identified, and meanwhile the following conclusion is obtained:
1) aiming at the problem that the identification characteristic quantity of the power quality disturbance is difficult to obtain, S disturbance characteristic quantities are extracted based on S transformation;
2) the problem of identifying a single variable in the traditional electric energy quality disturbance identification method is solved, and an electric energy quality disturbance mode identification method based on fuzzy clustering is established.
Simulation analysis shows that the electric energy quality disturbance mode identification method can effectively extract disturbance characteristic quantity, has high accuracy of cluster analysis, and can be considered to be combined with electric energy quality control in future research to carry out engineering popularization and application.
The invention also discloses a system for identifying the power quality disturbance of the power grid, which comprises the following steps:
the monitoring module is used for monitoring the voltage value of each node of the power distribution network in real time, if the sudden change delta U of the voltage value exceeds a preset threshold delta UsetImmediately starting disturbance mode identification;
the feature extraction module is used for acquiring real-time data from the monitored line and extracting a real-time feature sample x 'consisting of S disturbance feature quantities by utilizing S transformation'g；
A calculation module for calculating a sample x 'to be measured'gCarrying out standardization processing to obtain a sample x to be detectedgCalculating a sample x to be measuredgThe angle similarity between the photovoltaic disturbance cluster center and the public power grid disturbance cluster centerg1、g2；
A judging module for judgingg1Andg2the size of (2): if it isg1＞g2If so, photovoltaic disturbance occurs on the monitored line; if it isg1＜g2And the monitored line generates public power grid disturbance.
The identification system of the invention, for performing the identification method as described above, also has the advantages as described above for the identification method.
The invention further discloses a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, performs the steps of the method for identifying a grid power quality disturbance as described above.
The invention also discloses a computer device comprising a memory and a processor, wherein the memory is stored with a computer program, and the computer program executes the steps of the identification method of the power quality disturbance of the power grid when being executed by the processor.
All or part of the flow of the method of the embodiments may be implemented by a computer program, which may be stored in a computer-readable storage medium and executed by a processor, to implement the steps of the embodiments of the methods. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like. The memory may be used to store computer programs and/or modules, and the processor may perform various functions by executing or executing the computer programs and/or modules stored in the memory, as well as by invoking data stored in the memory. The memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.

Claims (10)

1. A method for identifying power quality disturbance of a power grid is characterized by comprising the following steps:
1) monitoring voltage values of nodes of the power distribution network in real time, wherein if sudden change delta U of the voltage values exceeds preset threshold delta UsetImmediately starting disturbance mode identification;
2) acquiring real-time data from a monitored line, and extracting a real-time feature sample x 'consisting of S disturbance feature quantities by utilizing S transformation'g；
3) To sample x 'to be tested'gCarrying out standardization processing to obtain a sample x to be detectedgCalculating a sample x to be measuredgThe angle similarity between the photovoltaic disturbance cluster center and the public power grid disturbance cluster centerg1、g2；
4) Judgment ofg1Andg2the size of (2): if it isg1＞g2If so, photovoltaic disturbance occurs on the monitored line; if it isg1＜g2And the monitored line generates public power grid disturbance.
2. The method for identifying the power quality disturbance of the power grid according to claim 1, wherein in the step 3), the obtaining process of the photovoltaic disturbance class center and the public power grid disturbance class center is as follows:
3.1) extracting historical characteristic sample x 'under various running states'kStandardized to obtain xk；
3.2) historical feature sample x by fuzzy clustering algorithmkClassifying to obtain a clustering center c of the photovoltaic disturbance clustering center and the public power grid disturbance clustering centeri。
3. The method for identifying the power quality disturbance of the power grid according to claim 2, wherein the specific process of the step 3.1) is as follows:
3.1.1) gather s kinds of disturbance characteristic vector of monitored line under n kinds of operating condition through current distribution lines measuring equipment, define as n historical feature samples, wherein the kth sample is:
x′k＝(x′k1,x′k2,…,x′ks)T(6)
in the formula: k is an integer and the value interval is [1, n]，x′k1、…、x′ksThe specific values of the s disturbance characteristic quantities extracted under the kth running state are respectively;
3.1.2) carrying out standardization treatment on various voltage change samples to obtain the following results:
Figure FDA0002509849240000011
in the formula, the value interval of j is [1, s ];
after normalization, the kth sample xk＝(xk1,xk2,…,xks)T；
3.1.3) calculating real-time characteristic sample x of monitored line in real-time running state of power distribution networkg＝(xg1,…,xgj,…,xgs)T，xg1、...、xgsAnd the specific values of the s full-band voltage sudden change characteristic quantities extracted in real time for the monitored line are respectively.
4. The method for identifying the power quality disturbance of the power grid according to claim 3, wherein the specific process of the step 3.2) is as follows:
3.2.1) normalization of the samplesx1，...，xnCarrying out fuzzy clustering analysis, dividing the sample into a photovoltaic disturbance class and a public power grid disturbance class, and solving a clustering center of the photovoltaic disturbance and the public power grid disturbance;
the objective function is set as:
Figure FDA0002509849240000021
in the formula: i has a value of 1 or 2, c1As a photovoltaic disturbance cluster center, c2Disturbing a clustering center for the public power grid; mu.sikRepresenting a cluster sample xkMembership degree belonging to the i-th cluster type, satisfying the condition
Figure FDA0002509849240000022
The clustering center and the membership degree can be solved through the extreme value:
Figure FDA0002509849240000023
Figure FDA0002509849240000024
3.2.2) assumed clustering center ciBy μikAnd ciAnd (4) performing mutual iteration to obtain the photovoltaic disturbance clustering center and the public power grid disturbance clustering center when the membership degree is smaller than an iteration stop threshold value.
5. The method for identifying the power quality disturbance of the power grid according to claim 4, wherein in the step 3), the sample x to be measured is calculatedgThe angle similarity between the photovoltaic disturbance cluster center and the public power grid disturbance cluster centerg1、g2The process comprises the following steps:
sample x to be testedgWith class i center ciThe full-band voltage amplitude variation between the two is as follows:
Figure FDA0002509849240000025
in the formula: delta Ug1Representing the sample x to be measuredgA variation from a photovoltaic perturbation class center; delta Ug2Representing the sample x to be measuredgVariation from public grid disturbance class center;
defining the photovoltaic disturbance measure as:
Figure FDA0002509849240000031
the public power grid disturbance measure is as follows:
Figure FDA0002509849240000032
6. the identification method for the power quality disturbance of the power grid according to any one of claims 1 to 5, wherein in the step 4), the disturbance type of the public power grid comprises one or more of voltage sag, voltage interruption, harmonic wave, electromagnetic pulse, oscillation transient, voltage flicker, voltage sag, and voltage spike; the photovoltaic disturbance comprises two composite disturbance types of harmonic wave + sag and harmonic wave + sag.
7. The method for identifying the power quality disturbance of the power grid according to any one of claims 1 to 5, wherein in the step 2), the s disturbance characteristic quantities comprise one or more of a fundamental frequency amplitude characteristic, a middle frequency range amplitude characteristic, a fundamental frequency standard deviation characteristic and a signal minimum value characteristic near a moment corresponding to a fundamental frequency amplitude minimum value;
the fundamental frequency amplitude feature extraction process comprises the following steps:
defining a fundamental frequency curve of amplitude variation with time at fundamental frequency corresponding to S transform mode time-frequency matrix as Vfb(l) The expression is as follows:
Vfb(l)＝Sa(l,fb) (1)
in the formula: l represents the sampling instant: f. ofbRepresents the fundamental frequency;
the fundamental frequency amplitude mean value characteristic of the S transformation is as follows:
Figure FDA0002509849240000033
in the formula: l is the total sampling point number; favReflecting the amplitude variation of the fundamental frequency.
8. The method for identifying the power quality disturbance of the power grid according to claim 7, wherein the extraction process of the amplitude feature of the intermediate frequency band comprises the following steps:
the time-varying function of the square sum of the amplitudes corresponding to the intermediate-frequency band frequencies in the S-transform mode time-frequency matrix and the variation with time is defined as Vfh(l)：
Figure FDA0002509849240000034
In the formula (3), the values in the mode time-frequency matrix are squared to reduce the influence of noise on signal processing, wherein m and n are the lower bound value and the upper bound value of the line range of the corresponding middle frequency band in the S transformation mode time-frequency matrix respectively, and the lower bound value and the upper bound value are used for Vfh(l) Obtaining a medium frequency range amplitude characteristic Fz by taking an average value, wherein the characteristic value is used for distinguishing electromagnetic pulses and other types of disturbance; by Fz and FavThe two eigenvalues distinguish between impulses and interruptions.
9. The method for identifying the power quality disturbance of the power grid according to claim 7, wherein the minimum value of the fundamental frequency amplitude corresponds to the characteristic of the signal minimum value near the moment, and is used for distinguishing the sag disturbance and the interruption disturbance, and the method can be calculated according to the following two steps:
① finding the fundamental frequency curve VfbThe minimum value point of (D) is denoted as Kmin；
Sa(Kmin,fb)＝Sa(l,fb) (4)
② calculating fundamental frequency minimum value point KminObtaining the minimum value characteristic of the signal near the moment corresponding to the minimum value of the fundamental frequency amplitude value by the root mean square value of the sampling points of the front and the back half cycles, and recording the minimum value characteristic as Fav；
Figure FDA0002509849240000041
Wherein x [ kT ] represents a disturbed sampling signal, and the square of the disturbed sampling signal is used for reducing the influence of noise on a signal processing result.
10. A system for identifying power quality disturbance of a power grid is characterized by comprising:
the monitoring module is used for monitoring the voltage value of each node of the power distribution network in real time, if the sudden change delta U of the voltage value exceeds a preset threshold delta UsetImmediately starting disturbance mode identification;
the feature extraction module is used for acquiring real-time data from the monitored line and extracting a real-time feature sample x 'consisting of S disturbance feature quantities by utilizing S transformation'g；
A calculation module for calculating a sample x 'to be measured'gCarrying out standardization processing to obtain a sample x to be detectedgCalculating a sample x to be measuredgThe angle similarity between the photovoltaic disturbance cluster center and the public power grid disturbance cluster centerg1、g2；
A judging module for judgingg1Andg2the size of (2): if it isg1＞g2If so, photovoltaic disturbance occurs on the monitored line; if it isg1＜g2And the monitored line generates public power grid disturbance.
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