CN101034389A - Electrical energy power quality disturbance automatic identification method and system based on information fusion - Google Patents
Electrical energy power quality disturbance automatic identification method and system based on information fusion Download PDFInfo
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- CN101034389A CN101034389A CNA2007100517107A CN200710051710A CN101034389A CN 101034389 A CN101034389 A CN 101034389A CN A2007100517107 A CNA2007100517107 A CN A2007100517107A CN 200710051710 A CN200710051710 A CN 200710051710A CN 101034389 A CN101034389 A CN 101034389A
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Abstract
The invention is a automatic identification method and system based on the power quality disturbances of the information fusion, characterized by: collecting the transient and steady-state measurement datum associated with the power quality disturbances from the Power Quality Monitoring System and other automation systems, disposing of noise such as pretreatments; Using the method combining Fourier analysis, small wave multi-resolution decomposition and analysis of the correlation functions, distilling the from the disturbance datum, establishing the disturbance eigenvector, and as a the input characteristic vector of three probabilistic neural networks, realizing the mapping from a feature space to the disturbance space; the output of three probabilistic neural networks regarded as the evidence body of independent of each other, realizing Information Fusion by the use of D-S evidence theory, obtaining recognition results. This invention through the correct selection and extraction of disturbance eigenvectors, can input neural network parallel in classification and reflect the disturbance situation from the various aspects, thus effectively enhancing the correct identification rate of disturbance, a first step in Intelligent Recognition of the power quality disturbances.
Description
Technical field
The invention belongs to power system automatic field, especially a kind of electrical energy power quality disturbance automatic identification method and system based on information fusion.
Background technology
The essence of electrical energy power quality disturbance identification is pattern-recognition, at present the basic thought of this problem of research is according to Fast Fourier Transform (FFT) and wavelet transformation or other Time-Frequency Analysis Method signal to be analyzed, extract feature, carry out the automatic identification of electrical energy power quality disturbance in conjunction with neural network, the recognition methods of fuzzy expert system isotype.The simple mostly characteristic quantity that extracts an aspect from the distortion time domain waveform of electric energy quality monitoring system of existing method is discerned electrical energy power quality disturbance, lacks associated treatment and comprehensive utilization to the multi-source multidimensional information.Because the perturbation features amount utilized of this method is limited, if these characteristic quantities are influenced by various noise, may reduce disturbance accuracy of identification and reliability, defective of various degrees all at aspects such as accuracy, reliability and practicality simultaneously.
Summary of the invention
Purpose of the present invention is exactly that a kind of electrical energy power quality disturbance identification of proposition accuracy is good, reliability is high, the electrical energy power quality disturbance recognition methods based on information fusion of highly versatile; Another object of the present invention provides a kind of rational in infrastructure, can satisfy the electrical energy power quality disturbance recognition system based on information fusion of accuracy, reliability and practicality.
Electrical energy power quality disturbance recognition methods based on information fusion of the present invention may further comprise the steps:
(1) data acquisition and pre-service
Data acquisition and pre-service are directly to merge on the raw data layer that collects, from electric energy quality monitoring system and other automated system (as electric energy metered system, load management system etc.) in, (for example gather steady state measurement data relevant and transient measuring data with electrical energy power quality disturbance, historical data, statistics and wave file) without just carrying out the comprehensive of data before the pre-service and analyzing, it is the fusion of minimum level, mainly finish the screening of raw data, error detection and check, Data Format Transform, the data processing that normalized etc. are preliminary, mainly be used to from different aforementioned sources, the information that reflects same target has similar features, judge the true and false of the data that these features are incomplete same, eliminate the false and retain the true, remedy the untrue property of some information, inexactness.
(2) feature extraction and local identification
Feature extraction and local identification belong to the intermediate level, except that the state estimation of finishing data, main is to the raw information from the different pieces of information source, the method of utilizing Fourier analysis, the decomposition of wavelet multiresolution rate and correlation function analysis to combine is extracted time domain, frequency domain and the linked character of noisy data, and characteristic information carried out analysis-by-synthesis and processing, set up 3 proper vectors of reflection perturbation features, 3 proper vectors are input to 3 probabilistic neural networks respectively, realize the part identification of disturbance.
(3) comprehensive identification
Comprehensive identification is a kind of high-level fusion, the ability of utilization D-S evidential reasoning theoretical treatment uncertain problem, time domain, frequency domain and related disturbance recognition result that step (2) is provided carry out Combined Treatment, further optimize recognition result by its rule of combination, make final identification.
Electrical energy power quality disturbance recognition system based on information fusion of the present invention is divided into Visual C++, Matlab and Excel working environment, uses Visual C++ to carry out the collection of data, the transmission of order and complicated flow process control; The complicated algorithm of finishing the com component that uses Matlab to develop down realizes and the digital signal processing programming; Use Excel to make up database.Specifically, the quality of power supply recognition system that the present invention is based on information fusion is made of data acquisition and pretreatment module, disturbance identification database, characteristic extracting module, local dip identification module, fusion recognition module and user interactive module.
The beneficial effect of the electrical energy power quality disturbance recognition methods based on information fusion of the present invention is:
1, the probabilistic neural network recognition technology is combined with wavelet analysis technology, realized the Intelligent Recognition of electrical energy power quality disturbance, overcome the strong shortcoming of classic method subjectivity;
2, this method is carried out information fusion to the output of 3 probabilistic neural networks, obtains final recognition result, discrimination height, and good reliability.
The electrical energy power quality disturbance recognition system that the present invention is based on information fusion is rational in infrastructure, and acquired signal accurately, has truly tentatively realized the intellectuality of electric energy quality monitoring.
Description of drawings
Fig. 1 is the electrical energy power quality disturbance recognition methods synoptic diagram based on information fusion;
Fig. 2 is the electrical energy power quality disturbance recognition system structural representation based on information fusion;
Fig. 3 is the electrical energy power quality disturbance identification process figure based on information fusion.
Embodiment
Below in conjunction with drawings and the specific embodiments the present invention is described in further detail.
As shown in Figure 1, a kind of quality of power supply recognition methods based on information fusion comprises the steps:
(1) data acquisition and pre-service
Utilize data access interface from the database of electric energy quality monitoring system, gather relevant informations such as the steady state data relevant, Temporal Data (as indexs such as harmonic wave, voltage fluctuation and flickering, tri-phase unbalance factor, temporary overvoltage and transient overvoltages), temporal pattern and effective value situation of change and duration, peak value size with electrical energy power quality disturbance; By the standard access interface, obtain other automated system (as electric energy metered system, load management system etc.) data relevant with this disturbance; Utilizing wavelet transformation that signal is carried out denoising handles.
(2) feature extraction and local identification
Distorted waveform to the in-site measurement sampling, adopt fast fourier transform that the digital signal of time domain promptly is transformed to its pairing spectrum, just from spectrum, can obtain various features (as the amplitude of the number of times of the contained harmonic wave of signal, each harmonic and initial phase angle thereof etc.) as the proper vector of disturbance about signal; Adopt the wavelet multiresolution rate to decompose distorted waveform is transformed into different frequency fields, extract the signal energy of the various perturbation features of reflection; Adopt the relation of correlation function analysis methods analyst electrical energy power quality disturbance and the operating condition of electric locomotive, by correlation function analysis and calculate facies relationship numerical value.Like this, the characteristic information of the reflection electrical energy power quality disturbance that extracts from Various types of data can be divided three classes:
1. 6 kinds of temporal signatures (voltage waveform, current waveform, effective value, fundamental voltage amplitude and phase place, disturbance duration);
2. 10 kinds of frequency domain characters: 3, the voltage of 5,7,9 subharmonic, current amplitude and phase place, total percent harmonic distortion, wavelet package transforms extracts the energy value in the different frequency bands);
3. 3 kinds of linked characters (related coefficient of disturbance and electric locomotive speed, the related coefficient of disturbance and electric locomotive load, the related coefficient of disturbance and electric locomotive rate of traffic flow).
At above 3 class perturbation features, adopt 3 three layers of probabilistic neural networks arranged side by side, constitutive characteristic grades of fusion neural network.The input of neural network is corresponding with 3 proper vectors, exports correspondingly with the disturbance kind, and promptly voltage dip is (1,0,0,0,0), and voltage is upgraded to (0,1 temporarily, 0,0,0), voltage flicker is (0,0,1,0,0), and voltage fluctuation is (0,0,0,1,0), voltage interruption is (0,0,0,0,1).Judge that confidence value threshold value θ=0.5 that certain class disturbance takes place is benchmark, take place if the neural network output valve, then shows certain class disturbance greater than threshold value and near " 1 ", otherwise, then near " 0 ".
From electric energy quality monitoring system, collect in the past noisy data and test figure, select to be suitable for the disturbance training sample of 3 sub neural networks respectively, set up 3 neural networks and it is trained, finish mapping separately from perturbation features parameter subspace to whole disturbance space.After network training is finished, just can discern according to the perturbation features data of input.
Neural network need be the data normalization of input in the scope of learning algorithm restriction, therefore, with the perturbation features data conversion is positive number between 0 or 1, floods Partial Feature information to avoid excessive weight to regulate, and eliminates the interference of each characteristic parameter physical unit.
(3) comprehensive identification
The ability of each recognition network is different, so there is the discount that reliability coefficient is an evidence in each network.The real output value of each local nerve network is carried out normalized, multiply by the total discrimination of neural network again.If the individual output valve of j (j=1,2,3,4,5) of the individual network of i (i=1,2,3) is O
i(A
j), its correspondence is assigned as the reliability of output j on this evidence basis so
In the formula, α
iBe the discrimination of each network, n is the number of neural network,
Total output for each node of neural network under the perfect condition.A
jBe the basic perturbation mode in the disturbance identification; m
i(A
j) be to disturbance A on the disturbance framework
jBelief function distribute.After normalized, obtain the basic reliability distribution of each evidence.
Adopt the D-S evidence theory that the recognition result combination of 3 localized networks is comprehensively discerned.According to the characteristic of electrical energy power quality disturbance, mainly analyze several typical disturbances, i.e. voltage dip (E
1), voltage rises (E temporarily
2), voltage flicker (E
3), voltage fluctuation (E
4), voltage interruption (E
5), the identification framework in the conclusion evidence theory is designated as ={ E
1, E
2, E
3, E
4, E
5.According to evidence fusion formula (2) evidence of a plurality of neural networks is carried out fusion treatment, each the disturbance basic reliability distribution after can obtaining merging can realize the accurate identification to disturbance thus.
In the formula, Φ is an empty set; M (A
i), m (B
j) be called on the disturbance framework disturbance A
i, B
jBelief function distribute.C is the perturbation mode A of hypothesis
iAnd B
jA synthetic proposition of boolean combination; The belief function value m (C) of C comprises the disturbance hypothesis A that do not conflict
iAnd B
jAll belief function sum of products.Can recursion obtain the fusion results of a plurality of disturbance evidences by formula (2), like this, just unite several evidence groups and form a new comprehensive evidence, for the decision-making of electrical energy power quality disturbance pattern provides comprehensively, information accurately by the D-S fusion rule.
The present invention is based on Visual C++ platform, set up whole electrical energy power quality disturbance recognition system in conjunction with Matlab, be divided into Visual C++, Matlab and Excel working environment, use Visual C++ to carry out the collection of data, the transmission of order and complicated flow process control; The complicated algorithm of finishing the com component that uses Matlab to develop down realizes and the digital signal processing programming; Use Excel to make up database.Specifically, the quality of power supply recognition system that the present invention is based on information fusion mainly is made of data acquisition and pretreatment module 1, disturbance identification database 2, characteristic extracting module 3, local dip identification module 4, fusion recognition module 5 and user interactive module 6, as shown in Figure 2.
Wherein, data acquisition and pretreatment module 1 from the database of electric energy quality monitoring system and other automated system, filter out the related data that can reflect perturbation features according to measuring place, date, time by data access interface.
Disturbance identification database 2 is responsible for the required training sample of record neural network, the data that module 1 is gathered are saved in the Excel database, under the Excel environment, carry out the data management maintenance work, functions such as data storage, increase, deletion, statistics are provided, other module can use the ODBC interface to obtain related data, need not to be concerned about the position and the form of database, also need not consider the data consistency problem.
Characteristic extracting module 3, local dip identification module 4 and fusion recognition module 5 are used the Matlab com component realization of exploitation down, finish complicated algorithm such as Fourier transform, wavelet analysis, correlation function analysis, probabilistic neural network identification, D-S evidence theory fusion recognition and realize and the digital signal processing function.
User interactive module 6 is responsible for showing all recognition results at software interface, receives user's various operation signals simultaneously.
As shown in Figure 3, the electrical energy power quality disturbance identification realization flow that the present invention is based on information fusion has been described.When the voltage waveform of field electric energy quality monitoring device monitoring reaches default distortion degree (exceeding standard as the total percent harmonic distortion of voltage), the disturbance recognition system just can be carried out data acquisition, signal after the collection takes out contained noise in the signal earlier through pre-service, and then realizes the collection to signal.
Claims (5)
1, the automatic disturbance recognition methods of a kind of quality of power supply based on information fusion, it is characterized in that: it comprises the steps:
(1) from electric energy quality monitoring system electric energy metered system and load management system, gather steady state measurement data and the transient measuring data relevant, and data are carried out denoising handle with electrical energy power quality disturbance, realize unified markers, integrated management;
(2) adopt Fourier analysis, wavelet multiresolution rate to decompose and the data of correlation function analysis method after to denoising are carried out feature extraction, obtain reflecting 3 kinds of multidimensional characteristic vectors of perturbation features;
(3) multidimensional characteristic vectors that extracts is sent into probabilistic neural network respectively and train, the neural network that trains can be discerned unknown disturbance;
(4) with the output result of each neural network as evidence independent of each other, utilize the D-S evidence theory to carry out information fusion, obtain final recognition result.
2, the electrical energy power quality disturbance recognition methods based on information fusion according to claim 1, it is characterized in that: described step (1) is to utilize data access interface from the database of electric energy quality monitoring system, gather steady state data, the Temporal Data relevant with electrical energy power quality disturbance, by the application access interface, obtain data relevant in electric energy metered system, the load management system, utilize wavelet transformation that signal is carried out denoising and handle with electrical energy power quality disturbance.
3, the electrical energy power quality disturbance automatic identification method based on information fusion according to claim 1, it is characterized in that: described step (2) is the distorted waveform to the in-site measurement sampling, adopt fast fourier transform that the distorted waveform of time domain is transformed to its pairing spectrum, from spectrum, obtain about the feature of distorted waveform proper vector as disturbance; Adopt the wavelet multiresolution rate to decompose distorted waveform is transformed into different frequency fields, extract the signal energy of the various perturbation features of reflection; By correlation function analysis with calculate facies relationship numerical value; The characteristic information of the reflection electrical energy power quality disturbance that extracts from Various types of data is divided three classes: a, 6 kinds of temporal signatures: voltage waveform, current waveform, effective value, fundamental voltage amplitude and phase place, disturbance duration; B, 10 kinds of frequency domain characters: 3, the voltage of 5,7,9 subharmonic, current amplitude and phase place, total percent harmonic distortion, wavelet package transforms extract the energy value in the different frequency bands; C, 3 kinds of linked characters: the related coefficient of related coefficient, disturbance and the electric locomotive rate of traffic flow of the related coefficient of disturbance and electric locomotive speed, disturbance and electric locomotive load.
4, the electrical energy power quality disturbance automatic identification method based on information fusion according to claim 1 is characterized in that: the artificial neural network that adopts in the described step (3) is a probabilistic neural network, and 3 proper vectors that step (2) is obtained are as input, and output is with to discern the disturbance kind corresponding, and promptly voltage dip is (1,0,0,0,0), voltage is upgraded to (0 temporarily, 1,0,0,0), voltage flicker is (0,0,1,0,0), voltage fluctuation is (0,0,0,1,0), voltage interruption is (0,0,0,0,1); Set up 3 neural networks and it is trained, the network that trains is finished the mapping from feature space to the disturbance space.5, the electrical energy power quality disturbance automatic identification method based on information fusion according to claim 1, it is characterized in that: the information fusion that adopts in the described step (4) is finished according to theory by D-S, according to identification disturbance kind, the identification framework in the conclusion evidence theory is designated as ={ E
1, E
2, E
3, E
4, E
5.Each disturbance basic reliability distribution after obtaining merging according to the evidence blending theory can realize the accurate identification to disturbance thus.
6, a kind of electrical energy power quality disturbance automatic recognition system based on information fusion is characterized in that: it is made of data acquisition and pretreatment module (1), disturbance identification database (2), characteristic extracting module (3), local dip identification module (4), fusion recognition module (5) and user interactive module (6).
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