CN111626093B - Method for identifying related bird species of power transmission line based on sound power spectral density - Google Patents
Method for identifying related bird species of power transmission line based on sound power spectral density Download PDFInfo
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- 238000007781 pre-processing Methods 0.000 claims abstract description 12
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Abstract
The invention discloses a method for identifying related bird species of a power transmission line based on the power spectrum density of sound. According to the method, a bird species singing signal preprocessing algorithm module and a characteristic extraction algorithm module are constructed by establishing a transmission line interference bird fault related bird species singing database, and a discrete Fourier transform and power spectrum estimation method is used for extracting a power spectrum density value of a singing signal to be used as a characteristic vector for distinguishing different bird species; constructing a machine learning algorithm module for classifying and identifying bird species singing signals, and training a multi-classification model by utilizing a power spectrum density characteristic set of bird species singing signals related to bird faults to obtain an intelligent bird species identification model; and importing the bird species singing signals recorded in the inspection process of the power transmission line operation and maintenance personnel into a preprocessing module, a characteristic extraction module and an intelligent recognition model, and outputting corresponding bird species information. The method is beneficial to improving the accuracy of classifying and identifying the bird species, and further improving the pertinence and the effectiveness of the transmission line in bird fault prevention and control.
Description
Technical Field
The invention relates to the field of operation and maintenance of transmission lines, in particular to a method for identifying related bird species of a transmission line based on a sound power spectrum density.
Background
Bird activity is one of the important reasons for causing the faults of the overhead transmission line, different birds can cause bird-related faults of different types of transmission lines, and the prevention and treatment measures of the bird-related faults are different. At present, various bird prevention devices are widely applied in actual operation, but still have larger blindness, cannot effectively inhibit the rising trend of bird related faults, and line tripping faults caused by failure of the bird prevention devices also occur. Since bird faults are transient, it is often difficult for operation and maintenance personnel to judge the bird species causing the faults after the faults occur, and it is difficult to take bird fault prevention measures in a targeted manner due to the lack of an intelligent bird species identification and fault cause judgment method. Therefore, intelligent identification research of bird species related to bird faults of the overhead transmission line is necessary to be carried out, and basis is provided for line operation and maintenance personnel to correctly identify birds.
At present, related researchers propose various methods for identifying bird nests on a tower of a power transmission line, and bird nests on the tower can be identified through aerial images, however, bird faults are involved in one type of bird nest faults, but also bird droppings, bird body short circuits and bird pecking faults, and the root of the bird faults is that birds close to the power transmission line or stay on the tower, so that different bird species must be accurately identified, and prevention and treatment measures can be taken in a targeted manner. In the prior art of bird species identification, classification of bird species is mainly achieved by images and sounds, and a method for identifying based on a bird song signal mainly extracts a main frequency, a resonance frequency, a frequency amplitude, a resonance peak, and the like of the signal as feature vectors of bird species. Such characteristics would cross for different species of birds in the same family and would not be effectively distinguishable. The other method is to draw spectrograms of different bird species, and take the texture information with distinction degree on the spectrograms as feature vectors. The method is used for extracting 24-dimensional Meyer cepstrum coefficient (MFCC) of the bird song signal as a feature vector, and because the feature parameters are fewer, the intersection parts exist among different bird species, the distinction degree is insufficient, and the identification accuracy is low.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention aims to provide a method for identifying the bird species related to the power transmission line based on the power spectrum density of the ringing sound, which provides a basis for correctly identifying birds for line operation and maintenance personnel.
In order to achieve the aim of the invention, the technical scheme adopted by the invention is that the method for identifying the bird species related to the power transmission line based on the power spectrum density of the ringing sound comprises the following steps:
s1: counting main dangerous bird species causing line bird interference faults according to the running experience, geographic environment characteristics and bird inhabitation habit of a power transmission line, collecting the sound signals of related bird species, and establishing a bird species sound database related to the bird interference faults;
s2: constructing a bird species singing signal preprocessing algorithm module, converting the singing signals of bird species related to bird faults into time domain waveforms, and preprocessing;
s3: constructing a bird species singing signal characteristic extraction algorithm module, performing discrete Fourier transform on the preprocessed bird species singing signals, calculating power spectrum density values of each singing signal through a power spectrum estimation method, performing logarithmic conversion, drawing a relation curve of the power spectrum density values of each bird and corresponding frequency points, extracting power spectrum density values corresponding to N frequency points from the curve as characteristic vectors for distinguishing different bird species, and establishing a characteristic set of bird species singing signals related to bird fault;
s4: constructing a bird species singing signal classification and identification algorithm module, adopting a machine learning algorithm to construct a multi-classification model, and training the model by utilizing a power spectrum density characteristic set of bird species singing signals related to bird faults to obtain an intelligent bird species identification model;
s5: and recording bird species singing in the inspection process by using a recording device by using an operation and maintenance personnel of the power transmission line, guiding the bird species singing into a singing signal preprocessing algorithm module and a characteristic extraction algorithm module, acquiring a singing power spectrum density characteristic set of the bird species related to the power transmission line, guiding the singing power spectrum density characteristic set into a bird species intelligent recognition model, and outputting corresponding bird species information.
Further, the preprocessing algorithm module in S2 includes analog-to-digital conversion, pre-emphasis, framing and windowing, and endpoint detection.
Further, the power spectrum estimation method in S3 may be selected from an average periodic chart method, a Bartlett method or a weighted overlap average method (Welch method).
Further, the machine learning method in S4 may select a random forest or a multi-classification support vector machine.
Compared with the prior art, the invention has the beneficial effects that:
the method for identifying the related bird species of the power transmission line based on the sound power spectral density overcomes the limitations of insufficient bird species sound characteristic information and insufficient distinguishing degree in the prior art, and the difference of sound signals of different bird species can be effectively represented through the power spectral density characteristics corresponding to different frequency points, so that the method is beneficial to improving the accuracy of classifying and identifying the bird species, and the pertinence and the effectiveness of preventing and controlling bird faults of the power transmission line can be improved.
Drawings
FIG. 1 is a flow chart of a method for identifying related bird species of a power transmission line based on the spectral density of sound power in the invention;
FIG. 2 is a time domain waveform of a pretreated bird song signal according to an embodiment of the present invention; (a) - (h) respectively the time domain waveform of the sound signal of the Geranium, the aigrette, the grazing with small mouth, the falcon, the magpie, the silver gull, the hawk and the cormorum;
FIG. 3 is a graph of power spectral density of different bird song signals according to an embodiment of the present invention; (a) - (h) power spectral density curves of Geranium, egret, rabdosia, tacrolimus, happy, silver gull, hawk, and cormorum, respectively.
Detailed Description
The invention will now be further described with reference to the following examples, which are given solely for the purpose of illustration and are not to be construed as limitations on the scope of the invention, as will be apparent to those skilled in the art upon examination of the foregoing disclosure.
The following describes in detail the processing of the acoustic signal, the feature extraction and the classification recognition of the bird species of a typical bird fault by means of the transmission line, and the flowchart is shown in fig. 1. The method comprises the following steps:
s1: according to the running experience, geographical environment characteristics and bird inhabitation habit of a power transmission line, counting main dangerous bird species causing line bird interference faults, collecting the sound signals of related bird species, and establishing a bird species sound database related to the bird interference faults.
In this embodiment, according to the running experience of the overhead transmission line in China, it is summarized that the main dangerous bird species causing bird faults are selected from the related bird species causing 4 bird nest types, bird droppings, bird body short circuits and bird pecking types, 8 representative bird species such as the oriental geranium, aigrette, small mouth crow, red hawk, magpie, silver gull, hawk and corm are taken as identification objects, and sound files of the bird species are downloaded from websites such as world wild bird sound nets and the like to establish a sound database.
S2: and constructing a bird species singing signal preprocessing algorithm module, converting the singing signals of bird species related to bird faults into time domain waveforms, and preprocessing.
An algorithm program of analog-to-digital conversion, pre-emphasis, framing and windowing and endpoint detection of the bird song signals is written by MATLAB software, and the bird song files are converted into time domain waveforms through analog-to-digital conversion, and 1 song sample is taken as an example respectively, and the time domain waveforms of the song signals of 8 bird strains in the embodiment are shown in figure 2. The method comprises the steps of pre-emphasizing a bird song signal by adopting a first-order high-pass filter, emphasizing a high-frequency part of the bird song signal, removing radiation of a bird beak, increasing high-frequency resolution of the bird song, attenuating a low-frequency part, and reducing dynamic range of a frequency spectrum; then, the Hamming window is adopted to carry out framing and windowing on the ringing signal x (n), and the process is as follows:
where y (n) is the windowed chirp signal, w (n) is the window function, and Hamming window can be expressed as
Where N is the window length.
After framing and windowing, endpoint detection is carried out on the bird song signals by adopting a double-threshold method based on short-time energy and short-time average zero-crossing rate, and short-time energy E (i) and short-time average zero-crossing rate Z (i) of the i-th frame bird song signals are obtained according to the following formula.
And setting a second-level criterion according to the short-time energy and the short-time average zero-crossing rate, and detecting by using a double-threshold method to obtain the sound section in the bird song signal.
S3: constructing a bird species singing signal characteristic extraction algorithm module, performing discrete Fourier transform on the preprocessed bird species singing signals, calculating power spectrum density values of each singing signal through a power spectrum estimation method, performing logarithmic conversion, drawing a relation curve of the power spectrum density values of each bird and corresponding frequency points, extracting power spectrum density values corresponding to N frequency points from the curve as characteristic vectors for distinguishing different bird species, and establishing a characteristic set of bird species singing signals related to bird fault.
And dividing the bird song signal subjected to framing windowing and end point detection into L sections according to 256 sampling points of each section. FFT transforming 256 sampling points of each segment of the bird song signal, i.e
Where w (n) is a window function, and the power spectral density is obtained by squaring the modulus:
in the method, in the process of the invention,is the power spectrum of the window function.
Then the average value of the power spectrum density of the L-section signal is obtained, namely
Logarithmic conversion is carried out on the extracted power spectrum density value, and the conversion relation is as follows: p' =10×log 10 P, drawing a power spectral density curve, and extracting power spectral density characteristics corresponding to 129 frequency points of the bird song signal from the curve as shown in fig. 3 to form a characteristic set for representing the bird song signal.
S4: and constructing a bird species singing signal classification and identification algorithm module, adopting a machine learning algorithm to construct a multi-classification model, and training the model by utilizing a power spectrum density characteristic set of bird species singing signals related to bird faults to obtain the bird species intelligent identification model.
The multi-classification model can be constructed by adopting a random forest and a support vector machine, and in the embodiment, an 8-classification machine learning model is established by adopting the random forest, and is an integrated learning algorithm combined by a plurality of decision tree classifiers. And training the random forest model by adopting 8 kinds of bird song signal power spectrum density characteristic sets in the training sample set to obtain the bird song intelligent identification model.
S5: and recording bird species singing in the inspection process by using a recording device by using an operation and maintenance personnel of the power transmission line, guiding the bird species singing into a singing signal preprocessing algorithm module and a characteristic extraction algorithm module, acquiring a singing power spectrum density characteristic set of the bird species related to the power transmission line, guiding the singing power spectrum density characteristic set into a bird species intelligent recognition model, and outputting corresponding bird species information.
And taking the bird species singing signal to be predicted as a test sample, preprocessing the singing signal, extracting the characteristics to obtain a power spectrum density characteristic set, inputting the power spectrum density characteristic set into the bird species intelligent recognition model based on the random forest, outputting a bird species recognition result, and counting, classifying and recognizing the accuracy.
In this embodiment, the sample number and the classification and identification results of 8 typical bird species causing the transmission line to interfere with the bird fault, such as the Geranium, egre, dilucrow, tacrow, magpie, silver gull, hawk, and corm, are shown in table 1, and it can be seen that the total identification accuracy is 94.87%, and the identification accuracy of Geranium, egre, dilucrow, dilujuu, hawk, and corm is up to 100%.
TABLE 1
It should be understood that parts of the specification not specifically set forth herein are all prior art.
While particular embodiments of the present invention have been described above with reference to the accompanying drawings, it will be understood by those skilled in the art that these are by way of example only, and that various changes and modifications may be made to these embodiments without departing from the principles and spirit of the invention. The scope of the invention is limited only by the appended claims.
Claims (1)
1. A method for identifying related bird species of a power transmission line based on the acoustic power spectral density is characterized by comprising the following steps: the method comprises the following steps:
s1: counting main dangerous bird species causing line bird interference faults according to the running experience, geographic environment characteristics and bird inhabitation habit of a power transmission line, collecting the sound signals of related bird species, and establishing a bird species sound database related to the bird interference faults;
s2: constructing a bird species singing signal preprocessing algorithm module, converting the singing signals of bird species related to bird faults into time domain waveforms, and performing analog-to-digital conversion, pre-emphasis, framing and windowing and endpoint detection;
s3: constructing a bird species singing signal characteristic extraction algorithm module, performing discrete Fourier transform on the preprocessed bird species singing signals, calculating power spectrum density values of each singing signal through an average periodic chart method, a Bartlett method or a weighted overlap average method, performing logarithmic conversion, drawing a relation curve of the power spectrum density values of each bird and corresponding frequency points, extracting power spectrum density values corresponding to N frequency points from the curve as characteristic vectors for distinguishing different bird species, and establishing a characteristic set of bird species singing signals related to bird fault;
s4: constructing a bird species singing signal classification and identification algorithm module, adopting a machine learning algorithm to construct a multi-classification model, and training the model by utilizing a power spectrum density characteristic set of bird species singing signals related to bird faults to obtain an intelligent bird species identification model; the machine learning method in the S4 can select a random forest or a multi-classification support vector machine;
s5: and recording bird species singing in the inspection process by using a recording device by using an operation and maintenance personnel of the power transmission line, guiding the bird species singing into a singing signal preprocessing algorithm module and a characteristic extraction algorithm module, acquiring a singing power spectrum density characteristic set of the bird species related to the power transmission line, guiding the singing power spectrum density characteristic set into a bird species intelligent recognition model, and outputting corresponding bird species information.
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| CN113111618B (en) * | 2021-03-09 | 2022-10-18 | 电子科技大学 | Analog circuit fault diagnosis method based on improved empirical wavelet transform |
| CN113255661B (en) * | 2021-04-15 | 2022-07-12 | 南昌大学 | Bird species image identification method related to bird-involved fault of power transmission line |
| CN113707159B (en) * | 2021-08-02 | 2024-05-03 | 南昌大学 | Power grid bird-involved fault bird species identification method based on Mel language graph and deep learning |
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